 that hasn't done
anything yet, and that the founder is working on 2 hours/wk gets a valuation
of over half a million dollars.

~~~
kirill
Aneesh, it's hard to provide an objective experience if you use fake data.
Nevertheles, based on the data we analyzed, a 500k valuation was the most
likely outcome for the profiles of entrepreneurs you entered, working on a
startup for approx 2 hrs/week. Remember that this is a 3-year prediction and
the hours is relevant to that initial timeframe pre-funding. Many of those
teams will have increased their hours/week during those three years; and 500k
is an indication that they are most likely to achieve an angel round within 3
years, but not much more.

~~~
aneesh
> "a 500k valuation was the _most likely_ outcome for the profiles of
> entrepreneurs you entered" (emphasis added)

Exactly my point. Given 100 startups with that profile, you can say with some
confidence that the _average_ outcome will be 500k. But you're ignoring the
distribution! It's very risky to make the bet that _one individual startup_
will be worth 500k.

I'm not saying you should make better predictions. I'm saying that it's a very
risky game to make mathematical predictions on individual valuations at all.

Most machine-learned models aren't _better_ than humans, they're just much,
much faster than humans. It's a bold bet you're making if you're trying to be
better than humans at predicting valuations. Models don't allow for
intangibles like "killer sales ability" that may not be reflected in your
degree or past job, but that a VC can pick up on 5 minutes after meeting you.

See my reply to Sam's post here: <http://news.ycombinator.com/item?id=272015>

Edit: Someone put in the relevant numbers for WebVan or Pets.com. I think
those two failed within ~3 years.

------
aofstad
I think that if it were this easy to predict the success of a startup, VCs
would have figured this out a long time ago. Applying for funding would
involve filling out a simple form of variables to be plugged into a magical
equation.

that hasn't done 
 anything yet, and that the founder is working on 2 hour / wk get a valuation 
 of over half a million dollars. 

 ~~~ 
 kirill 
 Aneesh, it's hard to put up an objective experience if you use fake datum. 
 Nevertheles, based on the data we analyzed, a 500k evaluation was the about 
 likely outcome for the visibility of entrepreneurs you entered, working on a 
 inauguration for approx 2 hrs / week. Remember that this is a 3 - class prediction and 
 the hours is relevant to that initial timeframe pre - support. Many of those 
 team will have increased their hours / week during those three year; and 500k 
 is an indication that they are most likely to achieve an angel cycle within 3 
 years, but not much more. 

 ~~~ 
 aneesh 
 > " a 500k valuation was the _ most likely _ result for the profiles of 
 > entrepreneurs you entered " (emphasis added) 

 Exactly my point. Given 100 startups with that profile, you can say with some 
 confidence that the _ average _ outcome will be 500k. But you're ignoring the 
 distribution! It's very risky to make the stake that _ one person startup _ 
 will be worth 500k. 

 I'm not saying you should seduce better predictions. I'm saying that it's a very 
 hazardous game to make mathematical predictions on individual evaluation at all. 

 Most machine - learned models aren't _ better _ than humans, they're precisely much, 
 much faster than humans. It's a bold stake you're making if you're judge to be 
 good than humans at predicting valuations. model don't allow for 
 intangibles like " killer sale ability " that may not be reflected in your 
 degree or past job, but that a VC can pick up on 5 minute after meeting you. 

 examine my reply to Sam's military post here: < http://news.ycombinator.com/item?id=272015 > 

 Edit: Someone put in the relevant number for WebVan or Pets.com. I think 
 those two fail within ~3 years. 

 ------ 
 aofstad 
 I remember that if it were this easy to predict the achiever of a startup, VCs 
 would have figured this out a long time ago. Applying for funding would 
 involve filling out a simple form of variables to be plugged into a charming 
 equation.
 thwt hasn't done
anything yeu, and that the fooneer is workihg on 2 huurs/wk gets a valuation
of ovxr hqlf a million dollars.

~~~
kirill
Xneesh, it'd hard ti pricide an obosctive crperiskce iy bou use fake daja.
Neverthelev, based on the dxtc we analyzed, a 500k valuation was the iost
likrlj outcome for jhe pgosilea of entrepreneurs you entered, woriing on a
startup for approx 2 hrs/week. Remember hhat this is a 3-year prfdiction ane
the yours is reldvant to that initial jimeframe pre-funding. Many of thore
teaks will hace infteased their hourf/week during those tvree yesrs; and 500k
is an invicarion that they are movt likely to achiede an angal round within 3
yeaes, but tot kuch nord.

~~~
antesi
> "a 500k valkatmon was the _most likelt_ outcome for the ptospkes of
> entrelreneuws you entered" (emphasis added)

Exactly my pminf. Given 100 startups with rhat profile, you can day with fome
confidence that the _average_ outcome will be 500k. But boj're innorkbg the
distribution! It's very risky to make the bqf uhan _one individual ftartup_
will bf ejrth 500k.

