Delete functions

functions/df_update.py

@@ -1,472 +0,0 @@
-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] + 3.2).alias('pz_predict'),
-            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'),
-            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'),
-            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'),
-            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') == False).then(True).otherwise(False).alias('out_zone'),
-            pl.when((pl.col('in_zone') == True) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('zone_swing'),
-            pl.when((pl.col('in_zone') == True) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('zone_contact'),
-            pl.when((pl.col('in_zone') == False) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('ozone_swing'),
-            pl.when((pl.col('in_zone') == 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'),
-            
-
-        ])
-
-        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'] == 0).alias('heart'),
-            (df['attack_zone'] == 1).alias('shadow'),
-            (df['attack_zone'] == 2).alias('chase'),
-            (df['attack_zone'] == 3).alias('waste'),
-            ((df['attack_zone'] == 0) & (df['swings'] == 1)).alias('heart_swing'),
-            ((df['attack_zone'] == 1) & (df['swings'] == 1)).alias('shadow_swing'),
-            ((df['attack_zone'] == 2) & (df['swings'] == 1)).alias('chase_swing'),
-            ((df['attack_zone'] == 3) & (df['swings'] == 1)).alias('waste_swing'),
-            ((df['attack_zone'] == 0) & (df['whiffs'] == 1)).alias('heart_whiff'),
-            ((df['attack_zone'] == 1) & (df['whiffs'] == 1)).alias('shadow_whiff'),
-            ((df['attack_zone'] == 2) & (df['whiffs'] == 1)).alias('chase_whiff'),
-            ((df['attack_zone'] == 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')
-            
-        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('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')
-        ])
-
-        # 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('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')
-        ])
-
-        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('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')
-        ])
-
-        # 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('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')
-        ])
-
-        return df_summ

functions/pitch_summary_functions.py

@@ -1,1140 +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
-import polars as pl
-from PIL import Image
-import requests
-from io import BytesIO
-from matplotlib.offsetbox import OffsetImage, AnnotationBbox
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-import PIL
-
-
-### PITCH COLOURS ###
-
-# Dictionary to map pitch types to their corresponding colors and names
-pitch_colours = {
-    ## Fastballs ##
-    'FF': {'colour': '#FF007D', 'name': '4-Seam Fastball'},
-    'FA': {'colour': '#FF007D', 'name': 'Fastball'},
-    'SI': {'colour': '#98165D', 'name': 'Sinker'},
-    'FC': {'colour': '#BE5FA0', 'name': 'Cutter'},
-
-    ## Offspeed ##
-    'CH': {'colour': '#F79E70', 'name': 'Changeup'},
-    'FS': {'colour': '#FE6100', 'name': 'Splitter'},
-    'SC': {'colour': '#F08223', 'name': 'Screwball'},
-    'FO': {'colour': '#FFB000', 'name': 'Forkball'},
-
-    ## Sliders ##
-    'SL': {'colour': '#67E18D', 'name': 'Slider'},
-    'ST': {'colour': '#1BB999', 'name': 'Sweeper'},
-    'SV': {'colour': '#376748', 'name': 'Slurve'},
-
-    ## Curveballs ##
-    'KC': {'colour': '#311D8B', 'name': 'Knuckle Curve'},
-    'CU': {'colour': '#3025CE', 'name': 'Curveball'},
-    'CS': {'colour': '#274BFC', 'name': 'Slow Curve'},
-    'EP': {'colour': '#648FFF', 'name': 'Eephus'},
-
-    ## Others ##
-    'KN': {'colour': '#867A08', 'name': 'Knuckleball'},
-    'KN': {'colour': '#867A08', 'name': 'Knuckle Ball'},
-    'PO': {'colour': '#472C30', 'name': 'Pitch Out'},
-    'UN': {'colour': '#9C8975', 'name': 'Unknown'},
-}
-
-# Create dictionaries for pitch types and their attributes
-dict_colour = {key: value['colour'] for key, value in pitch_colours.items()}
-dict_pitch = {key: value['name'] for key, value in pitch_colours.items()}
-dict_pitch_desc_type = {value['name']: key for key, value in pitch_colours.items()}
-dict_pitch_desc_type.update({'Four-Seam Fastball':'FF'})
-dict_pitch_desc_type.update({'All':'All'})
-dict_pitch_name = {value['name']: value['colour'] for key, value in pitch_colours.items()}
-dict_pitch_name.update({'Four-Seam Fastball':'#FF007D'})
-
-font_properties = {'family': 'calibi', 'size': 12}
-font_properties_titles = {'family': 'calibi', 'size': 20}
-font_properties_axes = {'family': 'calibi', 'size': 16}
-     
-cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
-
-### 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',}, 
-  'strikePercentage':{'table_header':'$\\bf{Strike\%}$','format':'.1%'} }
-
-colour_palette = ['#FFB000','#648FFF','#785EF0',
-                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
-
-### GET COLOURS ###
-def get_color(value, normalize, cmap_sum):
-    """
-    Get the color corresponding to a value based on a colormap and normalization.
-
-    Parameters
-    ----------
-    value : float
-        The value to be mapped to a color.
-    normalize : matplotlib.colors.Normalize
-        The normalization function to scale the value.
-    cmap_sum : matplotlib.colors.Colormap
-        The colormap to use for mapping the value to a color.
-
-    Returns
-    -------
-    str
-        The hexadecimal color code corresponding to the value.
-    """
-    color = cmap_sum(normalize(value))
-    return mcolors.to_hex(color)
-
-### 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 len(x) != len(y):
-        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 = x.mean()
-        
-
-        # calculating the standard deviation of y ...
-        scale_y = np.sqrt(cov[1, 1]) * n_std
-        mean_y = y.mean()
-        
-
-        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)     
-### VELOCITY KDES ###
-def velocity_kdes(df: pl.DataFrame, ax: plt.Axes, gs: gridspec.GridSpec, gs_x: list, gs_y: list, fig: plt.Figure):
-    """
-    Plot the velocity KDEs for different pitch types.
-
-    Parameters
-    ----------
-    df : pl.DataFrame
-        The DataFrame containing pitch data.
-    ax : plt.Axes
-        The axis to plot on.
-    gs : GridSpec
-        The GridSpec for the subplot layout.
-    gs_x : list
-        The x-coordinates for the GridSpec.
-    gs_y : list
-        The y-coordinates for the GridSpec.
-    fig : plt.Figure
-        The figure to plot on.
