bonus

app.py

@@ -330,7 +330,7 @@ def display_leaderboard():
             leaderboard['Rank'] = range(1, len(leaderboard) + 1)
             
             # Select and order the columns for display
-            leaderboard = leaderboard[['Rank', 'User', 'Points', 'Last 5 Bids']]
+            leaderboard = leaderboard[['Rank', 'User', 'Points', 'Redistribution Bonus', 'Last 5 Bids']]
             
             st.dataframe(leaderboard, hide_index=True)
         except Exception as e:
@@ -561,29 +561,55 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
         new_leaderboard.sort(key=lambda x: x[1], reverse=True)
         new_ranks = {user: rank for rank, (user, _) in enumerate(new_leaderboard)}
 
-        # Step 3: Try redistributing lost points to remaining users
-        remaining_users = [u for u in users_df.columns if u not in top5_usernames]
-        redistribution_possible = True
-        redistributed_outcomes = {}
-
-        if remaining_users and lost_points_by_top5 > 0:
-            share = lost_points_by_top5 // len(remaining_users)
+        # Step 3: Redistribute proportionally based on users' updated points
+        remaining_users = [user for user in users_df.columns if user not in top5_usernames]
+        redistribution_possible = lost_points_by_top5 > 0 and len(remaining_users) > 0
 
-            # Simulate redistribution
-            for user in user_outcomes:
-                redistributed_outcomes[user] = user_outcomes[user]["updated_points"]
-                if user in remaining_users:
-                    redistributed_outcomes[user] += share
+        redistributed_outcomes = {}
+        bonus_distribution = {}
 
-            # Check if new ranks remain same
-            redistributed_leaderboard = [(u, p) for u, p in redistributed_outcomes.items()]
-            redistributed_leaderboard.sort(key=lambda x: x[1], reverse=True)
-            redistributed_ranks = {user: rank for rank, (user, _) in enumerate(redistributed_leaderboard)}
+        if redistribution_possible:
+            remaining_users_points = {
+                u: user_outcomes[u]["updated_points"] for u in remaining_users
+            }
+            total_remaining_points = sum(remaining_users_points.values())
+
+            # Edge case: all remaining users have 0 points
+            if total_remaining_points == 0:
+                share = lost_points_by_top5 // len(remaining_users)
+                for i, user in enumerate(remaining_users):
+                    bonus = share if i < len(remaining_users) - 1 else lost_points_by_top5 - share * (len(remaining_users) - 1)
+                    bonus_distribution[user] = bonus
+            else:
+                cumulative_bonus = 0
+                for i, user in enumerate(remaining_users):
+                    if i == len(remaining_users) - 1:
+                        bonus = lost_points_by_top5 - cumulative_bonus  # Remaining bonus
+                    else:
+                        share_fraction = remaining_users_points[user] / total_remaining_points
+                        bonus = int(lost_points_by_top5 * share_fraction)
+                        cumulative_bonus += bonus
+                    bonus_distribution[user] = bonus
+
+            # Final points with bonus, but only update if leaderboard stays unchanged
+            redistributed_outcomes = {
+                user: user_outcomes[user]["updated_points"] + bonus_distribution.get(user, 0)
+                for user in users_df.columns
+            }
 
-            for user in user_outcomes:
-                if new_ranks[user] != redistributed_ranks[user]:
-                    redistribution_possible = False
-                    break
+            # Check if new leaderboard ranks remain the same
+            post_redistribution = [(u, redistributed_outcomes[u]) for u in redistributed_outcomes]
+            post_redistribution.sort(key=lambda x: x[1], reverse=True)
+            redistributed_ranks = {user: rank for rank, (user, _) in enumerate(post_redistribution)}
+
+            rank_changes = any(new_ranks[u] != redistributed_ranks[u] for u in new_ranks)
+            if rank_changes:
+                # Rollback redistribution
+                bonus_distribution = {}
+                redistributed_outcomes = {
+                    user: user_outcomes[user]["updated_points"]
+                    for user in users_df.columns
+                }
 
         # Step 4: Apply the appropriate update
         for user in users_df.columns: