Adding playoff bonus

app.py

@@ -162,7 +162,7 @@ def submit_prediction(
 
     if bid_points > max_bid_points:
         st.error(
-            f"Oops, your bid is too high! 🚫 Maximum allowed bid is {max_bid_points} (50% of your points)."
+            f"Oops, your bid is too high! 🚫 Maximum allowed bid is {max_bid_points}."
         )
         return
 
@@ -224,7 +224,7 @@ def get_user_total_points(user_name):
 def calculate_min_max_bid_points(user_name):
     total_points = get_user_total_points(user_name)
     min_bid_points = math.ceil(total_points * 0.10)  # round up
-    max_bid_points = math.floor(total_points * 0.50)  # round down
+    max_bid_points = total_points  # math.floor(total_points * 0.50)  # round down
     return  int(min_bid_points), int(max_bid_points)
 
 
@@ -466,6 +466,8 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
         users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
         users_df = pd.DataFrame(users)
 
+        submitted_users = set(predictions['user_name'])
+
         # Capture previous leaderboard (top 3 users and their points)
         prev_scores = [(user, users_df[user][0]['points']) for user in users_df.columns]
         prev_scores.sort(key=lambda x: x[1], reverse=True)
@@ -481,7 +483,7 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
             user_points = user_data['points']
             user_initial_points = user_points
 
-            if user_name in set(predictions['user_name']):
+            if user_name in submitted_users:
                 prediction = predictions[predictions['user_name'] == user_name].iloc[0]
                 predicted_winner = prediction['predicted_winner']
                 predicted_motm = prediction['predicted_motm']
@@ -516,13 +518,13 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
         new_leaderboard.sort(key=lambda x: x[1], reverse=True)
         third_place_points = new_leaderboard[2][1] if len(new_leaderboard) >= 3 else 0
 
-        # Step 3: Redistribute lost points using difference-from-3rd-place logic
+        # Step 3: Redistribute lost points using difference-from-3rd-place logic (only for users who submitted prediction)
         redistribution_pool = lost_points_by_top3
         redistribution_weights = {}
         redistribution_total_weight = 0
 
         for user, data in user_outcomes.items():
-            if user not in top3_usernames:
+            if user not in top3_usernames and user in submitted_users:
                 diff_from_3rd = max(third_place_points - data['updated_points'], 0)
                 redistribution_weights[user] = diff_from_3rd
                 redistribution_total_weight += diff_from_3rd

leaders/users.json

@@ -7,7 +7,7 @@
       "🔴",
       "🔴"
     ],
-    "points": 42301,
+    "points": 90301,
     "redistributed_bonus": 641,
     "wildcard": [
       0,
@@ -55,7 +55,7 @@
       "🔴",
       "🔴"
     ],
-    "points": 10262,
+    "points": 34262,
     "redistributed_bonus": 1494,
     "wildcard": [
       0,
@@ -87,7 +87,7 @@
       "🟢",
       "🔴"
     ],
-    "points": 23003,
+    "points": 55003,
     "redistributed_bonus": 1155,
     "wildcard": [
       0,
@@ -119,7 +119,7 @@
       "⚪",
       "⚪"
     ],
-    "points": 24504,
+    "points": 52504,
     "redistributed_bonus": 1115,
     "wildcard": [
       0,
@@ -167,7 +167,7 @@
       "⚪",
       "⚪"
     ],
-    "points": 65323,
+    "points": 79323,
     "redistributed_bonus": 28,
     "wildcard": [
       0,
@@ -183,7 +183,7 @@
       "🟢",
       "🔴"
     ],
-    "points": 175826,
+    "points": 199826,
     "redistributed_bonus": 0,
     "wildcard": [
       0,
@@ -231,7 +231,7 @@
       "🔴",
       "🔴"
     ],
-    "points": 48117,
+    "points": 80117,
     "redistributed_bonus": 486,
     "wildcard": [
       0,
@@ -279,7 +279,7 @@
       "🔴",
       "🟢"
     ],
-    "points": 20605,
+    "points": 48605,
     "redistributed_bonus": 1218,
     "wildcard": [
       0,
@@ -327,7 +327,7 @@
       "🔴",
       "🔴"
     ],
-    "points": 45021,
+    "points": 73021,
     "redistributed_bonus": 568,
     "wildcard": [
       0,