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tag:blogger.com,1999:blog-121333352014-12-19T23:45:40.171-05:00Walk Like a SabermetricianOccasional commentary on baseball and sabermetricsphttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-56776994745221219142014-12-11T21:29:00.000-05:002014-12-11T21:29:30.710-05:00Hitting by Position, 2014Of all the annual repeat posts I write, this is the one which most interests me--I have always been fascinated by patterns of offensive production by fielding position, particularly trends over baseball history and cases in which teams have unusual distributions of offense by position. I also contend that offensive positional adjustments, when carefully crafted and appropriately applied, remain a viable and somewhat more objective competitor to the defensive positional adjustments often in use, although this post does not really address those broad philosophical questions.

The first obvious thing to look at is the positional totals for 2014, with the data coming from Baseball-Reference.com. "MLB" is the overall total for MLB, which is not the same as the sum of all the positions here, as pinch-hitters and runners are not included in those. "POS" is the MLB totals minus the pitcher totals, yielding the composite performance by non-pitchers. "PADJ" is the position adjustment, which is the position RG divided by the overall major league average (this is a departure from past posts; I'll discuss this a little at the end). "LPADJ" is the long-term positional adjustment that I use, based on 2002-2011 data. The rows "79" and "3D" are the combined corner outfield and 1B/DH totals, respectively:

The most notable deviations from historical norms (which, when limited to one year, are strictly trivia rather than trends) were present in the outfield, where all three spots provided essentially equal production. The shape was not equal--centerfielders had a higher batting average and lower secondary average (.213 to .236) than did corner outfielders. Catchers also outhit their usual levels, bringing the three rightmost positions on the defensive spectrum together around 95% of league average production. DHs rebounded from a poor 2013 showing (102 PADJ) to get back to their historical level.

Of course, a DH-supporter like myself can't avoid commenting on pitchers, who are wont to set a new low every couple years but went so far as to fall below the negative absolute RC threshold in 2014, with a -4 PADJ eclipsing 2012's 1 as the worst in history. Pitchers struck out in 41% of their plate appearances, double the rate of position players (20%). Still, I'll take a moment and provide the list of NL pitching staffs by runs above average. I need to stress that the runs created method I'm using here does not take into account sacrifices, which usually is not a big deal but can be significant for pitchers. Note that all team figures from this point forward in the post are park-adjusted. The RAA figures for each position are baselined against the overall major league average RG for the position, except for left field and right field which are pooled. So for pitchers, the formula for RAA was fun to write this year, since it involved adding the league average performance (well, subtracting the negative):

RAA = (RG + .15)*(AB - H + CS)/25.5

This marked the second straight triumph for Dodger pitchers as most productive, with Zack Greinke again the standout, although his numbers were much less gaudy than in 2013 (this year he hit .200/.262/.350). Pittsburgh's hurlers extended a complete power outage to a third season; while they topped Miami and Milwaukee in isolated power thanks to a Gerrit Cole home run, their two extra base hits (Vance Worley doubled) were the fewest. This comes on the heels of 2012 (one double) and 2013 (zero extra base hits), giving them a three year stretch of 894 at bats with two doubles and a home run (.006 ISO).

I don't run a full chart of the leading positions since you will very easily be able to go down the list and identify the individual primarily responsible for the team's performance and you won't be shocked by any of them, but the teams with the highest RAA at each spot were:

C--MIL, 1B--DET, 2B--HOU, 3B--WAS, SS--COL, LF--CLE, CF--PIT, RF--LA, DH--DET

More interesting are the worst performing positions; the player listed is the one who started the most games at that position for the team:

I will take the poor performance of the Jeter-led Yankee shortstops as an opportunity to share some wholly unoriginal thoughts about the Didi Gregorius trade. I have no particularly strong feelings on Gregorius' long-term outlook; I'll leave that to the projection mavens and the scouts. However, some remarkably silly columns have been written about his assuming Saint Derek's mantle. One sneered that he wasn't likely to be a long-term solution. This is probably true, but most major league lineup spots are filled by guys who are long-term solutions. A minority of teams have a long-term answer at shortstop, let alone a ten-year answer.

But more importantly is that even a static Gregorius could be an immediate boost to the Yankees. No team in baseball got less out of the position offensively, and fielding? (Rhetorical question). Last year, Gregorius hit .222/.279/.356 in 292 PA (3.4 RG); Jeter hit .253/.294/.309 in 616 PA (3.1 RG).

I like to attempt to measure each team's offensive profile by position relative to a typical profile. I've found it frustrating as a fan when my team's offensive production has come disproportionately from "defensive" positions rather than offensive positions ("Why can't we just find a corner outfielder who can hit?") The best way I've yet been able to come up with to measure this is to look at the correlation between RG at each position and the long-term positional adjustment. A positive correlation indicates a "traditional" distribution of offense by position--more production from the positions on the right side of the defensive spectrum. (To calculate this, I use the long-term positional adjustments that pool 1B/DH as well as LF/RF, and because of the DH I split it out by league):

My comments on frustration are based on the Indians, who have often had a negative correlation but this year exhibited a more normal profile. The Tigers +.88 is about as high as you'll see. Of course, offensive positions were their biggest producers with Cabrera and the two Martinezes, and their right fielders were their fourth most productive position. They did get more out of second than third or right, and more out of catcher than shortstop, but otherwise they were fell right in place.

The following charts, broken out by division, display RAA for each position, with teams sorted by the sum of positional RAA. Positions with negative RAA are in red, and positions that are +/-20 RAA are bolded:

Washington led the majors in RAA from both their corner infield spots and the entire infield. Giancarlo Stanton almost single-handedly led Miami to the majors top outfield RAA total. Atlanta had the majors worst RAA from middle infielders. Philadelphia's corner infielders had the lowest RAA in the NL.

I was bullish on Milwaukee this year, which looked smart for four and a half months before they wound up with an overall season record close to where most people picked them to finish. One factor I cited was how bad their production from first base had been in 2013. While the Brewers did not replicate their dreadful -37 RAA first base performance from '13, they only gained a win or so by improving to -26, still the worst first base production in the NL. 84% of their first base PA went to Lyle Overbay (628 OPS as a first baseman) and Mark Reynolds (632), who had basically the same overall production with different shapes. And so it was no surprise that for the second straight year, Milwaukee had the NL's most oddly distributed offense by position (based on the correlation approach described above). Cincinnati had the majors worst outfield production, and consistently so from left to right (-24, -20, -18).

The Dodgers were the only NL team to be above average at seven of the eight positions, but their catchers went all-in as the NL's least productive unit. Los Angeles tied Pittsburgh with 123 total RAA, but they did it with opposite production from the backstops (the Pirates' +24 was a perfect offset for LA's -24). Dodger middle infielders led the NL in RAA. San Diego was on the other end of the spectrum, the NL's only team with just one above-average position, with catcher once again serving as the exception. The Padres infield was the least productive in MLB.

Boston went from having one below-average position in 2013 to having just two above-average in 2014, which is how a team can go from leading the majors in total RAA to ranking second-last in the AL. Their infielders were the worst in the AL with a total of -46 RAA; their outfielders only tied for second-last, but matched the total of -46. Yet much of the winter discussion regarding the Red Sox has involved how they will parcel out their outfield surplus.

Detroit led the AL in corner infield RAA thanks to first base. Just eyeballing the charts, the Indians may have been the most average in terms of combining roughly average overall RAA with close to average production at many positions. Minus the outfield corners, there weren't many extremes in Cleveland.

The Angels led the AL in outfield RAA; the corner outfielders washed each other out for a total of -4 RAA, but the Trout-led centerfielders could not be washed out. Houston had the majors best middle infield production, but the worst corner infield production, and the latter exceeded the former by 24 absolute runs. The Astros had three -20 positions (the corner infielders and left field, so get your JD Martinez victim of their own success jokes in); so did the Reds, but just barely (two of theirs were -20 and the other -24). Seattle had the AL's worst outfield, largely due to trotting James Jones out there for 72 starts, then seeing Austin Jackson crater when they acquired him to address the problem. Texas had eight below-average positions, but made their one bright spot count with the AL's best third base RAA.

The data for each team-position is available in this spreadsheet.
phttp://email@example.com:blogger.com,1999:blog-12133335.post-17902621439670288972014-12-03T20:36:00.000-05:002014-12-03T20:36:03.924-05:00Hitting by Lineup Position, 2014 I devoted a whole post to leadoff hitters, whether justified or not, so it's only fair to have a post about hitting by batting order position in general. I certainly consider this piece to be more trivia than sabermetrics, since there's no analytic content.

The data in this post was taken from Baseball-Reference. The figures are park-adjusted. RC is ERP, including SB and CS, as used in my end of season stat posts. When I started I didn't have easy access to HB, so they are not included in any of the stats, including OBA. The weights used are constant across lineup positions; there was no attempt to apply specific weights to each position, although they are out there and would certainly make this a little bit more interesting.

This is the sixth consecutive season in which NL #3 hitters were the top producing lineup spot, while AL teams demonstrated more balance between #3 and #4. This is a fairly consistent pattern and the most interesting thing I've found from doing this every year. I have no explanation for this phenomenon and suspect that there really is none--the NL has had a run of outstanding hitters who happened to bat third in the lineup (e.g. Pujols, Votto, Braun, Gonzalez). NL hitters were more productive at spots 2-3 and 5-7, while the AL got more production at leadoff, cleanup, #8, and #9 (the latter is a given, of course).

The position that sticks out the most to me is AL #6; even with hit batters included, they managed an OBA of just .300 and outhit only AL #9, NL #8, and NL #9. I'd assume this is a one-year oddity and nothing more; in 2013 they created 4.55 runs, trailing only 3-5 among AL slots.

Next are the team leaders and trailers in RG at each lineup position. The player listed is the one who appeared in the most games in that spot (which can be misleading, particularly when there is no fixed regular as in the case of the Astros #5 spot). Or poor Matt Dominguez, who was perhaps the worst regular hitter in MLB (.215/.253/.330 for 2.6 RG, only Zack Cozart was worse among those with 500 PA), but doesn't deserve to be blamed for sinking two Houston lineup spots as he had plenty of help in both.

Some random thoughts:

* You can see why Seattle felt they needed Nelson Cruz, with the worst production out of the cleanup spot in the AL.

* Texas #3 hitters were a complete disaster. At 2.54 RG, they managed to outhit only five non-pitcher lineup spots. No #1, #2, #3 (obviously), #4, #5, or #6 spots in the majors were worse.

* Kansas City's production was oddly distributed. Their #1-3 hitters combined for 3.59 RG (no park adjustment applied in this bullet), their #4-6 for 4.86, and their #7-9 for 3.76. I'll call that a 3-1-2 pattern of hitting by batting order third (1-3 least productive, 4-6 most productive, 7-9 in the middle). 22 teams exhibited a 1-2-3 pattern; 4 teams a 2-1-3; and 2 teams each with 1-3-2 and 3-1-2. Texas was the other team with a 3-1-2 pattern.

* And then there are the Padres, who take on the role that was filled so well by the Mariners for many years of being the source of ridiculous offensive futility factoids. As you can see, San Diego got the NL's worst production at four lineup spots, all at the top or middle of the order, but also had the most productive #7 and #8 hitters. In fact, Padre #7 hitters were the most productive of any of their lineup spots. Two teams got their top production from leadoff hitters, six from #2, seventeen from #3, two from #4, two from #5, but only one from #7:

The next list is the ten best positions in terms of runs above average relative to average for their particular league spot (so AL leadoff spots are compared to the AL average leadoff performance, etc.):

And the worst:

The -54 figure for Texas #3 hitters is a big number; I've been running this report since 2009 and that is the worst performance by a team batting spot, topping the -53 runs turned in by KC's Mike Jacobs-led cleanup hitters in 2009. Considering that the AL average RPG in 2014 was 13% lower than in 2009, that one run difference is approximately a full win difference.

The last set of charts show each team's RG rank within their league at each lineup spot. The top three are bolded and the bottom three displayed in red to provide quick visual identification of excellent and poor production:

If you are interested in digging in yourself, see the spreadsheet here.phttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-19747103648676421932014-11-24T21:52:00.001-05:002014-11-24T21:52:51.144-05:00Leadoff Hitters, 2014This post kicks off a series of posts that I write every year, and therefore struggle to infuse with any sort of new perspective. However, they're a tradition on this blog and hold some general interest, so away we go.

First, the offensive performance of teams' leadoff batters. I will try to make this as clear as possible: the statistics are based on the players that hit in the #1 slot in the batting order, whether they were actually leading off an inning or not. It includes the performance of all players who batted in that spot, including substitutes like pinch-hitters.

Listed in parentheses after a team are all players that started in twenty or more games in the leadoff slot--while you may see a listing like "OAK (Crisp)" this does not mean that the statistic is only based solely on Crisp's performance; it is the total of all Oakland batters in the #1 spot, of which Crisp was the only one to start in that spot in twenty or more games. I will list the top and bottom three teams in each category (plus the top/bottom team from each league if they don't make the ML top/bottom three); complete data is available in a spreadsheet linked at the end of the article. There are also no park factors applied anywhere in this article.

That's as clear as I can make it, and I hope it will suffice. I always feel obligated to point out that as a sabermetrician, I think that the importance of the batting order is often overstated, and that the best leadoff hitters would generally be the best cleanup hitters, the best #9 hitters, etc. However, since the leadoff spot gets a lot of attention, and teams pay particular attention to the spot, it is instructive to look at how each team fared there.

The conventional wisdom is that the primary job of the leadoff hitter is to get on base, and most simply, score runs. It should go without saying on this blog that runs scored are heavily dependent on the performance of one's teammates, but when writing on the internet it's usually best to assume nothing. So let's start by looking at runs scored per 25.5 outs (AB - H + CS):

1. MIL (Gomez/Gennett), 5.9
2. MIN (Santana/Dozier), 5.8
3. STL (Carpenter), 5.6
Leadoff average, 4.8
28. CHN (Bonifacio/Coghlan), 4.1
ML average, 4.0
29. SD (Cabrera/Solarte/Venable/Denorfia), 3.5
30. SEA (Jackson/Chavez/Jones/Almonte), 3.5

The Twins leading the AL in run scoring rate for leadoff hitters is a surprise--usually leading teams in this category are good offenses or high OBA guys, neither category describes Minnesota. They combined for a .324 OBA,
just a tick above the major league average in the other obvious measure to look at. The figures here exclude HB and SF to be directly comparable to earlier versions of this article, but those categories are available in the spreadsheet if you'd like to include them:

1. STL (Carpenter), .366
2. HOU (Altuve/Grossman/Fowler), .352
3. WAS (Span), .346
Leadoff average, .322
ML average, .310
28. CIN (Hamilton), .295
29. SD (Cabrera/Solarte/Venable/Denorfia), .293
30. SEA (Jackson/Chavez/Jones/Almonte), .287

The next statistic is what I call Runners On Base Average. The genesis for ROBA is the A factor of Base Runs. It measures the number of times a batter reaches base per PA--excluding homers, since a batter that hits a home run never actually runs the bases. It also subtracts caught stealing here because the BsR version I often use does as well, but BsR versions based on initial baserunners rather than final baserunners do not.

My 2009 leadoff post was linked to a Cardinals message board, and this metric was the cause of a lot of confusion (this was mostly because the poster in question was thick-headed as could be, but it's still worth addressing). ROBA, like several other methods that follow, is not really a quality metric, it is a descriptive metric. A high ROBA is a good thing, but it's not necessarily better than a slightly lower ROBA plus a higher home run rate (which would produce a higher OBA and more runs). Listing ROBA is not in any way, shape or form a statement that hitting home runs is bad for a leadoff hitter. It is simply a recognition of the fact that a batter that hits a home run is not a baserunner. Base Runs is an excellent model of offense and ROBA is one of its components, and thus it holds some interest in describing how a team scored its runs, rather than how many it scored:

1. STL (Carpenter), .349
2. HOU (Altuve/Grossman/Fowler), .326
3. WAS (Span), .325
Leadoff average, .294
ML average, .281
27. SEA (Jackson/Chavez/Jones/Almonte), .272
28. MIL (Gomez/Gennett), .270
29. SD (Cabrera/Solarte/Venable/Denorfia), .266
30. CIN (Hamilton), .253

Milwaukee's leadoff hitters are a good example of why ROBA is not a quality metric. Their .325 OBA was slightly above average, but they also led leadoff hitters with 26 home runs. They also were caught stealing 13 times, which tied for the fourth-most among leadoff hitters, which brought it down some more. It's CS that really brings down the Reds, as the Hamilton-led leadoff hitters led all teams by getting caught 20 times.

I will also include what I've called Literal OBA here--this is just ROBA with HR subtracted from the denominator so that a homer does not lower LOBA, it simply has no effect. You don't really need ROBA and LOBA (or either, for that matter), but this might save some poor message board out there twenty posts, by not implying that I think home runs are bad, so here goes. LOBA = (H + W - HR - CS)/(AB + W - HR):

1. STL (Carpenter), .353
2. HOU (Altuve/Grossman/Fowler), .331
3. WAS (Span), .328
Leadoff average, .298
ML average, .287
28. SEA (Jackson/Chavez/Jones/Almonte), .274
29. SD (Cabrera/Solarte/Venable/Denorfia), .269
30. CIN (Hamilton), .257

There is a high degree of repetition for the various OBA lists, which shouldn't come as a surprise since they are just minor variations on each other.

The next two categories are most definitely categories of shape, not value. The first is the ratio of runs scored to RBI. Leadoff hitters as a group score many more runs than they drive in, partly due to their skills and partly due to lineup dynamics. Those with low ratios don't fit the traditional leadoff profile as closely as those with high ratios (at least in the way their seasons played out):

1. LA (Gordon), 2.5
2. PHI (Revere), 2.4
3. BOS (Holt/Pedroia/Betts), 2.3
Leadoff average, 1.7
28. NYA (Gardner/Ellsbury), 1.3
29. DET (Kinsler/Davis/Jackson), 1.3
30. COL (Blackmon), 1.3
ML average, 1.1

A similar gauge, but one that doesn't rely on the teammate-dependent R and RBI totals, is Bill James' Run Element Ratio. RER was described by James as the ratio between those things that were especially helpful at the beginning of an inning (walks and stolen bases) to those that were especially helpful at the end of an inning (extra bases). It is a ratio of "setup" events to "cleanup" events. Singles aren't included because they often function in both roles.

Of course, there are RBI walks and doubles are a great way to start an inning, but RER classifies events based on when they have the highest relative value, at least from a simple analysis:

1. STL (Carpenter), 1.6
2. LA (Gordon), 1.5
3. PHI (Revere), 1.5
4. KC (Aoki/Cain), 1.5
Leadoff average, 1.0
ML average, .7
28. PIT (Harrison/Polanco/Marte), .6
29. LAA (Calhoun/Cowgill), .5
30. DET (Kinsler/Davis/Jackson), .5

Since stealing bases is part of the traditional skill set for a leadoff hitter, I've included the ranking for what some analysts call net steals, SB - 2*CS. I'm not going to worry about the precise breakeven rate, which is probably closer to 75% than 67%, but is also variable based on situation. The ML and leadoff averages in this case are per team lineup slot:

1. PHI (Revere), 34
2. TOR (Reyes), 29
3. LA (Gordon), 23
Leadoff average, 8
ML average, 3
28. LAA (Calhoun/Cowgill), -3
29. CHA (Eaton), -4
30. SD (Cabrera/Solarte/Venable/Denorfia), -7

Last year I noted that since 2007, the percentage of major league stolen base attempts from leadoff hitters has declined. It was up to 28.8% in 2014, so the 2007-14 figures are (2007 is an arbitrary endpoint due to it being the first year I have the data at my finger tips):

30.2%, 29.6%, 27.8%, 25.9%, 27.9%, 25.1%, 25.9%, 28.8%

Shifting back to quality measures, beginning with one that David Smyth proposed when I first wrote this annual leadoff review. Since the optimal weight for OBA in a x*OBA + SLG metric is generally something like 1.7, David suggested figuring 2*OBA + SLG for leadoff hitters, as a way to give a little extra boost to OBA while not distorting things too much, or even suffering an accuracy decline from standard OPS. Since this is a unitless measure anyway, I multiply it by .7 to approximate the standard OPS scale and call it 2OPS:

1. HOU (Altuve/Grossman/Fowler), 789
2. MIL (Gomez/Gennett), 781
3. WAS (Span), 776
Leadoff average, 724
ML average, 704
28. NYN (Young/Granderson/Lagares), 654
29. SD (Cabrera/Solarte/Venable/Denorfia), 637
30. SEA (Jackson/Chavez/Jones/Almonte), 625

Along the same lines, one can also evaluate leadoff hitters in the same way I'd go about evaluating any hitter, and just use Runs Created per Game with standard weights (this will include SB and CS, which are ignored by 2OPS):

1. HOU (Altuve/Grossman/Fowler), 5.4
2. MIL (Gomez/Gennett), 5.2
3. NYA (Gardner/Ellsbury), 5.2
Leadoff average, 4.4
ML average, 4.1
28. NYN (Young/Granderson/Lagares), 3.6
29. SEA (Jackson/Chavez/Jones/Almonte), 3.1
30. SD (Cabrera/Solarte/Venable/Denorfia), 3.1

You may note that the spread in RG between team leadoff spots is not that great, ranging from just 3.1 to 5.4. This seemed very unusual to me, so I checked the last five years and it was in fact an unusual year (chart shows standard deviation and coefficient of variation of leadoff RG by team):

Originally I just included the most recent five seasons, but I'm glad I dug up the 2009 data, because the COV was similar to that in 2014. However, the history does indicate that this is an unusually small spread in production from the leadoff spot. It seems far more likely to a blip than anything of note, though.

Allow me to close with a crude theoretical measure of linear weights supposing that the player always led off an inning (that is, batted in the bases empty, no outs state). There are weights out there (see The Book) for
the leadoff slot in its average situation, but this variation is much easier to calculate (although also based on a silly and impossible premise).