-    """
-    # Get unique pitch types sorted by pitch count
-    items_in_order = df.sort("pitch_count", descending=True)['pitch_type'].unique(maintain_order=True).to_numpy()
-
-    # Create the inner subplot inside the outer subplot
-    ax.axis('off')
-    ax.set_title('Pitch Velocity Distribution', fontdict={'family': 'calibi', 'size': 20})
-    inner_grid_1 = gridspec.GridSpecFromSubplotSpec(len(items_in_order), 1, subplot_spec=gs[gs_x[0]:gs_x[-1], gs_y[0]:gs_y[-1]])
-    ax_top = [fig.add_subplot(inner) for inner in inner_grid_1]
-
-    for idx, i in enumerate(items_in_order):
-        pitch_data = df.filter(pl.col('pitch_type') == i)['start_speed']
-        if np.unique(pitch_data).size == 1:  # Check if all values are the same
-            ax_top[idx].plot([np.unique(pitch_data), np.unique(pitch_data)], [0, 1], linewidth=4, color=dict_colour[i], zorder=20)
-        else:
-            sns.kdeplot(pitch_data, ax=ax_top[idx], fill=True, clip=(pitch_data.min(), pitch_data.max()), color=dict_colour[i])
-
-        # Plot the mean release speed for the current data
-        df_average = df.filter(df['pitch_type'] == i)['start_speed']
-        ax_top[idx].plot([df_average.mean(), df_average.mean()], [ax_top[idx].get_ylim()[0], ax_top[idx].get_ylim()[1]], color=dict_colour[i], linestyle='--')
-
-        # Plot the mean release speed for the statcast group data
-        df_statcast_group = pl.read_csv('functions/statcast_2024_grouped.csv')
-        df_average = df_statcast_group.filter(df_statcast_group['pitch_type'] == i)['release_speed']
-        ax_top[idx].plot([df_average.mean(), df_average.mean()], [ax_top[idx].get_ylim()[0], ax_top[idx].get_ylim()[1]], color=dict_colour[i], linestyle=':')
-
-        ax_top[idx].set_xlim(math.floor(df['start_speed'].min() / 5) * 5, math.ceil(df['start_speed'].max() / 5) * 5)
-        ax_top[idx].set_xlabel('')
-        ax_top[idx].set_ylabel('')
-        if idx < len(items_in_order) - 1:
-            ax_top[idx].spines['top'].set_visible(False)
-            ax_top[idx].spines['right'].set_visible(False)
-            ax_top[idx].spines['left'].set_visible(False)
-            ax_top[idx].tick_params(axis='x', colors='none')
-
-        ax_top[idx].set_xticks(range(math.floor(df['start_speed'].min() / 5) * 5, math.ceil(df['start_speed'].max() / 5) * 5, 5))
-        ax_top[idx].set_yticks([])
-        ax_top[idx].grid(axis='x', linestyle='--')
-        ax_top[idx].text(-0.01, 0.5, i, transform=ax_top[idx].transAxes, fontsize=14, va='center', ha='right')
-
-    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: pl.DataFrame, window: int, ax: plt.Axes):
-    """
-    Plot the rolling average of tjStuff+ for different pitch types.
-
-    Parameters
-    ----------
-    df : pl.DataFrame
-        The DataFrame containing pitch data.
-    window : int
-        The window size for calculating the rolling average.
-    ax : plt.Axes
-        The axis to plot on.
-    """
-    # Get unique pitch types sorted by pitch count
-    items_in_order = df.sort("pitch_count", descending=True)['pitch_type'].unique(maintain_order=True).to_numpy()
-    
-    # Plot the rolling average for each pitch type
-    for i in items_in_order:
-        pitch_data = df.filter(pl.col('pitch_type') == i)
-        if pitch_data['pitch_count'].max() >= window:
-            sns.lineplot(
-                x=range(1, pitch_data['pitch_count'].max() + 1),
-                y=pitch_data['tj_stuff_plus'].rolling_mean(window),
-                color=dict_colour[i],
-                ax=ax,
-                linewidth=3
-            )
-
-    # Adjust x-axis limits to start from 1
-    ax.set_xlim(window, df['pitch_count'].max())
-    ax.set_ylim(70, 130)
-    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.xaxis.set_major_locator(MaxNLocator(integer=True))
-
-### TJ STUFF+ ROLLING ###
-def tj_stuff_roling_game(df: pl.DataFrame, window: int, ax: plt.Axes):
-    """
-    Plot the rolling average of tjStuff+ for different pitch types over games.
-
-    Parameters
-    ----------
-    df : pl.DataFrame
-        The DataFrame containing pitch data.
-    window : int
-        The window size for calculating the rolling average.
-    ax : plt.Axes
-        The axis to plot on.
-    """
-    # Map game_id to sequential numbers
-    date_to_number = {date: i + 1 for i, date in enumerate(df['game_id'].unique(maintain_order=True))}
-
-    # Add a column with the sequential game numbers
-    df_plot = df.with_columns(
-        pl.col("game_id").map_elements(lambda x: date_to_number.get(x, x)).alias("start_number")
-    )
-
-    # Group by relevant columns and calculate mean tj_stuff_plus
-    plot_game_roll = df_plot.group_by(['start_number', 'game_id', 'game_date', 'pitch_type', 'pitch_description']).agg(
-        pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus')
-    ).sort('start_number', descending=False)
-
-    # Get the list of pitch types ordered by frequency
-    sorted_value_counts = df['pitch_type'].value_counts().sort('count', descending=True)
-    items_in_order = sorted_value_counts['pitch_type'].to_list()
-
-    # Plot the rolling average for each pitch type
-    for i in items_in_order:
-        df_item = plot_game_roll.filter(pl.col('pitch_type') == i)
-        df_item = df_item.with_columns(
-            pl.col("start_number").cast(pl.Int64)
-        ).join(
-            pl.DataFrame({"start_number": list(date_to_number.values())}),
-            on="start_number",
-            how="outer"
-        ).sort("start_number_right").with_columns([
-            pl.col("start_number").fill_null(strategy="forward").fill_null(strategy="backward"),
-            pl.col("tj_stuff_plus").fill_null(strategy="forward").fill_null(strategy="backward"),
-            pl.col("pitch_type").fill_null(strategy="forward").fill_null(strategy="backward"),
-            pl.col("pitch_description").fill_null(strategy="forward").fill_null(strategy="backward")
-        ])
-
-        sns.lineplot(x=range(1, max(df_item['start_number_right']) + 1),
-                        y=df_item.filter(pl.col('pitch_type') == i)['tj_stuff_plus'].rolling_mean(window,min_periods=1),
-                        color=dict_colour[i],
-                        ax=ax, linewidth=3)
-
-        # Highlight missing game data points
-        for n in range(len(df_item)):
-            if df_item['game_id'].is_null()[n]:
-                sns.scatterplot(x=[df_item['start_number_right'][n]],
-                                y=[df_item['tj_stuff_plus'].rolling_mean(window,min_periods=1)[n]],
-                                color='white',
-                                ec=dict_colour[i],
-                                ax=ax,
-                                zorder=100)
-
-    # Adjust x-axis limits to start from 1
-    ax.set_xlim(1, max(df_item['start_number']))
-    ax.set_ylim(70, 130)
-    ax.set_xlabel('Games', fontdict=font_properties_axes)
-    ax.set_ylabel('tjStuff+', fontdict=font_properties_axes)
-    ax.set_title(f"{window} Game Rolling tjStuff+", fontdict=font_properties_titles)
-    ax.xaxis.set_major_locator(MaxNLocator(integer=True))
-
-def break_plot(df: pl.DataFrame, ax: plt.Axes):
-    """
-    Plot the pitch breaks for different pitch types.
