Refactor Player Data Initialization with Consistent Projection Scaling

Update the player data initialization process to:
- Remove redundant duplicate row selection
- Align kill, death, and assist projection scaling methods
- Use consistent scaling approach across win and loss scenarios
- Simplify projection calculation logic

app.py

@@ -336,8 +336,7 @@ def init_team_data(game_count, team, opponent, win_loss_settings, kill_predictio
                     raw_deaths = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1)
                     raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1)
                     kill_scale = kill_predictions[game] / raw_kills.sum()
-                    death_scale = min(1.0, death_predictions[game] / raw_deaths.sum()) if raw_deaths.sum() > 0 else 1.0
-                    assist_scale = min(1.0, kill_predictions[game] / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0
+                    death_scale = death_predictions[game] / raw_deaths.sum()
                     team_data['Kill_Proj'] = raw_kills * kill_scale
                     team_data['Death_Proj'] = raw_deaths * death_scale
                     team_data['Assist_Proj'] = raw_assists * kill_scale
@@ -348,9 +347,8 @@ def init_team_data(game_count, team, opponent, win_loss_settings, kill_predictio
                     raw_kills = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
                     raw_deaths = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1)
                     raw_assists = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1)
-                    kill_scale = min(1.0, kill_predictions[game] / raw_kills.sum()) if raw_kills.sum() > 0 else 1.0
-                    death_scale = min(1.0, death_predictions[game] / raw_deaths.sum()) if raw_deaths.sum() > 0 else 1.0
-                    assist_scale = min(1.0, kill_predictions[game] / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0
+                    kill_scale = kill_predictions[game] / raw_kills.sum()
+                    death_scale = death_predictions[game] / raw_deaths.sum()
                     team_data['Kill_Proj'] = raw_kills * kill_scale
                     team_data['Death_Proj'] = raw_deaths * death_scale
                     team_data['Assist_Proj'] = raw_assists * kill_scale
@@ -370,16 +368,14 @@ def init_team_data(game_count, team, opponent, win_loss_settings, kill_predictio
                     team_data['Kill_Proj'] = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1)
                     team_data['Death_Proj'] = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1)
                     team_data['Assist_Base'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1)
-                    assist_scale = min(1.0, team_data['Kill_Proj'].sum() / team_data['Assist_Base'].sum()) if team_data['Assist_Base'].sum() > 0 else 1.0
-                    team_data['Assist_Proj'] = team_data['wAssist%'] * assist_scale
+                    team_data['Assist_Proj'] = team_data['Assist_Base']
                     team_data['CS_Proj'] = team_data.apply(lambda row: row['wCS'] * opp_pos_cs_boost_win.get(row['position'], 1), axis=1)
                     team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']]
                 else:
                     team_data['Kill_Proj'] = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
                     team_data['Death_Proj'] = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1)
                     team_data['Assist_Base'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
-                    assist_scale = min(1.0, team_data['Kill_Proj'].sum() / team_data['Assist_Base'].sum()) if team_data['Assist_Base'].sum() > 0 else 1.0
-                    team_data['Assist_Proj'] = team_data['wAssist%'] * assist_scale
+                    team_data['Assist_Proj'] = team_data['Assist_Base']
                     team_data['CS_Proj'] = team_data.apply(lambda row: row['lCS'] * opp_pos_cs_boost_loss.get(row['position'], 1), axis=1)
                     team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']]
             
@@ -551,31 +547,35 @@ def init_player_data(game_count, players, opponent, win_loss_settings, kill_pred
 
         for game in range(game_count):
             if kill_predictions[game] > 0:
-                working_tables = player_tables[['playername', 'teamname', 'position', 'playername_avg_kill_share_win', 'playername_avg_death_share_win','playername_avg_assist_share_win',
-                                        'playername_avg_total_cs_win', 'playername_avg_kill_share_loss', 'playername_avg_death_share_loss', 'playername_avg_assist_share_loss', 'playername_avg_total_cs_loss']]
-                working_tables = working_tables.rename(columns = {'playername_avg_kill_share_win': 'wKill%', 'playername_avg_death_share_win': 'wDeath%', 'playername_avg_assist_share_win': 'wAssist%',
-                                                            'playername_avg_total_cs_win': 'wCS', 'playername_avg_kill_share_loss': 'lKill%', 'playername_avg_death_share_loss': 'lDeath%',
-                                                            'playername_avg_assist_share_loss': 'lAssist%', 'playername_avg_total_cs_loss': 'lCS'})
+                working_tables = player_tables[['playername', 'teamname', 'position', 'playername_avg_kills_win', 'playername_avg_deaths_win', 'playername_avg_assists_win', 'playername_avg_total_cs_win',
+                                        'playername_avg_kills_loss', 'playername_avg_deaths_loss', 'playername_avg_assists_loss', 'playername_avg_total_cs_loss']]
+                working_tables = working_tables.rename(columns = {'playername_avg_kills_win': 'wKill%', 'playername_avg_deaths_win': 'wDeath%', 'playername_avg_assists_win': 'wAssist%',
+                                                            'playername_avg_total_cs_win': 'wCS', 'playername_avg_kills_loss': 'lKill%', 'playername_avg_deaths_loss': 'lDeath%',
+                                                            'playername_avg_assists_loss': 'lAssist%', 'playername_avg_total_cs_loss': 'lCS'})
                 team_data = working_tables.drop_duplicates(subset = ['playername'])
-                
+                team_data = working_tables.drop_duplicates(subset = ['position'])
 
