2024_mlb_pitch_heat_maps / pitcher_update.py
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import pandas as pd
import numpy as np
import joblib
import math
import pickle
loaded_model = joblib.load('joblib_model/barrel_model.joblib')
in_zone_model = joblib.load('joblib_model/in_zone_model_knn_20240410.joblib')
attack_zone_model = joblib.load('joblib_model/model_attack_zone.joblib')
xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
px_model = joblib.load('joblib_model/linear_reg_model_x.joblib')
pz_model = joblib.load('joblib_model/linear_reg_model_z.joblib')
def percentile(n):
def percentile_(x):
return np.nanpercentile(x, n)
percentile_.__name__ = 'percentile_%s' % n
return percentile_
def df_update(df=pd.DataFrame()):
df.loc[df['sz_top']==0,'sz_top'] = np.nan
df.loc[df['sz_bot']==0,'sz_bot'] = np.nan
df['in_zone'] = [x < 10 if x > 0 else np.nan for x in df['zone']]
if len(df.loc[(~df['x'].isnull())&(df['px'].isnull()),'px']) > 0:
df.loc[(~df['x'].isnull())&(df['px'].isnull()),'px'] = px_model.predict(df.loc[(~df['x'].isnull())&(df['px'].isnull())][['x']])
df.loc[(~df['y'].isnull())&(df['pz'].isnull()),'pz'] = px_model.predict(df.loc[(~df['y'].isnull())&(df['pz'].isnull())][['y']]) + 3.2
# df['in_zone'] = [x < 10 if x > 0 else np.nan for x in df['zone']]
if len(df.loc[(~df['px'].isna())&
(df['in_zone'].isna())&
(~df['sz_top'].isna())]) > 0:
print('We found missing data')
df.loc[(~df['px'].isna())&
(df['in_zone'].isna())&
(~df['sz_top'].isna())&
(~df['pz'].isna())&
(~df['sz_bot'].isna())
,'in_zone'] = in_zone_model.predict(df.loc[(~df['px'].isna())&
(df['in_zone'].isna())&
(~df['sz_top'].isna())&
(~df['pz'].isna())&
(~df['sz_bot'].isna())][['px','pz','sz_top','sz_bot']].values)
hit_codes = ['single',
'double','home_run', 'triple']
ab_codes = ['single', 'strikeout', 'field_out',
'grounded_into_double_play', 'fielders_choice', 'force_out',
'double', 'field_error', 'home_run', 'triple',
'double_play',
'fielders_choice_out', 'strikeout_double_play',
'other_out','triple_play']
obp_true_codes = ['single', 'walk',
'double','home_run', 'triple',
'hit_by_pitch', 'intent_walk']
obp_codes = ['single', 'strikeout', 'walk', 'field_out',
'grounded_into_double_play', 'fielders_choice', 'force_out',
'double', 'sac_fly', 'field_error', 'home_run', 'triple',
'hit_by_pitch', 'double_play', 'intent_walk',
'fielders_choice_out', 'strikeout_double_play',
'sac_fly_double_play',
'other_out','triple_play']
contact_codes = ['In play, no out',
'Foul', 'In play, out(s)',
'In play, run(s)',
'Foul Bunt']
conditions_hit = [df.event_type.isin(hit_codes)]
choices_hit = [True]
df['hits'] = np.select(conditions_hit, choices_hit, default=False)
conditions_ab = [df.event_type.isin(ab_codes)]
choices_ab = [True]
df['ab'] = np.select(conditions_ab, choices_ab, default=False)
conditions_obp_true = [df.event_type.isin(obp_true_codes)]
choices_obp_true = [True]
df['on_base'] = np.select(conditions_obp_true, choices_obp_true, default=False)
conditions_obp = [df.event_type.isin(obp_codes)]
choices_obp = [True]
df['obp'] = np.select(conditions_obp, choices_obp, default=False)
bip_codes = ['In play, no out', 'In play, run(s)','In play, out(s)']
conditions_bip = [df.play_description.isin(bip_codes)]
choices_bip = [True]
df['bip'] = np.select(conditions_bip, choices_bip, default=False)
conditions = [
(df['launch_speed'].isna()),
(df['launch_speed']*1.5 - df['launch_angle'] >= 117 ) & (df['launch_speed'] + df['launch_angle'] >= 124) & (df['launch_speed'] > 98) & (df['launch_angle'] >= 8) & (df['launch_angle'] <= 50)
]
