Update ownership average calculation in predict_dupes function to reflect a new ratio of 0.66, improving accuracy in ownership metrics for both portfolio and team ownership assessments.

global_func/predict_dupes.py

@@ -471,7 +471,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['FLEX5_Own'] = portfolio.iloc[:,5].map(maps_dict['own_map']).astype('float32') / 100
 
                 portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
-                portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
+                portfolio['own_average'] = (portfolio['Own'].max() * .66) / 100
                 portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
                 
@@ -520,7 +520,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio['Team_Own'] = portfolio.iloc[:,6].map(maps_dict['own_map']).astype('float32') / 100
 
                 portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
-                portfolio['own_average'] = (portfolio['Own'].max() * .33) / 100
+                portfolio['own_average'] = (portfolio['Own'].max() * .66) / 100
                 portfolio['own_sum'] = portfolio[own_columns].sum(axis=1)
                 portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)