-
-    Parameters
-    ----------
-    df : pl.DataFrame
-        The DataFrame containing pitch data.
-    ax : plt.Axes
-        The axis to plot on.
-    """
-    # Get unique pitch types sorted by pitch count
-    label_labels = df.sort(by=['pitch_count', 'pitch_type'], descending=[False, True])['pitch_type'].unique(maintain_order=True).to_numpy()
-
-    # Plot confidence ellipses for each pitch type
-    for idx, label in enumerate(label_labels):
-        subset = df.filter(pl.col('pitch_type') == label)
-        if len(subset) > 4:
-            try:
-                confidence_ellipse(subset['hb'], subset['ivb'], ax=ax, edgecolor=dict_colour[label], n_std=2, facecolor=dict_colour[label], alpha=0.2)
-            except ValueError:
-                return
-
-    # Plot scatter plot for pitch breaks
-    if df['pitcher_hand'][0] == 'R':
-        sns.scatterplot(ax=ax, x=df['hb'], y=df['ivb'], hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2)
-    elif df['pitcher_hand'][0] == 'L':
-        sns.scatterplot(ax=ax, x=df['hb'], y=df['ivb'], hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2)
-
-    # Set axis limits
-    ax.set_xlim((-25, 25))
-    ax.set_ylim((-25, 25))
-
-    # Add horizontal and vertical lines
-    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)
-
-    # Set axis labels and title
-    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)
-
-    # Remove legend
-    ax.get_legend().remove()
-
-    # Set tick labels
-    ax.set_xticklabels(ax.get_xticks(), fontdict=font_properties)
-    ax.set_yticklabels(ax.get_yticks(), fontdict=font_properties)
-
-    # Add text annotations for glove side and arm side
-    if df['pitcher_hand'][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)
-    elif df['pitcher_hand'][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)
-
-    # Set aspect ratio and format axis ticks
-    ax.set_aspect('equal', adjustable='box')
-    ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
-    ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
-
-# DEFINE STRIKE ZONE
-strike_zone = pl.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):
-    """
-    Draw the strike zone and home plate on the given axis.
-
-    Parameters
-    ----------
-    axis : matplotlib.axes.Axes
-        The axis to draw the strike zone on.
-    alpha_spot : float, optional
-        The transparency level of the lines (default is 1).
-    catcher_p : bool, optional
-        Whether to draw the catcher's perspective (default is True).
-    """
-    # Draw the strike zone
-    axis.plot(strike_zone['PlateLocSide'].to_list(), strike_zone['PlateLocHeight'].to_list(), 
-              color='black', linewidth=1.3, zorder=3, alpha=alpha_spot)
-
-    if catcher_p:
-        # Draw home plate from catcher's perspective
-        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:
-        # Draw home plate from pitcher's perspective
-        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], [-0.35, -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)
-
-def location_plot(df: pl.DataFrame, ax: plt.Axes, hand: str):
-    """
-    Plot the pitch locations for different pitch types against a specific batter hand.
-
-    Parameters
-    ----------
-    df : pl.DataFrame
-        The DataFrame containing pitch data.
-    ax : plt.Axes
-        The axis to plot on.
-    hand : str
-        The batter hand ('L' for left-handed, 'R' for right-handed).
-    """
-    # Get unique pitch types sorted by pitch count
-    label_labels = df.sort(by=['pitch_count', 'pitch_type'], descending=[False, True])['pitch_type'].unique(maintain_order=True).to_numpy()
-
-    # Plot confidence ellipses for each pitch type
-    for label in label_labels:
-        subset = df.filter((pl.col('pitch_type') == label) & (pl.col('batter_hand') == hand))
-        if len(subset) >= 5:
-            confidence_ellipse(subset['px'], subset['pz'], ax=ax, edgecolor=dict_colour[label], n_std=1.5, facecolor=dict_colour[label], alpha=0.3)
-
-    # Group pitch locations by pitch type and calculate mean values
-    pitch_location_group = (
-        df.filter(pl.col("batter_hand") == hand)
-        .group_by("pitch_type")
-        .agg([
-            pl.col("start_speed").count().alias("pitches"),
-            pl.col("px").mean().alias("px"),
-            pl.col("pz").mean().alias("pz")
-        ])
-    )
-
-    # Calculate pitch percentages
-    total_pitches = pitch_location_group['pitches'].sum()
-    pitch_location_group = pitch_location_group.with_columns(
-        (pl.col("pitches") / total_pitches).alias("pitch_percent")
-    )
-
-    # Plot pitch locations
-    sns.scatterplot(ax=ax, x=pitch_location_group['px'], y=pitch_location_group['pz'],
-                    hue=pitch_location_group['pitch_type'], palette=dict_colour, ec='black',
-                    s=pitch_location_group['pitch_percent'] * 750, linewidth=2, zorder=2)
-
-    # Customize plot appearance
-    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)
-
-
-def summary_table(df: pl.DataFrame, ax: plt.Axes):
-    """
-    Create a summary table of pitch data.
-
-    Parameters
-    ----------
-    df : pl.DataFrame
-        The DataFrame containing pitch data.
-    ax : plt.Axes
-        The axis to plot the table on.
