stats.py

import polars as pl
from data import data_df

from types import SimpleNamespace

from convert import verify_and_return_presult


valid_pitch = pl.col('x').is_not_null() & pl.col('y').is_not_null() & (pl.col('ballSpeed') > 0)


def filter_data_by_date_and_game_kind(data, start_date=None, end_date=None, game_kind=None):
  if start_date is not None:
    data = data.filter(pl.col('date') >= start_date)
  if end_date is not None:
    data = data.filter(pl.col('date') <= end_date)
  if game_kind is not None:
    data = data.filter(pl.col('coarse_game_kind') == game_kind)
  return data

def compute_team_games(data):
  data = (
      data
      .with_columns(
          pl.col('gameId').unique().len().over('HomeTeamNameES').alias('home_games'),
          pl.col('gameId').unique().len().over('VisitorTeamNameES').alias('visitor_games')
      )
  )
  game_data = (
      data
      .group_by('HomeTeamNameES')
      .first()
      [['HomeTeamNameES', 'home_games']]
      .rename({'HomeTeamNameES': 'team'})
      .join(
          (
              data
              .group_by('VisitorTeamNameES')
              .first()
              [['VisitorTeamNameES', 'visitor_games']]
              .rename({'VisitorTeamNameES': 'team'})
          ),
          on='team',
          how='full'
      )
      .fill_null(0)
      .with_columns(
        (pl.col('home_games')+pl.col('visitor_games')).alias('games'),
        pl.when(pl.col('team').is_null())
        .then(pl.col('team_right'))
        .otherwise(pl.col('team')).alias('team')
      )
  )


  return (
      data
      .drop('home_games', 'visitor_games')
      .join(
          game_data[['team', 'games']].rename({'games': 'home_games'}),
          left_on='HomeTeamNameES',
          right_on='team'
      )
      .join(
          game_data[['team', 'games']].rename({'games': 'visitor_games'}),
          left_on='VisitorTeamNameES',
          right_on='team'
      )
  )


def compute_pitch_stats(data, player_type, pitch_class_type, min_pitches=1, pitcher_lr='both', batter_lr='both', group_by_team=False):
  assert pitcher_lr in ('both', 'l', 'r')
  assert batter_lr in ('both', 'l', 'r')
  assert player_type in ('pitcher', 'batter')
  assert pitch_class_type in ('general', 'specific')

  if pitcher_lr != 'both':
    data = data.filter(pl.col('pitLR') == pitcher_lr)

  if batter_lr != 'both':
    data = data.filter(pl.col('batLR') == batter_lr)
    
