aiming to fix dupes dropping while increasing size

global_func/predict_dupes.py

@@ -288,7 +288,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 0, 
                 np.round(portfolio['Dupes'], 0) - 1
             )
-        if sport_var == 'LOL':
+        elif sport_var == 'LOL':
             dup_count_columns = ['CPT_Own_percent_rank', 'TOP_Own_percent_rank', 'JNG_Own_percent_rank', 'MID_Own_percent_rank', 'ADC_Own_percent_rank', 'SUP_Own_percent_rank', 'Team_Own_percent_rank']
             own_columns = ['CPT_Own', 'TOP_Own', 'JNG_Own', 'MID_Own', 'ADC_Own', 'SUP_Own', 'Team_Own']
             calc_columns = ['own_product', 'own_average', 'own_sum', 'avg_own_rank', 'dupes_calc', 'low_own_count', 'Ref_Proj', 'Max_Proj', 'Min_Proj', 'Avg_Ref', 'own_ratio']
@@ -378,7 +378,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 portfolio[f'player_{i}_percent_rank'] = portfolio.iloc[:,i-1].map(maps_dict['own_percent_rank'])
                 portfolio[f'player_{i}_own'] = portfolio.iloc[:,i-1].map(maps_dict['own_map']).astype('float32') / 100
             
-            portfolio['own_product'] = (portfolio[own_columns].product(axis=1))
+            portfolio['own_product'] = (portfolio[own_columns].product(axis=1)) * max(Contest_Size / 10000, 1)
             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)