Refactor assist projection calculation in player data initialization

Update the assist projection method to:
- Separate base assist calculation from scaling
- Use different assist percentage columns for win and loss scenarios
- Improve clarity and maintainability of the projection logic

app.py

@@ -584,17 +584,17 @@ def init_player_data(game_count, players, opponent, win_loss_settings, kill_pred
                 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)
-                    raw_assists = team_data.apply(lambda row: row['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * team_data['Kill_Proj'].sum()
-                    assist_scale = min(1.0, team_data['Kill_Proj'].sum() / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0
-                    team_data['Assist_Proj'] = raw_assists * assist_scale
+                    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['Assist_Base'] * assist_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)
                     team_data['Death_Proj'] = 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['wAssist%'] * opp_pos_assists_boost_win.get(row['position'], 1), axis=1) * team_data['Kill_Proj'].sum()
-                    assist_scale = min(1.0, team_data['Kill_Proj'].sum() / raw_assists.sum()) if raw_assists.sum() > 0 else 1.0
-                    team_data['Assist_Proj'] = raw_assists * assist_scale
+                    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['Assist_Base'] * assist_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']]
             
@@ -705,7 +705,7 @@ if st.button("Run"):
     st.session_state.overall_sim_df = pd.DataFrame(overall_sim_results)
     st.session_state.overall_sim_df = st.session_state.overall_sim_df.drop_duplicates(subset = ['Player', 'Stat'])
 
-tab1, tab2, tab3, tab4 = st.tabs(["Gamelogs", "Overall Data", "Individual Game Data", "Opponent Data"])
+tab1, tab2, tab3, tab4 = st.tabs(["Gamelogs", "Individual Game Data", "Opponent Boosts", "Full Match Data"])
 with tab4:
     if 'player_summary' in st.session_state:
         st.subheader("Full Match Data")