  id_cols = ['pitId' if player_type == 'pitcher' else 'batId']
  if group_by_team:
    id_cols.append('pitcher_team_name_short')
  name_col = 'pitcher_name' if player_type == 'pitcher' else 'batter_name'
  pitch_col = 'ballKind_code' if pitch_class_type == 'specific' else 'general_ballKind_code'
  pitch_name_col = 'ballKind' if pitch_class_type == 'specific' else 'general_ballKind'
  pitch_stats = (
      data
      .with_columns((pl.col('ballSpeed') / 1.609).round(1).alias('mph'))
      .group_by(*id_cols, pitch_col)
      .agg(
          pl.first(name_col),
          pl.col('pitLR').first().str.to_uppercase().alias('Throws'),
          *([pl.first('general_ballKind')] if pitch_class_type == 'specific' else []),
          pl.first(pitch_name_col),
          pl.len().alias('count'),
          pl.when(pl.col('x').is_not_null() & pl.col('y').is_not_null() & (pl.col('ballSpeed') > 0)).then('ballSpeed').mean().alias('Avg KPH'),
          pl.col('ballSpeed').max().alias('Max KPH'),
          pl.when(pl.col('x').is_not_null() & pl.col('y').is_not_null() & (pl.col('ballSpeed') > 0)).then('mph').mean().round(1).alias('Avg MPH'),
          pl.col('mph').max().alias('Max MPH'),
          pl.col('aux_bresult').struct.field('batType').drop_nulls().value_counts(normalize=True),
          (pl.col('swing').sum() / pl.col('pitch').sum()).alias('Swing%'),
          ((pl.col('swing') & pl.col('zone')).sum() / pl.col('pitch').sum()).alias('Z-Swing%'),
          ((pl.col('swing') & ~pl.col('zone')).sum() / pl.col('pitch').sum()).alias('Chase%'),
          ((pl.col('swing') & ~pl.col('whiff')).sum()/pl.col('swing').sum()).alias('Contact%'),
          ((pl.col('zone') & pl.col('swing') & ~pl.col('whiff')).sum()/(pl.col('zone') & pl.col('swing')).sum()).alias('Z-Contact%'),
          ((~pl.col('zone') & pl.col('swing') & ~pl.col('whiff')).sum()/(~pl.col('zone') & pl.col('swing')).sum()).alias('O-Contact%'),
          (pl.col('whiff').sum() / pl.col('swing').sum()).alias('Whiff%'),
          (pl.col('whiff').sum() / pl.col('pitch').sum()).alias('SwStr%'),
          (pl.col('csw').sum() / pl.col('pitch').sum()).alias('CSW%'),
          (pl.col('zone').sum() / pl.col('pitch').sum()).alias('Zone%'),
          (pl.when(pl.col('pitLR') == 'r').then(pl.col('x') < 0).otherwise(pl.col('x') > 0)).mean().alias('Glove%'),
          (pl.when(pl.col('pitLR') == 'r').then(pl.col('x') >= 0).otherwise(pl.col('x') <= 0)).mean().alias('Arm%'),
          (pl.col('y') > 125).mean().alias('High%'),
          (pl.col('y') <= 125).mean().alias('Low%'),
          (pl.col('x').is_between(-20, 20) & pl.col('y').is_between(100, 100+50)).mean().alias('MM%')
      )
      .with_columns(
          (pl.col('count')/pl.sum('count').over('pitId')).alias('usage'),
          (pl.col('count') >= min_pitches).alias('qualified'),
      )
      .explode('batType')
      .unnest('batType')
      .pivot(on='batType', values='proportion')
      .fill_null(0)
      .with_columns(
          (pl.col('G') + pl.col('B')).alias('GB%'),
          (pl.col('F') + pl.col('P')).alias('FB%'),
          pl.col('L').alias('LD%').round(2),
      )
      .drop('G', 'F', 'B', 'P', 'L', 'null')
      .with_columns(
          (pl.when(pl.col('qualified')).then(pl.col(stat)).rank(descending=((stat in ['FB%', 'LD%'] or 'Contact%' in stat)))/pl.when(pl.col('qualified')).then(pl.col(stat)).count()).alias(f'{stat}_pctl')
          for stat in ['Avg KPH', 'Max KPH', 'Avg MPH', 'Max MPH', 'Swing%', 'Z-Swing%', 'Chase%', 'Contact%', 'Z-Contact%', 'O-Contact%', 'SwStr%', 'Whiff%', 'CSW%', 'GB%', 'FB%', 'LD%', 'Zone%']
      )
      .rename({pitch_col: 'ballKind_code', pitch_name_col: 'ballKind'} if pitch_class_type == 'general' else {})
      .sort(id_cols[0], 'count', descending=[False, True])
  )
  return pitch_stats

def compute_player_stats(data, player_type, min_ip='qualified', pitcher_lr='both', batter_lr='both', group_by_team=False):
  assert pitcher_lr in ('both', 'l', 'r')
  assert batter_lr in ('both', 'l', 'r')
  
  data = (
      compute_team_games(data)
      .with_columns(
          pl.when(pl.col('half_inning').str.ends_with('1')).then('home_games').otherwise('visitor_games').first().over('pitId').alias('games'),
          pl.col('inning_code').unique().len().over('pitId').alias('IP')
          # pl.col('presult').is_in(verify_and_return_presult([
            # 'Groundout', 'Flyout', 'Lineout', 'Groundout (Double play)',
            # 'Foul fly', 'Foul line (?)',
            # 'Sacrifice bunt', 'Sacrifice fly',
            # "Fielder's choice", "Sacrifice fielder's choice",
            # 'Bunt strikeout', 'Swinging strikeout', 'Looking strikeout'
          # ])).sum().over('pitId').mul(1/3).alias('IP')
      )
  )

  if min_ip == 'qualified':
    data = data.with_columns((pl.col('IP') >= pl.col('games')).alias('qualified'))
  else:
    data = data.with_columns((pl.col('IP') >= min_ip).alias('qualified'))

  if pitcher_lr != 'both':
    data = data.filter(pl.col('pitLR') == pitcher_lr)

  if batter_lr != 'both':
    data = data.filter(pl.col('batLR') == batter_lr)

