89deba6d65
Changed from range(1, 28) to empty list [] to automatically include all cardsets without future maintenance. This ensures new cardsets (like cardset 29) are automatically included in scouting reports. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
261 lines
10 KiB
Python
261 lines
10 KiB
Python
import asyncio
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import copy
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import datetime
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from functools import partial
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import multiprocessing
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import sys
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from db_calls import db_get
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from exceptions import logger, log_exception
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from scouting_batters import log_time, fetch_data
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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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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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('pitchingcardratings', [('vs_hand', 'vL'), *ratings_params]),
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('pitchingcardratings', [('vs_hand', 'vR'), *ratings_params]),
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('cardpositions', [('position', 'P'), *cardset_params])
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]
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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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api_data = await asyncio.gather(*tasks)
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log_time('end', f'Pulled {api_data[0]['count'] + api_data[1]['count']} batting card ratings and {api_data[2]['count']} positions', start_time=start_time)
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start_time = log_time('start', message='Building base dataframes')
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vl_vals = api_data[0]['ratings']
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for x in vl_vals:
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x.update(x['pitchingcard'])
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x['player_id'] = x['pitchingcard']['player']['player_id']
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x['player_name'] = x['pitchingcard']['player']['p_name']
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x['rarity'] = x['pitchingcard']['player']['rarity']['name']
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x['cardset_id'] = x['pitchingcard']['player']['cardset']['id']
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x['cardset_name'] = x['pitchingcard']['player']['cardset']['name']
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x['starter_rating'] = x['pitchingcard']['starter_rating']
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x['relief_rating'] = x['pitchingcard']['relief_rating']
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x['closer_rating'] = x['pitchingcard']['closer_rating']
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del x['pitchingcard'], x['player']
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vr_vals = api_data[1]['ratings']
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for x in vr_vals:
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x['player_id'] = x['pitchingcard']['player']['player_id']
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del x['pitchingcard']
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vl = pd.DataFrame(vl_vals)
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vr = pd.DataFrame(vr_vals)
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pit_df = pd.merge(vl, vr, on='player_id', suffixes=('_vl', '_vr')).set_index('player_id', drop=False)
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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 = api_data[2]['positions']
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series_list = [pd.Series(
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dict([(x['player']['player_id'], x['range']) for x in positions]),
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name=f'Range P'
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), pd.Series(
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dict([(x['player']['player_id'], x['error']) for x in positions]),
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name=f'Error P'
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)]
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log_time('end', f'Processed {len(positions)} defense series', start_time=start_time)
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logger.info(f'series_list: {series_list}')
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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.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 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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pitching_dfs = await get_scouting_dfs([])
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print(f'Received {pitching_dfs} rows')
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log_time('end', 'Pulled scouting data', start_time=start_time)
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start_time = log_time('start', 'Beginning basic scouting')
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await post_calc_basic(copy.deepcopy(pitching_dfs))
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log_time('end', 'Completed pitching scouting', start_time=start_time)
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start_time = log_time('start', 'Beginning ratings guide')
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await post_calc_ratings(copy.deepcopy(pitching_dfs))
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log_time('end', 'Completed ratings guide', start_time=start_time)
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log_time('end', 'Total pitcher scouting', print_to_console=False, start_time=overall_start_time)
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print('All done with pitchers!')
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if __name__ == '__main__':
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asyncio.run(main())
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