The weights I used were based on the 2010 run expectancy table from Baseball Prospectus. Ideally I would have used multiple seasons but this is a seat-of-the-pants metric. The 2010 post goes into the detail of how this measure is figured; this year, I'll just tell you that the out coefficient was -.215, the CS coefficient was -.582, and for other details refer you to that post. I then restate it per the number of PA for an average leadoff spot (736 in 2014):

1. HOU (Altuve/Grossman/Fowler), 16
2. STL (Carpenter), 12
3. NYA (Gardner/Ellsbury), 12
Leadoff average, 0
ML average, -5
28. CHN (Bonifacio/Coghlan), -12
29. SEA (Jackson/Chavez/Jones/Almonte), -21
30. SD (Cabrera/Solarte/Venable/Denorfia), -23

I doubt I would have guessed Houston in ten guesses at the most productive leadoff spot in MLB, but Altuve and Fowler both were very productive when leading off (Robbie Grossman started 43 games as a leadoff hitter but hit .262/.340/.337, and thus was not a major contributor to the Astros' #1 rank). Seattle managed to contend for a playoff spot despite woeful leadoff production, and attempted to address the issue (and the related center field woes) at the trade deadline by acquiring Austin Jackson. But Jackson hit just .229/.267/.260 in 236 PA in those roles after the trade. A center fielder led off for Seattle in 117 of 162 games, an outfielder in 148 of 162 games.

For the full lists and data, see the spreadsheet here.phttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-25155753912988738362014-11-11T23:13:00.000-05:002014-11-13T18:12:39.296-05:00Hypothetical Ballot: MVPLast year, I thought that Clayton Kershaw was the most valuable player in the National League. The BBWAA voters did not concur, placing Kershaw seventh in the voting; the IBA voters were more generous at third. This season, though, it appears Kershaw is going to win the MVP award.

When you compare Kershaw 2013 to Kershaw 2014, it's difficult to find good reasons for this (one obvious reason which I'll discuss in a minute is anything but good). Granted, MVP voting does not occur in a vacuum--the 2013 field had much more to offer from a position player perspective, with Yadier Molina have an outstanding full season, his teammate Matt Carpenter, a pair of big-mashing first basemen in Joey Votto and Paul Goldschmidt, and the one holdover, Andrew McCutchen. Thus it makes sense that more voters would turn to Kershaw in a season in which there are fewer alternatives. Still, Kershaw pitched 38 fewer innings over six fewer starts in 2014. His ERA dropped slightly (1.83 to 1.77), but that hardly makes up for 38 innings. A big factor for the BBWAA will be his win-loss record, 21-3 in 2014 rather than a more pedestrian 16-9 in 2013, but it goes without saying on this blog that W-L is a silly basis to vote for MVP.

It might be useful to take a look at Kershaw's performance in the categories that I feel are useful, with adjustments for league average since we are comparing across seasons, seasons in which the NL average RA dropped very slightly from 4.04 to 4.01 (the three RA figures have been divided by the league average RA; RAA and RAR have been very simply converted to WAA and WAR by dividing by the league average runs scored per game by both teams):

While Kershaw was slightly better in the pitching metrics that focus on actual results (RRA and eRA) and noticeably better in DIPS (dRA), the 36 inning difference looms large. I would take Kershaw's 2013 season over his 2014 season. Obviously both were outstanding, but the fact that he will be an MVP afterthought in one and a strong winner in another speaks to the arbitrary and narrative-driven voting that still reigns supreme in the BBWAA even as more sabermetric approaches gain some traction.

For my ballot, last year I chose Kershaw narrowly over McCutchen. This year I've done the opposite. McCutchen starts with a 77 to 70 lead over Kershaw in RAR, but he does give some of that back. Per Fangraphs McCutchen was an average baserunner, while Kershaw created three runs at the plate with a .178/.228/.211 line. Since pitchers essentially average zero runs created per out, I credit the three absolute RC with no baseline. McCutchen also doesn't fare particularly well in defensive metrics, -11 DRS, -11 UZR, -8 FRAA (Baseball Prospectus). Regressing these a little as I am wont to do, it's very close between Kershaw and McCutchen.

However, Kershaw's RAR is based on his actual runs allowed; were one to use eRA or dRA as the basis, he'd start from just 63 or 58 RAR respectively, and that would be too large of a gap to McCutchen to close with fielding, even with no regression. I have no issue with the notion of a pitcher being MVP, but I think it's a pretty high bar, and when the alternate ways of valuing pitchers don't support placing the pitcher ahead, I can't do it either.

Giancarlo Stanton would have made things very interesting had he not been injured, although in the end there was very little difference between the amount of time missed by McCutchen and Stanton. McCutchen played 146 games with 632 PA; Stanton played 145 games with 633 PA. Stanton came in at 67 RAR, ten fewer than McCutchen, partially due to the position adjustment difference between right field and center field, but not exclusively. Based on my estimates McCutchen created five more runs (121 to 116) in six fewer outs, making him the superior (albeit well within the margin of error) hitter (62 to 56 runs above average, hitting-only). While Stanton fares better in the fielding metrics, Fangraphs has him as a -2 baserunner, and so the ten run gap holds up for McCutchen. Kershaw/Stanton for second is a tossup, but I went with Stanton on the same reasoning discussed in the prior paragraph.

After them I have the top Cy Young challengers, who each pitched significantly more than Kershaw despite less impressive rates (Johnny Cueto and Adam Wainwright). The rest of my ballot is pretty self-explanatory from my RAR leaders, except Anthony Rendon is placed ahead of Jonathan Lucroy and Cole Hamels thanks to strong showing in baserunning (+6) and fielding metrics (16 DRS, 7 UZR, -1 FRAA):

1. CF Andrew McCutchen, PIT
2. RF Giancarlo Stanton, MIA
3. SP Clayton Kershaw, LA
4. SP Johnny Cueto, CIN
5. SP Adam Wainwright, STL
6. C Buster Posey, SF
7. 3B Anthony Rendon, WAS
8. C Jonathan Lucroy, MIL
9. SP Cole Hamels, PHI
10. RF Yasiel Puig, LA

Many words were used to discuss the 2012 and 2013 AL MVP votes; many fewer will be used in 2014, but the basic story is the same for me--Mike Trout was pretty clearly the most valuable player in the AL. This year, the Angels' record, Trout leading the league in RBI, and the lack of a triple crown stat standout other than Trout have combined to make the mainstream media agree. While comprehensive WAR metrics that include fielding with no regression may suggest this was the least valuable of Trout's three full seasons, I would point out that offensively, there's no pattern that could not be due to sheer random fluctuation. Trout's RG relative to the league average for the past three seasons is 196, 209, 186. Trout is such a towering figure among intelligent followers of the game that he has become subject to intense scrutiny--Trout death watch has become a bizarrely popular topic at sites that should know better. This is not to say that Trout will continue to dominate baseball for the next decade with no risk, or that Trout will ever match his 2012-2014 performances. But if you think you've found a clear decline trend in a 23 year old who was the best player in baseball for a third straight season, you are likely overanalyzing. You may want to take a gander at Alex Rodriguez 1997-1999 as well. It's more than a little uncouth if you ask me.

Rant aside, the rest of the ballot is not particular interesting, and I've mostly stuck with the RAR list. Some exceptions to note:

* Victor Martinez, a distant second among hitters with 64 RAR, just squeaks on to my ballot. Martinez was a -5 baserunner per Fangraphs, his RAR doesn't penalize him for being a DH at all, and when he did play the field, he was poor in just 280 innings (-4 DRS, -6 UZR, -4 FRAA). It was not an easy choice to keep Martinez on the ballot ahead of Josh Donaldson or Adrian Beltre, who spotted him around twenty RAR but did everything else better.

* Similarly, Jose Abreu drops off entirely--it's the same story except he starts from 55 RAR.

* Jose Altuve is knocked down a few pegs thanks to fielding metrics; I kept him ahead of Cano among second basemen on the basis of his forty plate appearance edge, but with no strong conviction:

* Corey Kluber beats out Michael Brantley as Most Valuable Indian; the two are tied at 61 RAR prior to considering Brantley's baserunning (good) and fielding (meh). Kluber would do worse using eRA, better using dRA, and the latter tipped the scales in his favor for me. After watching the Tribe all season, it would feel wrong to decide a tossup in favor of the pitcher rather than a fielder who played behind him. Cleveland's fielding was dreadful and Brantley, while not a main culprit, did not really help either. I remain unimpressed by Brantley as an outfielder, even in left; his arm is solid, but he's a left fielder, so...

1. CF Mike Trout, LAA
2. SP Felix Hernandez, SEA
3. SP Corey Kluber, CLE
4. LF Michael Brantley, CLE
5. SP Chris Sale, CHA
6. RF Jose Bautista, TOR
7. SP Jon Lester, BOS/OAK
8. 2B Jose Altuve, HOU
9. 2B Robinson Cano, SEA
10. DH Victor Martinez, DETphttp://email@example.com:blogger.com,1999:blog-12133335.post-21437291842613846832014-11-09T11:19:00.002-05:002014-11-09T11:19:16.416-05:00Hypothetical Ballot: Cy YoungThe National League Cy Young voting will not entail much intrigue. Clayton Kershaw will win in a romp, and will probably win the MVP as well. And while I agree that Kershaw deserves the Cy Young, I believe that the margin in the voting will greatly overstate the value difference between Kershaw and his closest competitors, Johnny Cueto and Adam Wainwright.

Kershaw and Cueto each were worth about 70 RAR through their pitching efforts based on actual runs allowed adjusted for bullpen support. Kershaw had a much better RRA at 1.98 while Cueto's was 2.55, but Cueto pitched an additional 45.1 IP. The difference between the two in runs allowed amounts to 45.1 innings of 5.07 RRA pitching. I estimate that the replacement level for starting pitchers is 128% of the league average runs allowed, which for the 2014 NL works out to 5.13. Thus there is essentially no difference between Cueto and Kershaw from a replacement level perspective. Cueto essentially tacked 45 innings of replacement level performance onto what Kershaw did.

Of course actual runs allowed are just one way to evaluate a pitcher. Cueto actually closes the gap when using eRA, which estimates runs allowed based on inputs, including actual hits allowed. Kershaw's eRA was 2.30 to Cueto's 2.73, a more narrow gap than the difference in actual runs allowed. Figuring RAR based on eRA, Cueto edges Kershaw 65 to 63. Kershaw has a significant advantage in DIPS measures, though, 2.47 to Cueto's 3.60 in dRA, as Cueto's BABIP allowed was just .246. And even considering just actual runs allowed, I am slightly biased towards the better performer on a rate basis rather than compiler. I concur that Kershaw is more deserving of the Cy than Cueto, but the gap just isn't that large given Kershaw's missed starts and 198 innings.

I've focused on Cueto v. Kershaw, but Wainwright is right on Cueto's heels with 67 RAR. Wainwright also has an edge on Cueto in dRA (3.38 to 3.60), and could easily place ahead if one values the DIPS metrics.

For the rest of the ballot, Cole Hamels is a comfortable pick for fourth, and I have fifth as a close battle between Washington teammates, Tanner Roark and Jordan Zimmermann. Roark ranks ahead in RAR 50 to 46, but Zimmermann's .78 dRA edge and superior peripherals are enough to slip ahead in my book:

1. Clayton Kershaw, LA
2. Johnny Cueto, CIN
3. Adam Wainwright, STL
4. Cole Hamels, PHI
5. Jordan Zimmermann, WAS

There will be more controversy associated with the AL award as Felix Hernandez and Corey Kluber jockey for the top spot. Kluber has become something of a darling among the DIPS-first portion of the sabermetric crowd, as his dRA is better than Hernandez' (2.88 to 3.07) and he had around 38 additional opponent plate appearances. Cleveland's fielders, in general, were bad--they ranked second-to-last in the AL in DER while Seattle led the AL.

However, I don't believe in throwing out all elements of pitching results outside of the three true outcomes, nor do I believe that it's a trivial matter to parcel out adjustments for fielding support amongst pitchers. For me, Hernandez' large edge in runs-based metrics (Hernandez has a 12 RAR lead; the two differed in innings pitched by a single out, but Hernandez' RRA of 2.54 was better than Kluber's 2.98; using eRA, Hernandez has an even larger advantage of 20 RAR) is too large to ignore.

One point to note when using runs allowed metrics--Hernandez got less help from his bullpen then did Kluber. Hernandez bequeathed 13 runners, and 7 of them came around to score. Kluber bequeathed 20 runners and only 2 were allowed to score. That's a seven run swing in the King's favor that is not apparent from the traditional stat line.

Using RRA (which considers bequeathed runners), Hernandez' advantage is 12 runs. Using eRA, which is just a component estimate, Hernandez leads by 20 runs. Using dRA, Kluber leads by 8. Quality of opposing hitters doesn't change the picture much; according to Baseball Prospectus, Hernandez' opponents combined for a .264 True Average, Kluber's for .263. To vault Kluber ahead, one must put a lot more stock in DIPS or in the quality of adjustments for fielding support than I am willing to grant. I'm not saying it's wrong to do so, but if you read any columns ripping the choice of Hernandez (and I don't know there will be any), it's likely you are dealing with a zealot.

And while I did not consciously consider it in my choice, another plus of choosing Hernandez is that I can dodge charges of pro-Indians bias.

For the rest of the ballot, I have stuck with the RAR order, as I see no particular reason to make any changes. Chris Sale is held back by just 174 innings, but he led the AL in RRA and dRA and was second in eRA to Garret Richards, who pitched five fewer innings:

1. Felix Hernandez, SEA
2. Corey Kluber, CLE
3. Chris Sale, CHA
4. Jon Lester, BOS/OAK
5. Max Scherzer, DET
phttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-67349694144289279342014-11-02T21:29:00.001-05:002014-11-02T21:29:14.062-05:00Hypothetical Ballot: Rookie of the YearJust off the top of my head, the National League rookie class is one of the least inspiring that I can remember. Not only were there no real standout performances, there's not a lot of competition for the top of the ballot, and there aren't a lot of big-time prospects who simply didn't produce in their first major league season (tragically, the closest to meeting this description is the late Oscar Taveras).

Jacob deGrom is the relatively clear choice with 33 RAR over just 22 starts. Over those 140 frames, though, he was excellent by any measure--his eRA and dRA were commensurate with his RRA, and it's hard to argue with 9.5/2.8 strikeout/walks per game. Another pitcher is a name that I don't recall seeing in much chatter about the award, Colorado lefty Tyler Matzek. Matzek pitched just 117 innings, which may help explain why he didn't draw much attention, and of course his statistics don't look very good without a park adjustment. After park adjustment, a 3.35 RRA was good for 23 RAR.

The other ballot spots go to batters; Ken Giles has drawn some attention, and he had an excellent season, ranking eighth among NL relievers with 17 RAR in just 45.2 innings thanks to sub-2 figures in all of the run average categories. Giles' strikeout rate of 14.1 trailed only the usual suspects among NL relievers (Chapman, Kimbrel, Jansen). However, 45.2 innings is the rub--it's hard for a reliever facing less than 200 batters to stand up against even average everyday rookies.

The NL had three such players worthy of recognition. Travis d'Arnaud led NL rookies with 22 RAR and also led with 4.5 RG. While his defensive reputation is not great, it would take a fair amount of credit for fielding and baserunning to move Kolten Wong (15 RAR) or Billy Hamilton (11 RAR) ahead. Both appear to be good fielders and baserunners, Hamilton's puzzling 23 caught stealings notwithstanding. Hamilton had 160 more PA thanks to leading off and not being subject to odd management by Michigan Mike, and he rates very highly in the various fielding metrics. After deGrom, it's splitting hairs to fill out the rest of the ballot:

1. SP Jacob deGrom, NYN
2. C Travis d'Arnaud, NYN
3. SP Tyler Matzek, COL
4. CF Billy Hamilton, CIN
5. 2B Kolten Wong, STL

Were they in the AL, only deGrom would crack the ballot, as the AL crop put the NL's to shame. Part of that is due to experienced international players, who are subject to bizarre treatment by the BBWAA. The BBWAA has rarely voted for Japanese rookies in recent years, but Jose Abreu will win the award despite having high-level experience in Cuba. Personally, I draw no distinction between international free agents and minor league graduates for award purposes.

Abreu is an easy choice for the top of the ballot, as his 55 RAR ranked sixth among all AL hitters and led first basemen, and his 7.2 RG ranked fourth in the league. The rest of the ballot spots go to pitchers, although Minnesota's Danny Santana could certainly be considered with 30 RAR, and George Springer might have been a contender even with his late recall had he not been injured.

The three starters who made my ballot were Collin McHugh, Masahiro Tanaka, and Yordano Ventura. Tanaka looked like a Cy Young contender until his injury, but McHugh ended up edging him in RRA (3.01 to 3.05) in addition to pitching eighteen more innings. And while I wouldn't have guessed it (largely due to McHugh toiling in obscurity with Houston), McHugh's peripherals were every bit a match for Tanaka's. It is worth noting that Tanaka, despite his experience pitching in NPB, is a year younger than McHugh.

Ventura pitched many more innings than either (183), but wasn't as good on a rate basis and despite his ridiculous velocity struck out two batters fewer per game than either. In the end, 40 RAR for McHugh, 35 for Ventura, and 34 for Tanaka make it easy to justify any order depending on what factors one values. I've slid Tanaka ahead of Ventura thanks to better peripherals.
Apologies to Matt Shoemaker and Marcus Stroman, but the last spot on my ballot goes to Dellin Betances. Betances, unlike Giles in the NL, was a workhorse out of the pen, throwing 90 innings which helped him lead all AL relievers with 33 RAR. Only Andrew Miller and Brad Boxberger topped his 15.0 strikeout rate, and Betances was outstanding in the peripheral run averages as well. Were there an award for best reliever, Betances would get my vote, but on the rookie ballot he's just fifth in a strong season for the AL:

1. 1B Jose Abreu, CHA
2. SP Collin McHugh, HOU
3. SP Masahiro Tanaka, NYA
4. SP Yordano Ventura, KC
5. RP Dellin Betances, NYA
phttp://email@example.com:blogger.com,1999:blog-12133335.post-54129450438198763952014-10-04T16:19:00.000-04:002014-10-30T22:19:49.676-04:00End of Season Statistics, 2014These reports will be trickling out over the next month as I don't have as much time as I'd like to devote to them right now. I wanted to get the park factors out as soon as possible and everything else will be added later.

The spreadsheets are published as Google Spreadsheets, which you can download in Excel format by changing the extension in the address from "=html" to "=xls". That way you can download them and manipulate things however you see fit.

The data comes from a number of different sources. Most of the basic data comes from Doug's Stats, which is a very handy site, or Baseball-Reference. KJOK's park database provided some of the data used in the park factors, but for recent seasons park data comes from B-R. Data on pitcher's batted ball types allowed, doubles/triples allowed, and inherited/bequeathed runners comes from Baseball Prospectus.

The basic philosophy behind these stats is to use the simplest methods that have acceptable accuracy. Of course, "acceptable" is in the eye of the beholder, namely me. I use Pythagenpat not because other run/win converters, like a constant RPW or a fixed exponent are not accurate enough for this purpose, but because it's mine and it would be kind of odd if I didn't use it.

If I seem to be a stickler for purity in my critiques of others' methods, I'd contend it is usually in a theoretical sense, not an input sense. So when I exclude hit batters, I'm not saying that hit batters are worthless or that they *should* be ignored; it's just easier not to mess with them and not that much less accurate.

I also don't really have a problem with people using sub-standard methods (say, Basic RC) as long as they acknowledge that they are sub-standard. If someone pretends that Basic RC doesn't undervalue walks or cause problems when applied to extreme individuals, I'll call them on it; if they explain its shortcomings but use it regardless, I accept that. Take these last three paragraphs as my acknowledgment that some of the statistics displayed here have shortcomings as well.

The League spreadsheet is pretty straightforward--it includes league totals and averages for a number of categories, most or all of which are explained at appropriate junctures throughout this piece. The advent of interleague play has created two different sets of league totals--one for the offense of league teams and one for the defense of league teams. Before interleague play, these two were identical. I do not present both sets of totals (you can figure the defensive ones yourself from the team spreadsheet, if you desire), just those for the offenses. The exception is for the defense-specific statistics, like innings pitched and quality starts. The figures for those categories in the league report are for the defenses of the league's teams. However, I do include each league's breakdown of basic pitching stats between starters and relievers (denoted by "s" or "r" prefixes), and so summing those will yield the totals from the pitching side. The one abbreviation you might not recognize is "N"--this is the league average of runs/game for one team, and it will pop up again.

The Team spreadsheet focuses on overall team performance--wins, losses, runs scored, runs allowed. The columns included are: Park Factor (PF), Home Run Park Factor (PFhr), Winning Percentage (W%), Expected W% (EW%), Predicted W% (PW%), wins, losses, runs, runs allowed, Runs Created (RC), Runs Created Allowed (RCA), Home Winning Percentage (HW%), Road Winning Percentage (RW%) [exactly what they sound like--W% at home and on the road], Runs/Game (R/G), Runs Allowed/Game (RA/G), Runs Created/Game (RCG), Runs Created Allowed/Game (RCAG), and Runs Per Game (the average number of runs scored an allowed per game). Ideally, I would use outs as the denominator, but for teams, outs and games are so closely related that I don't think it's worth the extra effort.

The runs and Runs Created figures are unadjusted, but the per-game averages are park-adjusted, except for RPG which is also raw. Runs Created and Runs Created Allowed are both based on a simple Base Runs formula. The formula is:

A = H + W - HR - CS
B = (2TB - H - 4HR + .05W + 1.5SB)*.76
C = AB - H
D = HR
Naturally, A*B/(B + C) + D.

I have explained the methodology used to figure the PFs before, but the cliff's notes version is that they are based on five years of data when applicable, include both runs scored and allowed, and they are regressed towards average (PF = 1), with the amount of regression varying based on the number of years of data used. There are factors for both runs and home runs. The initial PF (not shown) is:

iPF = (H*T/(R*(T - 1) + H) + 1)/2
where H = RPG in home games, R = RPG in road games, T = # teams in league (14 for AL and 16 for NL). Then the iPF is converted to the PF by taking x*iPF + (1-x), where x = .6 if one year of data is used, .7 for 2, .8 for 3, and .9 for 4+.

