Update 'own_rank_percentile' calculation in predict_dupes.py to use 'Own' column instead of duplicate count columns. This change enhances the accuracy of the duplicate prediction logic by directly reflecting player ownership rankings in the calculations.

global_func/predict_dupes.py

@@ -326,7 +326,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
             portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
             portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
-            portfolio['own_rank_percentile'] = portfolio[dup_count_columns].rank(pct=True)
+            portfolio['own_rank_percentile'] = portfolio['Own'].rank(pct=True)
             
             # Calculate dupes formula
             portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * (portfolio['Own'] / 100) * Contest_Size + ((portfolio['salary'] - (60000 - portfolio['Own'])) / 100) - ((60000 - portfolio['salary']) / 100)
@@ -355,7 +355,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
             portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
             portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
-            portfolio['own_rank_percentile'] = portfolio[dup_count_columns].rank(pct=True)
+            portfolio['own_rank_percentile'] = portfolio['Own'].rank(pct=True)
             
             portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (60000 - portfolio['Own'])) / 100) - ((60000 - portfolio['salary']) / 100)
             portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (100 + (Contest_Size / 1000)))
@@ -430,7 +430,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
             portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
             portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
-            portfolio['own_rank_percentile'] = portfolio[dup_count_columns].rank(pct=True)
+            portfolio['own_rank_percentile'] = portfolio['Own'].rank(pct=True)
             
             # Calculate dupes formula
             portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
@@ -478,7 +478,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
                 portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
-                portfolio['own_rank_percentile'] = portfolio[dup_count_columns].rank(pct=True)
+                portfolio['own_rank_percentile'] = portfolio['Own'].rank(pct=True)
                 
                 # Calculate dupes formula
                 portfolio['dupes_calc'] = ((portfolio['own_product'] * 10) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 50) - ((50000 - portfolio['salary']) / 50)
@@ -528,7 +528,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
                 portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
-                portfolio['own_rank_percentile'] = portfolio[dup_count_columns].rank(pct=True)
+                portfolio['own_rank_percentile'] = portfolio['Own'].rank(pct=True)
                 
                 # Calculate dupes formula
                 portfolio['dupes_calc'] = ((portfolio['own_product'] * 10) * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 50) - ((50000 - portfolio['salary']) / 50)
@@ -556,7 +556,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
                 portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
-                portfolio['own_rank_percentile'] = portfolio[dup_count_columns].rank(pct=True)
+                portfolio['own_rank_percentile'] = portfolio['Own'].rank(pct=True)
                 
                 portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (50000 - portfolio['Own'])) / 100) - ((50000 - portfolio['salary']) / 100)
                 portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (100 + (Contest_Size / 1000)))