import polars as pl import numpy as np import joblib 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') class df_update: def __init__(self): pass def update(self, df_clone: pl.DataFrame): df = df_clone.clone() # Assuming px_model is defined and df is your DataFrame 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'] bip_codes = ['In play, no out', 'In play, run(s)','In play, out(s)'] conditions_barrel = [ df['launch_speed'].is_null(), (df['launch_speed'] * 1.5 - df['launch_angle'] >= 117) & (df['launch_speed'] + df['launch_angle'] >= 124) & (df['launch_speed'] >= 98) & (df['launch_angle'] >= 4) & (df['launch_angle'] <= 50) ] choices_barrel = [False, True] 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] conditions_woba = [ df['event_type'].is_in(['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, 0.689, 0.720, 0.881, 1.254, 1.589, 2.048] 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'] 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': None, 'IN': None, 'PO': None, 'AB': None, 'AS': None, 'NP': None} df = df.with_columns([ pl.when(df['type_ab'].is_not_null()).then(1).otherwise(0).alias('pa'), pl.when(df['is_pitch']).then(1).otherwise(0).alias('pitches'), pl.when(df['sz_top'] == 0).then(None).otherwise(df['sz_top']).alias('sz_top'), pl.when(df['sz_bot'] == 0).then(None).otherwise(df['sz_bot']).alias('sz_bot'), pl.when(df['zone'] > 0).then(df['zone'] < 10).otherwise(None).alias('in_zone'), pl.Series(px_model.predict(df[['x']].fill_null(0).to_numpy())[:, 0]).alias('px_predict'), pl.Series(pz_model.predict(df[['y']].fill_null(0).to_numpy())[:, 0]).alias('pz_predict'), pl.when(df['event_type'].is_in(hit_codes)).then(True).otherwise(False).alias('hits'), pl.when(df['event_type'].is_in(ab_codes)).then(True).otherwise(False).alias('ab'), pl.when(df['event_type'].is_in(obp_true_codes)).then(True).otherwise(False).alias('on_base'), pl.when(df['event_type'].is_in(obp_codes)).then(True).otherwise(False).alias('obp'), pl.when(df['play_description'].is_in(bip_codes)).then(True).otherwise(False).alias('bip'), pl.when(conditions_barrel[0]).then(choices_barrel[0]).when(conditions_barrel[1]).then(choices_barrel[1]).otherwise(None).alias('barrel'), pl.when(df['launch_angle'].is_null()).then(False).when((df['launch_angle'] >= 8) & (df['launch_angle'] <= 32)).then(True).otherwise(None).alias('sweet_spot'), pl.when(df['launch_speed'].is_null()).then(False).when(df['launch_speed'] >= 94.5).then(True).otherwise(None).alias('hard_hit'), pl.when(conditions_tb[0]).then(choices_tb[0]).when(conditions_tb[1]).then(choices_tb[1]).when(conditions_tb[2]).then(choices_tb[2]).when(conditions_tb[3]).then(choices_tb[3]).otherwise(None).alias('tb'), pl.when(conditions_woba[0]).then(choices_woba[0]).when(conditions_woba[1]).then(choices_woba[1]).when(conditions_woba[2]).then(choices_woba[2]).when(conditions_woba[3]).then(choices_woba[3]).when(conditions_woba[4]).then(choices_woba[4]).when(conditions_woba[5]).then(choices_woba[5]).when(conditions_woba[6]).then(choices_woba[6]).otherwise(None).alias('woba'), pl.when((df['play_code'] == 'S') | (df['play_code'] == 'W') | (df['play_code'] == 