Enhance opponent performance data display in init_team_data function of app.py. Added a DataFrame to output opponent-specific metrics for kills, deaths, assists, and CS projections during both win and loss scenarios. Updated the return statement to include this new DataFrame, allowing for a more comprehensive analysis of opponent performance alongside team data. This change improves the visibility and usability of performance metrics for informed decision-making.

app.py

@@ -241,7 +241,16 @@ def init_team_data(team, opponent, win_loss, kill_prediction, death_prediction,
                 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']))
-
+                opp_boosts = pd.DataFrame({
+                    'opp_pos_kills_boost_win': opp_pos_kills_boost_win,
+                    'opp_pos_deaths_boost_win': opp_pos_deaths_boost_win,
+                    'opp_pos_assists_boost_win': opp_pos_assists_boost_win,
+                    'opp_pos_cs_boost_win': opp_pos_cs_boost_win,
+                    'opp_pos_kills_boost_loss': opp_pos_kills_boost_loss,
+                    'opp_pos_deaths_boost_loss': opp_pos_deaths_boost_loss,
+                    'opp_pos_assists_boost_loss': opp_pos_assists_boost_loss,
+                    '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',
@@ -284,7 +293,12 @@ def init_team_data(team, opponent, win_loss, kill_prediction, death_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']]
 
-        return team_data.dropna().set_index('playername')
+        return team_data.dropna().set_index('playername'), opp_boosts
 
 if st.button("Run"):
-    st.dataframe(init_team_data(selected_team, selected_opponent, win_loss, kill_prediction, death_prediction, start_date, end_date).style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True)
+    team_data, opp_boost = init_team_data(selected_team, selected_opponent, win_loss, kill_prediction, death_prediction, start_date, end_date)
+    tab1, tab2 = st.tabs(["Team Data", "Opponent Data"])
+    with tab1:
+        st.dataframe(team_data.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True)
+    with tab2:
+        st.dataframe(opp_boost.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)