Delete pitch_summary_functions.py

pitch_summary_functions.py

@@ -1,1005 +0,0 @@
-
-import pandas as pd
-import numpy as np
-import json
-from  matplotlib.ticker import FuncFormatter
-from matplotlib.ticker import MaxNLocator
-import math
-from matplotlib.patches import Ellipse
-import matplotlib.transforms as transforms
-import matplotlib.colors
-import matplotlib.colors as mcolors
-import seaborn as sns
-import matplotlib.pyplot as plt
-import requests
-
-font_properties = {'family': 'calibi', 'size': 12}
-font_properties_titles = {'family': 'calibi', 'size': 20}
-font_properties_axes = {'family': 'calibi', 'size': 16}
-     
-
-colour_palette = ['#FFB000','#648FFF','#785EF0',
-                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
-season_start = '2024-03-20'
-season_end = '2024-09-29'
-season_fg=2024 
-chad_fg = requests.get(f'https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&qual=0&season={season_fg}&season={season_fg}&month=1000&season1={season_fg}&ind=0&pageitems=2000000000&pagenum=1&ind=0&rost=0&players=&type=36&postseason=&sortdir=default&sortstat=sp_pitching').json()
-cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
-
-
-chadwick_df_small = pd.DataFrame(data={
-'key_mlbam':[x['xMLBAMID'] for x in chad_fg['data']],
-'key_fangraphs':[x['playerid'] for x in chad_fg['data']],
-'Name':[x['PlayerName'] for x in chad_fg['data']],
-})    
-
-pitcher_dicts = chadwick_df_small.set_index('key_mlbam')['Name'].sort_values().to_dict()
-mlb_fg_dicts = chadwick_df_small.set_index('key_mlbam')['key_fangraphs'].sort_values().to_dict()
-        
-        
-### DF UPDATE CODE ###
-def df_update_code(df):
-        print('Starting')
-        #df = pd.read_csv('2024_spring_data.csv',index_col=[0])
-        print('Starting')
-
-
-        df['vy_f'] = -(df['vy0']**2 - (2 * df['ay'] * (df['y0'] - 17/12)))**0.5
-        df['t'] = (df['vy_f'] - df['vy0']) / df['ay']
-        df['vz_f'] = (df['vz0']) + (df['az'] * df['t'])
-        df['vaa'] = -np.arctan(df['vz_f'] / df['vy_f']) * (180 / np.pi)
-
-        #df['vy_f'] = -(df['vy0']**2 - (2 * df['ay'] * (df['y0'] - 17/12)))**0.5
-        #df['t'] = (df['vy_f'] - df['vy0']) / df['ay']
-        df['vx_f'] = (df['vx0']) + (df['ax'] * df['t'])
-        df['haa'] = -np.arctan(df['vx_f'] / df['vy_f']) * (180 / np.pi)
-
-
-
-        end_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
-            'double', 'sac_fly', 'force_out', 'home_run',
-            'grounded_into_double_play', 'fielders_choice', 'field_error',
-            'triple', 'sac_bunt', 'double_play', 'intent_walk',
-            'fielders_choice_out', 'strikeout_double_play',
-            'sac_fly_double_play', 'catcher_interf', 'other_out']
-
-
-
-        df['pa'] = df.event_type.isin(end_codes)
-        #df['pa'] = 1
-        df['k'] = df.event_type.isin(list(filter(None, [x if 'strikeout' in x else '' for x in df.event_type.fillna('None').unique()])))
-        df['bb'] = df.event_type.isin(list(filter(None, [x if 'walk' in x else '' for x in df.event_type.fillna('None').unique()])))
-        df['k_minus_bb'] = df['k'].astype(np.float32)-df['bb'].astype(np.float32)
-
-        df = df.drop_duplicates(subset=['play_id'])
-        df = df.dropna(subset=['start_speed'])
-
-
-
-        swing_codes = ['Swinging Strike', 'In play, no out', 
-                'Foul', 'In play, out(s)', 
-            'In play, run(s)', 'Swinging Strike (Blocked)',
-            'Foul Bunt','Foul Tip', 'Missed Bunt','Foul Pitchout','Swinging Pitchout']
-
-        swings_in = ['Swinging Strike', 'In play, no out', 
-                'Foul', 'In play, out(s)', 
-            'In play, run(s)', 'Swinging Strike (Blocked)',
-            'Foul Bunt','Foul Tip', 'Missed Bunt','Foul Pitchout','Swinging Pitchout']       
-
-        swing_strike_codes = ['Swinging Strike',
-        'Swinging Strike (Blocked)','Missed Bunt','Foul Tip','Swinging Pitchout']
-
-
-        contact_codes = ['In play, no out', 
-                'Foul', 'In play, out(s)', 
-            'In play, run(s)', 
-            'Foul Bunt']
-
-        codes_in = ['In play, out(s)',
-        'Swinging Strike',
-        'Ball',
-        'Foul',
-        'In play, no out',
-        'Called Strike',
-        'Foul Tip',
-        'In play, run(s)',
-        'Hit By Pitch',
-        'Ball In Dirt',
-        'Pitchout',
-        'Swinging Strike (Blocked)',
-        'Foul Bunt',
-        'Missed Bunt',
-        'Foul Pitchout',
-        'Intent Ball',
-        'Swinging Pitchout']
-
-        df['in_zone'] = df['zone'] < 10
-
-
-        df = df.drop_duplicates(subset=['play_id'])
-
-
-
-        df_codes = df[df.play_description.isin(codes_in)].dropna(subset=['in_zone'])
-
-        df_codes['bip'] = ~df_codes.launch_speed.isna()
-        conditions = [
-            (df_codes['launch_speed'].isna()),
-            (df_codes['launch_speed']*1.5 - df_codes['launch_angle'] >= 117 ) & (df_codes['launch_speed'] + df_codes['launch_angle'] >= 124) & (df_codes['launch_speed'] > 98) & (df_codes['launch_angle'] >= 8) & (df_codes['launch_angle'] <= 50)
-        ]
-
-        choices = [False,True]
-        df_codes['barrel'] = np.select(conditions, choices, default=np.nan)
-
-        conditions_ss = [
-            (df_codes['launch_angle'].isna()),
-            (df_codes['launch_angle'] >= 8 ) * (df_codes['launch_angle'] <= 32 ) 
-        ]
-
-        choices_ss = [False,True]
-        df_codes['sweet_spot'] = np.select(conditions_ss, choices_ss, default=np.nan)
-        conditions_hh = [
-            (df_codes['launch_speed'].isna()),
-            (df_codes['launch_speed'] >= 94.5 ) 
-        ]
-
-        choices_hh = [False,True]
-        df_codes['hard_hit'] = np.select(conditions_hh, choices_hh, default=np.nan)
-
-