It is important to note, since there always seems to be confusion about this, that these park factors already incorporate the fact that the average player plays 50% on the road and 50% at home. That is what the adding one and dividing by 2 in the iPF is all about. So if I list Fenway Park with a 1.02 PF, that means that it actually increases RPG by 4%.

In the calculation of the PFs, I did not get picky and take out "home" games that were actually at neutral sites.

There are also Team Offense and Defense spreadsheets. These include the following categories:

Team offense: Plate Appearances, Batting Average (BA), On Base Average (OBA), Slugging Average (SLG), Secondary Average (SEC), Walks per At Bat (WAB), Isolated Power (SLG - BA), R/G at home (hR/G), and R/G on the road (rR/G) BA, OBA, SLG, WAB, and ISO are park-adjusted by dividing by the square root of park factor (or the equivalent; WAB = (OBA - BA)/(1 - OBA) and ISO = SLG - BA).

Team defense: Innings Pitched, BA, OBA, SLG, Innings per Start (IP/S), Starter's eRA (seRA), Reliever's eRA (reRA), Quality Start Percentage (QS%), RA/G at home (hRA/G), RA/G on the road (rRA/G), Battery Mishap Rate (BMR), Modified Fielding Average (mFA), and Defensive Efficiency Record (DER). BA, OBA, and SLG are park-adjusted by dividing by the square root of PF; seRA and reRA are divided by PF.

The three fielding metrics I've included are limited it only to metrics that a) I can calculate myself and b) are based on the basic available data, not specialized PBP data. The three metrics are explained in this post, but here are quick descriptions of each:

1) BMR--wild pitches and passed balls per 100 baserunners = (WP + PB)/(H + W - HR)*100

2) mFA--fielding average removing strikeouts and assists = (PO - K)/(PO - K + E)

3) DER--the Bill James classic, using only the PA-based estimate of plays made. Based on a suggestion by Terpsfan101, I've tweaked the error coefficient. Plays Made = PA - K - H - W - HR - HB - .64E and DER = PM/(PM + H - HR + .64E)

Next are the individual player reports. I defined a starting pitcher as one with 15 or more starts. All other pitchers are eligible to be included as a reliever. If a pitcher has 40 appearances, then they are included. Additionally, if a pitcher has 50 innings and less than 50% of his appearances are starts, he is also included as a reliever (this allows some swingmen type pitchers who wouldn't meet either the minimum start or appearance standards to get in).

For all of the player reports, ages are based on simply subtracting their year of birth from 2013. I realize that this is not compatible with how ages are usually listed and so "Age 27" doesn't necessarily correspond to age 27 as I list it, but it makes everything a heckuva lot easier, and I am more interested in comparing the ages of the players to their contemporaries, for which case it makes very little difference. The "R" category records rookie status with a "R" for rookies and a blank for everyone else; I've trusted Baseball Prospectus on this. Also, all players are counted as being on the team with whom they played/pitched (IP or PA as appropriate) the most.

For relievers, the categories listed are: Games, Innings Pitched, estimated Plate Appearances (PA), Run Average (RA), Relief Run Average (RRA), Earned Run Average (ERA), Estimated Run Average (eRA), DIPS Run Average (dRA), Strikeouts per Game (KG), Walks per Game (WG), Guess-Future (G-F), Inherited Runners per Game (IR/G), Batting Average on Balls in Play (%H), Runs Above Average (RAA), and Runs Above Replacement (RAR).

IR/G is per relief appearance (G - GS); it is an interesting thing to look at, I think, in lieu of actual leverage data. You can see which closers come in with runners on base, and which are used nearly exclusively to start innings. Of course, you can't infer too much; there are bad relievers who come in with a lot of people on base, not because they are being used in high leverage situations, but because they are long men being used in low-leverage situations already out of hand.

For starting pitchers, the columns are: Wins, Losses, Innings Pitched, Estimated Plate Appearances (PA), RA, RRA, ERA, eRA, dRA, KG, WG, G-F, %H, Pitches/Start (P/S), Quality Start Percentage (QS%), RAA, and RAR. RA and ERA you know--R*9/IP or ER*9/IP, park-adjusted by dividing by PF. The formulas for eRA and dRA are based on the same Base Runs equation and they estimate RA, not ERA.

* eRA is based on the actual results allowed by the pitcher (hits, doubles, home runs, walks, strikeouts, etc.). It is park-adjusted by dividing by PF.

* dRA is the classic DIPS-style RA, assuming that the pitcher allows a league average %H, and that his hits in play have a league-average S/D/T split. It is park-adjusted by dividing by PF.

The formula for eRA is:

A = H + W - HR
B = (2*TB - H - 4*HR + .05*W)*.78
C = AB - H = K + (3*IP - K)*x (where x is figured as described below for PA estimation and is typically around .93) = PA (from below) - H - W
eRA = (A*B/(B + C) + HR)*9/IP

To figure dRA, you first need the estimate of PA described below. Then you calculate W, K, and HR per PA (call these %W, %K, and %HR). Percentage of balls in play (BIP%) = 1 - %W - %K - %HR. This is used to calculate the DIPS-friendly estimate of %H (H per PA) as e%H = Lg%H*BIP%.

Now everything has a common denominator of PA, so we can plug into Base Runs:

A = e%H + %W
B = (2*(z*e%H + 4*%HR) - e%H - 5*%HR + .05*%W)*.78
C = 1 - e%H - %W - %HR
cRA = (A*B/(B + C) + %HR)/C*a

z is the league average of total bases per non-HR hit (TB - 4*HR)/(H - HR), and a is the league average of (AB - H) per game.

In the past couple years I've presented a couple of batted ball RA estimates. I've removed these this year, not just because batted ball data exhibits questionable reliability but because these metrics were complicated to figure, required me to collate the batted ball data, and were not personally useful to me. I figure these stats for my own enjoyment and have in some form or another going back to 1997. I share them here only because I would do it anyway, so if I'm not interested in certain categories, there's no reason to keep presenting them.

Instead, I'm showing strikeout and walk rate, both expressed as per game. By game I mean not 9 innings but rather the league average of PA/G. I have always been a proponent of using PA and not IP as the denominator for non-run pitching rates, and now the use of per PA rates is widespread. Usually these are expressed as K/PA and W/PA, or equivalently, percentage of PA with a strikeout or walk. I don't believe that any site publishes these as K and W per equivalent game as I am here. This is not better than K%--it's simply applying a scalar multiplier. I like it because it generally follows the same scale as the familiar K/9.

To facilitate this, I've finally corrected a flaw in the formula I use to estimate plate appearances for pitchers. Previously, I've done it the lazy way by not splitting strikeouts out from other outs. I am now using this formula to estimate PA (where PA = AB + W):

PA = K + (3*IP - K)*x + H + W
Where x = league average of (AB - H - K)/(3*IP - K)

Then KG = K*Lg(PA/G) and WG = W*Lg(PA/G).

G-F is a junk stat, included here out of habit because I've been including it for years. It was intended to give a quick read of a pitcher's expected performance in the next season, based on eRA and strikeout rate. Although the numbers vaguely resemble RAs, it's actually unitless. As a rule of thumb, anything under four is pretty good for a starter. G-F = 4.46 + .095(eRA) - .113(K*9/IP). It is a junk stat. JUNK STAT JUNK STAT JUNK STAT. Got it?

%H is BABIP, more or less--%H = (H - HR)/(PA - HR - K - W), where PA was estimated above. Pitches/Start includes all appearances, so I've counted relief appearances as one-half of a start (P/S = Pitches/(.5*G + .5*GS). QS% is just QS/(G - GS); I don't think it's particularly useful, but Doug's Stats include QS so I include it.

I've used a stat called Relief Run Average (RRA) in the past, based on Sky Andrecheck's article in the August 1999 By the Numbers; that one only used inherited runners, but I've revised it to include bequeathed runners as well, making it equally applicable to starters and relievers. I am using RRA as the building block for baselined value estimates for all pitchers this year. I explained RRA in this article , but the bottom line formulas are:

BRSV = BRS - BR*i*sqrt(PF)
IRSV = IR*i*sqrt(PF) - IRS
RRA = ((R - (BRSV + IRSV))*9/IP)/PF

The two baselined stats are Runs Above Average (RAA) and Runs Above Replacement (RAR). RAA uses the league average runs/game (N) for both starters and relievers, while RAR uses separate replacement levels for starters and relievers. Thus, RAA and RAR will be pretty close for relievers:

RAA = (LgRA - RRA)*IP/9
RAR (relievers) = (1.11*LgRA - RRA)*IP/9
RAR (starters) = (1.28*LgRA - RRA)*IP/9

All players with 300 or more plate appearances are included in the Hitters spreadsheets (along with some players close to the cutoff point who I was interested in). Each is assigned one position, the one at which they appeared in the most games. The statistics presented are: Games played (G), Plate Appearances (PA), Outs (O), Batting Average (BA), On Base Average (OBA), Slugging Average (SLG), Secondary Average (SEC), Runs Created (RC), Runs Created per Game (RG), Speed Score (SS), Hitting Runs Above Average (HRAA), Runs Above Average (RAA), Hitting Runs Above Replacement (HRAR), and Runs Above Replacement (RAR).

I do not bother to include hit batters, so take note of that for players who do get plunked a lot. Therefore, PA are simply AB + W. Outs are AB - H + CS. BA and SLG you know, but remember that without HB and SF, OBA is just (H + W)/(AB + W). Secondary Average = (TB - H + W)/AB = SLG - BA + (OBA - BA)/(1 - OBA). I have not included net steals as many people (and Bill James himself) do--it is solely hitting events.

BA, OBA, and SLG are park-adjusted by dividing by the square root of PF. This is an approximation, of course, but I'm satisfied that it works well. The goal here is to adjust for the win value of offensive events, not to quantify the exact park effect on the given rate. I use the BA/OBA/SLG-based formula to figure SEC, so it is park-adjusted as well.

Runs Created is actually Paul Johnson's ERP, more or less. Ideally, I would use a custom linear weights formula for the given league, but ERP is just so darn simple and close to the mark that it's hard to pass up. I still use the term "RC" partially as a homage to Bill James (seriously, I really like and respect him even if I've said negative things about RC and Win Shares), and also because it is just a good term. I like the thought put in your head when you hear "creating" a run better than "producing", "manufacturing", "generating", etc. to say nothing of names like "equivalent" or "extrapolated" runs. None of that is said to put down the creators of those methods--there just aren't a lot of good, unique names available. Anyway, RC = (TB + .8H + W + .7SB - CS - .3AB)*.322.

RC is park adjusted by dividing by PF, making all of the value stats that follow park adjusted as well. RG, the rate, is RC/O*25.5. I do not believe that outs are the proper denominator for an individual rate stat, but I also do not believe that the distortions caused are that bad. (I still intend to finish my rate stat series and discuss all of the options in excruciating detail, but alas you'll have to take my word for it now).

I have decided to switch to a watered-down version of Bill James' Speed Score this year; I only use four of his categories. Previously I used my own knockoff version called Speed Unit, but trying to keep it from breaking down every few years was a wasted effort.

Speed Score is the average of four components, which I'll call a, b, c, and d:

a = ((SB + 3)/(SB + CS + 7) - .4)*20
b = sqrt((SB + CS)/(S + W))*14.3
c = ((R - HR)/(H + W - HR) - .1)*25
d = T/(AB - HR - K)*450

James actually uses a sliding scale for the triples component, but it strikes me as needlessly complex and so I've streamlined it. I also changed some of his division to mathematically equivalent multiplications.

There are a whopping four categories that compare to a baseline; two for average, two for replacement. Hitting RAA compares to a league average hitter; it is in the vein of Pete Palmer's Batting Runs. RAA compares to an average hitter at the player's primary position. Hitting RAR compares to a "replacement level" hitter; RAR compares to a replacement level hitter at the player's primary position. The formulas are:

HRAA = (RG - N)*O/25.5
RAA = (RG - N*PADJ)*O/25.5
HRAR = (RG - .73*N)*O/25.5
RAR = (RG - .73*N*PADJ)*O/25.5

PADJ is the position adjustment, and it is based on 2002-2011 offensive data. For catchers it is .89; for 1B/DH, 1.17; for 2B, .97; for 3B, 1.03; for SS, .93; for LF/RF, 1.13; and for CF, 1.02. I had been using the 1992-2001 data as a basis for the last ten years, but finally have done an update. I'm a little hesitant about this update, as the middle infield positions are the biggest movers (higher positional adjustments, meaning less positional credit). I have no qualms for second base, but the shortstop PADJ is out of line with the other position adjustments widely in use and feels a bit high to me. But there are some decent points to be made in favor of offensive adjustments, and I'll have a bit more on this topic in general below.

That was the mechanics of the calculations; now I'll twist myself into knots trying to justify them. If you only care about the how and not the why, stop reading now.

The first thing that should be covered is the philosophical position behind the statistics posted here. They fall on the continuum of ability and value in what I have called "performance". Performance is a technical-sounding way of saying "Whatever arbitrary combination of ability and value I prefer".

With respect to park adjustments, I am not interested in how any particular player is affected, so there is no separate adjustment for lefties and righties for instance. The park factor is an attempt to determine how the park affects run scoring rates, and thus the win value of runs.

I apply the park factor directly to the player's statistics, but it could also be applied to the league context. The advantage to doing it my way is that it allows you to compare the component statistics (like Runs Created or OBA) on a park-adjusted basis. The drawback is that it creates a new theoretical universe, one in which all parks are equal, rather than leaving the player grounded in the actual context in which he played and evaluating how that context (and not the player's statistics) was altered by the park.

The good news is that the two approaches are essentially equivalent; in fact, they are equivalent if you assume that the Runs Per Win factor is equal to the RPG. Suppose that we have a player in an extreme park (PF = 1.15, approximately like Coors Field pre-humidor) who has an 8 RG before adjusting for park, while making 350 outs in a 4.5 N league. The first method of park adjustment, the one I use, converts his value into a neutral park, so his RG is now 8/1.15 = 6.957. We can now compare him directly to the league average:

RAA = (6.957 - 4.5)*350/25.5 = +33.72

The second method would be to adjust the league context. If N = 4.5, then the average player in this park will create 4.5*1.15 = 5.175 runs. Now, to figure RAA, we can use the unadjusted RG of 8:

RAA = (8 - 5.175)*350/25.5 = +38.77

These are not the same, as you can obviously see. The reason for this is that they take place in two different contexts. The first figure is in a 9 RPG (2*4.5) context; the second figure is in a 10.35 RPG (2*4.5*1.15) context. Runs have different values in different contexts; that is why we have RPW converters in the first place. If we convert to WAA (using RPW = RPG, which is only an approximation, so it's usually not as tidy as it appears below), then we have:

WAA = 33.72/9 = +3.75
WAA = 38.77/10.35 = +3.75

Once you convert to wins, the two approaches are equivalent. The other nice thing about the first approach is that once you park-adjust, everyone in the league is in the same context, and you can dispense with the need for converting to wins at all. You still might want to convert to wins, and you'll need to do so if you are comparing the 2014 players to players from other league-seasons (including between the AL and NL in the same year), but if you are only looking to compare Jose Bautista to Miguel Cabrera, it's not necessary. WAR is somewhat ubiquitous now, but personally I prefer runs when possible--why mess with decimal points if you don't have to?

The park factors used to adjust player stats here are run-based. Thus, they make no effort to project what a player "would have done" in a neutral park, or account for the difference effects parks have on specific events (walks, home runs, BA) or types of players. They simply account for the difference in run environment that is caused by the park (as best I can measure it). As such, they don't evaluate a player within the actual run context of his team's games; they attempt to restate the player's performance as an equivalent performance in a neutral park.

I suppose I should also justify the use of sqrt(PF) for adjusting component statistics. The classic defense given for this approach relies on basic Runs Created--runs are proportional to OBA*SLG, and OBA*SLG/PF = OBA/sqrt(PF)*SLG/sqrt(PF). While RC may be an antiquated tool, you will find that the square root adjustment is fairly compatible with linear weights or Base Runs as well. I am not going to take the space to demonstrate this claim here, but I will some time in the future.

Many value figures published around the sabersphere adjust for the difference in quality level between the AL and NL. I don't, but this is a thorny area where there is no right or wrong answer as far as I'm concerned. I also do not make an adjustment in the league averages for the fact that the overall NL averages include pitcher batting and the AL does not (not quite true in the era of interleague play, but you get my drift).

The difference between the leagues may not be precisely calculable, and it certainly is not constant, but it is real. If the average player in the AL is better than the average player in the NL, it is perfectly reasonable to expect the average AL player to have more RAR than the average NL player, and that will not happen without some type of adjustment. On the other hand, if you are only interested in evaluating a player relative to his own league, such an adjustment is not necessarily welcome.

The league argument only applies cleanly to metrics baselined to average. Since replacement level compares the given player to a theoretical player that can be acquired on the cheap, the same pool of potential replacement players should by definition be available to the teams of each league. One could argue that if the two leagues don't have equal talent at the major league level, they might not have equal access to replacement level talent--except such an argument is at odds with the notion that replacement level represents talent that is truly "freely available".

So it's hard to justify the approach I take, which is to set replacement level relative to the average runs scored in each league, with no adjustment for the difference in the leagues. The best justification is that it's simple and it treats each league as its own universe, even if in reality they are connected.

The replacement levels I have used here are very much in line with the values used by other sabermetricians. This is based both on my own "research", my interpretation of other's people research, and a desire to not stray from consensus and make the values unhelpful to the majority of people who may encounter them.

Replacement level is certainly not settled science. There is always going to be room to disagree on what the baseline should be. Even if you agree it should be "replacement level", any estimate of where it should be set is just that--an estimate. Average is clean and fairly straightforward, even if its utility is questionable; replacement level is inherently messy. So I offer the average baseline as well.

For position players, replacement level is set at 73% of the positional average RG (since there's a history of discussing replacement level in terms of winning percentages, this is roughly equivalent to .350). For starting pitchers, it is set at 128% of the league average RA (.380), and for relievers it is set at 111% (.450).

I am still using an analytical structure that makes the comparison to replacement level for a position player by applying it to his hitting statistics. This is the approach taken by Keith Woolner in VORP (and some other earlier replacement level implementations), but the newer metrics (among them Rally and Fangraphs' WAR) handle replacement level by subtracting a set number of runs from the player's total runs above average in a number of different areas (batting, fielding, baserunning, positional value, etc.), which for lack of a better term I will call the subtraction approach.

The offensive positional adjustment makes the inherent assumption that the average player at each position is equally valuable. I think that this is close to being true, but it is not quite true. The ideal approach would be to use a defensive positional adjustment, since the real difference between a first baseman and a shortstop is their defensive value. When you bat, all runs count the same, whether you create them as a first baseman or as a shortstop.

That being said, using "replacement hitter at position" does not cause too many distortions. It is not theoretically correct, but it is practically powerful. For one thing, most players, even those at key defensive positions, are chosen first and foremost for their offense. Empirical work by Keith Woolner has shown that the replacement level hitting performance is about the same for every position, relative to the positional average.

Figuring what the defensive positional adjustment should be, though, is easier said than done. Therefore, I use the offensive positional adjustment. So if you want to criticize that choice, or criticize the numbers that result, be my guest. But do not claim that I am holding this up as the correct analytical structure. I am holding it up as the most simple and straightforward structure that conforms to reality reasonably well, and because while the numbers may be flawed, they are at least based on an objective formula that I can figure myself. If you feel comfortable with some other assumptions, please feel free to ignore mine.

That still does not justify the use of HRAR--hitting runs above replacement--which compares each hitter, regardless of position, to 73% of the league average. Basically, this is just a way to give an overall measure of offensive production without regard for position with a low baseline. It doesn't have any real baseball meaning.

A player who creates runs at 90% of the league average could be above-average (if he's a shortstop or catcher, or a great fielder at a less important fielding position), or sub-replacement level (DHs that create 4 runs a game are not valuable properties). Every player is chosen because his total value, both hitting and fielding, is sufficient to justify his inclusion on the team. HRAR fails even if you try to justify it with a thought experiment about a world in which defense doesn't matter, because in that case the absolute replacement level (in terms of RG, without accounting for the league average) would be much higher than it is currently.

The specific positional adjustments I use are based on 2002-2011 data. I stick with them because I have not seen compelling evidence of a change in the degree of difficulty or scarcity between the positions between now and then, and because I think they are fairly reasonable. The positions for which they diverge the most from the defensive position adjustments in common use are 2B, 3B, and CF. Second base is considered a premium position by the offensive PADJ (.97), while third base and center field have similar adjustments in the opposite direction (1.03 and 1.02).

Another flaw is that the PADJ is applied to the overall league average RG, which is artificially low for the NL because of pitcher's batting. When using the actual league average runs/game, it's tough to just remove pitchers--any adjustment would be an estimate. If you use the league total of runs created instead, it is a much easier fix.

One other note on this topic is that since the offensive PADJ is a proxy for average defensive value by position, ideally it would be applied by tying it to defensive playing time. I have done it by outs, though.

The reason I have taken this flawed path is because 1) it ties the position adjustment directly into the RAR formula rather than leaving it as something to subtract on the outside and more importantly 2) there's no straightforward way to do it. The best would be to use defensive innings--set the full-time player to X defensive innings, figure how Derek Jeter's innings compared to X, and adjust his PADJ accordingly. Games in the field or games played are dicey because they can cause distortion for defensive replacements. Plate Appearances avoid the problem that outs have of being highly related to player quality, but they still carry the illogic of basing it on offensive playing time. And of course the differences here are going to be fairly small (a few runs). That is not to say that this way is preferable, but it's not horrible either, at least as far as I can tell.

To compare this approach to the subtraction approach, start by assuming that a replacement level shortstop would create .86*.73*4.5 = 2.825 RG (or would perform at an overall level of equivalent value to being an average fielder at shortstop while creating 2.825 runs per game). Suppose that we are comparing two shortstops, each of whom compiled 600 PA and played an equal number of defensive games and innings (and thus would have the same positional adjustment using the subtraction approach). Alpha made 380 outs and Bravo made 410 outs, and each ranked as dead-on average in the field.