'T')).then(1).otherwise(0).alias('whiffs'), pl.when((df['play_code'] == 'S') | (df['play_code'] == 'W') | (df['play_code'] == 'T') | (df['play_code'] == 'C')).then(1).otherwise(0).alias('csw'), pl.when(pl.col('is_swing').cast(pl.Boolean)).then(1).otherwise(0).alias('swings'), pl.col('event_type').is_in(['strikeout','strikeout_double_play']).alias('k'), pl.col('event_type').is_in(['walk', 'intent_walk']).alias('bb'), pl.lit(None).alias('attack_zone'), pl.lit(None).alias('woba_pred'), pl.lit(None).alias('woba_pred_contact') ]) df = df.with_columns([ pl.when(df['event_type'].is_in(woba_codes)).then(1).otherwise(None).alias('woba_codes'), pl.when(df['event_type'].is_in(woba_codes)).then(1).otherwise(None).alias('xwoba_codes'), pl.when((pl.col('tb') >= 0)).then(df['woba']).otherwise(None).alias('woba_contact'), ]) df = df.with_columns([ pl.Series(in_zone_model.predict(df[['px','pz','sz_top','sz_bot']].fill_null(0).to_numpy())[:]).alias('in_zone_predict'), pl.Series(attack_zone_model.predict(df[['px','pz','sz_top','sz_bot']].fill_null(0).to_numpy())[:]).alias('attack_zone_predict'), ]) df = df.with_columns([ pl.when(pl.col('px').is_null()).then(pl.col('px_predict')).otherwise(pl.col('px')).alias('px'), pl.when(pl.col('pz').is_null()).then(pl.col('pz_predict')).otherwise(pl.col('pz')).alias('pz'), pl.when(pl.col('in_zone').is_null()).then(pl.col('in_zone_predict')).otherwise(pl.col('in_zone')).alias('in_zone_final'), ]) df = df.with_columns([ pl.when(df['launch_speed'].is_null()).then(None).otherwise(df['barrel']).alias('barrel'), pl.lit('average').alias('average'), pl.when(pl.col('in_zone_final') == False).then(True).otherwise(False).alias('out_zone'), pl.when((pl.col('in_zone_final') == True) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('zone_swing'), pl.when((pl.col('in_zone_final') == True) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('zone_contact'), pl.when((pl.col('in_zone_final') == False) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('ozone_swing'), pl.when((pl.col('in_zone_final') == False) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('ozone_contact'), pl.when(pl.col('event_type').str.contains('strikeout')).then(True).otherwise(False).alias('k'), pl.when(pl.col('event_type').is_in(['walk', 'intent_walk'])).then(True).otherwise(False).alias('bb'), pl.when(pl.col('attack_zone').is_null()).then(pl.col('attack_zone_predict')).otherwise(pl.col('attack_zone')).alias('attack_zone_final'), ]) df = df.with_columns([ (df['k'].cast(pl.Float32) - df['bb'].cast(pl.Float32)).alias('k_minus_bb'), (df['bb'].cast(pl.Float32) - df['k'].cast(pl.Float32)).alias('bb_minus_k'), (df['launch_speed'] > 0).alias('bip_div'), (df['attack_zone_final'] == 0).alias('heart'), (df['attack_zone_final'] == 1).alias('shadow'), (df['attack_zone_final'] == 2).alias('chase'), (df['attack_zone_final'] == 3).alias('waste'), ((df['attack_zone_final'] == 0) & (df['swings'] == 1)).alias('heart_swing'), ((df['attack_zone_final'] == 1) & (df['swings'] == 1)).alias('shadow_swing'), ((df['attack_zone_final'] == 2) & (df['swings'] == 1)).alias('chase_swing'), ((df['attack_zone_final'] == 3) & (df['swings'] == 