-        conditions_tb = [
-            (df_codes['event_type']=='single'),
-            (df_codes['event_type']=='double'),
-            (df_codes['event_type']=='triple'),
-            (df_codes['event_type']=='home_run'),
-        ]
-
-        choices_tb = [1,2,3,4]
-
-        df_codes['tb'] = np.select(conditions_tb, choices_tb, default=np.nan)
-
-        conditions_woba = [
-            (df_codes['event_type']=='walk'),
-            (df_codes['event_type']=='hit_by_pitch'),    
-            (df_codes['event_type']=='single'),
-            (df_codes['event_type']=='double'),
-            (df_codes['event_type']=='triple'),
-            (df_codes['event_type']=='home_run'),
-        ]
-
-        choices_woba = [0.705,
-                        0.688,
-                        0.897,
-                        1.233,
-                        1.612,
-                        2.013]
-
-        df_codes['woba'] = np.select(conditions_woba, choices_woba, default=np.nan)
-
-
-        woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
-            'double', 'sac_fly', 'force_out', 'home_run',
-            'grounded_into_double_play', 'fielders_choice', 'field_error',
-            'triple', 'sac_bunt', 'double_play',
-            'fielders_choice_out', 'strikeout_double_play',
-            'sac_fly_double_play', 'other_out']
-
-
-
-
-
-        conditions_woba_code = [
-            (df_codes['event_type'].isin(woba_codes))
-        ]
-
-        choices_woba_code = [1]
-
-        df_codes['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan)
-
-
-        #df_codes['barrel']  = (df_codes.launch_speed >= 98) & (df_codes.launch_angle >= (26 - (-98 + df_codes.launch_speed))) & (df_codes.launch_angle <= 30 + (-98 + df_codes.launch_speed)) & (df_codes.launch_angle >= 8) & (df_codes.launch_angle <= 50)
-
-
-
-        #df_codes['barrel']  = (df_codes.launch_speed >= 98) & (df_codes.launch_angle >= (26 - (-98 + df_codes.launch_speed))) & (df_codes.launch_angle <= 30 + (-98 + df_codes.launch_speed)) & (df_codes.launch_angle >= 8) & (df_codes.launch_angle <= 50)
-        df_codes['pitches'] = 1
-        df_codes['whiffs'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')) else 0 for x in df_codes.play_code]
-        df_codes['csw'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')|(x == 'C')) else 0 for x in df_codes.play_code]
-        df_codes['swings'] = [1 if x in swings_in else 0 for x in df_codes.play_description]
-
-        df_codes['out_zone'] = df_codes.in_zone == False
-        df_codes['zone_swing'] = (df_codes.in_zone == True)&(df_codes.swings == 1)
-        df_codes['zone_contact'] = (df_codes.in_zone == True)&(df_codes.swings == 1)&(df_codes.whiffs == 0)
-        df_codes['ozone_swing'] = (df_codes.in_zone==False)&(df_codes.swings == 1)
-        df_codes['ozone_contact'] = (df_codes.in_zone==False)&(df_codes.swings == 1)&(df_codes.whiffs == 0)
-
-        return df_codes
-
-### GET COLOURS##
-def get_color(value,normalize,cmap_sum):
-    color = cmap_sum(normalize(value))
-    return mcolors.to_hex(color)
-
-### PERCENTILE ###
-def percentile(n):
-    def percentile_(x):
-        return x.quantile(n)
-    percentile_.__name__ = 'percentile_{:02.0f}'.format(n*100)
-    return percentile_
-
-### TJ STUFF+ DF CLEAN ###
-def df_clean(df):
-    df_copy = df.copy()
-    df_copy.loc[df_copy['pitcher_hand'] == 'L','hb'] *= -1
-    df_copy.loc[df_copy['pitcher_hand'] == 'L','x0'] *= -1
-    df_copy.loc[df_copy['pitcher_hand'] == 'L','spin_direction'] = 360 - df_copy.loc[df_copy['pitcher_hand'] == 'L','spin_direction']
-
-    df_copy['pitch_l'] = [1 if x == 'L' else 0 for x in df_copy['pitcher_hand']]
-    df_copy['bat_l'] = [1 if x == 'L' else 0 for x in df_copy['batter_hand']]
-    #df_copy = df_copy[~df_copy.pitch_type.isin(["EP", "PO", "KN", "CS", "SC", "FA"])].reset_index(drop=True)
-    #df_copy = df_copy[~df_copy.pitch_type.isin(["EP", "PO", "CS", "SC", "FA"])].reset_index(drop=True)
-
-    df_copy['pitch_type'] = df_copy['pitch_type'].replace({'FT':'SI',
-                                                           #'KC':'CU',
-                                                           'SV':'SL',
-                                                           'FO':'FS'})
-
-    df_copy_fb_sum = df_copy[df_copy.pitch_type.isin(["FF", "FC", "SI"])].groupby(['pitcher_id']).agg(
-        fb_velo = ('start_speed','mean'),
-        fb_max_ivb  = ('ivb',percentile(0.9)),
-        fb_max_x  = ('hb',percentile(0.9)),
-        fb_min_x  = ('hb',percentile(0.1)),
-        fb_max_velo  = ('start_speed',percentile(0.9)),
-        fb_axis  = ('spin_direction','mean'),
-    )
-
-    df_copy = df_copy.merge(df_copy_fb_sum,left_on='pitcher_id',right_index=True,how='left')
-
-    df_copy['fb_velo_diff'] = df_copy['start_speed']- df_copy['fb_velo']
-    df_copy['fb_max_ivb_diff'] = df_copy['ivb']- df_copy['fb_max_ivb']
-    df_copy['fb_max_hb_diff'] = -abs(df_copy['hb']- df_copy['fb_max_x'])
-    df_copy['fb_min_hb_diff'] = df_copy['hb']- df_copy['fb_min_x']
-    df_copy['fb_max_velo_diff'] = df_copy['start_speed']- df_copy['fb_max_velo']
-    df_copy['fb_axis_diff'] = df_copy['spin_direction']- df_copy['fb_axis']
-
-    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_velo_diff'] = 0
-    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_max_ivb_diff'] = 0
-    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_max_hb_diff'] = 0
-    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_min_hb_diff'] = 0
-    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_max_velo_diff'] = 0
-    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_axis_diff'] = 0
-
-
-    df_copy['max_speed'] = df_copy.groupby(['pitcher_id'])['start_speed'].transform('max')