The difference in overall RAR between the two using the subtraction approach would be equal to the difference between their offensive RAA compared to the league average. Assuming the league average is 4.5 runs, and that both Alpha and Bravo created 75 runs, their offensive RAAs are:

Alpha = (75*25.5/380 - 4.5)*380/25.5 = +7.94

Similarly, Bravo is at +2.65, and so the difference between them will be 5.29 RAR.

Using the flawed approach, Alpha's RAR will be:

(75*25.5/380 - 4.5*.73*.86)*380/25.5 = +32.90

Bravo's RAR will be +29.58, a difference of 3.32 RAR, which is two runs off of the difference using the subtraction approach.

The downside to using PA is that you really need to consider park effects if you, whereas outs allow you to sidestep park effects. Outs are constant; plate appearances are linked to OBA. Thus, they not only depend on the offensive context (including park factor), but also on the quality of one's team. Of course, attempting to adjust for team PA differences opens a huge can of worms which is not really relevant; for now, the point is that using outs for individual players causes distortions, sometimes trivial and sometimes bothersome, but almost always makes one's life easier.

I do not include fielding (or baserunning outside of steals, although that is a trivial consideration in comparison) in the RAR figures--they cover offense and positional value only). This in no way means that I do not believe that fielding is an important consideration in player valuation. However, two of the key principles of these stat reports are 1) not incorporating any data that is not readily available and 2) not simply including other people's results (of course I borrow heavily from other people's methods, but only adapting methodology that I can apply myself).

Any fielding metric worth its salt will fail to meet either criterion--they use zone data or play-by-play data which I do not have easy access to. I do not have a fielding metric that I have stapled together myself, and so I would have to simply lift other analysts' figures.

Setting the practical reason for not including fielding aside, I do have some reservations about lumping fielding and hitting value together in one number because of the obvious differences in reliability between offensive and fielding metrics. In theory, they absolutely should be put together. But in practice, I believe it would be better to regress the fielding metric to a point at which it would be roughly equivalent in reliability to the offensive metric.

Offensive metrics have error bars associated with them, too, of course, and in evaluating a single season's value, I don't care about the vagaries that we often lump together as "luck". Still, there are errors in our assessment of linear weight values and players that collect an unusual proportion of infield hits or hits to the left side, errors in estimation of park factor, and any number of other factors that make their events more or less valuable than an average event of that type.

Fielding metrics offer up all of that and more, as we cannot be nearly as certain of true successes and failures as we are when analyzing offense. Recent investigations, particularly by Colin Wyers, have raised even more questions about the level of uncertainty. So, even if I was including a fielding value, my approach would be to assume that the offensive value was 100% reliable (which it isn't), and regress the fielding metric relative to that (so if the offensive metric was actually 70% reliable, and the fielding metric 40% reliable, I'd treat the fielding metric as .4/.7 = 57% reliable when tacking it on, to illustrate with a simplified and completely made up example presuming that one could have a precise estimate of nebulous "reliability").

Given the inherent assumption of the offensive PADJ that all positions are equally valuable, once RAR has been figured for a player, fielding value can be accounted for by adding on his runs above average relative to a player at his own position. If there is a shortstop that is -2 runs defensively versus an average shortstop, he is without a doubt a plus defensive player, and a more valuable defensive player than a first baseman who was +1 run better than an average first baseman. Regardless, since it was implicitly assumed that they are both average defensively for their position when RAR was calculated, the shortstop will see his value docked two runs. This DOES NOT MEAN that the shortstop has been penalized for his defense. The whole process of accounting for positional differences, going from hitting RAR to positional RAR, has benefited him.

I've found that there is often confusion about the treatment of first baseman and designated hitters in my PADJ methodology, since I consider DHs as in the same pool as first baseman. The fact of the matter is that first baseman outhit DH. There is any number of potential explanations for this; DHs are often old or injured, players hit worse when DHing than they do when playing the field, etc. This actually helps first baseman, since the DHs drag the average production of the pool down, thus resulting in a lower replacement level than I would get if I considered first baseman alone.

However, this method does assume that a 1B and a DH have equal defensive value. Obviously, a DH has no defensive value. What I advocate to correct this is to treat a DH as a bad defensive first baseman, and thus knock another five or ten runs off of his RAR for a full-time player. I do not incorporate this into the published numbers, but you should keep it in mind. However, there is no need to adjust the figures for first baseman upwards --the only necessary adjustment is to take the DHs down a notch.

Finally, I consider each player at his primary defensive position (defined as where he appears in the most games), and do not weight the PADJ by playing time. This does shortchange a player like Ben Zobrist (who saw significant time at a tougher position than his primary position), and unduly boost a player like Buster Posey (who logged a lot of games at a much easier position than his primary position). For most players, though, it doesn't matter much. I find it preferable to make manual adjustments for the unusual cases rather than add another layer of complexity to the whole endeavor.

2014 Park Factors

2014 League

2014 Team

2014 Team Offense

2014 Team Defense

2014 AL Relievers

2014 NL Relievers

2014 AL Starters

2014 NL Starters

2014 AL Hitters

2014 NL Hittersphttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-31748821766539231002014-09-29T20:06:00.000-04:002014-09-29T20:06:35.056-04:00Crude Playoff Odds--2014In a world where there are plenty of sources for playoff odds that actually take into account the personnel currently available for each team, use projected rather than 2014-only performance, consider pitching matchups, and the like, there is no real reason for me to post this. Nonetheless, here are some very crude playoff odds. The key assumptions:

• Team strength is constant and is measured by my Crude Team Ratings, using an equal weight of W%, EW%, and PW% regressed with 69 games of .500
• Home field advantage is uniform and the home team wins 54.5% of the time

From there, the math is pretty simple and I will present with little explanation. First, the ratings which are used to feed the estimates:

These ratings don't know or care that Oakland has stopped scoring runs (or that the magic influence of Cespedes is gone); they don't know that Garret Richards is injured; they don't know anything other than these team's schedules, their wins and losses, runs and runs allowed, and BA/OBA/SLG and allowed.

From here, it's just plug and chug. Wildcard game odds:

Home field offsets OAK's perceived strength advantage over KC.

Division series:

Here, "P" is the probability that the series occurs; P(H win) is the probability that the home team wins should the series occur; and P(H) is the probability that the series occurs and that the home team wins [P*P(H win)].

LCS:

World Series:

The probability of one of the "rivalry" matchups (OAK/SF, LAA/LA, BAL/WAS, or KC/STL) occurring is 21.5%, which is not bad at all. LAA/LA is the second-most likely series; the least likely is KC/SF, which is good because that is the one that I would least like to see.

Putting it all together:

One might be surprised by OAK having better odds to win a Division Series and each subsequent round than KC even though the latter is favored in their wildcard game, but the ratings (perhaps incorrectly) think the A's are one of the strongest teams in the playoffs, and thus the Royals wild card game edge, solely due to home field, is insufficiently large to keep them ahead. KC would need a rating of around 122 to have an equal World Series win probability to the A's.

The odds suggest a 57.6% chance that the junior circuit wins it all, not a surprise given that the AL teams rank 1-2-3-6-7 in strength and they have home field in the World Series to boot. In fact, the AL is favored in 22 of 25 potential matchups, with the only exceptions being Washington against Detroit or Kansas City and Los Angeles against Kansas City.phttp://email@example.com:blogger.com,1999:blog-12133335.post-53689191324339679912014-09-06T09:40:00.000-04:002014-09-06T09:40:05.304-04:00Great Moments in Yahoo! Box Scores

phttp://email@example.com:blogger.com,1999:blog-12133335.post-49376670631937552472014-08-23T15:15:00.000-04:002014-08-23T15:15:26.373-04:00This House is Falling ApartOn May 4, Ohio State was suffering its worst ever loss in the eighteen-year history of Bill Davis Stadium, trailing Iowa 17-2. Only once had OSU lost a home game by a larger margin than the fifteen that they would wind up losing the game by--and that was in 1899. On two other occasions OSU had lost a home game by fifteen runs (to add insult to injury, both were against Michigan, in 1934 and 1989).

The music choice on the PA was unintentionally appropriate. Although ostensibly an upbeat song by an Ohio band, Walk the Moon's "Anna Sun" features the infectious chorus:

What do you know? This house is falling apart
What can I say? This house is falling apart
We got no money, but we got heart
We're gonna rattle this ghost town
This house is falling apart

I found it quite apropos for the moment, because the house that is the OSU baseball program is falling apart under the direction of Coach Greg Beals, and the historic home beatdown was the nadir of the season.

Or was it? Beals' tactics present any number of other crazy stupid baserunning events that encapsulate a program being run by a coach with the mind of a junior high coach more concerned with winning games than teaching kids how to actually play baseball. At least in that case, outlandish baserunning can produce victories, whereas at the collegiate level it simply produces embarrassment. (Search hashtag #BealsBall if you are interested in an account of these--the 2014 highlight was a batter who hit a home run being called out for passing the runner at first.)

I usually am not this cynical about OSU sports; I am a true believer, a fan who tends towards homerism. Baseball is a little tougher on this front for me, though, since I can't just turn off my analytical approach to the game just because the uniforms are scarlet and gray. But my sarcasm is aimed at squarely at the professional coaches, not at the student-athletes on partial scholarships who bust their tail for the greater glory of The Ohio State University.

The Buckeyes got off to a pretty solid start to the season, going 8-6 before playing their first home game; while that record does not sound particularly impressive, it included wins over Auburn, Oklahoma, and Oregon, with three of the losses coming in the final stretch of four games in Oregon (three against the Ducks and one against the Beavers). OSU then won its first five home games to fatten the record for 13-6 before Big Ten play opened.

OSU's first Big Ten series was scheduled to be played at East Lansing, but it was moved to Columbus on account of weather and the Bucks took two of three from the Spartans. Defending champs Indiana came in the next weekend and unceremoniously swept the series, and a trip to Lincoln the following weekend resulted in a Cornhusker sweep on three one-run wins, the latter two walkoffs including a three-run ninth inning implosion in the middle game. When Ohio lost the first game of its next home series against Penn State, the seven-game conference losing streak was the first for the program since 1987.

1987 was a year that came up repeatedly in 2014. While the Bucks won the final two games from PSU and two of three at Purdue and hosting Iowa (the debacle described to open this post notwithstanding), they closed the conference campaign by losing two of three at Ann Arbor and hosting Northwestern. The end result was a 10-14 Big Ten record, OSU's worst since 1987. And while all this was going on in Big Ten play, OSU was not exactly tearing it up in mid-week games, going 7-6 in such games (including taking two of three in a weekend series from Murray State). OSU was just 5-12 on the road (.294), the program's worst showing since 1972 (3-14).

In my season preview post, I made a grievous error, stating that the top six finishers in the conference qualified for the Big Ten tournament. I was mistaken--the field was expanded to eight this year in preparation for the thirteen-team Big Ten of 2015. It was a fortuitous change, since otherwise the seventh-seeded Bucks would have been on the outside looking in. It was little matter, though, as one-run losses to Nebraska and Illinois ended OSU's season.

The final tally was a 30-28 (.517) record, seventh among Big Ten teams. OSU was also seventh in the conference in EW% (.545) and PW% (.523); Indiana led in all three with figures of .746, .803, .733. OSU averaged 4.79 runs to a conference average of 4.87, while the Buckeyes allowed just 4.36 runs per game versus an average of 4.66 (although in comparing these figures it's worth considering that Bill Davis Stadium is a fairly strong pitcher's park). OSU's offense was pretty much in line with the Big Ten average not only in terms of output but shape as well, with a .267 batting average, .103 walk/AB ratio, and .096 isolated power compared to averages of .271, .101, and .089. It seems difficult to believe that OSU actually had a greater than average power output, but power is significantly down in college baseball, and the Big Ten, never a strong power league (talent and weather likely being contributing factors), is no exception.

Offensively, OSU had a couple bright spots, but they were outweighed by disappointments or puzzling coaching decisions. At catcher, Aaron Gretz was one of the team's more productive hitters, creating 6.3 runs per game on the strength of a team-high .156 W/AB ratio, but got only 129 PA to 102 to his backup Conor Sabanosh. To be fair, Sabanosh created 5.7 RG himself and also had a good walk rate, but hit for less power than Gretz. Beals has never been willing to let Gretz run with the catching duties despite him appearing to be an adequate defender and consistently outperforming the other catcher with whom he competes for playing time.

First baseman Zach Ratcliff was a disappointment in his sophomore season, failing to show the power he had as a freshman by hitting just 2 longballs in 99 AB and turning in the least effective overall performance of any OSU hitter (.232/.262/.313). 1B/DH Josh Dezse enjoyed a bounceback season, although his injuries prevented him from returning to the Buckeye bullpen as expected. Still, his 5.5 RG and 5 homers were an offensive bright spot and he improved as the season went on.

OSU's other infield positions were subject to some interesting coaching decisions. Sophomore Troy Kuhn started the year at second base and led the team with 6 home runs and was second with 6.3 RG and +9 RAA. Third base was manned in the early part of the season by sophomore Jacob Bosiokovic, who was expected to be a key offensive contributor and power source, but hit just one homer and turned in a perfectly average 5.0 RG. Shortstop was supposed to be the province of sophomore Craig Nennig, but an early season injury left him sideline and opened the door for sophomore Nick Sergakis, who provided a needed jolt to the offense and became the leadoff hitter despite a team-low .053 W/AB. His overall line of 5.4 RG was a definite upgrade for the middle infield. But when Nennig returned from injury, Beals shuffled the infield to get Nennig back on the field. While Nennig is a superior fielder, he has yet to show any indication of being a productive hitter (.231/.331/.256 in 146 PA was a steup up from his debut campaign). This shifted Sergakis to second and Kuhn to third, leaving Bosiokovic to come off the bench and eventually get a look in left field.

Bosiokovic's chance in left field came because senior Tim Wetzel failed to reverse his junior year offensive collapse, struggling to a .223/.284/.285 line in 185 PA. Also getting significant playing time in both left and center were a pair of freshman, Ronnie Dawson and Troy Montgomery. Dawson emerged as OSU's offensive star, a .337/.385/.454 (7.0 RG, +12 RAA) hitter with flair and a certain fan favorite. Montgomery's debut was less captivating (.235/.297/.353). And in right field, junior Pat Porter caught the Wetzel junior curse, tumbling to a .229/.311/.329 line after being penciled in as a reliable #3 hitter. The only other Buck to get significant plate appearances was sophomore Ryan Leffer, who had a solid offensive line (.303/.355/.343) while earning time at third base and DH.

OSU's pitching staff wound up with a surprising ace, as freshman Tanner Tully was named Big Ten Freshman of the Year with a team-high 93 innings with +14 RAA and team-low 2.22 ERA and .7 W/9. While Tully's 3.20 eRA and 5.1 KG suggest that he is a strong regression candidate, performance-wise he was the clear leader of the OSU staff. Junior Ryan Riga started the year as #1, but struggled through injuries and was not particularly effective (4.85 RA and 6.01 eRA over 68 innings and 11 starts). Senior Greg Greve turned in the best season of his career, winning a team-high seven games and contributing 12 RAA over 85 innings. The two most common mid-week starters were sophomore Jake Post and freshman Zach Farmer. Post was inconsistent but showed flashes of being an effective starter when filling in for Riga in the weekend rotation (+1 RAA but a 5.17 eRA). Farmer pitched solidly (4.01 RA over 49 innings) but was diagnosed with leukemia and will miss the 2015 campaign, although all indications are that his prognosis is good which of course is paramount.

OSU's bullpen took a big step back from 2013, largely due to Trace Dempsey's regression from stud closer to wild and ineffective (-7 RAA and 4.9 W/9). Freshman Travis Lakins was the bright spot of OSU's pen, pitching himself into a starting job for 2015 with +12 RAA (second only to Tully) and a team-leading 9.0 K/9. Senior Tyler Giannonatti moved into higher leverage innings and responded well with +3 RAA over 33 innings.

The rest of OSU's reserve pitchers were hit hard by injuries (particularly to promising freshman reliever Adam Niemeyer) and none logged enough innings to really evaluate their potential to help the team in 2015. As the season wore on, freshman Curtiss Irving got more work, but a 6.66 eRA with 5.7 W and 5.2 K per nine over nineteen frames means the jury is still out. Of the others, it's worth noting that freshman Shea Murray had the lowest RAA on the team (-8), on the basis of getting lit up for six walks and nine runs in just 2 1/3 innings.

2014 was not an encouraging year for the program. While Beals recruiting efforts continue to draw praise, after four seasons there has yet to be any tangible on-field results. Beals four-year record is 124-105 (.541), the worst four-year stretch for the program since 1987-1990 (117-111, .513), and that period featured an upward trajectory as Beals' predecessor Bob Todd took command of the program (you will note that 1987 came up multiple times as a low point for the program). In 1991, the Buckeyes exploded onto the national scene by going 52-13, coming within a game of reaching the College World Series and finishing as high as #13 in the national polls. Beals' four-year Big Ten record of 49-47 (.510) is the worst since 1986-89 (40-48, .455).

At some point, Beals' recruits need to start producing on the field, and the direction they are given needs to improve. OSU's baserunning is an atrocity. It is impossible for me to convey just how embarrassing the team-wide effort to give away outs is, and it has only gotten worse as #BealsBall has become the culture of the program. As a proud alumnus, it is infuriating that the university has discarded great men like Jim Tressel, Jon Waters, and Gordon Gee, as well as a promising coach in Mark Osiecki while athletic director Gene Smith is allowed to retain his job and retains the services of Beals. OSU baseball is a program that has demonstrated the ability to dominate the Big Ten, exhibit national relevance, draw fans to the park, and even occasionally turn a profit for the athletic department, a rarity in northern baseball. The powers that be would be wise to keep that in mind before allowing Beals to make all that a distant memory.phttp://email@example.com:blogger.com,1999:blog-12133335.post-55264259335875044072014-07-13T16:50:00.002-04:002014-07-13T16:50:43.631-04:00Drew Rucinski, #53On Thursday, twenty-five year old right-handed pitcher Drew Rucinski made his major debut in relief for the Angels against the Rangers. His performance was not memorable, as he came on in the ninth inning with a 15-4 lead, and allowed 4 hits and 2 runs while recording a strikeout. It was also a short stay in the majors for Rucinski, who was optioned back to AA two days later. My interest in Rucinski stems from the fact that he pitched at Ohio State, and is at least the 53rd former Buckeye to play in the majors as well as the fourth this season (Nick Swisher, Eric Fryer, and fellow Angel farmhand JB Shuck).

Rucinski's road to the majors has been interesting, both as an amateur and a professional. He came to OSU from Oklahoma, an oddity for a program that generally draws on Ohio and adjacent states for all its talent. He spent his first two seasons in the bullpen, including a 2009 sophomore campaign in which he did yeoman work in middle relief for a staff that really only had three reliable pitchers: ace Alex Wimmers, closer Jake Hale, and Rucinski. That team won OSU's first regular season Big Ten title in eight years and finished second at the Tallahassee regional.

Wimmers was a dominant pitcher at OSU, arguably the best in program history, and a first round pick of Minnesota, but has seen his career bog down first with control issues and then with injuries; he's still in high-A in his age 25 season. Hale was a 27th round pick from Arizona, stuck in the minors for two seasons, and currently is pitching in the Atlantic League. Had you told me that the first (and quite possibly only) of OSU's big three who would reach the majors would be Rucinski, I would have been surprised.

It wouldn't have been the first time I underestimated Rucinski. He moved into OSU's rotation for his junior season, a move I was all for, but when he was slated to be the ace in his senior season I expressed skepticism that he was up the typical standards of an OSU #1 pitcher. Rucinski pitched very well, though, leading to someone calling me out in the comments for having been wrong (I was thrilled to have been wrong!)

Rucinski was not drafted, but did sign with Cleveland as a free agent and spent 2011 in their system. He was subsequently released and pitching in the Frontier League before being signed by the Angels mid-season 2013. Last year he pitched well in five starts at high-A Inland Empire, and this year had a 2.35 ERA and 85 K/28 W in 95 innings at AA Arkansas. At the risk of underestimating him again, his prospect status is marginal, but it's great to see that his perseverance paid off with a cup of coffee, and hopefully much more.
phttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-56498011788954288632014-06-17T23:06:00.000-04:002014-06-17T23:06:50.464-04:001882 AAThe American Association was the first association of clubs to seriously challenge the National League as another major league. Previous organizations, like the International Association, were based on a fundamentally different notion of how to organize professional baseball teams. While everyone who is reading this knows that the NL survives today and the AA does not (unless you give much weight to its eventual shotgun merger with the NL), the AA certainly made its mark on the game. It lives on most obviously through the Cincinnati Reds, Los Angeles Dodgers, Pittsburgh Pirates, and St. Louis Cardinals.

As the AA was organized in 1881, the National League fielded eight teams in Buffalo, Boston, Chicago, Cleveland, Detroit, Providence, Troy, and Worcester. That left major cities like Cincinnati, New York, Philadelphia, and St. Louis out of luck. While independent clubs were still in existence and of high quality (although not in the numbers they once had been, particularly in the east), there was an opening available for a rival league to enter the fray.

William Hulbert's decision to start the NL was a consequence of his attempt to wrest the power in baseball from the eastern clubs to his own Chicago team, and the NL generally took on a midwestern feel. When the New York and Philadelphia clubs refused to make their final road trip of the 1876 campaign, they were expelled from the league. Neither city would get another NL team for some time; although some sources claim that this is because Hulbert held a grudge, others dismiss this theory and point out that neither city had a strong independent club worthy of admission to the NL. Regardless, two major eastern cities were without NL teams.

Cincinnati was dropped after 1880 because they allowed alcohol to be sold in their park. The NL also generally insisted on a fifty cent admission fee. The combination of high ticket price and no booze encouraged a more cosmopolitan atmosphere, but it also forsook a great number of potential customers.