df['bip_div'] = ~df.launch_speed.isna()
choices = [False,True]
df['barrel'] = np.select(conditions, choices, default=np.nan)
df['barrel'] = loaded_model.predict(df[['launch_speed','launch_angle']].fillna(0).values)
conditions_ss = [
(df['launch_angle'].isna()),
(df['launch_angle'] >= 8 ) * (df['launch_angle'] <= 32 )
]
choices_ss = [False,True]
df['sweet_spot'] = np.select(conditions_ss, choices_ss, default=np.nan)
conditions_hh = [
(df['launch_speed'].isna()),
(df['launch_speed'] >= 94.5 )
]
choices_hh = [False,True]
df['hard_hit'] = np.select(conditions_hh, choices_hh, default=np.nan)
conditions_tb = [
(df['event_type']=='single'),
(df['event_type']=='double'),
(df['event_type']=='triple'),
(df['event_type']=='home_run'),
]
choices_tb = [1,2,3,4]
df['tb'] = np.select(conditions_tb, choices_tb, default=np.nan)
conditions_woba = [
(df['event_type'].isin(['strikeout', 'field_out', 'sac_fly', 'force_out',
'grounded_into_double_play', 'fielders_choice', 'field_error',
'sac_bunt', 'double_play', 'fielders_choice_out', 'strikeout_double_play',
'sac_fly_double_play', 'other_out'])),
(df['event_type']=='walk'),
(df['event_type']=='hit_by_pitch'),
(df['event_type']=='single'),
(df['event_type']=='double'),
(df['event_type']=='triple'),
(df['event_type']=='home_run'),
]
choices_woba = [0,
0.696,
0.726,
0.883,
1.244,
1.569,
2.004]
df['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['event_type'].isin(woba_codes))
]
choices_woba_code = [1]
df['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan)
df['woba_contact'] = [df['woba'].values[x] if df['bip'].values[x] == 1 else np.nan for x in range(len(df['woba_codes']))]
#df['in_zone'] = [x < 10 if type(x) == int else np.nan for x in df['zone']]
# df['in_zone_2'] = in_zone_model.predict(df[['x','y','sz_bot','sz_top']].fillna(0).values)
# df['in_zone_3'] = df['in_zone_2'] < 10
# df.loc[df['in_zone'].isna(),'in_zone'] = df.loc[df['in_zone'].isna(),'in_zone_3'].fillna(0)
df['whiffs'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')) else 0 for x in df.play_code]
df['csw'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')|(x == 'C')) else 0 for x in df.play_code]
df['swings'] = [1 if x == True else 0 for x in df.is_swing]
df['out_zone'] = df.in_zone == False
df['zone_swing'] = (df.in_zone == True)&(df.swings == 1)
df['zone_contact'] = (df.in_zone == True)&(df.swings == 1)&(df.whiffs == 0)
df['ozone_swing'] = (df.in_zone==False)&(df.swings == 1)
df['ozone_contact'] = (df.in_zone==False)&(df.swings == 1)&(df.whiffs == 0)
df['k'] = df.event_type.isin(list(filter(None, [x if 'strikeout' in x else '' for x in df.event_type.dropna().unique()])))
df['bb'] = df.event_type.isin(['walk','intent_walk'])
df['k_minus_bb'] = df['k'].astype(np.float32)-df['bb'].astype(np.float32)
df['bb_minus_k'] = df['bb'].astype(np.float32)-df['k'].astype(np.float32)
df['pa'] = [1 if isinstance(x, str) else 0 for x in df.event_type]
df['pitches'] = [1 if x else 0 for x in df.is_pitch]
df.loc[df['launch_speed'].isna(),'barrel'] = np.nan
pitch_cat = {'FA':'Fastball',
'FF':'Fastball',
'FT':'Fastball',
'FC':'Fastball',
'FS':'Off-Speed',
'FO':'Off-Speed',
'SI':'Fastball',
'ST':'Breaking',
'SL':'Breaking',
'CU':'Breaking',
'KC':'Breaking',
'SC':'Off-Speed',
'GY':'Off-Speed',
'SV':'Breaking',
'CS':'Breaking',
'CH':'Off-Speed',
'KN':'Off-Speed',
'EP':'Breaking',
'UN':np.nan,
'IN':np.nan,
'PO':np.nan,
'AB':np.nan,
'AS':np.nan,
'NP':np.nan}
#df['pitch_type'] = df['pitch_type'].map(pitch_cat).fillna('Unknown')
df['average'] = 'average'
df.loc[df['trajectory'] == 'bunt_popup','trajectory'] = 'popup'