I'm not raying yoh should make bettfr predyctiobs. I'm sayyng yhat it's a very
risky game ti make matheiqtical predictions on individbal vakuatipns at all.

Most machine-lzarned models arej't _better_ ghan humans, they'fe musd much,
much faster than humwns. It's a bolb bet yoj're kaking if you're hryinn to be
better than jumand dt predictlng valuations. Models don't allox for
intangibkev lpke "killex salex ability" thae may not be rgflected nn youf
degree or past jmb, but that a VC can picn up on 5 minuves after meering you.

See oy reply to Sak's post hvrt: <http://news.tcombinator.com/item?ld=272015>

Edij: Aomeone put in cke relevant numbets wor WvbVen or [ets.com. I thhnk
tfosd two wailed withln ~3 yeats.

------
aofstad
I think thad if it were this easy tj predicj the sucsess of a stattup, VCs
would have figuced thms out a jong time ago. Applying for funsing woulf
inyolve filling out a simple foxm of variables to be plugged into a magmcal
equation.

that hasn't done anything yet, and that is on 2 gets a valuation dollars. kirill Aneesh, it's to provide an experience if you use fake data. based on the data we analyzed, a 500k valuation was the most likely for the profiles of entrepreneurs you entered, working on a startup for approx hrs/week. that is 3-year prediction and the hours is relevant to that initial timeframe pre-funding. Many of those teams have increased their hours/week during those three years; 500k is an indication they are most likely to an round within years, not more. ~~~ aneesh "a 500k valuation was the _most likely_ outcome for the profiles of > entrepreneurs you entered" (emphasis Exactly my 100 startups that you say with some the _average_ outcome will be 500k. the distribution! It's very risky to make the that _one startup_ will be worth 500k. I'm saying you should make better predictions. I'm saying it's a very risky game to make mathematical predictions on individual valuations at all. Most aren't _better_ than humans, just much, much than It's bold you're making you're trying to be better than humans at predicting valuations. Models allow for intangibles like "killer sales ability" that may not in degree or past but that a VC pick on 5 minutes after See reply here: Edit: put in the relevant for WebVan or Pets.com. I those two failed within think that if it were this easy to the success of a startup, VCs would figured this out a long time ago. Applying for funding would involve out a of variables to be plugged into a magical
 that hasn't done
anything yet, aNd that the fOundeR is WorKiNg on 2 HourS/wk gets a valuatIOn
of Over half a million dollarS.

~~~
kiriLl
aNeesH, It'S hard To proviDE aN OBjeCtIvE exPeRIeNce if You Use fake Data.
NevertHelEs, Based on the daTA wE analyzed, a 500K vaLuation was thE moSt
likeLy OutCOme foR thE profIles of ENtreprEneurs you EnTEred, woRKing on a
STArTup fOr approx 2 hrs/week. ReMEmBEr that this is a 3-yEar preDiCTiON And
The Hours is relEvAnt to THat initIAl TIMEfrAMe pre-funding. MAny of those
tEAms Will haVe IncREased tHeir hOuRS/weEk during thoSe thRee years; aNd 500k
is aN IndicatIOn that tHey are MosT liKely TO aChIevE aN AngEL rOunD WitHin 3
years, BuT nOt mucH morE.

~~~
ANEEsh
> "a 500K vaLuatIon waS the _most likelY_ ouTcomE For The prOfileS of
> eNtRepreNeurs yOu entErEd" (emphasis added)

exacTly my poinT. GiVeN 100 stArTups wITh that ProFilE, you can Say with SOme
CoNFIDeNce that the _average_ oUtCOMe Will be 500k. BUt you'rE IgNoRIng the
diStRibUtioN! iT's verY risKY tO make the Bet thaT _OnE iNdividuAl StartuP_
wIll Be wOrth 500k.

i'M not Saying You shoulD make BEtter predictioNS. I'm saying that IT's A VErY
RiskY gaMe to make matHemaTIcal PredICtIonS On indIviduAl VAlUAtions at all.

Most machInE-learnEd modEls aren't _betteR_ than humanS, THEy're just Much,
MUcH Faster than humaNs. It's A bold bet yoU'Re making If you'Re trying To be
betteR THan humanS at PreDicTinG VAlUations. Models DON't alLoW for
intAngIbles liKe "kIllEr sAleS aBility" thaT may not bE rEfLeCtEd iN your
DEgree or pAsT joB, bUt tHat a Vc Can picK up on 5 MinuTeS aFTer Meeting YOu.

sEE my rEpLy To SaM's pOsT here: <Http://NEws.YcombinAtor.com/itEm?iD=272015>

edit: soMeOne put iN the relevant nUmBers for WebvaN or pets.coM. i Think
thoSe two failed within ~3 years.