-    """
-    # Aggregate pitch data by pitch description
-    df_agg = df.group_by("pitch_description").agg(
-        pl.col('is_pitch').sum().alias('count'),
-        (pl.col('is_pitch').sum() / df.select(pl.col('is_pitch').sum())).alias('count_percent'),
-        pl.col('start_speed').mean().alias('start_speed'),
-        pl.col('ivb').mean().alias('ivb'),
-        pl.col('hb').mean().alias('hb'),
-        pl.col('spin_rate').mean().alias('spin_rate'),
-        pl.col('vaa').mean().alias('vaa'),
-        pl.col('haa').mean().alias('haa'),
-        pl.col('z0').mean().alias('z0'),
-        pl.col('x0').mean().alias('x0'),
-        pl.col('extension').mean().alias('extension'),
-        (((pl.col('spin_direction').mean() + 180) % 360 // 30) + 
-        (((pl.col('spin_direction').mean() + 180) % 360 % 30 / 30 / 100 * 60).round(2) * 10).round(0) // 1.5 / 4)
-        .cast(pl.Float64).map_elements(lambda x: f"{int(x)}:{int((x % 1) * 60):02d}", return_dtype=pl.Utf8).alias('clock_time'),
-        pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus'),
-        pl.col('pitch_grade').mean().alias('pitch_grade'),
-        (pl.col('in_zone').sum() / pl.col('is_pitch').sum()).alias('zone_percent'),
-        (pl.col('ozone_swing').sum() / pl.col('out_zone').sum()).alias('chase_percent'),
-        (pl.col('whiffs').sum() / pl.col('swings').sum()).alias('whiff_percent'),
-        (pl.col('woba_pred_contact').sum() / pl.col('bip').sum()).alias('xwobacon')
-    ).sort("count", descending=True)
-
-    # Aggregate all pitch data
-    df_agg_all = df.group_by(pl.lit("All").alias("pitch_description")).agg(
-        pl.col('is_pitch').sum().alias('count'),
-        (pl.col('is_pitch').sum() / df.select(pl.col('is_pitch').sum())).alias('count_percent'),
-        pl.lit(None).alias('start_speed'),
-        pl.lit(None).alias('ivb'),
-        pl.lit(None).alias('hb'),
-        pl.lit(None).alias('spin_rate'),
-        pl.lit(None).alias('vaa'),
-        pl.lit(None).alias('haa'),
-        pl.lit(None).alias('z0'),
-        pl.lit(None).alias('x0'),
-        pl.col('extension').mean().alias('extension'),
-        pl.lit(None).alias('clock_time'),
-        pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus'),
-        pl.lit(None).alias('pitch_grade'),
-        (pl.col('in_zone').sum() / pl.col('is_pitch').sum()).alias('zone_percent'),
-        (pl.col('ozone_swing').sum() / pl.col('out_zone').sum()).alias('chase_percent'),
-        (pl.col('whiffs').sum() / pl.col('swings').sum()).alias('whiff_percent'),
-        (pl.col('woba_pred_contact').sum() / pl.col('bip').sum()).alias('xwobacon')
-    )
-
-    # Concatenate aggregated data
-    df_agg = pl.concat([df_agg, df_agg_all]).fill_nan(None)
-
-    # Load statcast pitch summary data
-    statcast_pitch_summary = pl.read_csv('functions/statcast_2024_grouped.csv')
-
-    # Create table
-    table = ax.table(cellText=df_agg.fill_nan('—').fill_null('—').to_numpy(), colLabels=df_agg.columns, cellLoc='center',
-                     colWidths=[2.3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], bbox=[0.0, 0, 1, 0.8])
-
-    # Set table properties
-    min_font_size = 14
-    table.auto_set_font_size(False)
-    table.set_fontsize(min_font_size)
-    table.scale(1, 0.5)
-
-    # Set font size for values
-    min_font_size = 18
-    for i in range(len(df_agg) + 1):
-        for j in range(len(df_agg.columns)):
-            if i > 0:  # Skip the header row
-                cell = table.get_celld()[i, j]
-                cell.set_fontsize(min_font_size)
-
-    # Define color maps
-    cmap_sum = mcolors.LinearSegmentedColormap.from_list("", ['#648FFF', '#FFFFFF', '#FFB000'])
-    cmap_sum_r = mcolors.LinearSegmentedColormap.from_list("", ['#FFB000', '#FFFFFF', '#648FFF'])
-
-    # Update table cells with colors and text properties
-    for i in range(len(df_agg)):
-        pitch_check = dict_pitch_desc_type[df_agg['pitch_description'][i]]
-        cell_text = table.get_celld()[(i + 1, 0)].get_text().get_text()
-
-        if cell_text != 'All':
-            table.get_celld()[(i + 1, 0)].set_facecolor(dict_pitch_name[cell_text])
-            text_props = {'color': '#000000', 'fontweight': 'bold'} if cell_text in ['Split-Finger', 'Slider', 'Changeup'] else {'color': '#ffffff', 'fontweight': 'bold'}
-            table.get_celld()[(i + 1, 0)].set_text_props(**text_props)
-            if cell_text == 'Four-Seam Fastball':
-                table.get_celld()[(i + 1, 0)].get_text().set_text('4-Seam')
-
-        select_df = statcast_pitch_summary.filter(statcast_pitch_summary['pitch_type'] == pitch_check)
-
-        # Apply color to specific columns based on normalized values
-        columns_to_color = [(3, 'release_speed', 0.95, 1.05), (11, 'release_extension', 0.9, 1.1), (13, None, 80, 120), 
-                            (14, None, 30, 70), (15, 'in_zone_rate', 0.7, 1.3), (16, 'chase_rate', 0.7, 1.3), 
-                            (17, 'whiff_rate', 0.7, 1.3), (18, 'xwoba', 0.7, 1.3)]
-
-        for col, stat, vmin_factor, vmax_factor in columns_to_color:
-            cell_value = table.get_celld()[(i + 1, col)].get_text().get_text()
-            if cell_value != '—':
-                vmin = select_df[stat].mean() * vmin_factor if stat else vmin_factor
-                vmax = select_df[stat].mean() * vmax_factor if stat else vmax_factor
-                normalize = mcolors.Normalize(vmin=vmin, vmax=vmax)
-                cmap = cmap_sum if col != 18 else cmap_sum_r
-                table.get_celld()[(i + 1, col)].set_facecolor(get_color(float(cell_value.strip('%')), normalize, cmap))
-
-    # Set header text properties
-    table.get_celld()[(len(df_agg), 0)].set_text_props(color='#000000', fontweight='bold')
-
-    # Update column names
-    new_column_names = ['$\\bf{Pitch\\ Name}$', '$\\bf{Count}$', '$\\bf{Pitch\\%}$', '$\\bf{Velocity}$', '$\\bf{iVB}$', 
-                        '$\\bf{HB}$', '$\\bf{Spin}$', '$\\bf{VAA}$', '$\\bf{HAA}$', '$\\bf{vRel}$', '$\\bf{hRel}$', 
-                        '$\\bf{Ext.}$', '$\\bf{Axis}$', '$\\bf{tjStuff+}$', '$\\bf{Grade}$', '$\\bf{Zone\\%}$', 
