Refactor overall_team_data structure in app.py by removing the 'game' column and renaming it to 'playername' for improved clarity. This change streamlines the data representation in the init_team_data function, enhancing the organization of player statistics and maintaining focus on key performance metrics.

app.py

@@ -151,7 +151,7 @@ def simulate_stats(row, num_sims=1000):
 @st.cache_data(ttl = 60)
 def init_team_data(team, opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date):  
         game_count = len(kill_predictions)
-        overall_team_data = pd.DataFrame(columns = ['playername', 'teamname', 'position', 'league', 'Opponent', 'result', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj', 'game'])
+        overall_team_data = pd.DataFrame(columns = ['playername', 'teamname', 'position', 'league', 'Opponent', 'result', 'Kill_Proj', 'Death_Proj', 'Assist_Proj', 'CS_Proj'])
         # Convert date objects to datetime strings in the correct format
         start_datetime = datetime.combine(start_date, datetime.min.time()).strftime("%Y-%m-%d %H:%M:%S")
         end_datetime = datetime.combine(end_date, datetime.max.time()).strftime("%Y-%m-%d %H:%M:%S")
@@ -340,7 +340,7 @@ def init_team_data(team, opponent, win_loss_settings, kill_predictions, death_pr
                     team_data['Assist_Proj'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1)
                     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']]
-            team_data['game'] = f'game {game + 1}'
+            team_data['playername'] = f'playername {game + 1}'
 
             overall_team_data = pd.concat([overall_team_data, team_data])