1)).alias('waste_swing'), ((df['attack_zone_final'] == 0) & (df['whiffs'] == 1)).alias('heart_whiff'), ((df['attack_zone_final'] == 1) & (df['whiffs'] == 1)).alias('shadow_whiff'), ((df['attack_zone_final'] == 2) & (df['whiffs'] == 1)).alias('chase_whiff'), ((df['attack_zone_final'] == 3) & (df['whiffs'] == 1)).alias('waste_whiff') ]) [0, 0.689, 0.720, 0.881, 1.254, 1.589, 2.048] df = df.with_columns([ pl.Series( [sum(x) for x in xwoba_model.predict_proba(df[['launch_angle', 'launch_speed']].fill_null(0).to_numpy()[:]) * ([0, 0.881, 1.254, 1.589, 2.048])] ).alias('woba_pred_predict') ]) df = df.with_columns([ pl.when(pl.col('event_type').is_in(['walk'])).then(0.689) .when(pl.col('event_type').is_in(['hit_by_pitch'])).then(0.720) .when(pl.col('event_type').is_in(['strikeout', 'strikeout_double_play'])).then(0) .otherwise(pl.col('woba_pred_predict')).alias('woba_pred_predict') ]) df = df.with_columns([ pl.when(pl.col('woba_codes').is_null()).then(None).otherwise(pl.col('woba_pred_predict')).alias('woba_pred'), pl.when(pl.col('bip')!=1).then(None).otherwise(pl.col('woba_pred_predict')).alias('woba_pred_contact'), ]) df = df.with_columns([ pl.when(pl.col('trajectory').is_in(['bunt_popup'])).then(pl.lit('popup')) .when(pl.col('trajectory').is_in(['bunt_grounder'])).then(pl.lit('ground_ball')) .when(pl.col('trajectory').is_in(['bunt_line_drive'])).then(pl.lit('line_drive')) .when(pl.col('trajectory').is_in([''])).then(pl.lit(None)) .otherwise(pl.col('trajectory')).alias('trajectory') ]) # Create one-hot encoded columns for the trajectory column dummy_df = df.select(pl.col('trajectory')).to_dummies() # Rename the one-hot encoded columns dummy_df = dummy_df.rename({ 'trajectory_fly_ball': 'trajectory_fly_ball', 'trajectory_ground_ball': 'trajectory_ground_ball', 'trajectory_line_drive': 'trajectory_line_drive', 'trajectory_popup': 'trajectory_popup' }) # Ensure the columns are present in the DataFrame for col in ['trajectory_fly_ball', 'trajectory_ground_ball', 'trajectory_line_drive', 'trajectory_popup']: if col not in dummy_df.columns: dummy_df = dummy_df.with_columns(pl.lit(0).alias(col)) # Join the one-hot encoded columns back to the original DataFrame df = df.hstack(dummy_df) # Check if 'trajectory_null' column exists and drop it if 'trajectory_null' in df.columns: df = df.drop('trajectory_null') pitch_cat = {'FA': None, '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': None, 'IN': None, 'PO': None, 'AB': None, 'AS': None, 'NP': None} df = df.with_columns( df["pitch_type"] .replace(pitch_cat) .fill_null("Unknown") .alias("pitch_group") ) df = df.with_columns([ (-(pl.col('vy0')**2 - (2 * pl.col('ay') * (pl.col('y0') - 17/12)))**0.5).alias('vy_f'), ]) df = df.with_columns([ ((pl.col('vy_f') - pl.col('vy0')) / pl.col('ay')).alias('t'), ]) df = df.with_columns([ (pl.col('vz0') + (pl.col('az') * pl.col('t'))).alias('vz_f'), (pl.col('vx0') + (pl.col('ax') * pl.col('t'))).alias('vx_f') ]) df = df.with_columns([ (-np.arctan(pl.col('vz_f') / pl.col('vy_f')) * (180 / np.pi)).alias('vaa'), (-np.arctan(pl.col('vx_f') / pl.col('vy_f')) * (180 / np.pi)).alias('haa') ]) # Mirror horizontal break for left-handed