-    df_copy['max_speed_diff'] = df_copy['start_speed'] - df_copy['max_speed']
-
-    df_copy['max_ivb'] = df_copy.groupby(['pitcher_id'])['ivb'].transform('max')
-    df_copy['max_ivb_diff'] = df_copy['ivb'] - df_copy['max_ivb']
-
-    df_copy['vy_f'] = -(df_copy['vy0']**2 - (2 * df_copy['ay'] * (df_copy['y0'] - 17/12)))**0.5
-    df_copy['t'] = (df_copy['vy_f'] - df_copy['vy0']) / df_copy['ay']
-    df_copy['vz_f'] = (df_copy['vz0']) + (df_copy['az'] * df_copy['t'])
-    df_copy['vaa'] = -np.arctan(df_copy['vz_f'] / df_copy['vy_f']) * (180 / np.pi)
-
-    #df_copy['vy_f'] = -(df_copy['vy0']**2 - (2 * df_copy['ay'] * (df_copy['y0'] - 17/12)))**0.5
-    #df_copy['t'] = (df_copy['vy_f'] - df_copy['vy0']) / df_copy['ay']
-    df_copy['vx_f'] = (df_copy['vx0']) + (df_copy['ax'] * df_copy['t'])
-    df_copy['haa'] = -np.arctan(df_copy['vx_f'] / df_copy['vy_f']) * (180 / np.pi)
-
-    # df_copy['x_diff'] = df_copy['x0'] - df_copy['px']
-    # df_copy['z_diff'] = df_copy['z0'] - df_copy['pz']
-
-    # df_copy['vaa'] = np.arctan(df_copy['z_diff'] / df_copy['release_pos_y']) * 360 / np.pi
-    # df_copy['haa'] = np.arctan(-df_copy['x_diff'] / df_copy['release_pos_y']) * 360 / np.pi
-
-    df_copy = df_copy.dropna(subset=['pitch_type']).fillna(0)
-    return df_copy
-
-### PITCH COLOURS ###
-pitch_colours = {
-        'Four-Seam Fastball':'#FF007D',#BC136F
-        'Sinker':'#98165D',#DC267F
-        'Cutter':'#BE5FA0',
-
-        'Changeup':'#F79E70',#F75233
-        'Splitter':'#FE6100',#F75233
-        'Screwball':'#F08223',
-        'Forkball':'#FFB000',
-        
-        'Slider':'#67E18D',#1BB999#785EF0
-        'Sweeper':'#1BB999',#37CD85#904039
-        'Slurve':'#376748',#785EF0#549C07#BEABD8
-
-        'Knuckle Curve':'#311D8B',  
-        'Curveball':'#3025CE',
-        'Slow Curve':'#274BFC',
-        'Eephus':'#648FFF',
-
-        'Knuckleball':'#867A08',
-
-        'Pitch Out':'#472C30',
-        'Other':'#9C8975',
-        }
-
-### PITCH ELLIPSE ###
-def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):
-    """
-    Create a plot of the covariance confidence ellipse of *x* and *y*.
-
-    Parameters
-    ----------
-    x, y : array-like, shape (n, )
-        Input data.
-
-    ax : matplotlib.axes.Axes
-        The axes object to draw the ellipse into.
-
-    n_std : float
-        The number of standard deviations to determine the ellipse's radiuses.
-
-    **kwargs
-        Forwarded to `~matplotlib.patches.Ellipse`
-
-    Returns
-    -------
-    matplotlib.patches.Ellipse
-    """
-    
-    if x.size != y.size:
-        raise ValueError("x and y must be the same size")
-    try:
-        cov = np.cov(x, y)
-        pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])
-        # Using a special case to obtain the eigenvalues of this
-        # two-dimensional dataset.
-        ell_radius_x = np.sqrt(1 + pearson)
-        ell_radius_y = np.sqrt(1 - pearson)
-        ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,
-                        facecolor=facecolor,linewidth=2,linestyle='--', **kwargs)
-        
-
-        # Calculating the standard deviation of x from
-        # the squareroot of the variance and multiplying
-        # with the given number of standard deviations.
-        scale_x = np.sqrt(cov[0, 0]) * n_std
-        mean_x = np.mean(x)
-        
-
-        # calculating the standard deviation of y ...
-        scale_y = np.sqrt(cov[1, 1]) * n_std
-        mean_y = np.mean(y)
-        
-
-        transf = transforms.Affine2D() \
-            .rotate_deg(45) \
-            .scale(scale_x, scale_y) \
-            .translate(mean_x, mean_y)
-        
-        
-
-        ellipse.set_transform(transf + ax.transData)
-    except ValueError:
-         return    
-        
-    return ax.add_patch(ellipse)
-
-# DEFINE STRIKE ZONE
-strike_zone = pd.DataFrame({
-'PlateLocSide': [-0.9, -0.9, 0.9, 0.9, -0.9],
-'PlateLocHeight': [1.5, 3.5, 3.5, 1.5, 1.5]
-})
-
-### STRIKE ZONE ###
-def draw_line(axis,alpha_spot=1,catcher_p = True):
-
-    axis.plot(strike_zone['PlateLocSide'], strike_zone['PlateLocHeight'], color='black', linewidth=1.3,zorder=3,alpha=alpha_spot,)
-
-    # ax.plot([-0.2833333, -0.2833333], [1.6, 3.5], color='black', linestyle='dashed',alpha=alpha_spot,zorder=3)
-    # ax.plot([0.2833333, 0.2833333], [1.6, 3.5], color='black', linestyle='dashed',alpha=alpha_spot,zorder=3)
-    # ax.plot([-0.85, 0.85], [2.2, 2.2], color='black', linestyle='dashed',alpha=alpha_spot,zorder=3)
-    # ax.plot([-0.85, 0.85], [2.9, 2.9], color='black', linestyle='dashed',alpha=alpha_spot,zorder=3)
-    if catcher_p:
-    # Add dashed line
-    # Add home plate
-        axis.plot([-0.708, 0.708], [0.15, 0.15], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-        axis.plot([-0.708, -0.708], [0.15, 0.3], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-        axis.plot([-0.708, 0], [0.3, 0.5], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-        axis.plot([0, 0.708], [0.5, 0.3], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-        axis.plot([0.708, 0.708], [0.3, 0.15], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-    else:
-        axis.plot([-0.708, 0.708], [0.4, 0.4], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-        axis.plot([-0.708, -0.9], [0.4, -0.1], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-        axis.plot([-0.9, 0], [-0.1, -0.35], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-        axis.plot([0, 0.9], [-.35, -0.1], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-        axis.plot([0.9, 0.708], [-0.1,0.4], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
-
-
-
-### FANGRAPHS STATS DICT ###
-fangraphs_stats_dict = {'IP':{'table_header':'$\\bf{IP}$','format':'.1f',} ,