In October of 1881 backers of the newly formed independent Cincinnati team (sportswriter O.P. Caylor and Justus Thorner) met in Pittsburgh with Denny McKnight, an area businessman who had managed (in the financial sense) the Allegheny club a few years earlier.

The group decided to sent telegrams to other team, inviting them to a second meeting. This meeting, held on November 2 in Cincinnati was a success and brought together six clubs (Again, I am using the modern [City Name] [Nickname] format even if it is not exactly applicable. In the case of "Pittsburgh" and "Philadelphia", it is closer to flat-out balderdash): the Brooklyn Atlantics, the Cincinnati Reds, the Louisville Eclipse, the Philadelphia Athletics, the Pittsburgh Alleghenys, and the St. Louis Browns. McKnight was named president.

The AA allowed twenty five cent admission, Sunday baseball, and alcohol sales. Critics of the league and NL snobs derided it as the "beer and whiskey league", because in addition to allowing sales, most of the teams were in some way backed by beer money. Before the season could start, the AA had to adjust on the fly as the Atlantics withdrew in March due to financial problems. The AA had also attempted to attract the New York Metropolitans, a strong independent club, but they were courted by the NL as well and decided to wait the situation out.

Raiding National League rosters was not a priority for AA clubs, and the NL did not initially react to the new circuit with hostility, just indifference. Some NL and AA teams even met in pre-season exhibitions. NL players were not a necessity for AA teams; several of them had been independent teams and already had a base of talent. However, two backup, unreserved infielders from Detroit became the center of controversy. Dasher Troy signed with Philadelphia and Sam Wise signed with Cincinnati. Troy backed out, claiming that he was not aware of the AA's intentions to play on Sunday when he signed, and went back to the Wolverines. Wise wound up signing with Boston, which infuriated the AA, and the association decided to drop its policy of honoring the NL blacklist.

The playing rules of the AA were largely similar to those of the NL. The AA did not fine pitchers for hitting batters, used the Mahn ball, and continued the use of courtesy runners. The association decided to determine its standings by winning percentage rather than total wins. Beginning in July, the association itself employed the umpires rather than the teams, a move that seemed downright prescient in light of the NL's Higham scandal.

The first AA game was played in Cincinnati on May 2; Allegheny defeated the Reds 10-9. Louisville pitcher Tony Mullane worked ambidextrously in a July 18 game against Baltimore, the first to do so in a major league game (while doubtful, Mullane's handsomeness is credited with starting the custom of Lady's Day at the ballpark). Mullane added the AA's first no-hitter to his accomplishments on September 11, winning 2-0 at Cincinnati. His teammate Guy Hecker tossed his own no-no eight days later, 3-1 at Allegheny.

The Athletics got to the early lead, but Cincinnati was 20-10 at the end of June and rolled home from there, going 35-15 the rest of the way. Their final margin over the Athletics was 11.5 games. After the season, they met the NL pennant winners from Chicago in a two game engagement. The homestanding Reds took the first game 4-0 on October 6, but they were defeated 2-0 in Chicago the next day. Some histories claim that the series was stopped when Denny McKnight threatened to expel Cincinnati, but the more credible explanation is that both teams had other engagements to attend to (in the White Stockings' case, their series with Providence) and that it was never intended as a championship series.

Even though the pennant race was anti-climactic, Sunday ball, alcohol, and quarter admission proved a winning combination for the AA. The Association, which boasted a combined population in its markets around half a million greater than the NL, outdrew the league despite having two less teams (with obvious caveats regarding attendance figures). All six AA teams claimed to be in the black.

Relatively flush with success, the AA teams made runs at NL talent. Detroit's star catcher Charlie Bennett accepted a $100 advance from the Alleghenys in August, but then refused to sign in October as expected. Pud Galvin and Ed Williamson also were said to have backed out of AA deals. The club sued Bennett, but the court ruled that there was no valid contract. Regardless of the outcome of any particular skirmish, it was now clear that there were two major leagues.

STANDINGS

Cincinnati was clearly the class of the league, leading in both runs scored and allowed with an impressive EW%. Louisville looked like a contender on paper but not on the field, and Philadelphia was the opposite. The league was pretty well-balanced except for Cincinnati on top and, unsurprisingly, the last-minute replacement team in Baltimore on the other end of the spectrum.

In 1882, the AA hit .244/.271/.312 for a .105 SEC, 5.21 runs and 23.66 outs per game. The AA scored .2 runs less per game, with a batting average seven points lower, the same OBA, and a thirty point deficiency in SLG. As you can see, the major difference in offense between the two circuits was power, with more of it in the NL.

CINCINNATI

This incarnation of the Reds was founded in 1881 by Caylor to play a weekend series in St. Louis against the Browns. Apparently, it was a successful gate attraction and illustrated the desire for high-quality baseball in non-League cities. While this is the franchise that carries on today, it is not the same club as the original Red Stockings or the two or three different incarnations of Cincinnati NL teams between 1876-1880. In my experience, many fans of the modern day Reds think that their team is a direct descendant of the 1869 juggernaut--they are wrong, and they don't like to be told that they are wrong.

Every regular player on the team had previous major league experience with the exception of Bid McPhee; Joe Sommer is considered a rookie for my purposes as well, but he had 88 PA for the 1880 Reds. Snyder (BSN), Stearns (DET), Carpenter (WOR), White (DET), and McCormick (WOR) had all played in the NL in 1881. Fulmer had not played in the league since 1880 (BUF), Macullar since 1879 (SYR), and Wheeler was another 1880 Red. None of the other five clubs had such a wealth of established talent, so it can't be considered much of a surprise that Cincinnati won the pennant.

Prior to playing the two games with Chicago, the Reds had played their in-state NL counterpart from Cleveland at home, but had won just one out of three, and the win came when Dave Rowe, an outfielder by trade, pitched for Cleveland.

Reserve first baseman Henry Luff was apparently fined $5 for making a catch one-handed, and quit the team in response.

PHILADELPHIA

The Athletics had played in the Eastern Championship Association in 1881, and were picked to join the AA over another Philadelphia club, a new outfit being organized by sporting goods magnate Al Reach and Horace Phillips. The AA went with the more established club, backed by Bill Sharsig, a theatre producer (thanks to Richard Hershberger for sharing his research on the potential membership of the Philadelphias).

Athletics regulars with NL experience (last team and year) were: Dorgan (WOR, 1880), Latham (LOU, 1877), Lou Say (CIN, 1880), and Sam Weaver (MIL, 1878). All others plus Dorgan were rookies. Say's younger brother Jimmy was a reserve shortstop.

LOUISVILLE

The Eclipse was made up of players who had already been together over the preceding seasons. Only two of the regulars had any NL experience: Denny Mack (BUF, 1880) and Tony Mullane (DET, 1881). Only Mack is not considered a rookie by my standard. It is noteworthy then that he was the team's least valuable regular in terms of WAR.

Louisville may have had the most balanced combination of good-hitting pitchers yet seen. Mullane and 1B/P Guy Hecker each hit at 110 ARG or better. Among teams using two pitchers, there are none from 1876 until this point that had two pitchers each hit at such a high level. I'm not saying that they are the best hitting combination (as, say, Jim Whitney plus a marginal hitter would still create more runs than Mullane/Hecker), just that they both were solid contributors at the plate.

PITTSBURGH

This is one of the more blatant contemporary recastings of a team name as this team was the "Allegheny" club and a lot of people still spelled the name of the city in which they played "Pittsburg". Just so you know.

Regulars with NL experience: Taylor (CLE, 1881), Strief (CLE, 1879), Leary (DET, 1881 but still a rookie), Mansell (CIN, 1880), Swartwood (BUF, 1881 but still a rookie), and Salisbury (TRO, 1879).

John Peters may have been the biggest "name" player in the association, at least based on previous NL exploits. I suppose that one could make an argument for Will White, but Peters was one of the better players in the first few seasons of the senior circuit.

ST. LOUIS

After St. Louis dropped out of the NL during the 1877-78 offseason, independent teams billing themselves as the Browns continued to play in the city. This particular incarnation was backed by Chris Von der Ahe, a German immigrant who had succeeded in the beer business. He is one of the most interesting characters in nineteenth century baseball, and I will not be able to do him justice. He was prone to saying silly-sounding things, but what would you expect from someone speaking his non-native tongue? He was also criticized as lacking baseball knowledge (he supposedly boasted that his club's infield was the largest in the country). Whether that particular one is true or not, one needn't understand the nuances of the game on the field to succeed as a team owner. My point is that he gets a bad rap from some circles--he was a self-made man who was heavily involved in the operation of a successful major league and within a few years his team would be the AA's greatest dynasty.

In The Ball Clubs, Dewey and Accoella described him as "A German immigrant with a comic strip accent, a comic book physiognomy, and comic wardrobe of diamond stickpins, checkered pants, and spats." That gives you a decent summary of the common view of the man. For more balanced accounts, read some of the entries on him at This Game of Games, and go there if you want to know anything else about the 19th century game in St. Louis.

Browns regulars with NL experience were: Sullivan (BUF, 1881), Bill Gleason (STL, 1877 but still a rookie), Ned Cuthbert (CIN, 1877 and the manager), and Seward (NYN, 1876). According to Cliff Blau's article on this team, regulars Schappert and Walkera nd reserves Smiley and Fusselback were signed away from the Athletics and Atlantics, while Charlie Comiskey was signed from a Dubuque club, and Harry McCaffery was signed away from an area independent club in June. Jack Gleason missed April with injuries sustained during his off-season job as a fireman.

BALTIMORE

The Orioles, a late replacement for Brooklyn, were easily the least seasoned team in the AA, at least in terms of NL experience. Just three regulars had played in the NL: Henry Myers (PRO, 1881 and the manager of the Baltimores), Waitt (CHN, 1877), and Nichols (WOR, 1880). Only Nichols fails to qualify under my standard as a rookie, and one source claimed that Myers was the only player with NL experience on the opening day roster. Not surprisingly, the team was overmatched finishing in the cellar by 14.5 games. The pitching duo of Landis and Nichols was particularly bad, coming up 1-2 in the cellar in WAA (although this may also reflect heavily on the fielding in this day and age). Landis had been released by the Athletics after losing to Baltimore on May 4.

Twice (against Cincinnati in June and St. Louis in July) opposing catchers purposefully dropped Oriole third strikes with the bases loaded to turn triple plays. A June 28 game against the Reds set a still-standing record for the most runs scored in extra innings, as each club scored four in the tenth before Cincinnati won it with seven in the eleventh.

Most secondary sources claim that the Von der Horst family owned the team. Apparently they used baseball as the drawing card for their other interests--surrounding real estate that included an entertainment complex complete with restaurant, concert grounds, and dance hall.

However, Cliff Blau's research indicates that the Von der Horsts did not come to own the team until 1883 or 1884. There were rumors that this team would be replaced in the AA by a new AA club representing Baltimore and Washington, but in the end, this team withdrew from the circuit and was replaced by a new Baltimore entry. Blau's article was a source for some of the other information in this post.

Leaders and trailers:
BATTING AVERAGE
1. Pete Browning, LOU (.378)
2. Hick Carpenter, CIN (.342)
3. Ed Swartwood, PIT (.329)
Trailer: Charlie Waitt, BAL (.156)
ON BASE AVERAGE
1. Pete Browning, LOU (.430)
2. Ed Swartwood, PIT (.370)
3. Hick Carpenter, CIN (.360)
Trailer: Chappy Lane, PIT (.196)
SLUGGING AVERAGE
1. Pete Browning, LOU (.510)
2. Ed Swartwood, PIT (.489)
3. Billy Taylor, PIT (.452)
Trailer: Charlie Waitt, BAL (.172)
SECONDARY AVERAGE
1. Ed Swartwood, PIT (.225)
2. Pete Browning, LOU (.222)
3. Billy Taylor, PIT (.194)
Trailer: Will White, CIN (.043)
Trailing non-pitcher: Bill Smiley, STL (.047)
RUNS CREATED
1. Pete Browning, LOU (78)
2. Ed Swartwood, PIT (77)
3. Hick Carpenter, CIN (72)
4. Joe Sommer, CIN (66)
5. Mike Mansell, PIT (64)
ARG
1. Pete Browning, LOU (238)
2. Hick Carpenter, CIN (180)
3. Ed Swartwood, PIT (179)
4. Joe Sommer, CIN (152)
5. Jack O'Brien, PHA (149)
Trailer: Charlie Waitt, BAL (48)
WAA
1. Pete Browning, LOU (+4.5)
2. Hick Carpenter, CIN (+3.4)
3. Ed Swartwood, PIT (+3.2)
4. Joe Sommer, CIN (+2.4)
5. Chicken Wolf, LOU (+1.7)
Trailer: Charlie Waitt, BAL (-2.0)
WAR
1. Pete Browning, LOU (+5.8)
2. Hick Carpenter, CIN (+4.9)
3. Ed Swartwood, PIT (+4.4)
4. Joe Sommer, CIN (+3.8)
5. Pop Snyder, CIN (+3.1)
Trailer: Charlie Waitt, BAL (-.8)
ARA
1. Denny Driscoll, PIT (61)
2. Will White, CIN (65)
3. Harry McCormick, CIN (76)
4. Sam Weaver, PHA (83)
5. Tony Mullane, LOU (83)
Trailer: Tricky Nichols, BAL (162)
WAA
1. Will White, CIN (+4.7)
2. Denny Driscoll, PIT (+2.2)
3. Tony Mullane, LOU (+2.1)
4. Sam Weaver, PHA (+1.7)
5. Harry McCormick, CIN (+1.5)
Trailer: Doc Landis, BAL (-2.5)
T WAR
1. Will White, CIN (+6.3)
2. Tony Mullane, LOU (+4.7)
3. Sam Weaver, PHA (+2.7)
4. Guy Hecker, LOU (+2.4)
5. Denny Driscoll, PIT (+2.1)
Trailer: Doc Landis, BAL (-2.7)

My all-star team:
C: Pop Snyder, CIN
1B: Guy Hecker, LOU
2B: Pete Browning, LOU
3B: Hick Carpenter, CIN
SS: Chick Fulmer, CIN
LF: Joe Sommer, CIN
CF: Oscar Walker, STL
RF: Ed Swartwood, PIT
P: Will White, CIN
P: Tony Mullane, LOU
MVP: 2B Pete Browning, LOU
Rookie Hitter: 2B Pete Browning, LOU
Rookie Pitcher: Tony Mullane, LOU

These choices were all pretty straightforward. The only position where I did not end up going with the highest WAR was center, where Walker was +1.6 with 6 FR and John Reccius was +1.8 with -3 FR.phttp://email@example.com:blogger.com,1999:blog-12133335.post-59420507118419214862014-06-11T17:18:00.002-04:002014-06-11T17:18:37.803-04:00Great Moments in Yahoo! Box Scores

phttp://email@example.com:blogger.com,1999:blog-12133335.post-19541462841209874642014-06-09T00:08:00.000-04:002014-06-09T00:08:27.688-04:00Great Moments in Yahoo! StandingsAs of midnight on June 9, this is how Yahoo! renders the NL wildcard standings:

WAS and ATL at 32-29 are somehow half a game ahead of 33-30 MIA, although if you were to look at the NL East standings on Yahoo!, they are all listed as tied (yes, I realize that W% is what really defines standings, not GB, but there is no dispute that by the rules of GB, those teams are tied).

It gets even more ridiculous, though, when you see that 33-31 LA and STL are tied with MIA. Obviously, they are .5 game behind MIA.phttp://email@example.com:blogger.com,1999:blog-12133335.post-5117122079652308372014-05-25T21:51:00.001-04:002014-05-25T21:51:35.285-04:00OPS at the ParkSaturday night I was at the Reds game against the Cardinals. On the big scoreboard they displayed BA/OPS/HR/RBI and on the ribbon boards they displayed BA/HR/RBI/OBA/SLG/R (I didn't write this down, so I might be off a little on the categories, but I think the preceding is a pretty fair representation). In any event, there was a group of four people sitting in the row in front of me. It appeared to be a father, two adult daughters, and one of the daughter's boyfriend/husband.

Late in the game, the gentleman asked the group what "OPS" was. The boyfriend said something to the effect of "It's a stat that's supposed to tell you how good of a hitter a player is", but didn't seem to know any details. This being 2014, he got out his phone and looked it up. It appeared that this took him to the Wikipedia page, which after explaining that OPS is the sum of OBA and SLG , says it can also be computed in one equation as follows:

OPS = (AB*(H + W + HB) + TB*(AB + W + HB + SF))/(AB*(AB + W + HB + SF))

He proceeded to show this to the rest of the group, and the reaction appeared to be "wow, that's really complicated". It wasn't "that stat must be bunk because it's complicated", but seemed to be more of a "I'm not interested in learning more about this because it's too complicated".

In observing this I started thinking about what I might have said was I somehow involved in this conversation. Would I have thrown OPS (a metric about which I have written negative things about more times than I can count) under the bus by agreeing that it was too complicated, all the while concealing the fact that my preferred metrics might appear to be even more complicated to a layperson? Would I have said that the equation above was more complex than it needed to be, because it could be written as the equivalent and decomposed (H + W + HB)/(AB + W + HB + SF) + TB/AB--hand-waiving away the fact that the more complex version provides insight into how events are actually weighted by OPS? Would I, not wishing to contribute to any anti-sabermetric sentiment but also with no desire to teach a saber 101 course, have offered a lame defense of OPS?

Of course, I wasn't involved in this conversation, and certainly wasn't going to insert myself into it, so this was all hypothetical. But I think the thought exercise is worthwhile to consider for those who do have an interest in what I'll call for lack of a better term sabermetric evangelization. I am not one of them, but if you are interested in getting more casual fans interested in sabermetrics, you should consider whether you could explain the metrics that you are advocating if asked to do so. I would draw a distinction between a metric that is complex in order to achieve precision or theoretical considerations, and one that has properties that defy explanation. If you think you could explain why total bases are weighted by plate appearances and times on base are weighted by at bats, then by all means, go ahead and point people to the Wikipedia page for OPS. Good luck.
phttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-57362285561179701032014-05-20T21:39:00.000-04:002014-05-20T21:39:38.569-04:001882 NL1882 would be a very eventful year for baseball, but that was belied by the NL's lack of pre-season franchise changes; this was be the first season in which all league members returned. For the first time, the NL had a serious rival operating in a similar fashion to itself, the American Association. Also significantly, William Hulbert died on April 10, leaving the league without its founder and president. Arthur Soden of Boston was his successor.

There were no significant rule changes. Players were required to account for their own bats and uniforms when they were on the road, and were also obligated to purchase at least two uniform sets. In one of the final actions of Hulbert's tenure, reacting in response to a supposed AA plan, the league adopted a uniform color-coding scheme in which each position would wear a different color uniform (there is some disagreement in the secondary sources about this, and I'd like to believe that's because it never really happened). Pitchers wore baby blue, catchers scarlet, first baseman scarlet and white, second baseman orange and black, third baseman blue and white, shortstops maroon, left fielders white, center fielders red and black, and right fielders gray. The ridiculousness of this should be obvious, and it was quickly quashed if it ever had existed at all (there are varying reports in the secondary sources about whether players actually took the field dressed like clowns or not).

Arthur Irwin, the Providence shortstop, is said to have been the first to use a padded glove, and his teammate Paul Hines has been cited as the first player to wear sunglasses. As with other such firsts, these are potentially dubious in their accuracy. Cap Anson was called out during a May 5 game for walking back to the base after a foul ball instead of running; this minor rule was done away with after the season. On May 25, Curry Foley of Buffalo became the first major leaguer to hit for the cycle in a 20-1 rout of Cleveland. On August 17, Providence defeated Detroit 1-0 in eighteen innings on Hoss Radbourn's home run; it would be the longest major league shutout until 1967. Chicago's brilliant Larry Corcoran no-hit Boston 5-0 on September 20 to become the first author of multiple no-nos.

Umpire Dick Higham, employed by Detroit (umpires in this time were employed by teams and went on road trips with them) purportedly wrote a gambler a letter telling him how to bet on a May 30 Wolverine game against Providence. He was subsequently dropped by Detroit and umpired for Troy on June 20-22, but then was called to a hearing on June 24 in Detroit. Higham claimed that he was innocent, but nonetheless was removed. He is the only umpire ever explicitly expelled for dishonesty.

The pennant race turned into a two team affair. Two-time defending champ Chicago put up some impressive performances (exemplified by a 35-4 thumping of Cleveland in late July), but Providence had the lead by three games with sixteen to play. The White Stockings went on a 15-1 tear to take the pennant by three games.

However, controversy abounded. The final three scheduled White Stocking games at Buffalo were moved to Chicago for financial reasons. This was objected to by Providence, and the Grays wanted a special series after the regular season to determine the pennant. Chicago assented, although it is unclear whether this was ever actually regarded as championship series.

By the time it ended, there were no such questions. Chicago played the first three games of the nine game series with players out of position (and Abner Dalrymple and Joe Quest on the bench) and fell behind 3-0. They wound up winning the series 5-4 with a 19-7 win in the ninth game, played at Fort Wayne long after general interest had been exhausted.

Entering the offseason, the National League would be looking for two new members and dealing with the realization that they finally had a rival worthy of their respect.

STANDINGS

Chicago was even better in EW% than their actual record, but game scores like 35-4 will do that for you. The standings wound up playing out in the exact order of EW%, except for the third place tie between Boston and Buffalo. It was also another fairly balanced year for the league as only Worcester was dreadful and no one ran away and hid.

In 1882, the league hit .251/.271/.342 for a .130 SEC, 5.41 runs and 24.33 outs per game.

CHICAGO

The White Stockings returned the entire regular lineup from their 1881 pennant winner, although there were minor shuffles in the lineup; second baseman Joe Quest was benched, Tom Burns slid from short to second, King Kelly came in from right to play short, and Hugh Nicol came off the bench to take over right. However, for the late season pennant push, Anson reinstalled Quest and benched Nicol. Neither man hit much; Quest was pretty much at replacement level in his 167 PA.