                 if win_loss_settings[game] == "Win":
-                    team_data['Kill_Proj'] = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1) * kill_predictions[game]
-                    team_data['Death_Proj'] = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1) * death_predictions[game]
-                    # Calculate assists and scale them to not exceed total kills
-                    raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * kill_predictions[game]
-                    assist_scale = min(1.0, kill_predictions[game] / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0
-                    team_data['Assist_Proj'] = raw_assists * assist_scale
+                    raw_kills = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1)
+                    raw_deaths = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1)
+                    raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1)
+                    kill_scale = kill_predictions[game] / raw_kills.sum()
+                    death_scale = death_predictions[game] / raw_deaths.sum()
+                    team_data['Kill_Proj'] = raw_kills * kill_scale
+                    team_data['Death_Proj'] = raw_deaths * death_scale
+                    team_data['Assist_Proj'] = raw_assists * kill_scale
                     
                     team_data['CS_Proj'] = team_data.apply(lambda row: row['wCS'] * opp_pos_cs_boost_win.get(row['position'], 1), axis=1)
                     team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']]
                 else:
-                    team_data['Kill_Proj'] = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1) * kill_predictions[game]
-                    team_data['Death_Proj'] = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1) * death_predictions[game]
-                    # Calculate assists and scale them to not exceed total kills
-                    raw_assists = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1) * kill_predictions[game]
-                    assist_scale = min(1.0, kill_predictions[game] / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0
-                    team_data['Assist_Proj'] = raw_assists * assist_scale
+                    raw_kills = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
+                    raw_deaths = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1)
+                    raw_assists = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1)
+                    kill_scale = kill_predictions[game] / raw_kills.sum()
+                    death_scale = death_predictions[game] / raw_deaths.sum()
+                    team_data['Kill_Proj'] = raw_kills * kill_scale
+                    team_data['Death_Proj'] = raw_deaths * death_scale
+                    team_data['Assist_Proj'] = raw_assists * kill_scale
                     
                     team_data['CS_Proj'] = team_data.apply(lambda row: row['lCS'] * opp_pos_cs_boost_loss.get(row['position'], 1), axis=1)
                     team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']]
@@ -591,16 +591,14 @@ def init_player_data(game_count, players, opponent, win_loss_settings, kill_pred
                     team_data['Kill_Proj'] = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1)
                     team_data['Death_Proj'] = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1)
                     team_data['Assist_Base'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1)
-                    assist_scale = min(1.0, team_data['Kill_Proj'].sum() / team_data['Assist_Base'].sum()) if team_data['Assist_Base'].sum() > 0 else 1.0
-                    team_data['Assist_Proj'] = team_data['wAssist%']
+                    team_data['Assist_Proj'] = team_data['Assist_Base']
                     team_data['CS_Proj'] = team_data.apply(lambda row: row['wCS'] * opp_pos_cs_boost_win.get(row['position'], 1), axis=1)
                     team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']]
                 else:
                     team_data['Kill_Proj'] = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
                     team_data['Death_Proj'] = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1)
                     team_data['Assist_Base'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1)
-                    assist_scale = min(1.0, team_data['Kill_Proj'].sum() / team_data['Assist_Base'].sum()) if team_data['Assist_Base'].sum() > 0 else 1.0
-                    team_data['Assist_Proj'] = team_data['wAssist%']
+                    team_data['Assist_Proj'] = team_data['Assist_Base']
                     team_data['CS_Proj'] = team_data.apply(lambda row: row['lCS'] * opp_pos_cs_boost_loss.get(row['position'], 1), axis=1)
                     team_data = team_data[['playername', 'teamname', 'position', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj']]