- 'TBF':{'table_header':'$\\bf{PA}$','format':'.0f',} ,
- 'AVG':{'table_header':'$\\bf{AVG}$','format':'.3f',} ,
- 'K/9':{'table_header':'$\\bf{K\/9}$','format':'.2f',} ,
- 'BB/9':{'table_header':'$\\bf{BB\/9}$','format':'.2f',} ,
- 'K/BB':{'table_header':'$\\bf{K\/BB}$','format':'.2f',} ,
- 'HR/9':{'table_header':'$\\bf{HR\/9}$','format':'.2f',} ,
- 'K%':{'table_header':'$\\bf{K\%}$','format':'.1%',} ,
- 'BB%':{'table_header':'$\\bf{BB\%}$','format':'.1%',} ,
- 'K-BB%':{'table_header':'$\\bf{K-BB\%}$','format':'.1%',} ,
- 'WHIP':{'table_header':'$\\bf{WHIP}$','format':'.2f',} ,
- 'BABIP':{'table_header':'$\\bf{BABIP}$','format':'.3f',} ,
- 'LOB%':{'table_header':'$\\bf{LOB\%}$','format':'.1%',} ,
- 'xFIP':{'table_header':'$\\bf{xFIP}$','format':'.2f',} ,
- 'FIP':{'table_header':'$\\bf{FIP}$','format':'.2f',} ,
- 'H':{'table_header':'$\\bf{H}$','format':'.0f',} ,
- '2B':{'table_header':'$\\bf{2B}$','format':'.0f',} ,
- '3B':{'table_header':'$\\bf{3B}$','format':'.0f',} ,
- 'R':{'table_header':'$\\bf{R}$','format':'.0f',} ,
- 'ER':{'table_header':'$\\bf{ER}$','format':'.0f',} ,
- 'HR':{'table_header':'$\\bf{HR}$','format':'.0f',} ,
- 'BB':{'table_header':'$\\bf{BB}$','format':'.0f',} ,
- 'IBB':{'table_header':'$\\bf{IBB}$','format':'.0f',} ,
- 'HBP':{'table_header':'$\\bf{HBP}$','format':'.0f',} ,
- 'SO':{'table_header':'$\\bf{SO}$','format':'.0f',} ,
- 'OBP':{'table_header':'$\\bf{OBP}$','format':'.0f',} ,
- 'SLG':{'table_header':'$\\bf{SLG}$','format':'.0f',} ,
- 'ERA':{'table_header':'$\\bf{ERA}$','format':'.2f',} ,
- 'wOBA':{'table_header':'$\\bf{wOBA}$','format':'.3f',} ,
- 'G':{'table_header':'$\\bf{G}$','format':'.0f',} }
-
-
-    ## Fangraphs Table
-
-### FANGRAPHS SPLITS SCRAPE ###
-split_dict = {'all':[],
-                'left':['5'],
-                'right':['6']
-                }
-
-def fangraphs_scrape(pitcher_id=808967,
-                    split='all',
-                    start_date='2024-03-20',
-                    end_date='2024-09-29'):
-
-
-        url = "https://www.fangraphs.com/api/leaders/splits/splits-leaders"
-
-        payload = {
-            "strPlayerId": str(mlb_fg_dicts[pitcher_id]),
-            "strSplitArr": split_dict[split],
-            "strGroup": "season",
-            "strPosition": "P",
-            "strType": "2",
-            "strStartDate": str(pd.to_datetime(start_date).strftime('%Y-%m-%d')),
-            "strEndDate": str(pd.to_datetime(end_date).strftime('%Y-%m-%d')),
-            "strSplitTeams": False,
-            "dctFilters": [],
-            "strStatType": "player",
-            "strAutoPt": False,
-            "arrPlayerId": [],
-            "strSplitArrPitch": [],
-            "arrWxTemperature": None,
-            "arrWxPressure": None,
-            "arrWxAirDensity": None,
-            "arrWxElevation": None,
-            "arrWxWindSpeed": None
-        }
-        json_payload = json.dumps(payload)
-        headers = {'Content-Type': 'application/json'}
-        response = requests.post(url, data=json_payload, headers=headers)
-        data_pull = response.json()['data'][0]
-
-        payload_advanced = {
-            "strPlayerId": str(mlb_fg_dicts[pitcher_id]),
-            "strSplitArr": split_dict[split],
-            "strGroup": "season",
-            "strPosition": "P",
-            "strType": "1",
-            "strStartDate": str(pd.to_datetime(start_date).strftime('%Y-%m-%d')),
-            "strEndDate": str(pd.to_datetime(end_date).strftime('%Y-%m-%d')),
-            "strSplitTeams": False,
-            "dctFilters": [],
-            "strStatType": "player",
-            "strAutoPt": False,
-            "arrPlayerId": [],
-            "strSplitArrPitch": [],
-            "arrWxTemperature": None,
-            "arrWxPressure": None,
-            "arrWxAirDensity": None,
-            "arrWxElevation": None,
-            "arrWxWindSpeed": None
-        }
-
-        json_payload_advanced = json.dumps(payload_advanced)
-        headers = {'Content-Type': 'application/json'}
-        response_advanced = requests.post(url, data=json_payload_advanced, headers=headers)
-        data_pull_advanced = response_advanced.json()['data'][0]
-
-        data_pull.update(data_pull_advanced)
-            
-        return data_pull
-
-
-### FANGRAPHS TABLE PLOT ###
-def fangraphs_table(data,
-                    stats,
-                    ax):
-        
-        
-        fg_values = [data[x] if x in data else '---' for x in stats]
-        df_fg = pd.DataFrame(data=dict(zip(stats,fg_values)),index=[0])
-
-        df_fg.loc[0] = [format(df_fg[x][0],fangraphs_stats_dict[x]['format']) if df_fg[x][0] != '---' else '---' for x in df_fg]
-        table_fg = ax.table(cellText=df_fg.values, colLabels=df_fg.columns, cellLoc='center',
-                        bbox=[0.04, 0.2, 0.92, 0.8])
-        
-        min_font_size = 20
-        table_fg.set_fontsize(min_font_size)
-
-
-        new_column_names = [fangraphs_stats_dict[x]['table_header'] if x in data else '---' for x in stats]
-        # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%']
-        for i, col_name in enumerate(new_column_names):
-            table_fg.get_celld()[(0, i)].get_text().set_text(col_name)
-
-        ax.axis('off')
-
-
-        return table_fg
-
-### VELOCITY KDES ###
-def velocity_kdes(df,
-                  ax,
-                  gs,
-                  gs_list,
-                  fig):
-
-        sorted_value_counts = df['pitch_type'].value_counts().sort_values(ascending=False)
-
-        # Get the list of items ordered from most to least frequent
-        items_in_order = sorted_value_counts.index.tolist()
-    
-        # Create the inner subplot inside the outer subplot
-        import matplotlib.gridspec as gridspec