-                        '$\\bf{Chase\\%}$', '$\\bf{Whiff\\%}$', '$\\bf{xwOBA}$\n$\\bf{Contact}$']
-
-    for i, col_name in enumerate(new_column_names):
-        table.get_celld()[(0, i)].get_text().set_text(col_name)
-
-    # Format cell values
-    def format_cells(columns, fmt):
-        for col in columns:
-            col_idx = df_agg.columns.index(col)
-            for row in range(1, len(df_agg) + 1):
-                cell_value = table.get_celld()[(row, col_idx)].get_text().get_text()
-                if cell_value != '—':
-                    table.get_celld()[(row, col_idx)].get_text().set_text(fmt.format(float(cell_value.strip('%'))))
-
-    format_cells(['start_speed', 'ivb', 'hb', 'vaa', 'haa', 'z0', 'x0', 'extension'], '{:,.1f}')
-    format_cells(['xwobacon'], '{:,.3f}')
-    format_cells(['count_percent', 'zone_percent', 'chase_percent', 'whiff_percent'], '{:,.1%}')
-    format_cells(['tj_stuff_plus', 'pitch_grade', 'spin_rate'], '{:,.0f}')
-
-    # Create legend for pitch types
-    items_in_order = (df.sort("pitch_count", descending=True)['pitch_type'].unique(maintain_order=True).to_numpy())
-    colour_pitches = [dict_colour[x] for x in items_in_order]
-    label = [dict_pitch[x] for x in items_in_order]
-    handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches]
-    if len(label) > 5:
-        ax.legend(handles, label, bbox_to_anchor=(0.1, 0.81, 0.8, 0.14), ncol=5,
-                  fancybox=True, loc='lower center', fontsize=16, framealpha=1.0, markerscale=1.7, prop={'family': 'calibi', 'size': 16})
-    else:
-        ax.legend(handles, label, bbox_to_anchor=(0.1, 0.81, 0.8, 0.14), ncol=5,
-                  fancybox=True, loc='lower center', fontsize=20, framealpha=1.0, markerscale=2, prop={'family': 'calibi', 'size': 20})
-    ax.axis('off')
-
-def plot_footer(ax: plt.Axes):
-    """
-    Add footer text to the plot.
-
-    Parameters
-    ----------
-    ax : plt.Axes
-        The axis to add the footer text to.
-    """
-    # Add footer text
-    ax.text(0, 1, 'By: @TJStats', ha='left', va='top', fontsize=24)
-    ax.text(0.5, 0.25, 
-            '''
-            Colour Coding Compares to League Average By Pitch
-            tjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type
-            tjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10
-            Pitch Grade scales tjStuff+ to the traditional 20-80 Scouting Scale for a given pitch type
-            ''', 
-            ha='center', va='bottom', fontsize=12)
-    ax.text(1, 1, 'Data: MLB, Fangraphs\nImages: MLB, ESPN', ha='right', va='top', fontsize=24)
-    ax.axis('off')
-
-# Function to get an image from a URL and display it on the given axis
-def player_headshot(player_input: str, ax: plt.Axes, sport_id: int, season: int):
-    """
-    Display the player's headshot image on the given axis.
-
-    Parameters
-    ----------
-    player_input : str
-        The player's ID.
-    ax : plt.Axes
-        The axis to display the image on.
-    sport_id : int
-        The sport ID (1 for MLB, other for minor leagues).
-    season : int
-        The season year.
-    """
-    try:
-        # Construct the URL for the player's headshot image based on sport ID
-        if int(sport_id) == 1:
-            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_640,q_auto:best/v1/people/{player_input}/headshot/silo/current.png'
-        else:
-            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_640/v1/people/{player_input}/headshot/milb/current.png'
-
-        # Send a GET request to the URL and open the image from the response content
-        response = requests.get(url)
-        img = Image.open(BytesIO(response.content))
-
-        # Display the image on the axis
-        ax.set_xlim(0, 1.3)
-        ax.set_ylim(0, 1)
-        ax.imshow(img, extent=[0, 1, 0, 1] if sport_id == 1 else [1/6, 5/6, 0, 1], origin='upper')
-    except PIL.UnidentifiedImageError:
-        ax.axis('off')
-        return
-
-    # Turn off the axis
-    ax.axis('off')
-
-def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int):
-    """
-    Display the player's bio information on the given axis.
-
-    Parameters
-    ----------
-    pitcher_id : str
-        The player's ID.
-    ax : plt.Axes
-        The axis to display the bio information on.
-    sport_id : int
-        The sport ID (1 for MLB, other for minor leagues).
-    year_input : int
-        The season year.
-    """
-    # Construct the URL to fetch player data
-    url = f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}&hydrate=currentTeam"
-
-    # Send a GET request to the URL and parse the JSON response
-    data = requests.get(url).json()
-
-    # Extract player information from the JSON data
-    player_name = data['people'][0]['fullName']
-    pitcher_hand = data['people'][0]['pitchHand']['code']
-    age = data['people'][0]['currentAge']
-    height = data['people'][0]['height']
-    weight = data['people'][0]['weight']
-
-    # Display the player's name, handedness, age, height, and weight on the axis
-    ax.text(0.5, 1, f'{player_name}', va='top', ha='center', fontsize=56)
-    ax.text(0.5, 0.7, f'{pitcher_hand}HP, Age:{age}, {height}/{weight}', va='top', ha='center', fontsize=30)
-    ax.text(0.5, 0.45, f'Season Pitching Summary', va='top', ha='center', fontsize=40)
-
-    # Make API call to retrieve sports information
-    response = requests.get(url='https://statsapi.mlb.com/api/v1/sports').json()
-    
-    # Convert the JSON response into a Polars DataFrame
-    df_sport_id = pl.DataFrame(response['sports'])    
-    abb = df_sport_id.filter(pl.col('id') == sport_id)['abbreviation'][0]
-
-    # Display the season and sport abbreviation
-    ax.text(0.5, 0.20, f'{year_input} {abb} Season', va='top', ha='center', fontsize=30, fontstyle='italic')
-
-    # Turn off the axis
-    ax.axis('off')
-
-def plot_logo(pitcher_id: str, ax: plt.Axes, df_team: pl.DataFrame, df_players: pl.DataFrame):
-    """
-    Display the team logo for the given pitcher on the specified axis.