pitchers df = df.with_columns( pl.when(pl.col('pitcher_hand') == 'L') .then(-pl.col('ax')) .otherwise(pl.col('ax')) .alias('ax') ) # Mirror horizontal break for left-handed pitchers df = df.with_columns( pl.when(pl.col('pitcher_hand') == 'L') .then(-pl.col('hb')) .otherwise(pl.col('hb')) .alias('hb') ) # Mirror horizontal release point for left-handed pitchers df = df.with_columns( pl.when(pl.col('pitcher_hand') == 'L') .then(pl.col('x0')) .otherwise(-pl.col('x0')) .alias('x0') ) df = df.with_columns([ pl.when(df['swings'].is_null()).then(None).otherwise(df['swings']).alias('is_swing'), pl.when(df['bip'].is_null()).then(None).otherwise(df['bip']).alias('is_bip')]) df = df.with_columns([ (np.arctan((pl.col("hit_x")*-1 + 125.42) / (198.27 - pl.col("hit_y"))) * 180 / np.pi * 0.75).alias("spray_angle") ]) df = df.with_columns([ pl.when(pl.col("batter_hand") == "L") .then(-pl.col("spray_angle")) .otherwise(pl.col("spray_angle")) .alias("adj_spray_angle") ]).drop("spray_angle") df = df.with_columns([ pl.when(pl.col("adj_spray_angle").is_not_null() & (pl.col("adj_spray_angle") < -15)) .then(pl.lit("oppo")) .when(pl.col("adj_spray_angle").is_not_null() & (pl.col("adj_spray_angle") > 15)) .then(pl.lit("pull")) .when(pl.col("adj_spray_angle").is_not_null()) .then(pl.lit("straight")) .otherwise(None) # Keep null if adj_spray_angle is null .alias("hit_direction") ]) df = df.with_columns([ pl.when(pl.col("hit_direction") == "oppo").then(1).otherwise(None).alias("oppo"), pl.when(pl.col("hit_direction") == "pull").then(1).otherwise(None).alias("pull"), pl.when(pl.col("hit_direction") == "straight").then(1).otherwise(None).alias("straight") ]) df = df.with_columns([ pl.when(pl.col("event_type") == "single").then(1).otherwise(0).alias("single"), pl.when(pl.col("event_type") == "double").then(1).otherwise(0).alias("double"), pl.when(pl.col("event_type") == "triple").then(1).otherwise(0).alias("triple"), pl.when(pl.col("event_type") == "home_run").then(1).otherwise(0).alias("home_run") ]) return df # Assuming df is your Polars DataFrame def update_summary(self, df: pl.DataFrame, pitcher: bool = True) -> pl.DataFrame: """ Update summary statistics for pitchers or batters. Parameters: df (pl.DataFrame): The input Polars DataFrame containing player statistics. pitcher (bool): A flag indicating whether to calculate statistics for pitchers (True) or batters (False). Returns: pl.DataFrame: A Polars DataFrame with aggregated and calculated summary statistics. """ # Determine the position based on the pitcher flag if pitcher: position = 'pitcher' else: position = 'batter' # Group by position_id and position_name, then aggregate various statistics df_summ = df.group_by([f'{position}_id', f'{position}_name']).agg([ pl.col('pa').sum().alias('pa'), pl.col('ab').sum().alias('ab'), pl.col('obp').sum().alias('obp_pa'), pl.col('hits').sum().alias('hits'), pl.col('on_base').sum().alias('on_base'), pl.col('k').sum().alias('k'), pl.col('bb').sum().alias('bb'), pl.col('k_minus_bb').sum().alias('k_minus_bb'), pl.col('bb_minus_k').sum().alias('bb_minus_k'), pl.col('csw').sum().alias('csw'), pl.col('bip').sum().alias('bip'), pl.col('bip_div').sum().alias('bip_div'), pl.col('tb').sum().alias('tb'), pl.col('woba').sum().alias('woba'), pl.col('woba_contact').sum().alias('woba_contact'), pl.col('woba_pred').sum().alias('xwoba'), pl.col('woba_pred_contact').sum().alias('xwoba_contact'), pl.col('woba_codes').sum().alias('woba_codes'), pl.col('xwoba_codes').sum().alias('xwoba_codes'), pl.col('hard_hit').sum().alias('hard_hit'), pl.col('barrel').sum().alias('barrel'), pl.col('sweet_spot').sum().alias('sweet_spot'), pl.col('launch_speed').max().alias('max_launch_speed'), pl.col('launch_speed').quantile(0.90).alias('launch_speed_90'), pl.col('launch_speed').mean().alias('launch_speed'), pl.col('launch_angle').mean().alias('launch_angle'), pl.col('is_pitch').sum().alias('pitches'), pl.col('swings').sum().alias('swings'), pl.col('in_zone').sum().alias('in_zone'), pl.col('out_zone').sum().alias('out_zone'), pl.col('whiffs').sum().alias('whiffs'), pl.col('zone_swing').sum().alias('zone_swing'), pl.col('zone_contact').sum().alias('zone_contact'), pl.col('ozone_swing').sum().alias('ozone_swing'), pl.col('ozone_contact').sum().alias('ozone_contact'), pl.col('trajectory_ground_ball').sum().alias('ground_ball'), pl.col('trajectory_line_drive').sum().alias('line_drive'), pl.col('trajectory_fly_ball').sum().alias('fly_ball'), pl.col('trajectory_popup').sum().alias('pop_up'), pl.col('attack_zone').count().alias('attack_zone'), pl.col('heart').sum().alias('heart'), pl.col('shadow').sum().alias('shadow'), pl.col('chase').sum().alias('chase'), pl.col('waste').sum().alias('waste'), pl.col('heart_swing').sum().alias('heart_swing'), pl.col('shadow_swing').sum().alias('shadow_swing'), pl.col('chase_swing').sum().alias('chase_swing'), pl.col('waste_swing').sum().alias('waste_swing'), pl.col('heart_whiff').sum().alias('heart_whiff'), pl.col('shadow_whiff').sum().alias('shadow_whiff'), pl.col('chase_whiff').sum().alias('chase_whiff'), pl.col('waste_whiff').sum().alias('waste_whiff'), pl.col('pull').sum().alias('pull'), pl.col('straight').sum().alias('straight'), pl.col('oppo').sum().alias('oppo'), ((pl.col('trajectory_fly_ball') == 1) | (pl.col('trajectory_line_drive') == 1)).sum().alias('fly_line_bip'), (pl.col('pull') & ((pl.col('trajectory_fly_ball') == 1) | (pl.col('trajectory_line_drive') == 1))).sum().alias('pull_fly_ball'), pl.col('single').sum().alias('single'), pl.col('double').sum().alias('double'), pl.col('triple').sum().alias('triple'), pl.col('home_run').sum().alias('home_run'), (pl.col('extension').mean()).alias('extension'), (pl.col('start_speed').filter(pl.col('pitch_type').is_in(['FF','SI'])).mean().alias('avg_start_speed_ff')), ]) # Add calculated columns to the summary DataFrame df_summ = df_summ.with_columns([ (pl.col('hits') / pl.col('ab')).alias('avg'), (pl.col('on_base') / pl.col('obp_pa')).alias('obp'), (pl.col('tb') / pl.col('ab')).alias('slg'), (pl.col('on_base') / pl.col('obp_pa') + pl.col('tb') / pl.col('ab')).alias('ops'), (pl.col('k') / pl.col('pa')).alias('k_percent'), (pl.col('bb') / pl.col('pa')).alias('bb_percent'), (pl.col('k_minus_bb') / pl.col('pa')).alias('k_minus_bb_percent'), (pl.col('bb_minus_k') / pl.col('pa')).alias('bb_minus_k_percent'), (pl.col('bb') / pl.col('k')).alias('bb_over_k_percent'), (pl.col('csw') / pl.col('pitches')).alias('csw_percent'), (pl.col('sweet_spot') / pl.col('bip_div')).alias('sweet_spot_percent'), (pl.col('woba') / pl.col('woba_codes')).alias('woba_percent'), (pl.col('woba_contact') / pl.col('bip')).alias('woba_percent_contact'), (pl.col('hard_hit') / pl.col('bip_div')).alias('hard_hit_percent'), (pl.col('barrel') / pl.col('bip_div')).alias('barrel_percent'), (pl.col('zone_contact') / pl.col('zone_swing')).alias('zone_contact_percent'), (pl.col('zone_swing') / pl.col('in_zone')).alias('zone_swing_percent'), (pl.col('in_zone') / pl.col('pitches')).alias('zone_percent'), (pl.col('ozone_swing') / (pl.col('out_zone'))).alias('chase_percent'), (pl.col('ozone_contact') / pl.col('ozone_swing')).alias('chase_contact'), (pl.col('swings') / pl.col('pitches')).alias('swing_percent'), (pl.col('whiffs') / pl.col('swings')).alias('whiff_rate'), (pl.col('whiffs') / pl.col('pitches')).alias('swstr_rate'), (pl.col('ground_ball') / pl.col('bip')).alias('ground_ball_percent'), (pl.col('line_drive') / pl.col('bip')).alias('line_drive_percent'), (pl.col('fly_ball') / pl.col('bip')).alias('fly_ball_percent'), (pl.col('pop_up') / pl.col('bip')).alias('pop_up_percent'), (pl.col('heart') / pl.col('attack_zone')).alias('heart_zone_percent'), (pl.col('shadow') / pl.col('attack_zone')).alias('shadow_zone_percent'), (pl.col('chase') / pl.col('attack_zone')).alias('chase_zone_percent'), (pl.col('waste') / pl.col('attack_zone')).alias('waste_zone_percent'), (pl.col('heart_swing') / pl.col('heart')).alias('heart_zone_swing_percent'), (pl.col('shadow_swing') / pl.col('shadow')).alias('shadow_zone_swing_percent'), (pl.col('chase_swing') / pl.col('chase')).alias('chase_zone_swing_percent'), (pl.col('waste_swing') / pl.col('waste')).alias('waste_zone_swing_percent'), (pl.col('heart_whiff') / pl.col('heart_swing')).alias('heart_zone_whiff_percent'), (pl.col('shadow_whiff') / pl.col('shadow_swing')).alias('shadow_zone_whiff_percent'), (pl.col('chase_whiff') / pl.col('chase_swing')).alias('chase_zone_whiff_percent'), (pl.col('waste_whiff') / pl.col('waste_swing')).alias('waste_zone_whiff_percent'), (pl.col('xwoba') / pl.col('xwoba_codes')).alias('xwoba_percent'), (pl.col('xwoba_contact') / pl.col('bip')).alias('xwoba_percent_contact'), (pl.col('pull') / pl.col('bip')).alias('pull_percent'), (pl.col('straight') / pl.col('bip')).alias('straight_percent'), (pl.col('oppo') / pl.col('bip')).alias('oppo_percent'), (pl.col('pull_fly_ball') / pl.col('fly_line_bip')).alias('pulled_fly_ball_percent'), ]) return df_summ # Assuming df is your Polars DataFrame def update_summary_select(self, df: pl.DataFrame, selection: list) -> pl.DataFrame: """ Update summary statistics for pitchers or batters. Parameters: df (pl.DataFrame): The input Polars DataFrame containing player statistics. pitcher (bool): A flag indicating whether to calculate statistics for pitchers (True) or batters (False). Returns: pl.DataFrame: A Polars DataFrame with aggregated and calculated summary statistics. """ # Group by position_id and position_name, then