-        ax.axis ('off')
-        #ax.set_ylabel('Pitch Velocity Distribution', fontdict=font_properties_axes)
-        ax.set_title('Pitch Velocity Distribution', fontdict={'family': 'calibi', 'size': 20})
-
-        inner_grid_1 = gridspec.GridSpecFromSubplotSpec(len(items_in_order),1, subplot_spec=gs[2,gs_list])
-        ax_top = []
-        for inner in inner_grid_1:
-            ax_top.append(fig.add_subplot(inner))
-
-
-        ax_number = 0
-
-        for i in items_in_order[0:]:
-            if np.unique(df[df['pitch_type']==i]['start_speed']).size == 1:  # Check if all values are the same
-                print('just')
-                ax_top[ax_number].plot([np.unique(df[df['pitch_type']==i]['start_speed']),np.unique(df[df['pitch_type']==i]['start_speed'])],[0,1], linewidth=4,
-                                color=pitch_colours[df[df['pitch_type']==i]['pitch_description'].values[0]],zorder=20)
-                #    ax_top[ax_number].plot(np.unique(df_melt[df_melt['Player']==i]['value']), [0.5]*len(np.unique(df_melt[df_melt['Player']==i]['value'])), linewidth=4)
-            else:
-                sns.kdeplot(df[df['pitch_type']==i]['start_speed'],ax=ax_top[ax_number],fill=True,
-                            clip=(df[df['pitch_type']==i]['start_speed'].min(),df[df['pitch_type']==i]['start_speed'].max()),
-                            color=pitch_colours[df[df['pitch_type']==i]['pitch_description'].values[0]])
-            ax_top[ax_number].set_xlim(math.floor(df['start_speed'].min()/5)*5,math.ceil(df['start_speed'].max()/5)*5)
-            ax_top[ax_number].set_xlabel('')
-            ax_top[ax_number].set_ylabel('')
-            if ax_number < len(items_in_order)-1:
-                ax_top[ax_number].spines['top'].set_visible(False)
-                ax_top[ax_number].spines['right'].set_visible(False)
-                ax_top[ax_number].spines['left'].set_visible(False)
-                ax_top[ax_number].tick_params(axis='x', colors='none')
-
-
-            ax_top[ax_number].set_xticks(range(math.floor(df['start_speed'].min()/5)*5,math.ceil(df['start_speed'].max()/5)*5,5))
-            ax_top[ax_number].set_yticks([])
-            ax_top[ax_number].grid(axis='x', linestyle='--')
-            ax_top[ax_number].text(-0.01, 0.5, i, transform=ax_top[ax_number].transAxes,
-                        fontsize=14, va='center', ha='right')
-            ax_number =   ax_number + 1
-        ax_top[-1].spines['top'].set_visible(False)
-        ax_top[-1].spines['right'].set_visible(False)
-        ax_top[-1].spines['left'].set_visible(False)
-
-
-        ax_top[-1].set_xticks(list(range(math.floor(df['start_speed'].min()/5)*5,math.ceil(df['start_speed'].max()/5)*5,5)))
-        ax_top[-1].set_xlabel('Velocity (mph)')
-
-### TJ STUFF+ ROLLING ###
-def tj_stuff_roling(df,
-                    window,
-                    ax):
-        ## Velocity Plot
-        sorted_value_counts = df['pitch_type'].value_counts().sort_values(ascending=False)
-
-        # Get the list of items ordered from most to least frequent
-        items_in_order = sorted_value_counts.index.tolist()
-        
-
-        for i in items_in_order:
-            if max(df[df['pitch_type']==i]['pitch_type_count_each']) >= window:
-                sns.lineplot(x=range(1,max(df[df['pitch_type']==i]['pitch_type_count_each'])+1),
-                        y=df[df['pitch_type']==i]['tj_stuff_plus'].rolling(window).sum()/window,
-                    color=pitch_colours[df[df['pitch_type']==i]['pitch_description'].values[0]],
-                    ax=ax,linewidth=3)
-
-        # Adjust x-axis limits to start from 1
-        ax.set_xlim(window,max(df['pitch_type_count_each']))
-        ax.set_ylim(70,130)
-        #ax.get_legend().remove()
-        ax.set_xlabel('Pitches', fontdict=font_properties_axes)
-        ax.set_ylabel('tjStuff+', fontdict=font_properties_axes)
-        ax.set_title(f"{window} Pitch Rolling tjStuff+",fontdict=font_properties_titles)
-        # ax.axis('square')
-        # ax.set_xlim(left=1)
-        ax.xaxis.set_major_locator(MaxNLocator(integer=True))
-
-### BREAK PLOT ###
-def break_plot(df,
-               ax): 
-
-        label_labels = df.sort_values(by=['prop','pitch_type'],ascending=[False,True]).pitch_description.unique()   
-        j = 0
-        for label in label_labels:
-                subset = df[df['pitch_description'] == label]
-                print(label)
-                if len(subset) > 4:
-                    if df['pitcher_hand'].values[0] == 'R':
-                        subset['hb'] = subset['hb']*1
-                    if df['pitcher_hand'].values[0] == 'L':
-                        subset['hb'] = subset['hb']*1
-                    subset['ivb'] = subset['ivb']*1
-                    
-                    try:
-                        confidence_ellipse(subset['hb'], subset['ivb'], ax=ax,edgecolor =  pitch_colours[label],n_std=2,facecolor=  pitch_colours[label],alpha=0.2)
-                    except ValueError:
-                        return
-                    j=j+1
-                else:
-                    j=j+1  
-                
-        if df['pitcher_hand'].values[0] == 'R':
-            sns.scatterplot(ax=ax,x=df.hb*1,y=df.ivb*1,hue=df.pitch_description,palette=pitch_colours,ec='black',alpha=1,zorder=2)
-        if df['pitcher_hand'].values[0] == 'L':
-            sns.scatterplot(ax=ax,x=df.hb*1,y=df.ivb*1,hue=df.pitch_description,palette=pitch_colours,ec='black',alpha=1,zorder=2)
-
-        ax.set_xlim((-25,25))
-        ax.set_ylim((-25,25))
-
-        ax.hlines(y=0,xmin=-50,xmax=50,color=colour_palette[8],alpha=0.5,linestyles='--',zorder=1)
-        ax.vlines(x=0,ymin=-50,ymax=50,color=colour_palette[8],alpha=0.5,linestyles='--',zorder=1)