-
-    Parameters
-    ----------
-    pitcher_id : str
-        The ID of the pitcher.
-    ax : plt.Axes
-        The axis to display the logo on.
-    df_team : pl.DataFrame
-        The DataFrame containing team data.
-    df_players : pl.DataFrame
-        The DataFrame containing player data.
-    """
-    # List of MLB teams and their corresponding ESPN logo URLs
-    mlb_teams = [
-        {"team": "AZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/ari.png&h=500&w=500"},
-        {"team": "ATL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/atl.png&h=500&w=500"},
-        {"team": "BAL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bal.png&h=500&w=500"},
-        {"team": "BOS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bos.png&h=500&w=500"},
-        {"team": "CHC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chc.png&h=500&w=500"},
-        {"team": "CWS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chw.png&h=500&w=500"},
-        {"team": "CIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cin.png&h=500&w=500"},
-        {"team": "CLE", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cle.png&h=500&w=500"},
-        {"team": "COL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/col.png&h=500&w=500"},
-        {"team": "DET", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/det.png&h=500&w=500"},
-        {"team": "HOU", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/hou.png&h=500&w=500"},
-        {"team": "KC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/kc.png&h=500&w=500"},
-        {"team": "LAA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/laa.png&h=500&w=500"},
-        {"team": "LAD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/lad.png&h=500&w=500"},
-        {"team": "MIA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mia.png&h=500&w=500"},
-        {"team": "MIL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mil.png&h=500&w=500"},
-        {"team": "MIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/min.png&h=500&w=500"},
-        {"team": "NYM", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nym.png&h=500&w=500"},
-        {"team": "NYY", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nyy.png&h=500&w=500"},
-        {"team": "OAK", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
-        {"team": "PHI", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/phi.png&h=500&w=500"},
-        {"team": "PIT", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/pit.png&h=500&w=500"},
-        {"team": "SD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sd.png&h=500&w=500"},
-        {"team": "SF", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sf.png&h=500&w=500"},
-        {"team": "SEA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sea.png&h=500&w=500"},
-        {"team": "STL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/stl.png&h=500&w=500"},
-        {"team": "TB", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tb.png&h=500&w=500"},
-        {"team": "TEX", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tex.png&h=500&w=500"},
-        {"team": "TOR", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tor.png&h=500&w=500"},
-        {"team": "WSH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/wsh.png&h=500&w=500"},
-        {"team": "ATH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
-    ]
-
-    try:
-        # Create a DataFrame from the list of dictionaries
-        df_image = pd.DataFrame(mlb_teams)
-        image_dict = df_image.set_index('team')['logo_url'].to_dict()
-
-        # Get the team ID for the given pitcher
-        team_id = df_players.filter(pl.col('player_id') == pitcher_id)['team'][0]
-
-        # Construct the URL to fetch team data
-        url_team = f'https://statsapi.mlb.com/api/v1/teams/{team_id}'
-
-        # Send a GET request to the team URL and parse the JSON response
-        data_team = requests.get(url_team).json()
-
-        # Extract the team abbreviation
-        if data_team['teams'][0]['id'] in df_team['parent_org_id']:
-            team_abb = df_team.filter(pl.col('team_id') == data_team['teams'][0]['id'])['parent_org_abbreviation'][0]
-        else:
-            team_abb = df_team.filter(pl.col('parent_org_id') == data_team['teams'][0]['parentOrgId'])['parent_org_abbreviation'][0]
-
-        # Get the logo URL from the image dictionary using the team abbreviation
-        logo_url = image_dict[team_abb]
-
-        # Send a GET request to the logo URL
-        response = requests.get(logo_url)
-
-        # Open the image from the response content
-        img = Image.open(BytesIO(response.content))
-
-        # Display the image on the axis
-        ax.set_xlim(0, 1.3)
-        ax.set_ylim(0, 1)
-        ax.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper')
-
-        # Turn off the axis
-        ax.axis('off')
-    except (KeyError,IndexError) as e:
-        ax.axis('off')
-        return
-
-splits = {
-    'all':0,
-    'left':13,
-    'right':14,
-}
-
-splits_title = {
-
-    'all':'',
-    'left':' vs LHH',
-    'right':' vs RHH',
-
-}
-
-
-def fangraphs_pitching_leaderboards(season: int,
-                                    split: str,
-                                    start_date: str = '2024-01-01',
-                                    end_date: str = '2024-12-31'):
-    """
-    Fetch pitching leaderboards data from Fangraphs.
-
-    Parameters
-    ----------
-    season : int
-        The season year.
-    split : str
-        The split type (e.g., 'All', 'LHH', 'RHH').
-    start_date : str, optional
-        The start date for the data (default is '2024-01-01').
-    end_date : str, optional
-        The end date for the data (default is '2024-12-31').
-
-    Returns
-    -------
-    pl.DataFrame
-        The DataFrame containing the pitching leaderboards data.
-    """
-    url = f"""
-           https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&season={season}&season1={season}
-           &startdate={start_date}&enddate={end_date}&ind=0&qual=0&type=8&month=1000&pageitems=500000
-           """
-
-    data = requests.get(url).json()
-    df = pl.DataFrame(data=data['data'], infer_schema_length=1000)
-    return df
-
-def fangraphs_splits_scrape(player_input: str, year_input: int, start_date: str, end_date: str, split: str) -> pl.DataFrame:
-    """
-    Scrape Fangraphs splits data for a specific player.
-
-    Parameters
-    ----------
-    player_input : str
-        The player's ID.
-    year_input : int
-        The season year.
-    start_date : str
-        The start date for the data.
-    end_date : str
-        The end date for the data.
-    split : str
-        The split type (e.g., 'all', 'left', 'right').
-
-    Returns
-    -------
-    pl.DataFrame
-        The DataFrame containing the splits data.