aggregate various statistics df_summ = df.group_by(selection).agg([ pl.col('pa').sum().alias('pa'), pl.col('ab').sum().alias('ab'), pl.col('obp').sum().alias('obp_pa'), pl.col('hits').sum().alias('hits'), pl.col('on_base').sum().alias('on_base'), pl.col('k').sum().alias('k'), pl.col('bb').sum().alias('bb'), pl.col('k_minus_bb').sum().alias('k_minus_bb'), pl.col('bb_minus_k').sum().alias('bb_minus_k'), pl.col('csw').sum().alias('csw'), pl.col('bip').sum().alias('bip'), pl.col('bip_div').sum().alias('bip_div'), pl.col('tb').sum().alias('tb'), pl.col('woba').sum().alias('woba'), pl.col('woba_contact').sum().alias('woba_contact'), pl.col('woba_pred').sum().alias('xwoba'), pl.col('woba_pred_contact').sum().alias('xwoba_contact'), pl.col('woba_codes').sum().alias('woba_codes'), pl.col('xwoba_codes').sum().alias('xwoba_codes'), pl.col('hard_hit').sum().alias('hard_hit'), pl.col('barrel').sum().alias('barrel'), pl.col('sweet_spot').sum().alias('sweet_spot'), pl.col('launch_speed').max().alias('max_launch_speed'), pl.col('launch_speed').quantile(0.90).alias('launch_speed_90'), pl.col('launch_speed').mean().alias('launch_speed'), pl.col('launch_angle').mean().alias('launch_angle'), pl.col('is_pitch').sum().alias('pitches'), pl.col('swings').sum().alias('swings'), pl.col('in_zone').sum().alias('in_zone'), pl.col('out_zone').sum().alias('out_zone'), pl.col('whiffs').sum().alias('whiffs'), pl.col('zone_swing').sum().alias('zone_swing'), pl.col('zone_contact').sum().alias('zone_contact'), pl.col('ozone_swing').sum().alias('ozone_swing'), pl.col('ozone_contact').sum().alias('ozone_contact'), pl.col('trajectory_ground_ball').sum().alias('ground_ball'), pl.col('trajectory_line_drive').sum().alias('line_drive'), pl.col('trajectory_fly_ball').sum().alias('fly_ball'), pl.col('trajectory_popup').sum().alias('pop_up'), pl.col('attack_zone').count().alias('attack_zone'), pl.col('heart').sum().alias('heart'), pl.col('shadow').sum().alias('shadow'), pl.col('chase').sum().alias('chase'), pl.col('waste').sum().alias('waste'), pl.col('heart_swing').sum().alias('heart_swing'), pl.col('shadow_swing').sum().alias('shadow_swing'), pl.col('chase_swing').sum().alias('chase_swing'), pl.col('waste_swing').sum().alias('waste_swing'), pl.col('heart_whiff').sum().alias('heart_whiff'), pl.col('shadow_whiff').sum().alias('shadow_whiff'), pl.col('chase_whiff').sum().alias('chase_whiff'), pl.col('waste_whiff').sum().alias('waste_whiff'), pl.col('pull').sum().alias('pull'), pl.col('straight').sum().alias('straight'), pl.col('oppo').sum().alias('oppo'), ((pl.col('trajectory_fly_ball') == 1) | (pl.col('trajectory_line_drive') == 1)).sum().alias('fly_line_bip'), (pl.col('pull') & ((pl.col('trajectory_fly_ball') == 1) | (pl.col('trajectory_line_drive') == 1))).sum().alias('pull_fly_ball'), pl.col('single').sum().alias('single'), pl.col('double').sum().alias('double'), pl.col('triple').sum().alias('triple'), pl.col('home_run').sum().alias('home_run'), (pl.col('extension').mean()).alias('extension'), (pl.col('start_speed').filter(pl.col('pitch_type').is_in(['FF','SI','FC'])).mean().alias('avg_start_speed_ff')), ]) # Add calculated