-        ax.set_xlabel('Horizontal Break (in)', fontdict=font_properties_axes)
-        ax.set_ylabel('Induced Vertical Break (in)', fontdict=font_properties_axes)
-        ax.set_title("Pitch Breaks",fontdict=font_properties_titles)
-
-
-        ax.get_legend().remove()
-
-
-        # ax1.set_xticklabels(ax1.get_xticks(), fontdict=font_properties)
-        ax.set_xticklabels(ax.get_xticks(), fontdict=font_properties)
-
-        # ax1.set_yticklabels(ax1.get_yticks(), fontdict=font_properties)
-        ax.set_yticklabels(ax.get_yticks(), fontdict=font_properties)
-
-
-
-        #ax1.set_aspect('equal', adjustable='box')
-        if df['pitcher_hand'].values[0] == 'R':
-            ax.text(-24.5,-24.5,s='← Glove Side',fontstyle='italic',ha='left',va='bottom',
-                    bbox=dict(facecolor='white', edgecolor='black'),fontsize=12,zorder=3)
-            ax.text(24.5,-24.5,s='Arm Side →',fontstyle='italic',ha='right',va='bottom',
-                        bbox=dict(facecolor='white', edgecolor='black'),fontsize=12,zorder=3)
-            #ax.invert_xaxis()
-        if df['pitcher_hand'].values[0] == 'L':
-            ax.invert_xaxis()    
-            ax.text(24.5,-24.5,s='← Arm Side',fontstyle='italic',ha='left',va='bottom',
-                    bbox=dict(facecolor='white', edgecolor='black'),fontsize=12,zorder=3)
-            ax.text(-24.5,-24.5,s='Glove Side →',fontstyle='italic',ha='right',va='bottom',
-                        bbox=dict(facecolor='white', edgecolor='black'),fontsize=12,zorder=3)
-        ax.set_aspect('equal', adjustable='box')
-        #ax1.yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
-        ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
-        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
-
-### TABLE SUMMARY ###
-def table_summary(df,
-                  pitcher_id,
-                  ax,
-                  df_group,
-                  df_group_all,
-                  statcast_pitch_summary):
-        cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
-
-        ax.axis('off')
-        df_group['spin_direction_adj'] = [(x + 180) for x in df_group['spin_direction']]
-        #(((df_group.groupby('pitch_description').mean()[['spin_direction_adj']] %360 % 30 / 30 /100 *60).round(2) *10).round(0)//1.5/4 )
-        clock_time = ((df_group.groupby('pitch_description').mean()['spin_direction_adj']) %360 // 30 )+  (((df_group.groupby('pitch_description').mean()['spin_direction_adj'] %360 % 30 / 30 /100 *60).round(2) *10).round(0)//1.5/4 )
-   #     print('Clocks')
-   #     print(clock_time)
-        clock_time = (clock_time.astype(int) + clock_time%1*60/100).round(2).astype(str).str.replace('.',':').str.replace(':0',':00').str.replace(':3',':30').str.replace('0:','12:').str.replace('112:','10:').to_frame()
-        df_group = df_group.merge(right=clock_time,left_on='pitch_description',right_index=True,suffixes=['','_clock'])
-
-
-        plot_table = df_group[df_group['pitcher_id']==pitcher_id].sort_values(
-            by=['pitches'],ascending=False)[['pitch_description','pitches','start_speed','ivb',
-                    'hb', 'spin_rate','vaa', 'haa', 'vertical_release','horizontal_release',
-                    'extension','tj_stuff_plus','spin_direction_adj_clock','zone_percent','chase_percent','whiff_rate']]
-
-        # if df['pitcher_hand'].values[0] == 'L':
-        #     plot_table['hb'] = plot_table['hb']*-1
-
-        #if df['pitcher_hand'].values[0] == 'R':
-        plot_table['horizontal_release'] = plot_table['horizontal_release']*-1
-
-        plot_table['pitch_percent'] = plot_table['pitches'] / plot_table['pitches'].sum()
-
-        plot_table = plot_table[['pitch_description','pitches','pitch_percent','start_speed','ivb',
-                    'hb', 'spin_rate','vaa', 'haa', 'vertical_release','horizontal_release', 
-                    'extension','spin_direction_adj_clock','tj_stuff_plus','zone_percent','chase_percent','whiff_rate']]
-
-        plot_table_all = pd.DataFrame(data={'pitch_description': 'All',
-        'pitches': plot_table['pitches'].sum(),
-        'pitch_percent': 1.0,
-        'start_speed': '—',
-        'ivb': '—',
-        'hb': '—',
-        'spin_rate': '—',
-        'vaa': '—',
-        'haa': '—',
-        'vertical_release': '—',
-        'horizontal_release': '—',
-        'extension': df['extension'].mean(),
-        'spin_direction_adj_clock': '—',
-        'tj_stuff_plus': df[df['pitcher_id']==pitcher_id]['tj_stuff_plus'].mean(),
-        'zone_percent': df_group_all[df_group_all['pitcher_id']==pitcher_id]['zone_percent'].values[0],
-        'chase_percent': df_group_all[df_group_all['pitcher_id']==pitcher_id]['chase_percent'].values[0],
-        'whiff_rate': df_group_all[df_group_all['pitcher_id']==pitcher_id]['whiff_rate'].values[0],
-        
-        
-        },index=[0]
-        )
-
-        plot_table = pd.concat([plot_table,plot_table_all]).fillna('—')
-
-
-
-        plt.rcParams['font.family'] = 'Calibri'
-        table = ax.table(cellText=plot_table.values, colLabels=plot_table.columns, cellLoc='center',
-                        colWidths=[2.3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], bbox=[0.04, 0, 0.92, 0.8])
-
-        min_font_size = 14
-        # Set table properties
-        table.auto_set_font_size(False)
-        #table.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
-        table.set_fontsize(min_font_size)
-        table.scale(1, 0.5)
-
-        min_font_size = 18
-        # Set font size for values
-        # Adjust the font size as needed
-        for i in range(len(plot_table)+1):
-            for j in range(len(plot_table.columns)):
-                if i > 0:  # Skip the header row
