had to add the used of the ownership baseline into the reg dupe predictions

global_func/predict_dupes.py

@@ -385,7 +385,9 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
             portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
             
             portfolio['dupes_calc'] = (portfolio['own_product'] * portfolio['avg_own_rank']) * Contest_Size + ((portfolio['salary'] - (max_salary - portfolio['Own'])) / 100) - ((max_salary - portfolio['salary']) / 100)
-            portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (90 + (Contest_Size / 1000)))
+            if sport_var == 'MMA':
+                own_baseline = 90
+            portfolio['dupes_calc'] = portfolio['dupes_calc'] * dupes_multiplier * (portfolio['Own'] / (own_baseline + (Contest_Size / 1000)))
             # Round and handle negative values
             portfolio['Dupes'] = np.where(
                 portfolio['salary'] == max_salary,