df.loc[df['trajectory'] == 'bunt_grounder','trajectory'] = 'ground_ball'
df.loc[df['trajectory'] == '','trajectory'] = np.nan
df.loc[df['trajectory'] == 'bunt_line_drive','trajectory'] = 'line_drive'
df[['trajectory_fly_ball','trajectory_ground_ball','trajectory_line_drive','trajectory_popup']] = pd.get_dummies(df['trajectory'], prefix='trajectory')
df['attack_zone'] = np.nan
df.loc[df[['px','pz','sz_top','sz_bot']].isnull().sum(axis=1)==0,'attack_zone'] = attack_zone_model.predict(df.loc[df[['px','pz','sz_top','sz_bot']].isnull().sum(axis=1)==0][['px','pz','sz_top','sz_bot']])
df['heart'] = df['attack_zone'] == 0
df['shadow'] = df['attack_zone'] == 1
df['chase'] = df['attack_zone'] == 2
df['waste'] = df['attack_zone'] == 3
df['heart_swing'] = (df['attack_zone'] == 0)&(df['swings']==1)
df['shadow_swing'] = (df['attack_zone'] == 1)&(df['swings']==1)
df['chase_swing'] = (df['attack_zone'] == 2)&(df['swings']==1)
df['waste_swing'] = (df['attack_zone'] == 3)&(df['swings']==1)
df['heart_whiff'] = (df['attack_zone'] == 0)&(df['whiffs']==1)
df['shadow_whiff'] = (df['attack_zone'] == 1)&(df['whiffs']==1)
df['chase_whiff'] = (df['attack_zone'] == 2)&(df['whiffs']==1)
df['waste_whiff'] = (df['attack_zone'] == 3)&(df['whiffs']==1)
df['woba_pred'] = np.nan
df['woba_pred_contact'] = np.nan
if len(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred']) > 0:
df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred'] = [sum(x) for x in xwoba_model.predict_proba(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
## Assign a value of 0.696 to every walk in the dataset
df.loc[df['event_type'].isin(['walk']),'woba_pred'] = 0.696
## Assign a value of 0.726 to every hit by pitch in the dataset
df.loc[df['event_type'].isin(['hit_by_pitch']),'woba_pred'] = 0.726
## Assign a value of 0 to every Strikeout in the dataset
df.loc[df['event_type'].isin(['strikeout','strikeout_double_play']),'woba_pred'] = 0
df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred_contact'] = [sum(x) for x in xwoba_model.predict_proba(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
return df
def df_update_summ(df=pd.DataFrame()):
df_summ = df.groupby(['pitcher_id','pitcher_name']).agg(
pa = ('pa','sum'),
ab = ('ab','sum'),
obp_pa = ('obp','sum'),
hits = ('hits','sum'),
on_base = ('on_base','sum'),
k = ('k','sum'),
bb = ('bb','sum'),
bb_minus_k = ('bb_minus_k','sum'),
csw = ('csw','sum'),
bip = ('bip','sum'),
bip_div = ('bip_div','sum'),
tb = ('tb','sum'),
woba = ('woba','sum'),
woba_contact = ('woba_contact','sum'),
xwoba = ('woba_pred','sum'),
xwoba_contact = ('woba_pred_contact','sum'),
woba_codes = ('woba_codes','sum'),
hard_hit = ('hard_hit','sum'),
barrel = ('barrel','sum'),
sweet_spot = ('sweet_spot','sum'),
max_launch_speed = ('launch_speed','max'),
launch_speed_90 = ('launch_speed',percentile(90)),
launch_speed = ('launch_speed','mean'),
launch_angle = ('launch_angle','mean'),
pitches = ('is_pitch','sum'),
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'),
ground_ball = ('trajectory_ground_ball','sum'),
line_drive = ('trajectory_line_drive','sum'),
fly_ball =('trajectory_fly_ball','sum'),
pop_up = ('trajectory_popup','sum'),
attack_zone = ('attack_zone','count'),
heart = ('heart','sum'),
shadow = ('shadow','sum'),
chase = ('chase','sum'),
waste = ('waste','sum'),
heart_swing = ('heart_swing','sum'),
shadow_swing = ('shadow_swing','sum'),
chase_swing = ('chase_swing','sum'),
waste_swing = ('waste_swing','sum'),
).reset_index()
return df_summ
def df_update_summ_avg(df=pd.DataFrame()):
df_summ_avg = df.groupby(['average']).agg(
