Refactor init_team_data function in app.py to streamline statistical calculations and enhance clarity. Removed redundant statistics calculations and reorganized the logic for player and team performance metrics, focusing on win/loss scenarios. This update improves the readability and efficiency of the code while maintaining the accuracy of performance evaluations based on opponent statistics.

app.py

@@ -121,12 +121,8 @@ def init_team_data(team, opponent, win_loss, kill_prediction, death_prediction,
         for loop in range(len(tables_to_loop)):
             tables = tables_to_loop[loop]
             calc_columns = ['kills', 'deaths', 'assists', 'total_cs']
-            league_win_stats = {}
-            league_loss_stats = {}
             league_pos_win_stats = {}   
             league_pos_loss_stats = {}
-            Opponent_win_allowed_stats = {}
-            Opponent_loss_allowed_stats = {}
             Opponent_pos_win_allowed_stats = {}
             Opponent_pos_loss_allowed_stats = {}
             playername_win_stats = {}
@@ -134,150 +130,118 @@ def init_team_data(team, opponent, win_loss, kill_prediction, death_prediction,
             teamname_win_stats = {}
             teamname_loss_stats = {}
 
-            for stats in calc_columns:
-                league_win_stats[stats] = tables[(tables['result'] == 1) & (tables['position'] != 'team')].groupby('league')[stats].mean().to_dict()
-                league_loss_stats[stats] = tables[(tables['result'] == 0) & (tables['position'] != 'team')].groupby('league')[stats].mean().to_dict()
-                Opponent_win_allowed_stats[stats] = tables[(tables['result'] == 1) & (tables['position'] != 'team')].groupby('Opponent')[stats].mean().to_dict()
-                Opponent_loss_allowed_stats[stats] = tables[(tables['result'] == 0) & (tables['position'] != 'team')].groupby('Opponent')[stats].mean().to_dict()
-
-            for stats in calc_columns:
-                league_pos_win_stats[stats] = {
-                    league: group.groupby('position')[stats].mean().to_dict()
-                    for league, group in tables[tables['result'] == 1].groupby('league')
-                }
-                league_pos_loss_stats[stats] = {
-                    league: group.groupby('position')[stats].mean().to_dict()
-                    for league, group in tables[tables['result'] == 0].groupby('league')
-                }
-                
-                Opponent_pos_win_allowed_stats[stats] = {
-                    opponent: group.groupby('position')[stats].mean().to_dict()
-                    for opponent, group in tables[tables['result'] == 1].groupby('Opponent')
-                }
-                Opponent_pos_loss_allowed_stats[stats] = {
-                    opponent: group.groupby('position')[stats].mean().to_dict()
-                    for opponent, group in tables[tables['result'] == 0].groupby('Opponent')
-                }
-
-            for stats in calc_columns:
-                playername_win_stats[stats] = tables[tables['result'] == 1].groupby(['playername'])[stats].mean().to_dict()
-                playername_loss_stats[stats] = tables[tables['result'] == 0].groupby(['playername'])[stats].mean().to_dict()
-                teamname_win_stats[stats] = tables[(tables['result'] == 1) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict()
-                teamname_loss_stats[stats] = tables[(tables['result'] == 0) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() 
-
-            for stat in calc_columns:
-
-                column_name = f'league_avg_{stat}_win'
-                tables[column_name] = tables.apply(
-                    lambda row: league_win_stats[stat].get(row['league'], 0), 
-                    axis=1
-                )
-
-                column_name = f'league_avg_{stat}_loss'
-                tables[column_name] = tables.apply(
-                    lambda row: league_loss_stats[stat].get(row['league'], 0), 
-                    axis=1
-                )
-
-                column_name = f'Opponent_avg_{stat}_allowed_win'
-                tables[column_name] = tables.apply(
-                    lambda row: Opponent_win_allowed_stats[stat].get(row['Opponent'], 0), 
-                    axis=1
-                )
-
-                column_name = f'Opponent_avg_{stat}_allowed_loss'
-                tables[column_name] = tables.apply(
-                    lambda row: Opponent_loss_allowed_stats[stat].get(row['Opponent'], 0), 
-                    axis=1
-                )
-
-                column_name = f'league_pos_avg_{stat}_win'
-                tables[column_name] = tables.apply(