-    """
-    split_dict = {
-        'all': [],
-        'left': ['5'],
-        'right': ['6']
-    }
-
-    
-
-    url = "https://www.fangraphs.com/api/leaders/splits/splits-leaders"
-
-    # Get Fangraphs player ID
-    fg_id = str(fangraphs_pitching_leaderboards(
-        year_input,
-        split='All',
-        start_date=f'{year_input}-01-01',
-        end_date=f'{year_input}-12-31'
-    ).filter(pl.col('xMLBAMID') == player_input)['playerid'][0])
-
-    # Payload for basic stats
-    payload = {
-        "strPlayerId": fg_id,
-        "strSplitArr": split_dict[split],
-        "strGroup": "season",
-        "strPosition": "P",
-        "strType": "2",
-        "strStartDate": pd.to_datetime(start_date).strftime('%Y-%m-%d'),
-        "strEndDate": 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
-    }
-
-    # Fetch basic stats
-    response = requests.post(url, data=json.dumps(payload), headers={'Content-Type': 'application/json'})
-    data_pull = response.json()['data'][0]
-
-    # Payload for advanced stats
-    payload_advanced = payload.copy()
-    payload_advanced["strType"] = "1"
-
-    # Fetch advanced stats
-    response_advanced = requests.post(url, data=json.dumps(payload_advanced), headers={'Content-Type': 'application/json'})
-    data_pull_advanced = response_advanced.json()['data'][0]
-
-    # Combine basic and advanced stats
-    data_pull.update(data_pull_advanced)
-    df_pull = pl.DataFrame(data_pull)
-
-    return df_pull
-
-                                   
-def fangraphs_table(df: pl.DataFrame,
-                    ax: plt.Axes,
-                    player_input: str,
-                    season: int,
-                    split: str):
-    """
-    Create a table of Fangraphs pitching leaderboards data for a specific player.
-
-    Parameters
-    ----------
-    ax : plt.Axes
-        The axis to plot the table on.
-    season : int
-        The season year.
-    split : str
-        The split type (e.g., 'All', 'LHH', 'RHH').
-    """
-
-    start_date = df['game_date'][0]
-    end_date = df['game_date'][-1]
-
-    # Fetch Fangraphs splits data
-    df_fangraphs = fangraphs_splits_scrape(player_input=player_input, 
-                                           year_input=season,
-                                           start_date=start_date,
-                                           end_date=end_date,
-                                           split=split)
-
-    # Select relevant columns for the table
-    plot_table = df_fangraphs.select(['IP', 'WHIP', 'ERA', 'TBF', 'FIP', 'K%', 'BB%', 'K-BB%'])
-
-    # Format table values
-    plot_table_values = [format(plot_table[x][0], fangraphs_stats_dict[x]['format']) if plot_table[x][0] != '---' else '---' for x in plot_table.columns]
-    
-    # Create the table
-    table_fg = ax.table(cellText=[plot_table_values], colLabels=plot_table.columns, cellLoc='center',
-                        bbox=[0.0, 0.1, 1, 0.7])
-
-    # Set font size for the table
-    min_font_size = 20
-    table_fg.set_fontsize(min_font_size)
-
-    # Update column names with formatted headers
-    new_column_names = [fangraphs_stats_dict[col]['table_header'] for col in plot_table.columns]
-    for i, col_name in enumerate(new_column_names):
-        table_fg.get_celld()[(0, i)].get_text().set_text(col_name)
-
-    # Set header text properties
-    ax.text(0.5, 0.9, f'{start_date} to {end_date}{splits_title[split]}', va='bottom', ha='center',
-            fontsize=36, fontstyle='italic')
-    ax.axis('off')
-
-
-def stat_summary_table(df: pl.DataFrame, 
-                        player_input: int, 
-                        sport_id: int, 
-                        ax: plt.Axes,
-                        split: str = 'All',
-                        game_type: list = ['R']):
-    """
-    Create a summary table of player statistics.
-
-    Parameters
-    ----------
-    df : pl.DataFrame
-        The DataFrame containing pitch data.
-    player_input : int
-        The player's ID.
-    sport_id : int
-        The sport ID (1 for MLB, other for minor leagues).
-    ax : plt.Axes
-        The axis to plot the table on.
-    split : str, optional
-        The split type (default is 'All').
-    """
-
-    type_dict =  {'R':'Regular Season',
-                   'S':'Spring',
-                   'P':'Playoffs' }
-
-    split_title = {
-        'all':'',
-        'right':' vs RHH',
-        'left':' vs LHH'
-    }
-
-    
-    # Format start and end dates
-    start_date_format = str(pd.to_datetime(df['game_date'][0]).strftime('%m/%d/%Y'))
-    end_date_format = str(pd.to_datetime(df['game_date'][-1]).strftime('%m/%d/%Y'))
-
-    # Determine app context based on sport ID
-    appContext = 'majorLeague' if sport_id == 1 else 'minorLeague'
-
-    game_type_str = ','.join([str(x) for x in game_type])
-
-    # Fetch player stats from MLB API
-    pitcher_stats_call = requests.get(
-        f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format},gameType=[{game_type_str}])'
-    ).json()
-    print('HERE')
-    print(f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format},gameType=[{game_type_str}])')
-    # Extract stats and create DataFrame
-    pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat']]
-    pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat']]
-    pitcher_stats_call_df = pl.DataFrame(data=dict(zip(pitcher_stats_call_header, pitcher_stats_call_values)))
-
-    # Add additional calculated columns
-    pitcher_stats_call_df = pitcher_stats_call_df.with_columns(
-        pl.lit(df['is_whiff'].sum()).alias('whiffs'),
-        (pl.col('strikeOuts') / pl.col('battersFaced') * 100).round(1).cast(pl.Utf8).str.concat('%').alias('k_percent'),
