diff --git a/scouting_batters.py b/scouting_batters.py index e0851c6..6c68d8e 100644 --- a/scouting_batters.py +++ b/scouting_batters.py @@ -173,7 +173,6 @@ async def post_calc_basic(batting_dfs: pd.DataFrame): return speed_raw start_time = log_time('start', 'Beginning Speed calcs') - overall_start_time = start_time raw_series = batting_dfs.apply(get_raw_speed, axis=1) rank_series = raw_series.rank(pct=True) @@ -385,7 +384,6 @@ async def post_calc_basic(batting_dfs: pd.DataFrame): async def post_calc_ratings(batting_dfs: pd.DataFrame): start_time = log_time('start', 'Beginning Ratings filtering') - overall_start_time = start_time output = batting_dfs first = ['player_id', 'player_name', 'cardset_name', 'rarity', 'hand', 'variant'] diff --git a/scouting_pitchers.py b/scouting_pitchers.py index a610db4..971ff94 100644 --- a/scouting_pitchers.py +++ b/scouting_pitchers.py @@ -12,12 +12,6 @@ from typing import Literal import pandas as pd async def get_scouting_dfs(cardset_id: list = None): - # all_ratings = PitchingCardRatings.select() - # if cardset_id is not None: - # set_players = Player.select(Player.player_id).where(Player.cardset_id << cardset_id) - # set_cards = PitchingCard.select(PitchingCard.id).where(PitchingCard.player << set_players) - # all_ratings = all_ratings.where(PitchingCardRatings.pitchingcard << set_cards) - cardset_params = [('cardset_id', x) for x in cardset_id] ratings_params = [('team_id', 31), ('ts', 's37136685556r6135248705'), *cardset_params] API_CALLS = [ @@ -26,9 +20,6 @@ async def get_scouting_dfs(cardset_id: list = None): ('cardpositions', [('position', 'P'), *cardset_params]) ] - # vl_query = all_ratings.where(PitchingCardRatings.vs_hand == 'L') - # vr_query = all_ratings.where(PitchingCardRatings.vs_hand == 'R') - start_time = log_time('start', message='Pulling all pitching card ratings and positions') tasks = [fetch_data(params) for params in API_CALLS] @@ -63,11 +54,6 @@ async def get_scouting_dfs(cardset_id: list = None): log_time('end', f'Base dataframes are complete', start_time=start_time) start_time = log_time('start', message='Building defense series') - # positions = CardPosition.select().where(CardPosition.position == 'P') - # if cardset_id is not None: - # set_players = Player.select(Player.player_id).where(Player.cardset_id << cardset_id) - # positions = positions.where(CardPosition.player << set_players) - positions = api_data[2]['positions'] series_list = [pd.Series( @@ -82,6 +68,172 @@ async def get_scouting_dfs(cardset_id: list = None): return pit_df.join(series_list) +async def post_calc_basic(pitching_dfs: pd.DataFrame): + raw_data = pitching_dfs + + def get_raw_leftcontrol(df_data): + return ((1 - (df_data['obp_vl'] - df_data['avg_vl'])) * 100) + (1 - (df_data['wild_pitch'] / 20)) + + start_time = log_time('start', 'Beginning Control L calcs') + + raw_series = raw_data.apply(get_raw_leftcontrol, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['Control L'] = round(rank_series * 100) + + log_time('end', 'Done Control L calcs', start_time=start_time) + start_time = log_time('start', 'Beginning Control R calcs') + + def get_raw_rightcontrol(df_data): + return ((1 - (df_data['obp_vr'] - df_data['avg_vr'])) * 100) + (1 - (df_data['wild_pitch'] / 20)) + + raw_series = raw_data.apply(get_raw_rightcontrol, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['Control R'] = round(rank_series * 100) + + log_time('end', 'Done Control R calcs', start_time=start_time) + start_time = log_time('start', 'Beginning Stuff L calcs') + + def get_raw_leftstuff(df_data): + return 10 - (df_data['slg_vl'] + df_data['slg_vl'] + ((df_data['homerun_vl'] + df_data['bp_homerun_vl']) / 108)) + + raw_series = raw_data.apply(get_raw_leftstuff, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['Stuff L'] = round(rank_series * 100) + + log_time('end', 'Done Stuff L calcs', start_time=start_time) + start_time = log_time('start', 'Beginning Stuff R calcs') + + def get_raw_rightstuff(df_data): + return 10 - (df_data['slg_vr'] + df_data['slg_vr'] + ((df_data['homerun_vr'] + df_data['bp_homerun_vr']) / 108)) + + raw_series = raw_data.apply(get_raw_rightstuff, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['Stuff R'] = round(rank_series * 100) + + log_time('end', 'Done Stuff R calcs', start_time=start_time) + start_time = log_time('start', 'Beginning Fielding calcs') + + def get_raw_fielding(df_data): + return ((6 - df_data['Range P']) * 10) + (50 - df_data['Error P']) + + raw_series = raw_data.apply(get_raw_fielding, axis=1) + rank_series = raw_series.rank(pct=True) + logger.info(f'max fld: {raw_series.max()} / min fld: {raw_series.min()}') + raw_data['Fielding'] = round(rank_series * 100) + + log_time('end', 'Done Fielding calcs', start_time=start_time) + start_time = log_time('start', 'Beginning Stamina calcs') + + def get_raw_stamina(df_data): + spow = df_data['starter_rating'] if pd.isna(df_data['starter_rating']) else -1 + rpow = df_data['relief_rating'] if pd.isna(df_data['relief_rating']) else -1 + this_pow = spow if spow > rpow else rpow + + return (((this_pow * (df_data['obp_vr'] * (2 / 3))) + (this_pow * (df_data['obp_vl'] / 3))) * 4.5) + this_pow + + raw_series = raw_data.apply(get_raw_stamina, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['Stamina'] = round(rank_series * 100) + + log_time('end', 'Done Stamina calcs', start_time=start_time) + start_time = log_time('start', 'Beginning H/9 calcs') + + def get_raw_hit(df_data): + return 1 - (df_data['avg_vr'] * (2 / 3)) + (df_data['avg_vl'] / 3) + + raw_series = raw_data.apply(get_raw_hit, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['H/9'] = round(rank_series * 100) + + log_time('end', 'Done H/9 calcs', start_time=start_time) + start_time = log_time('start', 'Beginning H/9 calcs') + + def get_raw_k(df_data): + return ((df_data['strikeout_vr'] / 108) * (2 / 3)) + ((df_data['strikeout_vl'] / 108) / 3) + + raw_series = raw_data.apply(get_raw_k, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['K/9'] = round(rank_series * 100) + + log_time('end', 'Done H/9 calcs', start_time=start_time) + start_time = log_time('start', 'Beginning BB/9 calcs') + + def get_raw_bb(df_data): + return ((df_data['walk_vr'] / 108) * (2 / 3)) + ((df_data['walk_vl'] / 108) / 3) + + raw_series = raw_data.apply(get_raw_bb, axis=1) + rank_series = raw_series.rank(pct=True, ascending=False) + raw_data['BB/9'] = round(rank_series * 100) + + log_time('end', 'Done BB/9 calcs', start_time=start_time) + start_time = log_time('start', 'Beginning BB/9 calcs') + + def get_raw_hr(df_data): + return 1 - ( + (((df_data['homerun_vr'] + df_data['bp_homerun_vr']) / 108) * (2 / 3)) + + (((df_data['homerun_vl'] + df_data['bp_homerun_vl']) / 108) / 3)) + + raw_series = raw_data.apply(get_raw_hr, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['HR/9'] = round(rank_series * 100) + + log_time('end', 'Done HR/9 calcs', start_time=start_time) + start_time = log_time('start', 'Beginning Rating calcs') + + def get_raw_rating(df_data): + spow = df_data['starter_rating'] if pd.isna(df_data['starter_rating']) else -1 + rpow = df_data['relief_rating'] if pd.isna(df_data['relief_rating']) else -1 + + if spow > rpow and spow >= 4: + return ( + ((df_data['H/9'] + df_data['K/9'] + df_data['BB/9'] + df_data['HR/9']) * 5) + + (df_data['Fielding']) + (df_data['Stamina'] * 5) + + (((df_data['Stuff L'] / 3) + (df_data['Stuff R'] * (2 / 3))) * 4) + + (((df_data['Control L'] / 3) + (df_data['Control R'] * (2 / 3))) * 2) + ) + else: + return ( + ((df_data['H/9'] + df_data['K/9'] + df_data['BB/9'] + df_data['HR/9']) * 5) + + (df_data['Fielding']) + (df_data['Stamina'] * 5) + + (((df_data['Stuff L'] / 3) + (df_data['Stuff R'] * (2 / 3))) * 4) + + (((df_data['Control L'] / 3) + (df_data['Control R'] * (2 / 3))) * 2) + ) + + raw_series = raw_data.apply(get_raw_rating, axis=1) + rank_series = raw_series.rank(pct=True) + raw_data['Rating'] = round(rank_series * 100) + + output = raw_data[[ + 'player_id', 'player_name', 'Rating', 'Control R', 'Control L', 'Stuff R', 'Stuff L', 'Stamina', 'Fielding', + 'H/9', 'K/9', 'BB/9', 'HR/9', 'hand', 'cardset_name' + ]] + + log_time('end', 'Done Rating calcs', start_time=start_time) + start_time = log_time('start', 'Beginning write csv') + + csv_file = pd.DataFrame(output).to_csv(index=False) + with open('scouting/pitching-basic.csv', 'w') as file: + file.write(csv_file) + + log_time('end', 'Done writing to file', start_time=start_time) + +async def post_calc_ratings(pitching_dfs: pd.DataFrame): + start_time = log_time('start', 'Beginning Ratings filtering') + + output = pitching_dfs + first = ['player_id', 'player_name', 'cardset_name', 'rarity', 'hand', 'variant'] + exclude = first + ['id_vl', 'id_vr', 'vs_hand_vl', 'vs_hand_vr'] + output = output[first + [col for col in output.columns if col not in exclude]] + + log_time('end', 'Done filtering ratings', start_time=start_time) + start_time = log_time('start', 'Beginning write to file') + + csv_file = pd.DataFrame(output).to_csv(index=False) + with open('scouting/pitching-ratings', 'w') as file: + file.write(csv_file) + + log_time('end', 'Done writing to file', start_time=start_time) + async def main(): start_time = log_time('start', 'Pulling scouting data') overall_start_time = start_time