columns to the summary DataFrame df_summ = df_summ.with_columns([ (pl.col('hits') / pl.col('ab')).alias('avg'), (pl.col('on_base') / pl.col('obp_pa')).alias('obp'), (pl.col('tb') / pl.col('ab')).alias('slg'), (pl.col('on_base') / pl.col('obp_pa') + pl.col('tb') / pl.col('ab')).alias('ops'), (pl.col('k') / pl.col('pa')).alias('k_percent'), (pl.col('bb') / pl.col('pa')).alias('bb_percent'), (pl.col('k_minus_bb') / pl.col('pa')).alias('k_minus_bb_percent'), (pl.col('bb_minus_k') / pl.col('pa')).alias('bb_minus_k_percent'), (pl.col('bb') / pl.col('k')).alias('bb_over_k_percent'), (pl.col('csw') / pl.col('pitches')).alias('csw_percent'), (pl.col('sweet_spot') / pl.col('bip_div')).alias('sweet_spot_percent'), (pl.col('woba') / pl.col('woba_codes')).alias('woba_percent'), (pl.col('woba_contact') / pl.col('bip')).alias('woba_percent_contact'), (pl.col('hard_hit') / pl.col('bip_div')).alias('hard_hit_percent'), (pl.col('barrel') / pl.col('bip_div')).alias('barrel_percent'), (pl.col('zone_contact') / pl.col('zone_swing')).alias('zone_contact_percent'), (pl.col('zone_swing') / pl.col('in_zone')).alias('zone_swing_percent'), (pl.col('in_zone') / pl.col('pitches')).alias('zone_percent'), (pl.col('ozone_swing') / (pl.col('pitches') - pl.col('in_zone'))).alias('chase_percent'), (pl.col('ozone_contact') / pl.col('ozone_swing')).alias('chase_contact'), (pl.col('swings') / pl.col('pitches')).alias('swing_percent'), (pl.col('whiffs') / pl.col('swings')).alias('whiff_rate'), (pl.col('whiffs') / pl.col('pitches')).alias('swstr_rate'), (pl.col('ground_ball') / pl.col('bip')).alias('ground_ball_percent'), (pl.col('line_drive') / pl.col('bip')).alias('line_drive_percent'), (pl.col('fly_ball') / pl.col('bip')).alias('fly_ball_percent'), (pl.col('pop_up') / pl.col('bip')).alias('pop_up_percent'), (pl.col('heart') / pl.col('attack_zone')).alias('heart_zone_percent'), (pl.col('shadow') / pl.col('attack_zone')).alias('shadow_zone_percent'), (pl.col('chase') / pl.col('attack_zone')).alias('chase_zone_percent'), (pl.col('waste') / pl.col('attack_zone')).alias('waste_zone_percent'), (pl.col('heart_swing') / pl.col('heart')).alias('heart_zone_swing_percent'), (pl.col('shadow_swing') / pl.col('shadow')).alias('shadow_zone_swing_percent'), (pl.col('chase_swing') / pl.col('chase')).alias('chase_zone_swing_percent'), (pl.col('waste_swing') / pl.col('waste')).alias('waste_zone_swing_percent'), (pl.col('heart_whiff') / pl.col('heart_swing')).alias('heart_zone_whiff_percent'), (pl.col('shadow_whiff') / pl.col('shadow_swing')).alias('shadow_zone_whiff_percent'), (pl.col('chase_whiff') / pl.col('chase_swing')).alias('chase_zone_whiff_percent'), (pl.col('waste_whiff') / pl.col('waste_swing')).alias('waste_zone_whiff_percent'), (pl.col('xwoba') / pl.col('xwoba_codes')).alias('xwoba_percent'), (pl.col('xwoba_contact') / pl.col('bip')).alias('xwoba_percent_contact'), (pl.col('pull') / pl.col('bip')).alias('pull_percent'), (pl.col('straight') / pl.col('bip')).alias('straight_percent'), (pl.col('oppo') / pl.col('bip')).alias('oppo_percent'), (pl.col('pull_fly_ball') / pl.col('fly_line_bip')).alias('pulled_fly_ball_percent'), ]) return df_summ