-                    lambda row: league_pos_win_stats[stat].get(row['league'], {}).get(row['position'], 0), 
-                    axis=1
-                )
-                
-                column_name = f'league_pos_avg_{stat}_loss'
-                tables[column_name] = tables.apply(
-                    lambda row: league_pos_loss_stats[stat].get(row['league'], {}).get(row['position'], 0), 
-                    axis=1
-                )
-
-                column_name = f'Opponent_pos_avg_{stat}_allowed_win'
-                tables[column_name] = tables.apply(
-                    lambda row: Opponent_pos_win_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), 
-                    axis=1
-                )
-
-                column_name = f'Opponent_pos_avg_{stat}_allowed_loss'
-                tables[column_name] = tables.apply(
-                    lambda row: Opponent_pos_loss_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), 
-                    axis=1
-                )
-
-                column_name = f'playername_avg_{stat}_win'
-                tables[column_name] = tables.apply(
-                    lambda row: playername_win_stats[stat].get(row['playername'], 0), 
-                    axis=1
-                )
-
-                column_name = f'playername_avg_{stat}_loss'
-                tables[column_name] = tables.apply(
-                    lambda row: playername_loss_stats[stat].get(row['playername'], 0), 
-                    axis=1
-                )
-
-                column_name = f'teamname_avg_{stat}_win'
-                tables[column_name] = tables.apply(
-                    lambda row: teamname_win_stats[stat].get(row['teamname'], 0), 
-                    axis=1
-                )
-
-                column_name = f'teamname_avg_{stat}_loss'
-                tables[column_name] = tables.apply(
-                    lambda row: teamname_loss_stats[stat].get(row['teamname'], 0), 
-                    axis=1
-                )
-
-            tables['overall_win_kills_boost'] = tables['Opponent_avg_kills_allowed_win'] / tables['league_avg_kills_win']
-            tables['overall_win_deaths_boost'] = tables['Opponent_avg_deaths_allowed_win'] / tables['league_avg_deaths_win']
-            tables['overall_win_assists_boost'] = tables['Opponent_avg_assists_allowed_win'] / tables['league_avg_assists_win']
-            tables['overall_win_total_cs_boost'] = tables['Opponent_avg_total_cs_allowed_win'] / tables['league_avg_total_cs_win']
-            tables['overall_loss_kills_boost'] = tables['Opponent_avg_kills_allowed_loss'] / tables['league_avg_kills_loss']
-            tables['overall_loss_deaths_boost'] = tables['Opponent_avg_deaths_allowed_loss'] / tables['league_avg_deaths_loss']
-            tables['overall_loss_assists_boost'] = tables['Opponent_avg_assists_allowed_loss'] / tables['league_avg_assists_loss']
-            tables['overall_loss_total_cs_boost'] = tables['Opponent_avg_total_cs_allowed_loss'] / tables['league_avg_total_cs_loss']
-
-            tables['overall_win_kills_boost_pos'] = tables['Opponent_pos_avg_kills_allowed_win'] / tables['league_pos_avg_kills_win']
-            tables['overall_win_deaths_boost_pos'] = tables['Opponent_pos_avg_deaths_allowed_win'] / tables['league_pos_avg_deaths_win']
-            tables['overall_win_assists_boost_pos'] = tables['Opponent_pos_avg_assists_allowed_win'] / tables['league_pos_avg_assists_win']
-            tables['overall_win_total_cs_boost_pos'] = tables['Opponent_pos_avg_total_cs_allowed_win'] / tables['league_pos_avg_total_cs_win']
-            tables['overall_loss_kills_boost_pos'] = tables['Opponent_pos_avg_kills_allowed_loss'] / tables['league_pos_avg_kills_loss']
-            tables['overall_loss_deaths_boost_pos'] = tables['Opponent_pos_avg_deaths_allowed_loss'] / tables['league_pos_avg_deaths_loss']
-            tables['overall_loss_assists_boost_pos'] = tables['Opponent_pos_avg_assists_allowed_loss'] / tables['league_pos_avg_assists_loss']
-            tables['overall_loss_total_cs_boost_pos'] = tables['Opponent_pos_avg_total_cs_allowed_loss'] / tables['league_pos_avg_total_cs_loss']
-
-            tables['playername_avg_kill_share_win'] = tables['playername_avg_kills_win'] / tables['teamname_avg_kills_win']
-            tables['playername_avg_death_share_win'] = tables['playername_avg_deaths_win'] / tables['teamname_avg_deaths_win']
-            tables['playername_avg_assist_share_win'] = tables['playername_avg_assists_win'] / tables['teamname_avg_kills_win']
-            tables['playername_avg_cs_share_win'] = tables['playername_avg_total_cs_win'] / tables['teamname_avg_total_cs_win']
