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