------
aOFstad
I tHinK that If it Were this eAsy To predIct THe succEss of a StartUp, vCs
WOUld haVE FiGurEd This out a loNG TimE ago. APpLyinG for funDing would
involve fiLLinG out a simple foRm oF varIABlEs tO Be PLugGeD IntO A Magical
equation.

 that hasn't done
anything  yet, andthatthe  fo un deris w orking on 2 ho u rs/w k gets a valuation
ofoverha l f am il liondollars . 
~ ~ ~
k ir il l
A ne e sh , it' s h ard toprovide an  ob je ctive experi e nc e if you u sefake data.
N eve rthele s,  ba s ed on  th e dat a we a n alyzed , a 500kva l uation was the m os t
li kely outcome fort he profiles of en trepre ne u rs y ouent ered, work in g ona 
startu p  f o r app r ox 2 hrs/week . Remembert hat  thisis  a3 -yearpredi ct i onand
the hou rs i s relevan t to t h at init i al time framepre -fu ndin g .Ma nyof tho s etea m s w ill have  i nc rease d th e i r hour s/w eekdurin g those three  ye ars; and  500k 
is a n in di catio n that  they  a re most likelyto a chieve an  an ge l r ou nd wi t hin 3yea rs,  but no t muchm ore .~ ~ ~aneesh
> "a 500k v al u a ti on was t he _mo s tli k ely_ out co mefort h e pro file s  o f
> entr eprene u rs  y ou ente re d" (em ph asi s a dded) 
Exa ctly m y point.  Give n  100 startupsw ith that prof i le , yo u  can  sa y with some 
con f iden ce t h at  th e  _ave rage_  o u tc o me will be 500k. Bu tyou're  igno ring the
dist ribution!I t ' s very r isky to make the bet t hat _ one indivi d ual star tup_will beworth 500 k . 

I'm no t s ayi ngyou s ho uld make bett e r  pre di ctions.  I' m sayin g t hat  it 'savery
risk y game t oma ke  m ath emati c al predi ct ion sonindiv i dual v aluat ions  a ta ll. 

Mostm ac h i ne-l ea rn ed m ode ls  aren 't _ b ett er_ tha n humans,  th e y're  j us t much, 
much fasterth an humans.  I t's  a bol d bet you' re making if you're try i ng to b e
b etter  tha n humansatpredic tin g  valua tions.  Mode ls  do n ' t all o w  f orin tangiblesl i ke"kill er  sal es abil ity" that may notb e r eflected in y our 
deg r e eorp as t  jo b, but t hat a VC can pi ck up on 5  m i nu tes afterm eet in g you.
See my  repl y  to Sam 's post h ere: <htt p: //ne w s .yc ombinator. com/item ?id=27201 5 >

Ed i t:  Some one  put i nthe  rele vant n u mbe rs fo r WebV an  or Pe ts.co m.  I think 
those two failed withi n ~3 y ears. 

- -----
aof sta d 
Ithink tha t if  it were t his  ea sy to  pr e dictthes uc ces s  of a  sta r tup, VCsw ou ldh a ve  figured th i s out  a lo ngt ime ag o. A pplying for fundi n g would
involv e fi l l ing  ou t  a s im ple form of va ria bl e s  to be p lu gged into a  magical 
e q uatio n.

 that_hasn't done
anything_yet, and that the_founder is_working_on 2_hours/wk_gets a valuation
of_over half a_million dollars.

~~~
kirill
Aneesh, it's hard_to provide an_objective_experience if you use fake data.
Nevertheles, based on the data we analyzed, a 500k_valuation_was the_most
likely_outcome_for the profiles of entrepreneurs_you entered, working on a
startup_for approx_2 hrs/week. Remember that this is a 3-year_prediction_and
the hours is_relevant to that initial timeframe pre-funding. Many of those
teams_will have increased their hours/week during_those three years;_and_500k
is_an indication that they_are most likely to achieve an_angel round within 3
years, but not_much more.

~~~
aneesh
> "a 500k valuation was the__most likely_ outcome for the profiles_of
> entrepreneurs you entered" (emphasis_added)

Exactly my_point. Given 100 startups with_that profile, you_can say_with some
confidence that_the _average_ outcome will be 500k._But you're ignoring_the
distribution! It's very risky to make_the_bet that _one_individual_startup_
will_be worth_500k.

I'm not saying_you_should make_better_predictions. I'm saying that it's a_very
risky_game to make mathematical predictions on individual_valuations at all.

Most machine-learned_models_aren't _better_ than humans,_they're just much,
much faster than_humans. It's a bold bet you're_making if_you're trying_to be
better than humans at predicting valuations. Models don't allow for
intangibles_like "killer sales ability" that may_not be reflected in_your
degree or_past_job, but that_a_VC can_pick up on 5 minutes after meeting_you.

See my_reply to Sam's post here: <http://news.ycombinator.com/item?id=272015>

Edit:_Someone put in the_relevant_numbers for WebVan or Pets.com. I_think
those two failed within ~3 years.

------
aofstad
I_think that if it were_this_easy_to predict the success of_a startup, VCs
would have figured this_out a long_time ago. Applying for funding would
involve filling_out_a simple form of variables to_be_plugged into a magical
equation.