-            tables['playername_avg_kill_share_loss'] = tables['playername_avg_kills_loss'] / tables['teamname_avg_kills_loss']
-            tables['playername_avg_death_share_loss'] = tables['playername_avg_deaths_loss'] / tables['teamname_avg_deaths_loss']
-            tables['playername_avg_assist_share_loss'] = tables['playername_avg_assists_loss'] / tables['teamname_avg_kills_loss']
-            tables['playername_avg_cs_share_loss'] = tables['playername_avg_total_cs_loss'] / tables['teamname_avg_total_cs_loss']
-
             if loop == 0:
+
+                for stats in calc_columns:
+                    playername_win_stats[stats] = tables[tables['result'] == 1].groupby(['playername'])[stats].mean().to_dict()
+                    playername_loss_stats[stats] = tables[tables['result'] == 0].groupby(['playername'])[stats].mean().to_dict()
+                    teamname_win_stats[stats] = tables[(tables['result'] == 1) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict()
+                    teamname_loss_stats[stats] = tables[(tables['result'] == 0) & (tables['position'] == 'team')].groupby(['teamname'])[stats].mean().to_dict() 
+
+                for stat in calc_columns:
+
+                    column_name = f'playername_avg_{stat}_win'
+                    tables[column_name] = tables.apply(
+                        lambda row: playername_win_stats[stat].get(row['playername'], 0), 
+                        axis=1
+                    )
+
+                    column_name = f'playername_avg_{stat}_loss'
+                    tables[column_name] = tables.apply(
+                        lambda row: playername_loss_stats[stat].get(row['playername'], 0), 
+                        axis=1
+                    )
+
+                    column_name = f'teamname_avg_{stat}_win'
+                    tables[column_name] = tables.apply(
+                        lambda row: teamname_win_stats[stat].get(row['teamname'], 0), 
+                        axis=1
+                    )
+
+                    column_name = f'teamname_avg_{stat}_loss'
+                    tables[column_name] = tables.apply(
+                        lambda row: teamname_loss_stats[stat].get(row['teamname'], 0), 
+                        axis=1
+                    )
+            
+                tables['playername_avg_kill_share_win'] = tables['playername_avg_kills_win'] / tables['teamname_avg_kills_win']
+                tables['playername_avg_death_share_win'] = tables['playername_avg_deaths_win'] / tables['teamname_avg_deaths_win']
+                tables['playername_avg_assist_share_win'] = tables['playername_avg_assists_win'] / tables['teamname_avg_kills_win']
+                tables['playername_avg_cs_share_win'] = tables['playername_avg_total_cs_win'] / tables['teamname_avg_total_cs_win']
+                tables['playername_avg_kill_share_loss'] = tables['playername_avg_kills_loss'] / tables['teamname_avg_kills_loss']
+                tables['playername_avg_death_share_loss'] = tables['playername_avg_deaths_loss'] / tables['teamname_avg_deaths_loss']
+                tables['playername_avg_assist_share_loss'] = tables['playername_avg_assists_loss'] / tables['teamname_avg_kills_loss']
+                tables['playername_avg_cs_share_loss'] = tables['playername_avg_total_cs_loss'] / tables['teamname_avg_total_cs_loss']
                 player_tables = tables
+
             else:
+
+                for stats in calc_columns:
+                    league_pos_win_stats[stats] = {
+                        league: group.groupby('position')[stats].mean().to_dict()
+                        for league, group in tables[tables['result'] == 1].groupby('league')
+                    }
+                    league_pos_loss_stats[stats] = {
+                        league: group.groupby('position')[stats].mean().to_dict()
+                        for league, group in tables[tables['result'] == 0].groupby('league')
+                    }
+                    
+                    Opponent_pos_win_allowed_stats[stats] = {
+                        opponent: group.groupby('position')[stats].mean().to_dict()
+                        for opponent, group in tables[tables['result'] == 1].groupby('Opponent')
+                    }
+                    Opponent_pos_loss_allowed_stats[stats] = {
+                        opponent: group.groupby('position')[stats].mean().to_dict()
+                        for opponent, group in tables[tables['result'] == 0].groupby('Opponent')
+                    }
+
+                for stat in calc_columns:
+
+                    column_name = f'league_pos_avg_{stat}_win'
+                    tables[column_name] = tables.apply(
+                        lambda row: league_pos_win_stats[stat].get(row['league'], {}).get(row['position'], 0), 
+                        axis=1
+                    )
+                    
+                    column_name = f'league_pos_avg_{stat}_loss'
+                    tables[column_name] = tables.apply(
+                        lambda row: league_pos_loss_stats[stat].get(row['league'], {}).get(row['position'], 0), 
+                        axis=1
+                    )
+
+                    column_name = f'Opponent_pos_avg_{stat}_allowed_win'
+                    tables[column_name] = tables.apply(
+                        lambda row: Opponent_pos_win_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), 
+                        axis=1
+                    )
+
+                    column_name = f'Opponent_pos_avg_{stat}_allowed_loss'
+                    tables[column_name] = tables.apply(
+                        lambda row: Opponent_pos_loss_allowed_stats[stat].get(row['Opponent'], {}).get(row['position'], 0), 
+                        axis=1
+                    )
+                
+                tables = tables[tables['Opponent'] == opponent]
+
+                tables['overall_win_kills_boost_pos'] = tables['Opponent_pos_avg_kills_allowed_win'] / tables['league_pos_avg_kills_win']
+                tables['overall_win_deaths_boost_pos'] = tables['Opponent_pos_avg_deaths_allowed_win'] / tables['league_pos_avg_deaths_win']
+                tables['overall_win_assists_boost_pos'] = tables['Opponent_pos_avg_assists_allowed_win'] / tables['league_pos_avg_assists_win']
+                tables['overall_win_total_cs_boost_pos'] = tables['Opponent_pos_avg_total_cs_allowed_win'] / tables['league_pos_avg_total_cs_win']
+                tables['overall_loss_kills_boost_pos'] = tables['Opponent_pos_avg_kills_allowed_loss'] / tables['league_pos_avg_kills_loss']
+                tables['overall_loss_deaths_boost_pos'] = tables['Opponent_pos_avg_deaths_allowed_loss'] / tables['league_pos_avg_deaths_loss']
+                tables['overall_loss_assists_boost_pos'] = tables['Opponent_pos_avg_assists_allowed_loss'] / tables['league_pos_avg_assists_loss']
+                tables['overall_loss_total_cs_boost_pos'] = tables['Opponent_pos_avg_total_cs_allowed_loss'] / tables['league_pos_avg_total_cs_loss']
+                
                 opp_tables = tables
-                opp_pos_kills_boost_win = dict(zip(tables['position'], tables['overall_win_kills_boost_pos']))
-                opp_pos_deaths_boost_win = dict(zip(tables['position'], tables['overall_win_deaths_boost_pos']))
-                opp_pos_assists_boost_win = dict(zip(tables['position'], tables['overall_win_assists_boost_pos']))
-                opp_pos_cs_boost_win = dict(zip(tables['position'], tables['overall_win_total_cs_boost_pos']))
-                opp_pos_kills_boost_loss = dict(zip(tables['position'], tables['overall_loss_kills_boost_pos']))
-                opp_pos_deaths_boost_loss = dict(zip(tables['position'], tables['overall_loss_deaths_boost_pos']))
-                opp_pos_assists_boost_loss = dict(zip(tables['position'], tables['overall_loss_assists_boost_pos']))
-                opp_pos_cs_boost_loss = dict(zip(tables['position'], tables['overall_loss_total_cs_boost_pos']))
+                opp_pos_kills_boost_win = dict(zip(opp_tables['position'], opp_tables['overall_win_kills_boost_pos']))
+                opp_pos_deaths_boost_win = dict(zip(opp_tables['position'], opp_tables['overall_win_deaths_boost_pos']))
+                opp_pos_assists_boost_win = dict(zip(opp_tables['position'], opp_tables['overall_win_assists_boost_pos']))
+                opp_pos_cs_boost_win = dict(zip(opp_tables['position'], opp_tables['overall_win_total_cs_boost_pos']))
+                opp_pos_kills_boost_loss = dict(zip(opp_tables['position'], opp_tables['overall_loss_kills_boost_pos']))
+                opp_pos_deaths_boost_loss = dict(zip(opp_tables['position'], opp_tables['overall_loss_deaths_boost_pos']))
+                opp_pos_assists_boost_loss = dict(zip(opp_tables['position'], opp_tables['overall_loss_assists_boost_pos']))
+                opp_pos_cs_boost_loss = dict(zip(opp_tables['position'], opp_tables['overall_loss_total_cs_boost_pos']))
+
 
         if kill_prediction > 0:
             player_tables = player_tables[['playername', 'teamname', 'position', 'playername_avg_kill_share_win', 'playername_avg_death_share_win','playername_avg_assist_share_win',