continuing to adjust dupe predictions

global_func/predict_dupes.py

@@ -116,6 +116,11 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     elif strength_var == 'Sharp':
         dupes_multiplier = 1.25
         percentile_multiplier = 1.10
+    
+    if sport_var == 'NFL':
+        own_baseline = 180
+    else:
+        own_baseline = 120
     max_ownership = max(maps_dict['own_map'].values()) / 100
     average_ownership = np.mean(list(maps_dict['own_map'].values())) / 100
 
@@ -186,8 +191,8 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
         portfolio['avg_own_rank'] = portfolio[dup_count_columns].mean(axis=1)
         
         # Calculate dupes formula
-        portfolio['dupes_calc'] = ((portfolio['own_product']+((portfolio['Own'] / 6) / max_salary)) * 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'] / (120 + (Contest_Size / 1000)))
+        portfolio['dupes_calc'] = ((portfolio['own_product']+((portfolio['Own'] / 6) / (max_salary / 2))) * 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'] / (own_baseline + (Contest_Size / 1000)))
         portfolio['dupes_calc'] = ((((portfolio['salary'] / (max_salary * 0.96)) - 1)*(max_salary / 10000)) + 1) * portfolio['dupes_calc']
         
         # Round and handle negative values