).reset_index()
return df_summ_avg
def df_summ_changes(df_summ=pd.DataFrame()):
df_summ['avg'] = [df_summ.hits[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['obp'] = [df_summ.on_base[x]/df_summ.obp_pa[x] if df_summ.obp_pa[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['slg'] = [df_summ.tb[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['ops'] = df_summ['obp']+df_summ['slg']
df_summ['k_percent'] = [df_summ.k[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['bb_percent'] =[df_summ.bb[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['bb_minus_k_percent'] =[(df_summ.bb_minus_k[x])/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['bb_over_k_percent'] =[df_summ.bb[x]/df_summ.k[x] if df_summ.k[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['csw_percent'] =[df_summ.csw[x]/df_summ.pitches[x] if df_summ.pitches[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['sweet_spot_percent'] = [df_summ.sweet_spot[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['woba_percent'] = [df_summ.woba[x]/df_summ.woba_codes[x] if df_summ.woba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['woba_percent_contact'] = [df_summ.woba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
#df_summ['hard_hit_percent'] = [df_summ.sweet_spot[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['hard_hit_percent'] = [df_summ.hard_hit[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['barrel_percent'] = [df_summ.barrel[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['zone_contact_percent'] = [df_summ.zone_contact[x]/df_summ.zone_swing[x] if df_summ.zone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['zone_swing_percent'] = [df_summ.zone_swing[x]/df_summ.in_zone[x] if df_summ.in_zone[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['zone_percent'] = [df_summ.in_zone[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
df_summ['chase_percent'] = [df_summ.ozone_swing[x]/(df_summ.pitches[x] - df_summ.in_zone[x]) if (df_summ.pitches[x]- df_summ.in_zone[x]) != 0 else np.nan for x in range(len(df_summ))]
df_summ['chase_contact'] = [df_summ.ozone_contact[x]/df_summ.ozone_swing[x] if df_summ.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['swing_percent'] = [df_summ.swings[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
df_summ['whiff_rate'] = [df_summ.whiffs[x]/df_summ.swings[x] if df_summ.swings[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['swstr_rate'] = [df_summ.whiffs[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
df_summ['ground_ball_percent'] = [df_summ.ground_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['line_drive_percent'] = [df_summ.line_drive[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['fly_ball_percent'] = [df_summ.fly_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['pop_up_percent'] = [df_summ.pop_up[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['heart_zone_percent'] = [df_summ.heart[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['shadow_zone_percent'] = [df_summ.shadow[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['chase_zone_percent'] = [df_summ.chase[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['waste_zone_percent'] = [df_summ.waste[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['heart_zone_swing_percent'] = [df_summ.heart_swing[x]/df_summ.heart[x] if df_summ.heart[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['shadow_zone_swing_percent'] = [df_summ.shadow_swing[x]/df_summ.shadow[x] if df_summ.shadow[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['chase_zone_swing_percent'] = [df_summ.chase_swing[x]/df_summ.chase[x] if df_summ.chase[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['waste_zone_swing_percent'] = [df_summ.waste_swing[x]/df_summ.waste[x] if df_summ.waste[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['xwoba_percent'] = [df_summ.xwoba[x]/df_summ.woba_codes[x] if df_summ.woba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ['xwoba_percent_contact'] = [df_summ.xwoba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