-                    cell = table.get_celld()[i, j]
-                    cell.set_fontsize(min_font_size)
-
-
-        for i in range(len(plot_table)):
-            
-            if table.get_celld()[(i+1, 0)].get_text().get_text() != 'All':
-                table.get_celld()[(i+1, 0)].set_facecolor(pitch_colours[table.get_celld()[(i+1, 0)].get_text().get_text()])  # Header cell color
-                if table.get_celld()[(i+1, 0)].get_text().get_text() in ['Split-Finger','Slider','Changeup']:
-                    table.get_celld()[(i+1, 0)].set_text_props(color='#000000',fontweight='bold')
-                else:
-                    table.get_celld()[(i+1, 0)].set_text_props(color='#ffffff',fontweight='bold')
-                    if table.get_celld()[(i+1, 0)].get_text().get_text() == 'Four-Seam Fastball':
-                        table.get_celld()[(i+1, 0)].get_text().set_text('4-Seam')
-
-            select_df = statcast_pitch_summary[statcast_pitch_summary['pitch_description'] == plot_table['pitch_description'].values[i]]
-
-            normalize = mcolors.Normalize(vmin=select_df['start_speed'].mean()-select_df.pitch_velocity_std.mean(), 
-                                        vmax=select_df['start_speed'].mean()+select_df.pitch_velocity_std.mean()) # Define the range of values
-            
-            if table.get_celld()[(i+1, 3)].get_text().get_text() != '—':
-                table.get_celld()[(i+1, 3)].set_facecolor(get_color(float(table.get_celld()[(i+1, 3)].get_text().get_text()),normalize,cmap_sum))  # Header cell color
-          
-
-            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',]) 
-            normalize = mcolors.Normalize(vmin=select_df['extension'].mean()*0.9, vmax=select_df['extension'].mean()*1.1)
-            if table.get_celld()[(i+1,11)].get_text().get_text() != '—':
-                table.get_celld()[(i+1,11)].set_facecolor(get_color(float(table.get_celld()[(i+1, 11)].get_text().get_text()),normalize,cmap_sum))  # Header cell color
-
-            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',]) 
-            normalize = mcolors.Normalize(vmin=80, vmax=120)
-            print(normalize)
-            if table.get_celld()[(i+1,13)].get_text().get_text() != '—':
-                
-                table.get_celld()[(i+1,13)].set_facecolor(get_color(float(table.get_celld()[(i+1, 13)].get_text().get_text()),normalize,cmap_sum))  # Header cell color
-
-            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',]) 
-            normalize = mcolors.Normalize(vmin=select_df['zone_percent'].mean()*0.7, vmax=select_df['zone_percent'].mean()*1.3)
-            if table.get_celld()[(i+1,14)].get_text().get_text() != '—':
-                table.get_celld()[(i+1,14)].set_facecolor(get_color(float(table.get_celld()[(i+1, 14)].get_text().get_text().strip('%')),normalize,cmap_sum))  # Header cell color
-
-            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',]) 
-            normalize = mcolors.Normalize(vmin=select_df['chase_percent'].mean()*0.7, vmax=select_df['chase_percent'].mean()*1.3)
-            if table.get_celld()[(i+1,15)].get_text().get_text() != '—':
-                table.get_celld()[(i+1,15)].set_facecolor(get_color(float(table.get_celld()[(i+1, 15)].get_text().get_text().strip('%')),normalize,cmap_sum))  # Header cell color
-
-
-            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',]) 
-            normalize = mcolors.Normalize(vmin=select_df['whiff_rate'].mean()*0.7, vmax=select_df['whiff_rate'].mean()*1.3)
-            if table.get_celld()[(i+1,16)].get_text().get_text() != '—':
-                table.get_celld()[(i+1,16)].set_facecolor(get_color(float(table.get_celld()[(i+1, 16)].get_text().get_text().strip('%')),normalize,cmap_sum))  # Header cell color
-
-            table.get_celld()[(len(plot_table), 0)].set_text_props(color='#000000',fontweight='bold') 
-            
-                
-            new_column_names = ['$\\bf{Pitch\ Name}$',
-            '$\\bf{Count}$',
-            '$\\bf{Pitch\%}$',
-            '$\\bf{Velocity}$',
-            '$\\bf{iVB}$',
-            '$\\bf{HB}$',
-            '$\\bf{Spin}$',
-            '$\\bf{VAA}$',
-            '$\\bf{HAA}$',
-            '$\\bf{vRel}$',    
-            '$\\bf{hRel}$',        
-
-            '$\\bf{Ext.}$',
-            '$\\bf{Axis}$',
-            '$\\bf{tjStuff+}$',
-            '$\\bf{Zone\%}$',
-            '$\\bf{Chase\%}$',
-            '$\\bf{Whiff\%}$',
-            ]
-
-            for i, col_name in enumerate(new_column_names):
-                table.get_celld()[(0, i)].get_text().set_text(col_name)
-
-        float_list = ['start_speed','ivb',
-                'hb', 'vaa', 'haa', 'vertical_release','horizontal_release', 'extension']
-        for fl in float_list:
-            # Subset of column names
-            subset_columns = [fl]
-            
-            # Get the list of column indices
-            column_indices = [plot_table.columns.get_loc(col) for col in subset_columns]
-            
-           # # print(column_indices)
-            for row_l in range(1,len(plot_table)+1):
-           #     print(row_l)
-                if table.get_celld()[(row_l,column_indices[0])].get_text().get_text() != '—':
-               #     print()
-               #     print(fl)
-                    table.get_celld()[(row_l,column_indices[0])].get_text().set_text('{:,.1f}'.format(float(table.get_celld()[(row_l,column_indices[0])].get_text().get_text().strip('%'))))
-
-
-
-        percent_list = ['pitch_percent','zone_percent','chase_percent','whiff_rate']
-        for fl in percent_list:
-            # Subset of column names
-            subset_columns = [fl]
-            
-            # Get the list of column indices
