Refactor game prediction settings in app.py to support multiple games. Introduced dynamic tab creation for each game, allowing users to input win/loss settings and kill/death predictions individually. This enhances the user experience by providing a more organized and intuitive interface for managing predictions across multiple games.

app.py

@@ -80,40 +80,56 @@ with st.sidebar:
         index=0
     )
     
-    col1, col2 = st.columns(2)
-    with col1:
-        win_loss = st.selectbox(
-            "Select Win/Loss",
-            options=["Win", "Loss"],
-            index=0
-        )
-    with col2:
-        game_settings = st.selectbox(
-            "Predict kills/deaths or use average?",
-            options=["Average", "Predict"],
-            index=0
-        )
-
-    if game_settings == "Average":
-        kill_prediction = 0
-        death_prediction = 0
-    else:
-        col1, col2 = st.columns(2)
-        with col1:
-            kill_prediction = st.number_input(
-                "Predicted Team Kills",
-                min_value=1,
-                max_value=100,
-                value=20
-            )
-        with col2:
-            death_prediction = st.number_input(
-                "Predicted Team Deaths",
-                min_value=1,
-                max_value=100,
-                value=5
+    # Convert BO format to number of games
+    game_count = int(num_games[2])
+    
+    # Create lists to store settings for each game
+    win_loss_settings = []
+    game_settings_list = []
+    kill_predictions = []
+    death_predictions = []
+    
+    # Create a tab for each game
+    game_tabs = st.tabs([f"Game {i+1}" for i in range(game_count)])
+    
+    for game_num, game_tab in enumerate(game_tabs, 1):
+        with game_tab:
+
+            win_loss_settings.append(st.selectbox(
+                f"Game {game_num} Win/Loss",
+                options=["Win", "Loss"],
+                index=0,
+                key=f"win_loss_{game_num}"
+            ))
+            game_setting = st.selectbox(
+                f"Game {game_num} Prediction Type",
+                options=["Average", "Predict"],
+                index=0,
+                key=f"game_settings_{game_num}"
             )
 
+            if game_setting == "Average":
+                kill_predictions.append(0)
+                death_predictions.append(0)
+            else:
+                col1, col2 = st.columns(2)
+                with col1:
+                    kill_predictions.append(st.number_input(
+                        f"Game {game_num} Predicted Team Kills",
+                        min_value=1,
+                        max_value=100,
+                        value=20,
+                        key=f"kills_{game_num}"
+                    ))
+                with col2:
+                    death_predictions.append(st.number_input(
+                        f"Game {game_num} Predicted Team Deaths",
+                        min_value=1,
+                        max_value=100,
+                        value=5,
+                        key=f"deaths_{game_num}"
+                    ))
+
 @st.cache_data(ttl = 60)
 def simulate_stats(row, num_sims=1000):
     """Simulate stats using normal distribution"""
@@ -133,8 +149,9 @@ def simulate_stats(row, num_sims=1000):
     return pd.Series(results)
 
 @st.cache_data(ttl = 60)
-def init_team_data(team, opponent, win_loss, kill_prediction, death_prediction, start_date, end_date):  
-        
+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'])
         # 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")
@@ -282,51 +299,55 @@ def init_team_data(team, opponent, win_loss, kill_prediction, death_prediction,
                     'opp_pos_cs_boost_loss': opp_pos_cs_boost_loss
                 }).set_index(pd.Index(list(opp_pos_kills_boost_win.keys()), name='position'))
 
-        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',
-                                       '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']]
-            player_tables = player_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'})
-            team_data = player_tables.drop_duplicates(subset = ['playername'])
-
-            if win_loss == "Win":
-                team_data['Kill_Proj'] = team_data.apply(lambda row: row['wKill%'] * opp_pos_kills_boost_win.get(row['position'], 1), axis=1) * kill_prediction
-                team_data['Death_Proj'] = team_data.apply(lambda row: row['wDeath%'] * opp_pos_deaths_boost_win.get(row['position'], 1), axis=1) * death_prediction
-                team_data['Assist_Proj'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * kill_prediction
-                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']]
+        for game in range(game_count):
+            if kill_predictions[game] > 0:
+                player_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']]
+                player_tables = player_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'})
+                team_data = player_tables.drop_duplicates(subset = ['playername'])
+
+                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]
+                    team_data['Assist_Proj'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * kill_predictions[game]
+                    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]
+                    team_data['Assist_Proj'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1) * kill_predictions[game]
+                    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']]
             else:
-                team_data['Kill_Proj'] = team_data.apply(lambda row: row['lKill%'] * opp_pos_kills_boost_loss.get(row['position'], 1), axis=1) * kill_prediction
-                team_data['Death_Proj'] = team_data.apply(lambda row: row['lDeath%'] * opp_pos_deaths_boost_loss.get(row['position'], 1), axis=1) * death_prediction
-                team_data['Assist_Proj'] = team_data.apply(lambda row: row['lAssist%'] * opp_pos_assists_boost_loss.get(row['position'], 1), axis=1) * kill_prediction
-                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']]
-        else:
-            player_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']]
-            player_tables = player_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 = player_tables.drop_duplicates(subset = ['playername'])
-
-            if win_loss == "Win":
-                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_Proj'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1)
-                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_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']]
-
-        return team_data.dropna().set_index('playername'), opp_boosts
+                player_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']]
+                player_tables = player_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 = player_tables.drop_duplicates(subset = ['playername'])
+
+                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)
+                    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_Proj'] = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1)
+                    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_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}'
+
+            overall_team_data = pd.concat([overall_team_data, team_data])
+
+        return overall_team_data.dropna().set_index('playername'), opp_boosts
 
 if st.button("Run"):
-    team_data, opp_boost = init_team_data(selected_team, selected_opponent, win_loss, kill_prediction, death_prediction, start_date, end_date)
+    team_data, opp_boost = init_team_data(selected_team, selected_opponent, win_loss_settings, kill_predictions, death_predictions, start_date, end_date)
 
     # Create simulated percentiles
     sim_results = []