Update 'Weighted Own' calculation in predict_dupes.py: adjust formula to account for the difference between maximum and minimum ownership values for improved accuracy in portfolio analysis.

global_func/predict_dupes.py

@@ -181,7 +181,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
     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) if row['Dupes'] > 0 else row['Lineup Edge'], axis=1)
     portfolio['Lineup Edge'] = portfolio['Lineup Edge'] - portfolio['Lineup Edge'].mean()
-    portfolio['Weighted Own'] = portfolio['Own'] * (portfolio[own_columns].max(axis=1) / 100)
+    portfolio['Weighted Own'] = portfolio['Own'] * ((portfolio[own_columns].max(axis=1) - portfolio[own_columns].min(axis=1)) / 100)
     portfolio = portfolio.drop(columns=dup_count_columns)
     portfolio = portfolio.drop(columns=own_columns)
     portfolio = portfolio.drop(columns=calc_columns)