Al Spalding decided that he wanted an anonymous official scorer who would therefore be free from the various pressures inherent to the job. He hired a woman named Elisa Green Williams, who did the job in secret for many years.

According to an anecdote in Bill James' Historical Baseball Abstract, the term "Charley horse" originated when the Chicago players attended a racetrack one day and witnessed a horse named Charley break down. The next day, when George Gore pulled up lame on a stolen base attempt, two and two came together. Do I buy it? No, but anecdotes that combine horse racing and baseball are hard to come by.

PROVIDENCE

The Grays also displayed a great deal of lineup stability; considering that Chicago and Providence had run 1-2 in the previous two seasons, it seemed to be wise policy. The only changes were Harry Wright as the new manager after his long stint in Boston, and his brother George coming out of retirement for his swan song at age 35. Wright was only a shadow of his former self, with a pitiful 35 ARG.

In an attempt to stimulate attendance, the Grays and their opponents paraded through the streets of the city before games.

BOSTON

The Reds replaced departed manager Harry Wright with longtime infielder John Morrill. There were no wholesale shits on the infield, only rookie Sam Wise (4 PA for Detroit in 1881) replacing Ross Barnes at shortstop (how that came about is a story for the AA section). Perhaps Morrill's own experience of being yanked around the infield (he had played every position on a regular basis for at least one season except short) influenced him to keep the infield intact.

Pat Deasley, a reserve in 1881, became the full-time catcher as Pop Smith moved to Cincinnati of the AA. Pete Hotaling of Worcester took over center field, rookie Ed Rowen inherited right field, and Bobby Mathews was brought in as Grasshopper Jim's #2 after pitching for the Grays in 1881. Whitney, always a fine hitter, paced the team in BA, OBA, SLG, RG, and WAA. His 61 runs created were only four off the team lead of Morrill and Joe Hornung, who made 70 and 90 more outs respectively.

BUFFALO

The Bisons shuffled their lineup a bit, moving players back to their more natural positions. Jim O'Rourke went from third to center, Deacon White from left to third, and Hardy Richardson from center to second. Blondie Purcell took the full-time job in left field after amassing 205 PA in 1881 between Buffalo and Cleveland. He would be fined $10 for cutting a wet ball in an attempt to make the umpire put a new one into play. Pud Galvin complained that he could not throw his curve properly with a wet ball.

Rookie #2 pitcher Hugh Daily was called One Arm because, well, he had one arm.

CLEVELAND

The Blues had two rookies (catcher Fatty Briody (4 PA for Troy in 1880) and third baseman Mike Muldoon) in their lineup. Left fielder Dude Esterbrook has not played in the league since 1880 (Buffalo) and center fielder John Richmond was brought in from Boston.

In the aforementioned 35-4 shellacking at the hands of the champion White Stockings, reserve outfielder Dave Rowe was in the box, and gave up 29 hits and 7 walks.

DETROIT

The Wolverines were at the center of the relatively limited player battles between the NL and the AA; again, this topic will be expounded upon in the AA entry, but backup infielders Dasher Troy (11 games in 1881) and Sam Wise (1 game) were not reserved after the 1881 season and signed with AA clubs. Eventually, Troy returned to Detroit and was the regular second baseman, while Wise wound up with Boston. Catcher Charlie Bennett was a major point of contention as well, but that was for an 1883 contract.

Rookie Joe Farrell was the third baseman, while Mike McGeary was imported from Cleveland to play short.

TROY

The Trojans featured two regular rookies, shortstop Fred Pfeffer and pitcher Jim Egan. John Smith started the year as the first baseman but later wound up in Worcester. Buck Ewing played third primarily (44 games), but also caught 25 games. Roger Connor as a center fielder seems interesting, but he actually spent more time at first (43 G) than he did in the outfield (24). Outside of Pete Gillespie and Chief Roseman, the outfield situation was quite murky.

The team finished only 19.5 games back, but it probably seemed a lot worse. They suffered through a sixteen game losing streak, lost money to the point where they had to be subsidized, and were eventually kicked to the curb, a casualty of the skirmish with the AA and good old-fashioned financial problems.

WORCESTER

The Brown Stockings finished 18.5 games behind Troy. The team featured two rookie outfielders, Tom O'Brien and Jackie Hayes. Left fielder Jim Clinton had not played in the National League since 1876 (with Louisville). An interesting transaction apparently resulted in Frank Mountain being loaned to Philadelphia of the AA for about a month beginning around the end of May.

The team drew just 11,000 fans for the entire season, around 260 a game. In an attempt to boost attendance, the team pioneered the single admission doubleheader. The roster was overhauled in an attempt to remove "rowdiness and dissension", and franchise pitcher Lee Richmond was hampered by a sore arm. Despite this, he was one of just two regulars to hit better than the league average (first baseman Harry Stovey was the other).

In September, the NL voted to drop Troy and Worcester, perhaps fearful of the AA's upper hand in attendance and entry into large cities long ignored by the NL, or just plain tired of having weak sisters in the circuit. Worcester apparently threatened to resign at this point, which would have caused all their games to be struck from the standings. This was confusion was another catalyst in some of the Chicago/Providence pennant controversy, but Worcester played 84 games, so apparently it wound up being an idle threat.

The departure of Troy and Worcester essentially marked the end of an era in which a very small city (relatively, of course) could field a "major league" team, with the exception of the future chaotic three league seasons.

Leaders and trailers:
BATTING AVERAGE
1. Dan Brouthers, BUF (.368)
2. Cap Anson, CHN (.362)
3. Roger Connor, TRO (.330)
Trailer: Doc Bushong, WOR (.158)
ON BASE AVERAGE
1. Dan Brouthers, BUF (.403)
2. Cap Anson, CHN (.397)
3. Jim Whitney, BSN (.382)
Trailer: Doc Bushong, WOR (.174)
SLUGGING AVERAGE
1. Dan Brouthers, BUF (.547)
2. Roger Connor, TRO (.530)
3. Jim Whitney, BSN (.510)
Trailer: George Wright, PRO (.189)
Doc Bushong's BA was .004 lower than Wright's, his OBA .006. I'm not sure that I'm aware of a superstar who ended his career in a sorrier state than George Wright did, essentially as the worst regular in the league or close to it. That doesn't mean there isn't one; I just can't think of one. This was a rapidly evolving and improving game.
SECONDARY AVERAGE
1. Jim Whitney, BSN (.283)
2. Dan Brouthers, BUF (.239)
3. Roger Connor, TRO (.238)
Trailer: George Wright, POR (.049)
RUNS CREATED
1. Dan Brouthers, BUF (90)
2. Cap Anson, CHN (84)
3. Roger Connor, TRO (79)
4. George Gore, CHN (77)
5. Paul Hines, PRO (75)
ARG
1. Dan Brouthers, BUF (197)
2. Jim Whitney, BSN (190)
3. Cap Anson, CHN (184)
4. Roger Connor, TRO (166)
5. Joe Start, PRO (163)
Trailer: Doc Bushong, WOR (34)
WAA
1. Dan Brouthers, BUF (+3.9)
2. Cap Anson, CHN (+3.4)
3. Paul Hines, PRO (+3.0)
4. Roger Connor, TRO (+2.8)
5. Jim Whitney, BSN (+2.8)
Trailer: Doc Bushong, PRO (-2.6)
WAR
1. Dan Brouthers, BUF (+4.8)
2. Paul Hines, PRO (+4.4)
3. Jim Whitney, BSN (+4.3)
4. Jack Glasscock, CLE (+4.3)
5. Cap Anson, CHN (+4.2)
Trailer: Doc Bushong, WOR (-1.0)
ARA
1. Larry Corcoran, CHN (68)
2. Fred Goldsmith, CHN (75)
3. Hoss Radbourn, PRO (84)
4. Jim McCormick, CLE (86)
5. Stump Wiedman, DET (86)
Trailer: Fred Corey, WOR (139)
WAA
1. Larry Corcoran, CHN (+3.0)
2. Fred Goldsmith, CHN (+2.7)
3. Jim McCormick, CLE (+2.3)
4. Hoss Radbourn, PRO (+2.0)
5. Stump Wiedman, DET (+1.5)
Trailer: George Derby, DET (-2.6)
T WAR
1. Jim Whitney, BSN (+5.0)
2. Hoss Radbourn, PRO (+3.9)
3. Larry Corcoran, CHN (+3.5)
4. Jim McCormick, CLE (+3.2)
5. Fred Goldsmith, CHN (+3.1)
Trailer: George Derby, DET (-2.5)

My all-star team:
C: Charlie Bennett, DET
1B: Dan Brouthers, BUF
2B: Fred Dunlap, CLE
3B: Ed Williamson, CHN
SS: Jack Glasscock, CLE
LF: Joe Hornung, BSN
CF: Paul Hines, PRO
RF: Curry Foley, BUF
P: Jim Whitney, BSN
P: Hoss Radbourn, PRO
MVP: SS Jack Glasscock, CLE
Rookie Hitter: 3B Mike Muldoon, CLE
Rookie Pitcher: One Arm Daily, BUF

I went with Joe Hornung over a close group in left field that included Dalrymple, York, and Wood on the strength of his +12 Fielding Runs (ESPN Baseball Encyclopedia, Palmer & Gillette).

The right field crop is very weak; Curry Foley at 2.2 WAR wouldn't be in the top four in left or the top three in center. Actually, I would pick the two runner-up centerfielders (Gore and Connor, with the caveat that Connor was playing first more than the outfield) over Hornung or Curry. However, my principle has been to pick guys who actually played the position for the "all-star team", not players at comparable positions who were actually more valuable. This is especially problematic in the early years of the NL as right field was still much-used as a place to hide a butcher or a change pitcher.

The rookie crop this year was very weak as well. In past seasons, there had always been rookies with a legitimate claim to being the all-star at their position. This season, Mike Muldoon was solid (2.2 WAR), but hardly star-caliber. Of course, there were only eight regular rookie hitters to choose from, and just two pitchers, each of whom ranked below replacement level. It is easy to speculate that the cause of this was the demand for players caused by six new major league teams. This would not have nearly the impact it would today, as several of the AA teams were already respectable independent clubs, but certainly talent was not as available as it had been.

Finally, my choice of Jack Glasscock as MVP is predicated on his 24 Fielding Runs. On offense and position, I had him .5 WAR behind Dan Brouthers. So all I have to do is believe that he was around 5 runs better in the field relative to his position than Brouthers. Given Glasscock's reputation and his perennial good standing in FR, I am more than willing to believe it.phttp://email@example.com:blogger.com,1999:blog-12133335.post-18410712994583675132014-05-06T23:14:00.000-04:002014-05-06T23:14:20.163-04:001882-1883 IntroductionIn my long-neglected effort to apply sabermetric measures to the nineteenth century game while also providing a perfunctory history, I have chosen to treat the seasons of 1882 and 1883 together. In fact, the statistical methods that I have used here are for the most part the same as those that I used for 1876-1881. It is not until 1884 that I felt the need to delineate a new era, so to speak (and of course subject to disagreement).

However, I split these two seasons off on the historical level because for the first time, the National League has a competitor engaging it on similar terms. What I mean by this is that there had previously been independent professional clubs, some of which were similar in quality to some of the NL teams. However, until the American Association began play in 1882, there had not been another league that behaved in the way which we would expect a major league to today--setting a schedule centrally (or at least proscribing the number of games that should be played between each club), setting uniform playing rules, restricting membership, attempting to improve the position of the league as a whole rather than solely worrying about the affairs of one club, etc.

In 1884, the Union Association will emerge as another challenger (at least in the dreams of its backers), and I think that is another good place to draw a line. I also found that run estimation in the mid-to-late 1880s is trickier than in the 1876-1883--not any less accurate, in most cases, but only after applying different formulas to smaller groups of seasons.

So, the methodology for the 1882 and 1883 recaps will be much the same as for 1876-1881; see the links on the right side of the page for a refresher. The Base Runs method being used is:

A = H + W - HR + E + .08SH
B = (.726S + 1.948D + 3.134T + 1.694HR + .052W + .799E + .727SH + 1.165WP + 1.174PB - .05(AB - H - E))*1.087
C = AB - H - E + .92SH
D = HR

As I mentioned in the earlier piece, the inclusion of SH was an embarrassing mistake on my part, but it all washes out since the SH are just an estimate based on singles, walks, and estimated errors--it just shifts around the values of those a bit, in a way that made the formula slightly more accurate. But there really were no sacrifice hits at this stage of the game's development, and I knew that, and I flat out forgot it when working on the run estimator. Embarrassing.

I would also be remiss if I did not point out that the formula does not work as well for the AA teams as it does for the NL teams, so all of the results for the AA in 1882-83 should be taken with an extra grain of salt. Again, I cannot stress enough that all of the metrics should be looked at with a much more jaundiced eye than similar figures for recent times.

The formulas used for the NL are the same, except I used a custom error estimate each year. E = x(AB - H - K), and the value for x is .1277 in 1882 and .1409 in 1883.

The BsR formula generates these linear weights for the seasons. The first set is presented as S, D, T, HR, W, E, AB-H-E, SH, PB, WP. The second set is the one that is actually applied to players, where the coefficients for the categories we don't have (SH, PB, and WP--we also don't have errors of course, but have estimated those) are "folded" into the weights for singles, walks, outs, etc. So those weights are displayed as S, D, T, HR, W, E, AB-H-E:

1882: .549, .836, 1.114, 1.397, .391, .566, -.143, .080, .275, .273
1882: .583, .867, 1.145, 1.397, .425, .600, -.143
1883: .561, .845, 1.120, 1.393, .405, .578, -.150, .073, .273, .270
1883: .594, .876, 1.151, 1.393, .438, .611, -.150

For the American Association, I have used different estimates for WP and PB (but the same estimate for SH):
WP = .0449*(H + W - HR + E)
PB = .0836*(H + W - HR + E)

The AA did not track batter strikeouts, so errors must be estimated as a proportion of (AB-H). This proportion is .1323 for 1882 and .1269 for 1883.

And the linear weights for the AA:
1882: .549, .846, 1.134, 1.411, .386, .587, -.145, .084, .285, .283
1882: .587, .881, 1.169, 1.411, .423, .605, -.145
1883: .558, .851, 1.136, 1.407, .396, .575, -.149, .079, .282, .280
1883: .595, .886, 1.171, 1.407, .433, .612, -.149

For teams, I have decided to stop using PW% and producing an estimate for Runs Created Allowed. Those estimates weren't particularly accurate, anyway, and it's a hassle to come up with different formulas each season for estimating all of the missing data from the defensive perspective--opponent at bats, doubles, etc. If I felt that those categories were adding something substantial, I would go through the effort.

I have also marked "rookies" in pink--I used 100 PA or 50 IP in the majors (NA, NL, AA for this time period) as the cutoff points.

In the next installment I will begin the yearly review with the 1882 NL. The details of the NL/AA relationship will mostly be saved for the AA portion, but they may be hinted at in the NL installment.phttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-18954157942515532712014-04-07T19:03:00.000-04:002014-04-07T19:03:08.494-04:00Great Moments in Yahoo! Schedules

Tripleheader!phttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-52362435773223552722014-04-04T06:49:00.000-04:002014-04-04T18:02:59.089-04:00April 4, 1994What was the most important game in the history of the major leagues? Perhaps it was one of the games that could be argued to have been the very first major league game (the National Association's first game on May 4, 1871 between Cleveland and Fort Wayne; the National League's on April 22, 1876 between Boston and Philadelphia; or the American League's on April 24, 1901, between Cleveland and Chicago). Maybe it was October 3, 1951 as Bobby Thomson hit The Shot Heard Round the World. You would get a lot of support for April 15, 1947 as Jackie Robinson broke the color barrier, and rightfully so. From a business standpoint, consider April 14, 1953, the Braves' first game in Milwaukee after fifty years of franchise stability, or April 10, 1961 as the second Senators hosted the White Sox to open the expansion era.

Or maybe you'd point out that I've not attempted to define what makes a game important as opposed to the circumstances that led to it, and that too many factors would go into doing so to produce a coherent result. There have literally been hundreds of thousands of games in major league history, and so this exercise is inherently silly. But one question I can answer with absolute certainty is "What was the most important game to you, personally?" And that was the 1994 Opening Day game between the Mariners and the Indians, the first regular season game ever played at Jacobs Field.

By late 1993, I was pretty interested in sports, but not baseball. Baseball simply had not captured my interest the way that football and basketball had. I really can't put my finger on why that was, as my personality even at a young age was well-suited to quiet contemplation, patient and passive spectatorship, and interest in numbers and factoids. It is obvious now that baseball is the perfect sport for me, but it was no less true long before I knew that.

I suppose I can assign some of the blame for this to the Indians. The organization was mired in a few decades of persistent losing, and there was no excitement surrounding the team. I was (sadly) a sports talk radio listener at this point and the Browns dominated the Cleveland scene (they still do, of course, but to a lesser extent). There was no social or peer pressure to follow the Indians, and thus in a pre-internet, no cable household, baseball in general. My dad was a casual baseball fan; I remember watching some of the 1993 World Series on a Friday or Saturday night. We also went to one of the Indians final games at Municipal Stadium in September 1993, but it was on a Sunday and my most indelible memory of the whole thing was fans huddling around a gentleman watching the Browns on a mini TV.

However, the tone started to shift a little bit in the spring of 1994, as there seemed to be genuine optimism regarding the Indians and excitement for the opening of Jacobs Field. I added free agent signings Dennis Martinez and Eddie Murray to the list of Indian players whose names I knew (Albert Belle, Carlos Baerga, Kenny Lofton, Jose Mesa, maybe Sandy Alomar), but was obviously still far from engaged as any sort of fan.

Then April 3, 1994 rolled around, the first regular season game at Jacobs Field between the Indians and the Mariners. Dennis Martinez v. Randy Johnson. Even as a kid I had a decent sense of history, so I believe that I was aware and interested in this event, but sadly not solely for it just being an Opening Day baseball game. It was pretty much the last time that I wouldn't be interested in a major league game just on the basis of it being a baseball game.

The details that follow are all courtesy of the play-by-play account, as I have very little memory of the specifics of this game. The details of most games become fuzzy over time, replaced by memories of specific moments and a general haze. This is especially true for me with the earliest games I remember, and while the (gulp) decades that have elapsed are certainly a contributing factor, I don't think it's the only one, or even the primary one. Rather, my memories from this time suffer from my inability at the time to process baseball in context--the lack of a real-time knowledge base from which to differentiate those events that were truly memorable from those that were more mundane. I also would cite the lack of keeping score, which could be dismissed as a crutch in lieu of actual memories, but in my opinion is an essential tool with which to place the events of the game within the context of the game itself, let alone across time. Even in a typical game with seventy-five plate appearances, I find it's easy to lose track of the truth and replace it with one's own narrative in absence of a trusty scoresheet.

Seattle scored in the first and third to take a 2-0 lead. Meanwhile, Randy Johnson was pitching a no-hitter, which was notable for a couple reasons beyond the obvious. One, Johnson, while not yet established as a Hall of Fame super-ace, had already pitched a no-hitter and was clearly one of the leading candidates to do so again among active pitchers. Secondly, the only Opening Day no-hitter had been thrown by Bob Feller for the Tribe, and Feller was in attendance for the opening of the new era in Indians baseball. It was sometime around the seventh inning when I got home from school and started listening to this familiar yet also newish (to me) sport of baseball on the radio. It may sound overly dramatic and absurd, but it is not untrue to say that, literally, my life would never be the same.

In the eighth, the Indians finally got something going offensively. Candy Maldonado led off with a walk (that was about the only thing he did well in 1994), the fifth surrendered by Johnson (a reminder that this was pre-super-ace Johnson). Sandy Alomar singled to break up the no-hitter, a wild pitch moved the runners up, and Manny Ramirez doubled them in to tie the game.

Johnson was relieved by Tim Davis in the ninth, and the Indians rallied with two out on an Albert Belle double and Eddie Murray infield single, but Paul Sorrento struck out. In the tenth, the Mariners scored off Jose Mesa thanks mainly to singles by Ken Griffey and Kevin Mitchell, but Bobby Ayala and Kevin King couldn't close it--the Indians rallied again. After Alomar struck out swinging to open the inning, Ramirez walked (Wayne Kirby pinch-ran) and Jim Thome hit a pinch-hit double. That's right--Mark Lewis started the game at third over Thome given the latter's perceived ineptness against southpaws. Somewhat bizarrely, Kenny Lofton was intentionally walked, Omar Vizquel hit into a fielder's choice to score Kirby, and Carlos Baerga flew out, pushing the 3-3 game to an eleventh inning.

Eric Plunk retired Seattle in order in the top of the inning. In the bottom, King got Belle to ground out before Murray doubled. Sorrento flied out and Murray took third. Seattle intentionally walked Alomar to get to Kirby, who was now batting in Ramirez's spot. Kirby lined a single down the left field line, and the Indians won the game 4-3.

It is difficult to exaggerate how much this game changed my attitude towards baseball. It might not have been entirely immediate, but if not it was pretty close. Memories are a bit fuzzy at this point, but for whatever reason, I remember being dragged along by my parents to shop for a refrigerator on Sunday April 10. The Indians' fifth game of the season had been rained out at Kansas City on Saturday, so Chris Nabholz was skipped in the rotation and Dennis Martinez was pitching again on the TV at the appliance store. This confused me, and my dad explained to me that the fifth starter is often skipped in such a situation. The point of this mundane story? I knew who the fifth starter was.

That spring and summer, I became obsessed with both the Indians and collecting baseball cards. I hated the White Sox as the Indians chased them in the division, I followed all the intrigue of Albert Belle's corked bat suspension and rejoiced when he homered in his return. I penciled Kenny Rogers' perfect game into the list in my Information Please Sports Almanac, the baseball section of which had previously not gotten much attention but now was well-worn. By the end of the year I had subscriptions to two baseball publications (Beckett Baseball Card Monthly and Baseball Digest). I was starting to apply my natural interest in figures and analysis into what would, by spring of the next year, be a nascent interest in sabermetrics and by the next year be a full-blown obsession.