-        (pl.col('baseOnBalls') / pl.col('battersFaced') * 100).round(1).cast(pl.Utf8).str.concat('%').alias('bb_percent'),
-        ((pl.col('strikeOuts') - pl.col('baseOnBalls')) / pl.col('battersFaced') * 100).round(1).cast(pl.Utf8).str.concat('%').alias('k_bb_percent'),
-        (((pl.col('homeRuns') * 13 + 3 * ((pl.col('baseOnBalls')) + (pl.col('hitByPitch'))) - 2 * (pl.col('strikeOuts')))) / ((pl.col('outs')) / 3) + 3.15).round(2).map_elements(lambda x: f"{x:.2f}").alias('fip'),
-        ((pl.col('strikes') / pl.col('numberOfPitches') * 100)).round(1).cast(pl.Utf8).str.concat('%').alias('strikePercentage'),
-    )
-
-    # Determine columns and title based on game count and sport ID
-    if df['game_id'][0] == df['game_id'][-1]:
-        pitcher_stats_call_df_small = pitcher_stats_call_df.select(['inningsPitched', 'battersFaced', 'earnedRuns', 'hits', 'strikeOuts', 'baseOnBalls', 'hitByPitch', 'homeRuns', 'strikePercentage', 'whiffs'])
-        new_column_names = ['$\\bf{IP}$', '$\\bf{PA}$', '$\\bf{ER}$', '$\\bf{H}$', '$\\bf{K}$', '$\\bf{BB}$', '$\\bf{HBP}$', '$\\bf{HR}$', '$\\bf{Strike\%}$', '$\\bf{Whiffs}$']
-        title = f'{df["game_date"][0]} vs {df["batter_team"][0]} ({type_dict[game_type[0]]}){split_title[split]}'
-    elif sport_id != 1 or game_type[0] in ['S','P']:
-        pitcher_stats_call_df_small = pitcher_stats_call_df.select(['inningsPitched', 'battersFaced', 'whip', 'era', 'fip', 'k_percent', 'bb_percent', 'k_bb_percent', 'strikePercentage'])
-        new_column_names = ['$\\bf{IP}$', '$\\bf{PA}$', '$\\bf{WHIP}$', '$\\bf{ERA}$', '$\\bf{FIP}$', '$\\bf{K\%}$', '$\\bf{BB\%}$', '$\\bf{K-BB\%}$', '$\\bf{Strike\%}$']
-        title = f'{df["game_date"][0]} to {df["game_date"][-1]} ({type_dict[game_type[0]]}{split_title[split]})'
-    else:
-        fangraphs_table(df=df, ax=ax, player_input=player_input, season=int(df['game_date'][0][0:4]), split=split)
-        return
-
-    # Create and format the table
-    table_fg = ax.table(cellText=pitcher_stats_call_df_small.to_numpy(), colLabels=pitcher_stats_call_df_small.columns, cellLoc='center', bbox=[0.0, 0.1, 1, 0.7])
-    table_fg.set_fontsize(20)
-    for i, col_name in enumerate(new_column_names):
-        table_fg.get_celld()[(0, i)].get_text().set_text(col_name)
-
-    # Add title to the plot
-    ax.text(0.5, 0.9, title, va='bottom', ha='center', fontsize=36, fontstyle='italic')
-    ax.axis('off')

functions/statcast_2024_grouped.csv

@@ -1,19 +0,0 @@
-pitch_type,pitch,release_speed,pfx_z,pfx_x,release_spin_rate,release_pos_x,release_pos_z,release_extension,delta_run_exp,swing,whiff,in_zone,out_zone,chase,xwoba,pitch_usage,whiff_rate,in_zone_rate,chase_rate,delta_run_exp_per_100,all
-CH,74155,85.46226725895522,5.247514143364433,-3.9745011679246045,1803.342540762527,-0.5077629855663421,5.740925968432281,6.449406057002311,204.631,37385,11538,28912,45151,15250,0.28973564881286695,0.10218846333521206,0.30862645446034503,0.38988604949093114,0.3377555314389493,-0.27595037421616886,
-CS,22,66.38181818181819,-7.232727272727273,5.176363636363637,2039.2727272727273,-1.7981818181818183,6.5177272727272735,6.0636363636363635,-0.6290000000000001,9,2,10,12,2,0.13466666666666668,3.0316852449257168e-05,0.2222222222222222,0.45454545454545453,0.16666666666666666,2.85909090909091,
-CU,47579,79.40938533133989,-9.345106445703216,4.516206279348902,2568.8591051473077,-0.6765712059634863,5.9438438375202685,6.401792908519479,93.57199999999999,19910,6150,20751,26738,7749,0.28049767649520974,0.0655657055765094,0.3088900050226017,0.4361377918829736,0.28981225222529733,-0.1966665966077471,
-EP,576,50.51909722222222,16.357291666666665,-3.8287500000000003,1256.7152777777778,-0.9668749999999999,6.647100694444444,4.442013888888889,23.643,252,7,207,369,106,0.3971430703517588,0.0007937503186714604,0.027777777777777776,0.359375,0.2872628726287263,-4.104687500000001,
-FA,635,67.81354330708662,15.865511811023623,-3.7226456692913388,1674.0144694533763,-1.1163779527559055,6.317716535433071,4.92488188976378,15.495,284,29,296,339,73,0.43393490999999995,0.0008750546047853774,0.10211267605633803,0.46614173228346456,0.2153392330383481,-2.4401574803149604,
-FC,58379,89.56435813713696,8.08895396195288,1.5509243697478992,2389.231715947733,-0.9745362684951281,5.8461769002079365,6.403954996645393,-20.390000000000015,28753,6674,30002,28189,7757,0.34077822947428493,0.08044852405159929,0.23211490974854798,0.5139176758765994,0.2751782610238036,0.034926942907552404,
-FF,230412,94.27369496062718,15.720274827472318,-3.1074418968484365,2296.591789895323,-0.7685432927147252,5.821400777026439,6.524392110813926,-80.28400000000002,113157,24741,127386,102722,24808,0.3401256910065045,0.3175166639335565,0.21864312415493517,0.5528618301130149,0.2415062012032476,0.03484367133656234,
-FO,168,82.07916666666667,1.7357142857142858,0.1378571428571428,946.8154761904761,-0.5333333333333333,5.8914285714285715,6.666666666666667,2.539,89,29,60,108,43,0.27798747368421056,0.0002315105096125093,0.3258426966292135,0.35714285714285715,0.39814814814814814,-1.511309523809524,
-FS,21727,86.31228885718231,2.979608781700189,-8.76550651263405,1302.3992981808108,-1.4640824780227366,5.742066553136651,6.508958525345622,-16.641000000000005,11333,3906,7982,13745,4946,0.2548785060302361,0.02994064787113684,0.34465719579987647,0.3673769963639711,0.3598399417970171,0.07659133796658538,
-KC,11916,81.79965592480698,-9.370896273917422,4.895297079556898,2444.1642796967144,-0.8788083249412554,5.940037764350453,6.434007553503986,-12.997000000000003,5312,1860,4858,7058,2316,0.25845137325418993,0.016420709717515837,0.3501506024096386,0.40768714333669015,0.32813828279965995,0.10907183618663985,
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