bonus

app.py

@@ -492,7 +492,7 @@ def redistribute_lost_points(match_id):
 
 
 def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
-    outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")  
+    outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")
     outcomes_df = pd.DataFrame(outcomes)
 
     # Update or add match outcome
@@ -560,71 +560,34 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
         # Step 2: Build new leaderboard after applying outcome
         new_leaderboard = [(u, d["updated_points"]) for u, d in user_outcomes.items()]
         new_leaderboard.sort(key=lambda x: x[1], reverse=True)
-        new_ranks = {user: rank for rank, (user, _) in enumerate(new_leaderboard)}
-
-        # 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
-
-        redistributed_outcomes = {}
+        
+        # Step 3: Redistribute lost points with reverse leaderboard weighting
         bonus_distribution = {}
 
-        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
-            }
-
-            # 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)}
+        remaining_users = [u for u in user_outcomes if u not in top5_usernames]
+        if remaining_users and lost_points_by_top5 > 0:
+            sorted_remaining = sorted(remaining_users, key=lambda u: user_outcomes[u]['updated_points'])
+            weights = {u: 1 / (i + 1) for i, u in enumerate(sorted_remaining)}
+            total_weight = sum(weights.values())
 
-            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
-                }
+            cumulative_bonus = 0
+            for i, user in enumerate(sorted_remaining):
+                if i == len(sorted_remaining) - 1:
+                    bonus = lost_points_by_top5 - cumulative_bonus
+                else:
+                    share_fraction = weights[user] / total_weight
+                    bonus = int(lost_points_by_top5 * share_fraction)
+                    cumulative_bonus += bonus
+                bonus_distribution[user] = bonus
 
         # Step 4: Apply the appropriate update
         for user in users_df.columns:
-            if redistribution_possible and user in redistributed_outcomes:
-                final_points = redistributed_outcomes[user]
-                bonus = final_points - user_outcomes[user]["updated_points"]
-            else:
-                final_points = user_outcomes[user]["updated_points"]
-                bonus = 0  # No redistribution received
+            bonus = bonus_distribution.get(user, 0)
+            final_points = user_outcomes[user]["updated_points"] + bonus
 
             users_df[user][0]['points'] = final_points
-            users_df[user][0]['redistributed_bonus'] = bonus  # Track redistributed bonus
+            users_df[user][0]['redistributed_bonus'] = bonus
 
-            # Maintain last 5 results
             result = user_outcomes[user]["result_indicator"]
             if "last_5_results" not in users_df[user][0]:
                 users_df[user][0]["last_5_results"] = []