And when the players went on strike? I was disappointed, to be sure, but an event that potentially could have crushed a young fan before he had a chance to be fully invested had zero impact on my newfound infatuation. The next spring I eagerly learned all the names of the Indians' replacement players and was ready to cheer for our ace Joe Slusarski, our middle-of-the-order thumper Joe Biasucci, our Kenny Lofton replacement Eric Yelding. After April 4, 1994, none of that really mattered. I was a baseball fanatic for good.

Would I have found the game in lieu of the excitement of this game? Given the Indians' 1994 resurgence, I assume I would have, just not as soon. But had the Indians not emerged as a competitive team at that point, it gets murkier. Had I grown much older without discovering the wonder of baseball, it would have been too late to have it as an integral part of my mid-childhood and I can't imagine being the same level of fan that I am today. But after the Opening Day game, I'm convinced that the Indians could have lost the rest of their games and the spark would not have been extinguished. I know precisely when I became a baseball fan--twenty years ago today.phttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-68164810391662838042014-03-31T18:16:00.000-04:002014-03-31T18:16:12.929-04:00Great Moments in Yahoo! Box ScoresYahoo! has once again modified the appearance of their sports data package...standings, schedules, box scores, etc. I prefer the old format, but that's really not the point. What it does not appear as if they've done (and they never do) is to fix the bugs that are endemic. Today, it appears from the BOS/BAL box score that the AL has made some serious changes to the DH rule; apparently teams must have a DH, but must remove one of the other fielders as a result. The Red Sox went without a third baseman, the Orioles without a second baseman. Never change, Yahoo! box scores.

phttp://email@example.com:blogger.com,1999:blog-12133335.post-65458801630519612582014-03-28T09:02:00.000-04:002014-04-02T18:07:50.246-04:002014 PredictionsIn lieu of a general disclaimer about the whole prediction game, from now on I can just point you to this piece. As a more specific disclaimer, please note that the missive that follows is only loosely based on objective sabermetric analysis—I do not have my own projection system, nor have I constructed detailed depth charts of each team, and thus my guesses are not in any way comparable to those of serious analysis. While there is no way I can approach making predictions without considering sabermetrics (since sabermetrics is a fundamental part of how I view anything in baseball), these are in no way "sabermetric predictions". If Milwaukee loses 95 games, it's not because sabermetrics have failed, I just would have made an awful guess. That has happened before (this in particular was a bloodbath) and it will happen again:

AL EAST

1. Tampa Bay
2. Boston (wildcard)
3. New York
4. Toronto
5. Baltimore

Picking this division is not fun; I'm assuming here that Tampa Bay can maintain a reasonably steady level of performance, that Boston will regress a bit, and that all of New York's free agents will only serve to paper over the loss of Cano and perhaps achieve underlying quality at the level of 2013's actual record. It's not at all hard to envision scenarios where the Rays' decline in minor league depth leave them unable to overcome injuries and they end up as sellers, or where the Yankees get a rebound from Sabathia, improvement from Nova, a resurrection of Pineda, and end up with a fearsome starting rotation. What I'm more confident about is that Toronto and Baltimore would need a lot to go right. Picking Toronto ahead of Baltimore may feel wrong, and may be an opportunity to chide sabermetricians about overlooking the Orioles again, but the 2013 Orioles were essentially what their statistics said they should have been. A late flourish to add Nelson Cruz and Ubaldo Jimenez only slightly pushes their projection forward, and is pretty much wiped out by potential regression/injury woes for Manny Machado. A lot of people (myself included) were way too high on the Blue Jays last year, but a lot went wrong and a .500 season is a reasonable target.

AL CENTRAL

1. Detroit
2. Kansas City
3. Cleveland
4. Chicago
5. Minnesota

The potential cracks show in the Tigers: their now ugly shortstop situation, the growing reliance on Miguel Cabrera in the middle of the order, aging complementary hitters; but given the competition, they are still among the safest bets to win a division. The Royals figure to be the new leader of the chase pack if their starting pitching patches (most importantly Yordano Ventura) can deliver. I'm not at all convinced that picking the Indians third with 80 wins was a good idea--the closer it is to the season, the worse I feel about the rotation and the more the possibility of one of those annoying White Sox resurrections looms. I attempt to be as neutral of a baseball observer as possible, but I can't help but be biased towards the Indians. Among the other teams, there are none that I have any particular antipathy against, with two exceptions (sure, at the moment I find Philadelphia and Arizona insufferable due to various management personalities, but I hold no inherent ill-will towards those franchises). Those two are the Tigers, simply because of the collegiate loyalty of many of their fans, and the White Sox, the one team I just flat out don't like. So I now tend to view the White Sox as possessing black magic. Jose Abreu could be a more reasonable explanation should they overplay expectations. I plan on seeing the Twins in Baltimore at the end of August, maybe they could call up Byron Buxton for me then?

AL WEST

1. Los Angeles
2. Texas (wildcard)
3. Oakland
4. Seattle
5. Houston

I keep waffling on this division, and the pitchers keep getting hurt. The Angels have fooled me each of the past two seasons, but made a valiant effort to deepen their staff with the Trumbo trade. With the carnage going on in Oakland and Texas, it's not outside the realm of reason to suggest that Los Angeles may have the most stable group of pitchers in the division. It's also easy to forget that Pujols is only a year removed from being a down-ballot MVP candidate. Things haven't gone great for the Rangers since Josh Hamilton was on fire and they were hailed by some as the greatest team since the 1998 Yankees (May 2012 or so). While this has been amusing, it would be easy to overstate the extent of their struggles given that they have made the playoffs once and barely missed in 2013. The trade for Fielder and signing of Choo make a lot of sense for the short-term; the biggest obvious concerns are the injuries that have piled up and a more general thinning of the starting pitching, but none of the other AL West contenders are free of such questions. I've never fully bought in to the 2012-13 A's, but that's a reflection on me, not the team. They've stocked the bullpen, but the injuries to Parker and Griffin have thinned the starting ranks, and Scott Kazmir is one of the still-healthy pitchers. Uh-oh. The Mariners are good enough that positive deviations from expectations can put them in the hunt for a playoff spot, but to this outsider #6org appears to be in job preservation rather than strategic planning mode. Maybe they've finally found their own competitive edge in black magic to destroy their opponents' pitching staffs. The Astros are still not good, but the league/division shift obscured the fact that even from a W-L record, they were no worse in '13 than '12 (CTR ticked up from 46 to 48). I was a tad too optimistic about their 2013 record, but people who are picking them to once again be historically bad are being a little silly. The bullpen alone is easy pickings for improvement, let alone players like Dexter Fowler and Scott Feldman brought in as placeholders.

NL EAST

1. Washington
2. Atlanta
3. New York
4. Philadelphia
5. Miami

The 2013 Nationals were a wonderful lesson in why it's dumb to pick teams to win 100 games, in why it's dumb to ignore the plexiglas principle, and why if everyone is picking one outcome, you can often look smart by picking anything else. The 2014 Nationals appear to me to present a wonderful lesson in why one should not get hung up on the previous year's performance in picking the next season, and in the benefits of playing in a weakened division. I'm picking them to win the World Series not because I'm convinced they are the best team in MLB (although they certainly could be), but because of the strongest teams, they're the ones with the best combination of talent, weak-ish division, and playing in the inferior league. I was going to pick the Braves second even before the UCLs started popping, but I might have picked them for a wildcard spot before Beachy and Medlen went down. The hope is there for a Jason Heyward monster season, but the loss of McCann and the loss of luster of the Upton brother combination make the offense a bigger question mark. No team is ever more underrated than a poor team playing in a huge media market; you'd think the Mets were closer to 1962 than 2001 based solely on media and fan kvetching. They seem like a perfectly normal mediocre team to me. It might be wise to pick the Phillies ahead of the Mets, but it's more fun to do it this way. The Marlins look lousy on paper but have to be one of the higher variance teams widely picked to have the worst record in their league.

NL CENTRAL

1. St. Louis
2. Milwaukee (wildcard)
3. Pittsburgh
4. Cincinnati
5. Chicago

The Cardinals organization is drawing high praise from all over the map, and for good reason--there is no other team that appears better positioned to weather injuries or implosions. Still, the smart money would bet on a decline in record from 2013, and in the mainstream narrative they are benefitting a bit from the halo of postseason success. They are still very well-positioned in the NL Central. Everyone should be granted one off-the-wall pick and mine is Milwaukee as a wildcard team. The addition of Garza makes their rotation quite respectable and they can easily gain a few wins on paper simply by using a non-Betancourt humanoid at first and avoiding last year's second base meltdown (either Weeks or Gennett should be able to handle that on their own). And while it has nothing to do with a rational evaluation of their chances, the absurd suspension of A-Rod makes Ryan Braun the de facto symbol of the forces of resistance against the steroid totalitarians, and gives him and the Brewers the moral high ground. A lot of things went right for the Pirates in 2013, and I really don't like their team better than Cincinnati's, but the Reds have very little depth, particular offensively. An injury to Votto or Bruce would be disastrous, as the rest of the lineup features declining veterans (Ludwick, Phillips), a thoroughly unremarkable Frazier, and a cadre of youngish guys with questionable on base ability (Mesoraco, Cozart, Hamilton). The most interesting thing about the Cubs season will likely be the countdown to Baez & Bryant, but bounce back seasons from Castro and Rizzo are just as important to the long-term plan.

NL WEST

1. Los Angeles
2. San Francisco (wildcard)
3. Arizona
4. San Diego
5. Colorado

The Dodgers are an interesting case, as they appear to me to be highly overrated even by objective systems (BP calls for 98 wins, a figure which I find shocking). A healthy and full potential Dodger team might indeed be a juggernaut, but they have plenty of star players with big question marks (Kemp, Ramirez, Puig, Crawford), and a couple very sketchy offensive positions (second and third base). I'm not saying they shouldn't be clear favorites in the NL West, and maybe I'm just missing something (and I picked them to win last year, which while hardly bold was not nearly as mundane as it is this year), but a 90% playoff probability doesn't compute. The Giants will attempt to continue their even year success, and I think they have a pretty good shot to do so; a reasonably effective Tim Hudson and return to form from Matt Cain may be key. The Diamondbacks improved last year, they just don't realize it because the Pythagorean fortune didn't persist. When your big moves are to trade for Mark Trumbo and sign Bronson Arroyo, staying at .500 begins to feel ambitious. The Padres have taken over the mantle of the most boring team in baseball; it's hard to see how they contend without a temporary blip like they experienced in 2010. The Rockies offer some more boom potential if for no reason other than Tulo, but contention is still a longshot.

WORLD SERIES

Washington over Tampa Bay

I believe that were this to come to fruition, it would be the most southern World Series in history, between two cities that are in no way southern.

AL Rookie of the Year: SP Yordano Ventura, KC

AL Cy Young: Yu Darvish, TEX

Assuming he only misses a start or two.

AL MVP: 3B Evan Longoria, TB

NL Rookie of the Year: 2B Kolten Wong, STL

NL Cy Young: Stephen Strasburg, WAS

NL MVP: OF Jason Heyward, ATL

Worst team in each league: HOU, MIA

Most likely to go .500 in each league: TOR, ATL

Over/under on media personalities who will have to choke back tears re: Derek Jeter on national television: Seven

Will at least one sane human being have a brain hemorrhage as a direct result of Derek Jeter overload?: Yesphttp://firstname.lastname@example.com:blogger.com,1999:blog-12133335.post-49899492154733724132014-03-01T20:07:00.002-05:002014-04-04T18:11:05.441-04:002014 IndiansWriting an Indians preview is something of an annual tradition here, and I see no reason to stop now. My interest at this point really lies in amusing myself by seeing if I can correctly predict the 25-man roster. There are a lot of places on the internet where you can find micro-analysis of teams and players; it's not my strength as an analyst, and so for the most part any such analysis here will be perfunctory by design.

The Indians certainly surpassed my expectations in 2013, winning 92 games and earning one of the two wildcard playoff berths before being shut out by Tampa Bay in the one-game playoff, which I had the mixed pleasure of attending (pro: first playoff game, con: lousy outcome for an Indians fan). However, the offseason that follows has felt a lot like 2008--standing pat rather than bolstering the roster to take another shot. While such a decision should hardly be surprising given the track record of the Dolan-era Indians, it was a little jarring coming on the heels of the team's aggressive attempt to contend in 2011 (most notably the Ubaldo Jimenez trade, which finally paid belated dividends and looks less of a disaster than anticipated as the prospects sent to Colorado have fallen upon hard times) and last season's off-season spending spree that netted Nick Swisher and Michael Bourn. The only free agent signing that is a lock for the 2014 roster is David Murphy, and a second LOOGY for the pen (Josh Outman) is the only trade acquisition that figures to make an impact. Meanwhile, two starters (Jimenez and Scott Kazmir) signed multi-year deals with other clubs.

This is not to say that these were not the right moves--I tend to think that the Indians need to make significant headway in player development before they can consider themselves a perennial contender given their financial resources--but they suggest that the front office doesn't buy its own rhetoric about the team's competitive prospects in 2014. I don't think I would have wanted to sign Jimenez and Kazmir to those contracts either, but it's hard to say that the Indians roster looks better today than it did on October 2.

Starting pitching remains a big question mark for the Tribe. Justin Masterson was good again last year, but he's yet to put together consecutive effective seasons, and his struggles with lefties and sinker/slider reliance make him a disquieting pitcher in which to place one's trust. He also is the owner of a big arbitration raise and a potential trade deadline casualty should Cleveland fail to contend. Danny Salazar was brilliant in the minors and his ten major league starts last year, and appears to have all the necessary stuff to be a top of the rotation arm, but is building off of a base of just 149 total innings pitched. Corey Kluber was a pleasant surprise, a reliable middle of the rotation arm earning cult status in the internet baseball community (or maybe just one extremely loyal devotee; the strength of the Kluber movement is unclear).

Zach McAllister represents a fourth righty with a rotation spot essentially sewn up, but his 3.75 ERA exaggerates his effectiveness (4.54 RA, 4.88 eRA), and he does not strike me as a pitcher with significant untapped potential. There are a variety of contenders for the fifth spot, from washed up vets (Aaron Harang and Shaun Marcum) to former top prospect (Carlos Carrasco) to soon-to-be former top prospect (Trevor Bauer) to junkballing righty (Josh Tomlin). The smart money is on Carrasco; the organization has remained positive about his potential and he is out of options. If he does not win the job, expect him to get a bullpen spot. Bauer will probably be given more time to work through his mechanical issues in AAA, while Marcum is unlikely to be ready for Opening Day, and Harang appears to be emergency depth. Tomlin is the pitcher most likely to give Carrasco a run for his money for the spot.

The bullpen will take on a very different look than it did on Opening Day 2013, as the seventh, eighth, and ninth inning options are all in very different places. The bullpen shakeup started with injury and legal trouble for Chris Perez, who was utilized as the closer when available but fell out of favor with the brass, leading to an unceremonious non-tender. Vinnie Pestano, who was extremely effective in the setup role in 2011-12, struggled all season, spending much of it in Columbus. Joe Smith was solid and moved up from the seventh to eighth inning, but signed a multi-year deal with the Angels.

In their stead, the Indians have returned to their standard closer strategy: someone else's reject, often with questionable stuff or command. John Axford represents the latter, and sad to say one of those 30 save seasons with a poor RA appears to be the optimum outcome. Cody Allen will serve as one of the top set up man, provided his arm doesn't fall off--Terry Francona's usage of Allen was bizarre last year, as even as he moved up the pecking order in trust, he was not held back from more mundane situations, leading to a largely unnecessary 77 appearances (second in the AL). He'll be joined by Bryan Shaw, who earned Francona's trust with a 2.84 eRA, 9.0 KG, and 3.5 WG over 75 innings.

The other two sure things for the pen are the LOOGYs. Marc Rzepczynski pitched very well after being plucked from St. Louis' bullpen excess, and Josh Outman was picked up from Colorado in exchange for Drew Stubbs. Neither are particularly exciting in terms of potential to outgrow their roles, although both have enough starting pedigree to warrant long relief duty.

That leaves two spots open. I expect one of them to go to Pestano, who has the highest upside of any of the candidates by far. Other options include Nick Hagadone, who'd be a third lefty and could provide long-ish relief; a few righties who had cups of coffee in 2013 in CC Lee, Preston Guilmet, and Blake Wood; lefty Scott Barnes and Colt Hynes; and non-roster invitees including David Aardsma, Scott Atchison, Matt Capps, JC Ramirez, and Mike Zagurski. Capps would have been a pretty good guess given the team's propensity to give the seventh spot to a veteran reclamantion project, but a sore shoulder will hold him back. My guess here is Wood, who had was serviceable for the Royals over 120 inning in 2010-11 but missed all of 2012 and spent 2013 working his way through the Indians farm save two September appearances in the bigs. Wood has walked too many batters throughout his career to be reliable, but his stuff and relative youth (28) will make him an attractive flier.

To talk about the composition of the roster as it relates to position players, the third base situation has to take center stage. Former first-round pick Lonnie Chisenhall has never really hit that well at any level (94 OPS+ over 682 PA in the majors, 821 career minor league OPS) which has made alternatives attractive, including moving Carlos Santana to third. Santana was not serving as the regular catcher down the stretch, as Yan Gomes superior defense and surprising offensive showing freed Santana up to DH. If Santana is passable at third, I expect him to play there, a sentiment which surprises me. When the Indians started talking about this over the winter, I sort of laughed it off, but I now am convinced of their sincerity.

Gomes will be the catcher and is a prime candidate for offensive regression. Nick Swisher is slated as a full-time first baseman, but can play right if need be and while he held his own offensively with 3 RAA, the Indians had hoped for more in the first year of his four-year deal. Second baseman Jason Kipnis was not nearly as good in the second half as he was in the first (149 OPS+ to 103), but the season as a whole was worthy of down-ballot MVP consideration. Asdrubal Cabrera had a disappointing year (just 2 RAA and a .297 OBA), but the only thing that would jeopardize his position at shortstop is a deadline selloff.

Left field goes to Michael Brantley and his shiny new contract extension; I had been a skeptic of Brantley's value, but he provided average production even for a corner spot last year, shedding concerns that he might be an over-extended fourth outfielder. If Swisher was a minor disappointment, Michael Bourn was a major disappointment; the center fielder had an OBA of just .322, created 4 runs/game, and missed 32 games due to injury. Right field figures to be a platoon of Ryan Raburn (right) and David Murphy (left) with Drew Stubbs shipped to Colorado. Raburn made a deal with the devil last year (.272/.357/.543 in 277 PA) and Murphy had a miserable year (.214/.273/.363, -5 RAR third lowest among AL regulars); together, more moderate luck might make them a mediocre whole.

The prospect of Santana being able to cut it at third should engender more excitement than it actually does, as Cleveland lacks a good DH candidate to plug in his place. Jason Giambi provided the single most memorable Indians moment since 2007, but his overall season line of .183/.282/.371 and his current age of 43 don't lie: he has no business getting regular playing time in the majors.

I'll assume that Chisenhall plays third and Santana is the regular DH, which still leaves a big roster question: will the Indians carry a third catcher or just use Santana as Gomes' caddy? Given that the options are veterans Matt Treanor and Luke Carlin or minor league non-hitter Roberto Perez (623 OPS in 2013, 685 career), I'm guessing no, leaving three bench spots. One clearly belongs to utility man Mike Aviles, who is fine in that role but got too many starts (87) in 2013. Brantley can play a passable center, which means that Raburn/Murphy is fine as fourth outfielder, which does leave room for Jason Giambi. The Indians also could consider Jeff Francoeur (just say no), Nyjer Morgan, or Matt Carson as fourth outfielders. Elliott Johnson could snag a utility man spot, while September pinch-runner Jose Ramirez will be farmed out for everyday playing time. It's conceivable that a hot spring from Bryan LaHair could put him in the mix for DH at bats as well. Frankly, my guess on the composition of the bench is no better than drawing names out of a hat.

I don't think this Indians team is well-positioned to repeat their success of 2013. The rotation isn't particularly strong, the bullpen is unproven; the offense should be fine, but I see no reason to expect it will be any better than it was in 2013. The Tigers remain the class of the division; they have never been as invulnerable as mainstream thought would have them, but the Royals are lurking around and while it may be completely irrational, I always am wary of a White Sox miracle. And as far as wildcard prospects go, the only AL teams I'd write off completely play in the Central except for Houston. I see the 50th percentile scenario for the Tribe as 80-82, third place.

1. 8 Michael Bourn
2. 3 Nick Swisher
3. 4 Jason Kipnis
4. D Carlos Santana
5. 7 Michael Brantley
6. 9 David Murphy
7. 6 Asdrubal Cabrera
8. 2 Yan Gomes
9. 5 Lonnie Chisenhall

Bench: IF Mike Aviles, IF Elliot Johnson, OF Ryan Raburn, DH Jason Giambi

1. Justin Masterson (R)
2. Danny Salazar (R)
3. Corey Kluber (R)
4. Zach McAllister (R)
5. Carlos Carrasco (R)

RP: Blake Wood (R)
RP: Vinnie Pestano (R)
LOOGY: Josh Outman (L)
LOOGY: Mark Rzepczynski (L)
SU: Bryan Shaw (R)
SU: Cody Allen (R)
CL: John Axford (R)phttp://email@example.com:blogger.com,1999:blog-12133335.post-42000829021759063142014-02-10T21:16:00.000-05:002014-04-04T18:11:35.914-04:00No Excuses2014 will mark Greg Beals' fourth season at the helm of the OSU baseball program, which means there are no excuses. He has had plenty of time to recruit his own players into the program and have them playing key roles, and perhaps more importantly he has had plenty of time in which to mold them to play baseball as Greg Beals believes it should be played. Unfortunately for the state of the program, 2013 was not encouraging on either front. One of the strengths that allowed OSU to contend in the Big Ten last season was a strong trio of weekend starters, a group that must now be replaced en masse. It is now time for Beals' recruits to lead the offense, and so far the most noticeable characteristic of a Beals' offense is baserunning that would make a JV high school coach blush.

One spot at which the Buckeyes are well-positioned is catcher, where junior Aaron Gretz will finally have the job to himself. Gretz, for whatever reason, was never able to fully overcome Greg Solomon in the eyes of Beals, despite being a better defensive catcher and possessing any sort of batting eye at all. Gretz figures to be an above-average Big Ten catcher, and is coming off a busy offseason in which the Minnesota native spent several weeks as a backup goalie for the OSU hockey team thanks to a transfer and an injury to the two scholarship goalies.

At first base, sophomore Zach Ratcliff figures to get the nod after emerging as one of OSU's few power threats mid-way through the Big Ten season. The other option is redshirt junior Josh Dezse, who had to sit out 2013 with a back injury. Dezse will also pull double-duty on the mound, and so it stands to reason that he will act as the primary DH to save unnecessary wear and tear. Sophomore Troy Kuhn will start at second base after serving as the utility infielder in his freshman campaign. Kuhn got off to hot start collecting base hits, but showed little in secondary skills as a freshman, particularly in the power department. His choice of walkup music ("Who Let the Dogs Out") may have been the most disappointing element of his campaign.

The first crack at the shortstop job will go to sophomore Craig Nennig, but there have to be serious questions about his ability to hold the job after hitting just .125/.143/.146 in 52 PA as a freshman, all against non-conference foes. At third base, sophomore Jacob Bosiokovic will be counted on to be a key cog in the offense. Bosiokovic showed flashes of potential, hitting for big power when he first got into the lineup, but his overall season line of .273/.327/.369 must improve greatly for the Buckeye offense to run as expected.

In the outfield, senior left fielder Tim Wetzel will look to rebound as an on-base guy and basestealing threat after a dismal junior season that is the outlier in his three-year track record (.215/.292/.304). Wetzel could also slide back over to center field in the event that true freshman Troy Montgomery stumbles. The Indians freshman is said to be a prototypical leadoff-hitting center fielder and will get a chance to assume both roles early in his career. In right field, junior Pat Porter was OSU's only consistent offensive threat last year, building on a solid freshman campaign, and along with Bosiokovic and Dezse will be counted on to man the middle part of the lineup.

With so many youngsters graduating to the starting lineup, the bench will be unproven. The backup catcher figures to be junior Connor Sabanosh, a junior college transfer from Arizona (Arizona JUCOs have continued to be a key pipeline for Beals). Freshman Jalen Washington is listed in the old Rico Washington role (C/IF), and will be an option at the infield spots as well as behind the plate.

The utility infielder should be redshirt sophomore Ryan Leffel, who showed promise as a fielder and with his approach at the plate as a freshman in 2012 before sitting out 2013 with an injury. Leffel may be the backup plan at shortstop if Nennig cannot hit enough to retain the job. Other infielders on the roster are redshirt sophomore Nick Sergakis (a transfer from Coastal Carolina) and true freshman Curtiss Irving (first baseman) and JP Sorma. The two key outfield reserves are sophomore Jake Brobst, who got limited playing time as a freshman and can play center, and true freshman Ronnie Dawson, a powerful hitter from the Columbus area who may challenge Wetzel for his left field playing time.

For most of the offensive positions that need to be replaced, the likely starters are pretty clear based on last year's roster utilization. The same is not true for the starting pitchers. Only one of the likely top three started a significant number of games in 2013, so uncertainty abounds.

The three most likely weekend starters in this observer's opinion are senior Greg Greve, junior Ryan Riga and sophomore Jake Post. Greve started in 2011-12, but worked exclusively in relief last year, pitching solidly (3.65 RA). His previous performance as a starter was poor (ERA over 5 in each of 2011-12), so his spot is far from a lock. Riga was OSU's top lefty reliever last year, also working in long relief, and was highly effective with 7.4 strikeouts and 1.7 walks per nine over 46 innings of work. Riga is probably the safest bet to actually be in OSU's weekend plans. Post showed that he has good stuff last year in starting seven midweek games, but the results (7.63 RA in 31 innings) did not match his peripherals (7.6 K/2.6 W).

The rotation wildcard is Dezse, who was slated to move into a starting role in 2013 after serving as closer in 2011-12. Dezse has an electric fastball, but didn't show much in the secondary offerings department when closing (and also didn't have the lockdown numbers to back up his reputation). Given his injury issue, he will be worked back into the fold slowly, and (based on absolutely no inside information) I would be surprised if he ended as a starter. That leaves the other top options as a pair of true freshman. Lefty Zach Farmer has received rave reviews and if half of what has been said is true, he'll be in the rotation by the time Big Ten play opens. Right hander Travis Lakins would be next in the pecking order.

Should Dezse wind up in the bullpen, he'll join with junior Trace Dempsey to give the Bucks two big right-handed arms. Dempsey was untouchable for much of last season with his three-quarters movement-heavy stuff, but faltered a bit in key games against Indiana and in the Big Ten Tournament. Still, a 1.50 RA in 35 innings is a sterling campaign.

Behind Dempsey, the pen also will have key parts to replace with Riga and Greve starting and the graduation of key right-handers Brett McKinney and David Fathalikhani. The only other returning reliever who pitched significantly is senior Tyler Giannonatti, who mopped up last year and probably won't be used in high leverage innings in 2013. Sophomore three-quarters lefty Matt Panek took a redshirt for injury last year after pitching sparingly as a freshman, but given Beals' propensity for playing matchups, a healthy Panek should see the mound.

According to Big Ten baseball guru Chris Webb, the other most likely relievers for key innings are redshirt freshman sidearmer Michael Koltak, true freshman Adam Niemeyer, true freshman lefty Tanner Tully, and redshirt freshman Shea Murray. Other pitchers on the roster include junior lefty Michael Horesjei, redshirt freshman lefty Joe Stoll, and true freshman right-handers Kyle Michalik, Brennan Milby, and Yianni Pavlopoulus.

OSU opens its season this weekend with four games in Port Charlotte, Florida (UConn, Auburn, and two with Indiana State). The following weekend they will back in the Sunshine State to play Central Florida, The Citadel, and Oklahoma in Orlando. The first weekend in March will see the Bucks in Greenville, North Carolina to face Pitt, Western Kentucky, and East Carolina. The destination portion of the schedule ends the following week with a three game series at Oregon followed by a single contest at Oregon State.

The home opener is slated for March 14 with a three-game series against Siena, then midweek games against Akron and Xavier. Other midweek opponents throughout the season will be Marshall, Ohio University, Toledo, Dayton, at West Virginia, Ball State (Beals' former employer), at Louisville, Miami, and Cincinnati.

Big Ten play opens March 21 with OSU at Michigan State. The weekends for the rest of the season will be hosting Indiana, at Nebraska, hosting Penn State, a bye week in which OSU will host Murray State, at Purdue, hosting Iowa, at Michigan, and hosting Northwestern. Should the Bucks qualify for the Big Ten Tournament by finishing in the top six, they will head to Omaha on May 21.

Will the Buckeyes get to Omaha? With Indiana breaking a twenty-nine year drought of Big Ten participation in the College World Series, this question might carry a different connotation than in the past. But given the current status of the program, the question should be limited to qualifying for the Big Ten Tournament. The Buckeyes have too many question marks to be considered a favorite in the conference race, and given that it's year four of Beals' tenure, I'm hoping that I am very wrong.

1. CF Troy Montgomery (FM)
2. 2B Troy Kuhn (SM)
3. RF Pat Porter (JR)
4. DH Jose Dezse (JR)
5. 3B Jacob Bosiokovic (SM)
6. 1B Zach Ratcliff (SM)
7. C Aaron Gretz (JR)
8. LF Tim Wetzel (SR)
9. SS Craig Nennig (SM)

SP #1: R Greg Greve (SR)
SP #2: L Ryan Riga (JR)
SP #3: R Jake Post (SM)
SP #4 (midweek): L Zach Farmer (FM)
SP #5 (midweek): R Travis Lakins (FM)

RP: R Tyler Giannonatti (SR)
RP: R Michael Koltak (FM)
RP: L Matt Panek (SM)
RP: R Trace Dempsey (JR)
CL: R Josh Dezse (JR)phttp://email@example.com:blogger.com,1999:blog-12133335.post-15337323482619550712014-01-28T08:56:00.000-05:002014-01-29T22:17:21.843-05:00Run Distribution and W%, 2013A couple of caveats apply to everything that follows in this post. The first is that there are no park adjustments anywhere. There's obviously a difference between scoring 5 runs at Petco and scoring 5 runs at Coors, but if you're using discrete data there's not much that can be done about it unless you want to use a different distribution for every possible context. Similarly, it's necessary to acknowledge that games do not always consist of nine innings; again, it's tough to do anything about this while maintaining your sanity.

All of the conversions of runs to wins are based only on 2013 data. Ideally, I would use an appropriate distribution for runs per game based on average R/G, but I've taken the lazy way out and used the empirical data for 2013 only. (I have a methodology I could use to do estimate win probabilities at each level of scoring that take context into account, but I've not been able to finish the full write-up it needs on this blog before I am comfortable using it without explanation).

The first breakout is record in blowouts versus non-blowouts. I define a blowout as a margin of five or more runs. This is not really a satisfactory definition of a blowout, as many five-run games are quite competitive--"blowout" is just a convenient label to use, and expresses the point succinctly. I use these two categories with wide ranges rather than more narrow groupings like one-run games because the frequency and results of one-run games are highly biased by the home field advantage. Drawing the focus back a little allows us to identify close games and not so close games with a margin built in to allow a greater chance of capturing the true nature of the game in question rather than a disguised situational effect.

In 2013, 74.7% of major league games were non-blowouts while the complement, 25.3%, were. Team record in non-blowouts:

And in blowouts:

Teams sorted by difference between blowout and non-blowout W%, as well as the percentage of blowouts for each team:

Baltimore is one of the teams that interest me here; their unbelievable one-run record in 2012 was well-documented, and so it shouldn't surprise that the Orioles ranked second in the majors in 2012 in non-blowout W% but were just over .500 in non-blowouts (23-21). In 2013, Baltimore just quit playing in blowouts, with only 15% of their games decided by five or more runs (only the White Sox at 17% joined them under 20% blowouts), but when they did they had a 14-11 record. Boston had the largest W% differential between blowouts and non-blowouts and were also the best team in the majors per most result-based perspectives.

A more interesting way to consider game-level results is to look at how teams perform when scoring or allowing a given number of runs. For the majors as a whole, here are the counts of games in which teams scored X runs:

The "marg" column shows the marginal W% for each additional run scored. In 2013, the second run was the marginally most valuable while the fourth was the cutoff point between winning and losing.

I use these figures to calculate a measure I call game Offensive W% (or Defensive W% as the case may be), which was suggested by Bill James in an old Abstract. It is a crude way to use each team's actual runs per game distribution to estimate what their W% should have been by using the overall empirical W% by runs scored for the majors in the particular season.

A theoretical distribution would be much preferable to the empirical distribution for this exercise, but as I mentioned earlier I haven't yet gotten around to writing up the requisite methodological explanation, so I've defaulted to the 2013 empirical data. Some of the drawbacks of this approach are:

1. The empirical distribution is subject to sample size fluctuations. In 2013, teams that scored 7 runs won 85.8% of the time while teams that scored 8 runs won 83.2% of the time. Does that mean that scoring 7 runs is preferable to scoring 8 runs? Of course not--it's a quirk in the data. Additionally, the marginal values don't necessary make sense even when W% increases from one runs scored level to another (In figuring the gEW% family of measures below, I lumped all games with 7 and 8 runs scored/allowed into one bucket, which smoothes any illogical jumps in the win function, but leaves the inconsistent marginal values unaddressed and fails to make any differentiation between scoring 7 and 8. The values actually used are displayed in the "use" column, and the "invuse" column is the complements of these figures--i.e. those used to credit wins to the defense. I've used 1.0 for 12+ runs, which is a horrible idea theoretically. In 2013, teams were 102-0 when scoring 12 or more runs).

2. Using the empirical distribution forces one to use integer values for runs scored per game. Obviously the number of runs a team scores in a game is restricted to integer values, but not allowing theoretical fractional runs makes it very difficult to apply any sort of park adjustment to the team frequency of runs scored.

3. Related to #2 (really its root cause, although the park issue is important enough from the standpoint of using the results to evaluate teams that I wanted to single it out), when using the empirical data there is always a tradeoff that must be made between increasing the sample size and losing context. One could use multiple years of data to generate a smoother curve of marginal win probabilities, but in doing so one would lose centering at the season's actual run scoring rate. On the other hand, one could split the data into AL and NL and more closely match context, but you would lose sample size and introduce more quirks into the data.

I will use my theoretical distribution (Enby, which you can read about here) for a few charts in this post. The first is a comparison of the frequency of scoring X runs in the majors to what would be expected given the overall major league average of 4.166 R/G (Enby distribution parameters are r = 3.922, B = 1.07, z = .0649):

Enby generally does a decent job of estimating the actual scoring distribution, and while I am certainly not an unbiased observer, I think it does so here as well.

I will not go into the full details of how gOW%, gDW%, and gEW% (which combines both into one measure of team quality) are calculated in this post, but full details were provided here. The "use" column here is the coefficient applied to each game to calculate gOW% while the "invuse" is the coefficient used for gDW%. For comparison, I have looked at OW%, DW%, and EW% (Pythagenpat record) for each team; none of these have been adjusted for park to maintain consistency with the g-family of measures which are not park-adjusted.

For most teams, gOW% and OW% are very similar. Teams whose gOW% is higher than OW% distributed their runs more efficiently (at least to the extent that the methodology captures reality); the reverse is true for teams with gOW% lower than OW%. The teams that had differences of +/- 2 wins between the two metrics were (all of these are the g-type less the regular estimate):

Positive: CHA, MIL, CHN, BAL, MIA, PIT, MIN
Negative: BOS, OAK, STL, TEX, CLE

There were an abnormally high number of teams this season whose gOW% diverged significantly from their standard OW%; as you'll see in a moment, the opposite was true for gDW%. The White Sox gOW% of .467 was 3.5 games better than their OW% of .445. Their gOW% was seventh-lowest in the majors, but their OW% was second-worst. So while their offense was still bad, they wound up distributing their runs in a manner that should have resulted in more wins than one would expect from their R/G average.

As such, Chicago makes for an interesting case study in how a measly 3.69 runs/game can be doled out more efficiently. The black line is Chicago's actual 2013 run distribution, the blue line is Enby's estimate for a team averaging 3.691 R/G (r = 3.662, B = 1.018, z = .0853), and the red line is that of the majors as a whole (Chicago did not actually score more than twelve runs in a game this season, but fifteen is the standard I've always used in these graphs):

Chicago scored 3, 4, and 5 runs significantly more often than Enby would expect and more often that the major league average despite having a poor offense. 3-5 runs is a good spot to be in, at least in the current scoring environment--in 2013, teams won 54% of the time when scoring 3-5.

I deliberately wrote the preceding paragraph to be a little misleading--Chicago's propensity to score 3-5 runs was not really a positive, since it meant fewer games in which they scored more than five runs. The White Sox were shutout more often than the major league average (8% to 6.8%), scored < 2 runs more often than average (19.1% to 18%), but scored < 3 runs less often than average (50.6% to 47.8%). That is the only step at which Chicago was above average, and they quickly fell into well below average territory--Chicago scored < 6 runs 82% of the time versus the average of 71.9%:

Teams with differences of +/- 2 wins between gDW% and standard DW%:

Positive: SEA
Negative: ATL, TEX, OAK

The 3.7 win discrepancy between Atlanta's gDW% (.570) and standard DW% (.592) was the largest such difference for any unit in the majors (greater than Chicago's gOW% difference). The Braves were the only team which did not allow eleven or more runs in a game; the average was 3.4% and only Oakland (one) and St. Louis (two) had fewer than three such games. Avoiding those disaster games helped keep their RA/G low, but the Braves allowed four and five runs more often than both the Enby expectation for a team allowing 3.383 runs per game (r = 3.478, B = .983, z = .1023) would predict and the major league average:

Teams with differences of +/- 2 wins between gEW% and EW% (standard Pythagenpat):

Positive: SEA, CHA, PHI, PIT, MIN, CHN
Negative: OAK, TEX, STL, ATL, BOS, CLE, CIN

The negative list includes all playoff teams which obviously were not too badly hampered by seemingly inefficient run distributions. Standard Pythagenpat had a freakishly good year predicting actual W% in 2013, with a RMSE of 3.66 while gEW% had a 3.95 RMSE. gEW% does not incorporate any knowledge about the joint distribution of runs scored and allowed; if you do that, you may as well just look at actual win-loss record. But since it doesn't have knowledge of the joint distribution, it's quite possible for standard EW% to perform better as a predictor.

For now most of the applications of this methodology, at least in my writings, have been freak show in nature. The more interesting questions will be easier to investigate once I've finished my update of the Enby methodology. Do certain types of offenses tend to bunch their runs more efficiently? Can the estimate of variance of runs scored (which is really the key assumption underpinning Enby) be improved by considering team characteristics? How well do efficient or non-efficient distributions by teams predict team performance in future years? I don't mean to imply that others have not investigated these questions, simply that I hope to have more interesting material in these year-end reviews starting in 2014. I said that last year too though.

phttp://email@example.com:blogger.com,1999:blog-12133335.post-75249219038423925552014-01-14T08:50:00.000-05:002014-01-14T08:50:00.495-05:00Crude Team Ratings, 2013For the last few years I have published a set of team ratings that I call "Crude Team Ratings". The name was chosen to reflect the nature of the ratings--they have a number of limitations, of which I documented several when I introduced the methodology.

I explain how CTR is figured in the linked post, but in short:

1) Start with a win ratio figure for each team. It could be actual win ratio, or an estimated win ratio.

2) Figure the average win ratio of the team's opponents.

3) Adjust for strength of schedule, resulting in a new set of ratings.

4) Begin the process again. Repeat until the ratings stabilize.

First, CTR based on actual wins and losses. In the table, "aW%" is the winning percentage equivalent implied by the CTR and "SOS" is the measure of strength of schedule--the average CTR of a team's opponents. The rank columns provide each team's rank in CTR and SOS:

This was a banner year for those of us who prefer the best teams to make it through the playoffs, as the pennant winners ranked one-two in MLB. The ten playoff teams were also the ten that had the most impressive win-loss records, with the exception of #9 Texas, but of course they had a shot in the one game playoff. Also, the Rangers were still only fifth in the AL so it's not as if their schedule unfairly kept them out. What is a departure from recent seasons is that no other also-ran AL teams finished with higher ratings than the NL playoff qualifiers. Still, the AL dominated the top spots again as can be seen by the fact that only St. Louis snuck into the top five.

Below are the mean ratings for each league and division, actually calculated as the geometric rather than arithmetic mean:

Last year, the AL-NL gap was 112-89, and if you count Houston with the NL it was 106-88 in 2013. In any event, the AL remains the stronger league based on the interleague results (which is what underpins any differences in these rankings), with an implied W% of .521 against the NL.

Speaking of Houston, they actually ticked up a bit in CTR, from 46 to 48. While I wouldn't claim that is a meaningful difference, it does indicate that their four win drop is largely a function of opponent quality, moving from the 21st most difficult schedule in 2012 to 4th in 2013. They also provide a good opportunity to point out that the schedule rankings are dependent on the quality of the team in question--Houston's schedule was tougher than that of their divisional opponents because they did not get the benefit of playing nineteen games against Houston.

Schedule can make a big difference when comparing two teams across leagues, in a tough and weak division--naturally, the largest schedule disparity is between the winner of the weakest division (NL East) and cellar dweller of the strongest (AL East). In the actual tallies, Atlanta was 96-66 and Toronto was 74-88. However, the ratings (as indicated by aW%) suggest that Atlanta was equivalent to a 92-70 team and Toronto to 78-84, an eight game swing in a head-to-head comparison. Atlanta's SOS of 90 and Toronto's of 112 implies that Toronto's average opponent would have a .554 W% against Atlanta's average opponent--comparable in 2013 CTR terms to the Dodgers or Rangers.

I will present the rest of the ratings with minimal comment. The next set is based on Pythagenpat record from R/RA:

Next is based on gEW%, which is explained in this post--some of the other exhibits for the annual post on that metric are a little more involved so I'm running these ratings first. The basic idea of gEW% is to take into account (separately) the distribution of runs scored and runs allowed per game for each team rather than simply using season totals as in Pythagenpat:

And finally, based on Runs Created and Allowed run through Pythagenpat:

These ratings are based on regular season data only, but one could also choose to include playoff results in the mix. Regardless of what your thoughts may be on the value of considering playoff data, it is most commonly omitted simply because of the way statistics are presented. It usually takes extra effort to combine regular season and playoff data.

So I decided to run the win-loss based ratings with playoff records and schedules included, and to see how large a difference it would create in the results. I was a little surprised by the results:

It's not a surprise of course that Boston strengthened its rating--the Red Sox went 11-5 against very good competition. What did surprise me was that the only other playoff team to have a noticeable change in rating was Atlanta. Their 1-3 record against the Dodgers pushed their rating down by four points. Much of the movement in ratings for the other teams was felt by non-playoff teams whose SOS numbers fluctuated, in particular the AL East in which each team gained a point, and the NL in general, whose collective rating was pushed further down.

An angle that could make the playoff-inclusive ratings more interesting would be if I included regression in the ratings, which I do not. My reasoning is that I intend the ratings to be a reflection of the actual results of the season rather than an attempt to measure true quality of the teams. Additionally, regression would have little impact on the rank order of teams--it would mostly serve to compress the variance of the ratings. On the other hand, even if one wants to use the actual record of a team untouched to establish its rating, the case can be made that its opponents' records should still be regressed, to avoid overcompensating for strength of schedule in ratings. Some purveyors of team ratings in other sports take a similar approach in basing calculations of opponent strength on those teams' point-based rankings, but still base each team's own rating on their actual wins and losses.

Again, though, these ratings are advertised as crude and are clearly only intended to be used in viewing 2013 retrospectively, so I've not bothered with regression here. I do use regression on the rare occasions when I use the CTRs to give crude estimates of win probabilities (such as playoff odds).phttp://email@example.com