df_summ = df_summ.dropna(subset=['bip'])
return df_summ
def df_summ_filter_out(df_summ=pd.DataFrame(),batter_select = 0):
df_summ_filter = df_summ[df_summ['pa'] >= min(math.floor(df_summ.xs(batter_select,level=0)['pa']/10)*10,500)]
df_summ_filter_pct = df_summ_filter.rank(pct=True,ascending=True)
df_summ_player = df_summ.xs(batter_select,level=0)
df_summ_player_pct = df_summ_filter_pct.xs(batter_select,level=0)
return df_summ_filter,df_summ_filter_pct,df_summ_player,df_summ_player_pct
def df_summ_batter_pitch_up(df=pd.DataFrame()):
df_summ_batter_pitch = df.dropna(subset=['pitch_type']).groupby(['pitcher_id','pitcher_name','pitch_type']).agg(
pa = ('pa','sum'),
ab = ('ab','sum'),
obp_pa = ('obp','sum'),
hits = ('hits','sum'),
on_base = ('on_base','sum'),
k = ('k','sum'),
bb = ('bb','sum'),
bb_minus_k = ('bb_minus_k','sum'),
csw = ('csw','sum'),
bip = ('bip','sum'),
bip_div = ('bip_div','sum'),
tb = ('tb','sum'),
woba = ('woba','sum'),
woba_contact = ('woba_pred_contact','sum'),
xwoba = ('woba_pred','sum'),
xwoba_contact = ('woba_pred','sum'),
woba_codes = ('woba_codes','sum'),
hard_hit = ('hard_hit','sum'),
barrel = ('barrel','sum'),
sweet_spot = ('sweet_spot','sum'),
max_launch_speed = ('launch_speed','max'),
launch_speed_90 = ('launch_speed',percentile(90)),
launch_speed = ('launch_speed','mean'),
launch_angle = ('launch_angle','mean'),
pitches = ('is_pitch','sum'),
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'),
ground_ball = ('trajectory_ground_ball','sum'),
line_drive = ('trajectory_line_drive','sum'),
fly_ball =('trajectory_fly_ball','sum'),
pop_up = ('trajectory_popup','sum'),
attack_zone = ('attack_zone','count'),
heart = ('heart','sum'),
shadow = ('shadow','sum'),
chase = ('chase','sum'),
waste = ('waste','sum'),
heart_swing = ('heart_swing','sum'),
shadow_swing = ('shadow_swing','sum'),
chase_swing = ('chase_swing','sum'),
waste_swing = ('waste_swing','sum'),
).reset_index()
#return df_summ_batter_pitch
df_summ_batter_pitch['avg'] = [df_summ_batter_pitch.hits[x]/df_summ_batter_pitch.ab[x] if df_summ_batter_pitch.ab[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['obp'] = [df_summ_batter_pitch.on_base[x]/df_summ_batter_pitch.obp_pa[x] if df_summ_batter_pitch.obp_pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['slg'] = [df_summ_batter_pitch.tb[x]/df_summ_batter_pitch.ab[x] if df_summ_batter_pitch.ab[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['ops'] = df_summ_batter_pitch['obp']+df_summ_batter_pitch['slg']
df_summ_batter_pitch['k_percent'] = [df_summ_batter_pitch.k[x]/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['bb_percent'] =[df_summ_batter_pitch.bb[x]/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['bb_minus_k_percent'] =[(df_summ_batter_pitch.bb_minus_k[x])/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['bb_over_k_percent'] =[df_summ_batter_pitch.bb[x]/df_summ_batter_pitch.k[x] if df_summ_batter_pitch.k[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['csw_percent'] =[df_summ_batter_pitch.csw[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['sweet_spot_percent'] = [df_summ_batter_pitch.sweet_spot[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['woba_percent'] = [df_summ_batter_pitch.woba[x]/df_summ_batter_pitch.woba_codes[x] if df_summ_batter_pitch.woba_codes[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['woba_percent_contact'] = [df_summ_batter_pitch.woba_contact[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
