Scouting complete :)

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']

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