Refine Lineup Edge calculation in predict_dupes.py to adjust the edge based on the mean of duplicates. This update improves the accuracy of the prediction model by incorporating a scaling factor related to the average number of duplicates.

global_func/predict_dupes.py

@@ -420,8 +420,8 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     portfolio['low_own_count'] = portfolio[own_columns].apply(lambda row: (row < 0.10).sum(), axis=1)
     portfolio['Finish_percentile'] = portfolio.apply(lambda row: row['Finish_percentile'] if row['low_own_count'] <= 0 else row['Finish_percentile'] / row['low_own_count'], axis=1)
     portfolio['Lineup Edge'] = portfolio['Win%'] * ((.5 - portfolio['Finish_percentile']) * (Contest_Size / 2.5))
-    portfolio['Lineup Edge'] = portfolio.apply(lambda row: ((row['Lineup Edge'] / (row['Dupes'] + 1)) + (row['Lineup Edge'] / ((row['Dupes'] + 1) / 2))) / 2 if row['Dupes'] > 0 else row['Lineup Edge'], axis=1)
-    portfolio['Lineup Edge'] = portfolio['Lineup Edge'] - portfolio['Lineup Edge'].mean()
+    portfolio['Lineup Edge'] = portfolio.apply(lambda row: row['Lineup Edge'] / (row['Dupes'] + 1) if row['Dupes'] > 0 else row['Lineup Edge'], axis=1)
+    portfolio['Lineup Edge'] = (portfolio['Lineup Edge'] - portfolio['Lineup Edge'].mean()) + ((portfolio['Dupes'] - portfolio['Dupes'].mean()) / 100)
     portfolio['Weighted Own'] = portfolio[own_columns].apply(calculate_weighted_ownership_wrapper, axis=1)
     portfolio['Geomean'] = np.power((portfolio[own_columns] * 100).product(axis=1), 1 / len(own_columns))