leaders/users.json

@@ -1,14 +1,14 @@
 {
     "Arpit": {
       "last_5_results": [
-        "🔴",
         "🟢",
         "🟢",
         "🔴",
-        "🔴"
+        "🔴",
+        "🟢"
       ],
-      "points": 12429,
-      "redistributed_bonus": 182,
+      "points": 12547,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -23,8 +23,8 @@
         "⚪",
         "⚪"
       ],
-      "points": 1014,
-      "redistributed_bonus": 14,
+      "points": 1200,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -36,11 +36,11 @@
         "🔴",
         "🔴",
         "🔴",
-        "🔴",
+        "⚪",
         "⚪"
       ],
-      "points": 1217,
-      "redistributed_bonus": 17,
+      "points": 1600,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -49,14 +49,14 @@
     },
     "Jay": {
       "last_5_results": [
-        "🟢",
         "🟢",
         "🟢",
         "🔴",
-        "🔴"
+        "🔴",
+        "🟢"
       ],
-      "points": 23588,
-      "redistributed_bonus": 346,
+      "points": 21142,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -68,10 +68,10 @@
         "🟢",
         "🟢",
         "🟢",
-        "🟢",
-        "⚪"
+        "⚪",
+        "🟢"
       ],
-      "points": 37300,
+      "points": 34800,
       "redistributed_bonus": 0,
       "wildcard": [
         0,
@@ -81,14 +81,14 @@
     },
     "Megha": {
       "last_5_results": [
-        "⚪",
         "🟢",
         "⚪",
         "🟢",
+        "⚪",
         "⚪"
       ],
-      "points": 21718,
-      "redistributed_bonus": 318,
+      "points": 21600,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -103,7 +103,7 @@
         "🟢",
         "🟢"
       ],
-      "points": 38800,
+      "points": 36000,
       "redistributed_bonus": 0,
       "wildcard": [
         0,
@@ -119,8 +119,8 @@
         "⚪",
         "⚪"
       ],
-      "points": 1014,
-      "redistributed_bonus": 14,
+      "points": 1200,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -135,8 +135,8 @@
         "⚪",
         "⚪"
       ],
-      "points": 8776,
-      "redistributed_bonus": 128,
+      "points": 8848,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -145,14 +145,14 @@
     },
     "Rakesh": {
       "last_5_results": [
-        "🟢",
         "🟢",
         "🔴",
         "🟢",
+        "🟢",
         "🟢"
       ],
-      "points": 22459,
-      "redistributed_bonus": 329,
+      "points": 20080,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -161,13 +161,13 @@
     },
     "Sai": {
       "last_5_results": [
-        "🔴",
         "🟢",
         "🟢",
         "🔴",
+        "🟢",
         "🟢"
       ],
-      "points": 38808,
+      "points": 39508,
       "redistributed_bonus": 0,
       "wildcard": [
         0,
@@ -180,11 +180,11 @@
         "🔴",
         "🔴",
         "🔴",
-        "🔴",
+        "🟢",
         "🟢"
       ],
-      "points": 23022,
-      "redistributed_bonus": 337,
+      "points": 28606,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -193,14 +193,14 @@
     },
     "Vaibhav": {
       "last_5_results": [
-        "🔴",
         "⚪",
         "⚪",
         "🟢",
-        "🔴"
+        "🔴",
+        "🟢"
       ],
-      "points": 27605,
-      "redistributed_bonus": 405,
+      "points": 27500,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -215,8 +215,8 @@
         "⚪",
         "⚪"
       ],
-      "points": 4851,
-      "redistributed_bonus": 71,
+      "points": 4980,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -225,14 +225,14 @@
     },
     "Anandh": {
       "last_5_results": [
-        "🟢",
         "🟢",
         "🔴",
         "🔴",
+        "🔴",
         "🔴"
       ],
-      "points": 6584,
-      "redistributed_bonus": 96,
+      "points": 4388,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -244,11 +244,11 @@
         "⚪",
         "⚪",
         "⚪",
-        "⚪",
-        "🟢"
+        "🟢",
+        "🔴"
       ],
-      "points": 5480,
-      "redistributed_bonus": 80,
+      "points": 5600,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -257,14 +257,14 @@
     },
     "Biswabarenya": {
       "last_5_results": [
-        "⚪",
         "⚪",
         "🟢",
         "🔴",
+        "⚪",
         "⚪"
       ],
-      "points": 6444,
-      "redistributed_bonus": 94,
+      "points": 6550,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -273,13 +273,13 @@
     },
     "Naitik": {
       "last_5_results": [
-        "🔴",
         "🟢",
         "🟢",
         "🔴",
-        "🟢"
+        "🟢",
+        "����"
       ],
-      "points": 39900,
+      "points": 41600,
       "redistributed_bonus": 0,
       "wildcard": [
         0,
@@ -292,11 +292,11 @@
         "⚪",
         "⚪",
         "⚪",
-        "⚪",
-        "🔴"
+        "🔴",
+        "🟢"
       ],
-      "points": 12341,
-      "redistributed_bonus": 181,
+      "points": 12360,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -305,14 +305,14 @@
     },
     "Siri Gowri": {
       "last_5_results": [
-        "⚪",
         "⚪",
         "⚪",
         "🔴",
+        "⚪",
         "⚪"
       ],
-      "points": 811,
-      "redistributed_bonus": 11,
+      "points": 1000,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
@@ -321,13 +321,13 @@
     },
     "Priyavrat Mohan": {
       "last_5_results": [
-        "🔴",
         "🟢",
         "🟢",
         "🔴",
-        "🔴"
+        "🔴",
+        "🟢"
       ],
-      "points": 32175,
+      "points": 32925,
       "redistributed_bonus": 0,
       "wildcard": [
         0,
@@ -337,18 +337,18 @@
     },
     "Satish": {
       "last_5_results": [
-        "🟢",
         "🔴",
         "🔴",
         "🔴",
-        "🟢"
+        "🟢",
+        "⚪"
       ],
-      "points": 35327,
-      "redistributed_bonus": 527,
+      "points": 28600,
+      "redistributed_bonus": 0,
       "wildcard": [
         0,
         0,
         0
       ]
     }
-  }
+}