  assert player_type in ('pitcher', 'batter') 
  id_cols = ['pitId' if player_type == 'pitcher' else 'batId']
  if group_by_team:
    id_cols.append('pitcher_team_name_short')
  name_col = 'pitcher_name' if player_type == 'pitcher' else 'batter_name'
  player_stats = (
    data
    .with_columns(pl.when(pl.col('general_ballKind_code').is_in(['4S', 'FC', 'SI'])).then(pl.when(valid_pitch).then('ballSpeed').mean().over('pitId', 'general_ballKind_code')).mul(1/1.609).round(1).alias('FB Velo'))
    .group_by(id_cols)
    .agg(
        pl.col(name_col).first(),
        *([] if group_by_team else [pl.col('pitcher_team_name_short').last()]),
        pl.col('pitLR').first().str.to_uppercase().alias('Throws'),
        pl.col('IP').first(),
        pl.col('pa_code').unique().len().alias('PA'),
        pl.col('FB Velo').max(),
        (pl.when(pl.col('presult').str.contains('strikeout')).then(1).otherwise(0).sum() / pl.col('pa_code').unique().len()).alias('K%'),
        (pl.when(pl.col('presult') == 'Walk').then(1).otherwise(0).sum() / pl.col('pa_code').unique().len()).alias('BB%'),
        pl.col('aux_bresult').struct.field('batType').drop_nulls().value_counts(normalize=True),
        (pl.col('swing').sum() / pl.col('pitch').sum()).alias('Swing%'),
        ((pl.col('swing') & pl.col('zone')).sum() / pl.col('pitch').sum()).alias('Z-Swing%'),
        ((pl.col('swing') & ~pl.col('zone')).sum() / pl.col('pitch').sum()).alias('Chase%'),
        ((pl.col('swing') & ~pl.col('whiff')).sum()/pl.col('swing').sum()).alias('Contact%'),
        ((pl.col('zone') & pl.col('swing') & ~pl.col('whiff')).sum()/(pl.col('zone') & pl.col('swing')).sum()).alias('Z-Contact%'),
        ((~pl.col('zone') & pl.col('swing') & ~pl.col('whiff')).sum()/(~pl.col('zone') & pl.col('swing')).sum()).alias('O-Contact%'),
        (pl.col('whiff').sum() / pl.col('swing').sum()).alias('Whiff%'),
        (pl.col('whiff').sum() / pl.col('pitch').sum()).alias('SwStr%'),
        (pl.col('csw').sum() / pl.col('pitch').sum()).alias('CSW%'),
        (pl.col('zone').sum() / pl.col('pitch').sum()).alias('Zone%'),
        (pl.when(pl.col('pitLR') == 'r').then(pl.col('x') < 0).otherwise(pl.col('x') > 0)).mean().alias('Glove%'),
        (pl.when(pl.col('pitLR') == 'r').then(pl.col('x') >= 0).otherwise(pl.col('x') <= 0)).mean().alias('Arm%'),
        (pl.col('y') > 125).mean().alias('High%'),
        (pl.col('y') <= 125).mean().alias('Low%'),
        (pl.col('x').is_between(-20, 20) & pl.col('y').is_between(100, 100+50)).mean().alias('MM%'),
        pl.first('qualified')
    )
    .explode('batType')
    .unnest('batType')
    .pivot(on='batType', values='proportion')
    .fill_null(0)
    .with_columns(
        (pl.col('G') + pl.col('B')).alias('GB%'),
        (pl.col('F') + pl.col('P')).alias('FB%'),
        pl.col('L').alias('LD%'),
    )
    .drop('G', 'F', 'B', 'P', 'L')
    .with_columns(
        (pl.when(pl.col('qualified')).then(pl.col(stat)).rank(descending=stat in ['BB%', 'FB%', 'LD%'] or 'Contact%' in stat)/pl.when(pl.col('qualified')).then(pl.col(stat)).count()).alias(f'{stat}_pctl')
        for stat in ['FB Velo', 'K%', 'BB%', 'Swing%', 'Z-Swing%', 'Chase%', 'Contact%', 'Z-Contact%', 'O-Contact%', 'SwStr%', 'Whiff%', 'CSW%', 'GB%', 'FB%', 'LD%', 'Zone%']
    )
    .sort('IP', descending=True)
  )
  return player_stats


def get_pitcher_stats(id, lr='both', game_kind=None, start_date=None, end_date=None, min_ip=1, min_pitches=1, pitch_class_type='specific'):
  
  source_data = data_df
  source_data = filter_data_by_date_and_game_kind(source_data, start_date=start_date, end_date=end_date, game_kind=game_kind)

  # if lr is not None:
    # source_data =

  pitch_stats = compute_pitch_stats(source_data, player_type='pitcher', pitch_class_type=pitch_class_type, min_pitches=min_pitches, batter_lr=lr, group_by_team=False).filter(pl.col('pitId') == id)

  pitch_shapes = (
      (source_data.filter(pl.col('batLR') == lr) if lr != 'both' else source_data)
      .filter(
          (pl.col('pitId') == id) &
          pl.col('x').is_not_null() &
          pl.col('y').is_not_null() &
          (pl.col('ballSpeed') > 0)
      )
      [['pitId', 'general_ballKind_code', 'ballKind_code', 'ballSpeed', 'x', 'y']]
      .with_columns((pl.col('ballSpeed')/1.609).alias('ballSpeed_mph'))
  )

  pitcher_stats = compute_player_stats(source_data, player_type='pitcher', min_ip=min_ip, batter_lr=lr, group_by_team=False).filter(pl.col('pitId') == id)
  return SimpleNamespace(pitcher_stats=pitcher_stats, pitch_stats=pitch_stats, pitch_shapes=pitch_shapes)