#df_summ_batter_pitch['hard_hit_percent'] = [df_summ_batter_pitch.sweet_spot[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['hard_hit_percent'] = [df_summ_batter_pitch.hard_hit[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['barrel_percent'] = [df_summ_batter_pitch.barrel[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['zone_contact_percent'] = [df_summ_batter_pitch.zone_contact[x]/df_summ_batter_pitch.zone_swing[x] if df_summ_batter_pitch.zone_swing[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['zone_swing_percent'] = [df_summ_batter_pitch.zone_swing[x]/df_summ_batter_pitch.in_zone[x] if df_summ_batter_pitch.in_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['zone_percent'] = [df_summ_batter_pitch.in_zone[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['chase_percent'] = [df_summ_batter_pitch.ozone_swing[x]/(df_summ_batter_pitch.pitches[x] - df_summ_batter_pitch.in_zone[x]) if (df_summ_batter_pitch.pitches[x]- df_summ_batter_pitch.in_zone[x]) != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['chase_contact'] = [df_summ_batter_pitch.ozone_contact[x]/df_summ_batter_pitch.ozone_swing[x] if df_summ_batter_pitch.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['swing_percent'] = [df_summ_batter_pitch.swings[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['whiff_rate'] = [df_summ_batter_pitch.whiffs[x]/df_summ_batter_pitch.swings[x] if df_summ_batter_pitch.swings[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['swstr_rate'] = [df_summ_batter_pitch.whiffs[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['heart_zone_percent'] = [df_summ_batter_pitch.heart[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['shadow_zone_percent'] = [df_summ_batter_pitch.shadow[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['chase_zone_percent'] = [df_summ_batter_pitch.chase[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['waste_zone_percent'] = [df_summ_batter_pitch.waste[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['heart_zone_swing_percent'] = [df_summ_batter_pitch.heart_swing[x]/df_summ_batter_pitch.heart[x] if df_summ_batter_pitch.heart[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['shadow_zone_swing_percent'] = [df_summ_batter_pitch.shadow_swing[x]/df_summ_batter_pitch.shadow[x] if df_summ_batter_pitch.shadow[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['chase_zone_swing_percent'] = [df_summ_batter_pitch.chase_swing[x]/df_summ_batter_pitch.chase[x] if df_summ_batter_pitch.chase[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['waste_zone_swing_percent'] = [df_summ_batter_pitch.waste_swing[x]/df_summ_batter_pitch.waste[x] if df_summ_batter_pitch.waste[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['xwoba_percent'] = [df_summ_batter_pitch.xwoba[x]/df_summ_batter_pitch.woba_codes[x] if df_summ_batter_pitch.woba_codes[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['xwoba_percent_contact'] = [df_summ_batter_pitch.xwoba_contact[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
df_summ_batter_pitch['bip'] = df_summ_batter_pitch['bip'].fillna(0)
return df_summ_batter_pitch