-            column_indices = [plot_table.columns.get_loc(col) for col in subset_columns]
-            
-           # # print(column_indices)
-            for row_l in range(1,len(plot_table)+1):
-           #     print(row_l)
-                if table.get_celld()[(row_l,column_indices[0])].get_text().get_text() != '—':
-                    
-               #     print(fl)
-                    table.get_celld()[(row_l,column_indices[0])].get_text().set_text('{:,.1%}'.format(float(table.get_celld()[(row_l,column_indices[0])].get_text().get_text().strip('%'))))
-
-
-        int_list = ['tj_stuff_plus','spin_rate']
-        for fl in int_list:
-            # Subset of column names
-            subset_columns = [fl]
-            
-            # Get the list of column indices
-            column_indices = [plot_table.columns.get_loc(col) for col in subset_columns]
-
-           # # print(column_indices)
-            for row_l in range(1,len(plot_table)+1):
-           #     print(row_l)
-                if table.get_celld()[(row_l,column_indices[0])].get_text().get_text() != '—':
-               #     print(fl)
-                    
-                    table.get_celld()[(row_l,column_indices[0])].get_text().set_text('{:,.0f}'.format(float(table.get_celld()[(row_l,column_indices[0])].get_text().get_text().strip('%'))))
-
-        return table
-
-### GROUED IVB CREATION ###
-def group_ivb_update(df,
-                     agg_list=['pitcher_id','pitcher_name','pitcher_hand','pitch_type','pitch_description']):
-    
-        grouped_ivb = df.groupby(agg_list).agg(
-        pitches = ('start_speed','count'),
-        
-        start_speed = ('start_speed','mean'),
-        ivb = ('ivb','mean'),
-        hb = ('hb','mean'),
-        spin_rate = ('spin_rate','mean'),
-        vaa = ('vaa','mean'),
-        haa = ('haa','mean'),
-        horizontal_release = ('x0','mean'),
-        vertical_release = ('z0','mean'),
-        extension = ('extension','mean'),
-        spin_direction = ('spin_direction','mean'),
-        tj_stuff_plus = ('tj_stuff_plus','mean'),
-        swings =  ('swings','sum'),
-        in_zone = ('in_zone','sum'),
-        out_zone = ('out_zone','sum'),
-        whiffs = ('whiffs','sum'),
-        zone_swing = ('zone_swing','sum'),
-        zone_contact = ('zone_contact','sum'),
-        ozone_swing = ('ozone_swing','sum'),
-        ozone_contact = ('ozone_contact','sum'),    
-        ).reset_index()
-
-
-        grouped_ivb['zone_contact_percent'] = [grouped_ivb.zone_contact[x]/grouped_ivb.zone_swing[x] if grouped_ivb.zone_swing[x] != 0 else np.nan for x in range(len(grouped_ivb))]
-
-        grouped_ivb['zone_swing_percent'] = [grouped_ivb.zone_swing[x]/grouped_ivb.in_zone[x] if grouped_ivb.pitches[x] != 0 else np.nan for x in range(len(grouped_ivb))]
-
-        grouped_ivb['zone_percent'] = [grouped_ivb.in_zone[x]/grouped_ivb.pitches[x] if grouped_ivb.pitches[x] != 0 else np.nan for x in range(len(grouped_ivb))]
-
-        grouped_ivb['chase_percent'] = [grouped_ivb.ozone_swing[x]/(grouped_ivb.pitches[x] - grouped_ivb.in_zone[x]) if (grouped_ivb.pitches[x]- grouped_ivb.in_zone[x]) != 0 else np.nan for x in range(len(grouped_ivb))]
-
-        grouped_ivb['chase_contact'] = [grouped_ivb.ozone_contact[x]/grouped_ivb.ozone_swing[x] if grouped_ivb.ozone_swing[x] != 0 else np.nan for x in range(len(grouped_ivb))]
-
-        grouped_ivb['swing_percent'] = [grouped_ivb.swings[x]/grouped_ivb.pitches[x] if grouped_ivb.pitches[x] != 0 else np.nan for x in range(len(grouped_ivb))]
-
-        grouped_ivb['whiff_rate'] = [grouped_ivb.whiffs[x]/grouped_ivb.swings[x] if grouped_ivb.swings[x] != 0 else np.nan for x in range(len(grouped_ivb))]
-
-        grouped_ivb['swstr_rate'] = [grouped_ivb.whiffs[x]/grouped_ivb.pitches[x] if grouped_ivb.pitches[x] != 0 else np.nan for x in range(len(grouped_ivb))]
-
-        return grouped_ivb
-
-
-####LHH
-def location_plot(df,ax,hand):
-        label_labels = df.sort_values(by=['prop','pitch_type'],ascending=[False,True]).pitch_description.unique()
-        j = 0
-        for label in label_labels:
-            
-            subset = df[(df['pitch_description'] == label)&(df['batter_hand'] == hand)]
-            print(label)
-            if len(subset) >= 5:
-                confidence_ellipse(subset['px'], subset['pz'], ax=ax,edgecolor =  pitch_colours[label],n_std=1.5,facecolor=  pitch_colours[label],alpha=0.3)
-                j=j+1
-            else:
-                j=j+1  
-
-        pitch_location_group = df[(df['batter_hand'] == hand)].groupby(['pitch_description']).agg(
-            pitches = ('start_speed','count'),
-            px = ('px','mean'),
-            pz = ('pz','mean')).reset_index()
-
-        pitch_location_group['pitch_percent'] = pitch_location_group['pitches']/pitch_location_group['pitches'].sum()
-
-
-        ## Location Plot
-        sns.scatterplot(ax=ax,x=pitch_location_group['px'],
-                                    y=pitch_location_group['pz'],
-                                    hue=pitch_location_group['pitch_description'],
-                                    palette=pitch_colours,ec='black',
-                                    s=pitch_location_group['pitch_percent']*750,
-                                    linewidth=2,
-                                    zorder=2)
-
-        ax.axis('square')
-        draw_line(ax,alpha_spot=0.75,catcher_p=False)
-        ax.axis('off')
-        ax.set_xlim((-2.75,2.75))
-        ax.set_ylim((-0.5,5))
-        if len(pitch_location_group['px'])>0:
-            ax.get_legend().remove()
-        ax.grid(False)
-        ax.set_title(f"Pitch Locations vs {hand}HB\n{pitch_location_group['pitches'].sum()} Pitches",fontdict=font_properties_titles)