Add weighted ownership calculation in predict_dupes.py: implement a new function to calculate weighted ownership values, enhancing the accuracy of ownership metrics in portfolio analysis.

global_func/predict_dupes.py

@@ -5,6 +5,34 @@ import time
 from fuzzywuzzy import process
 import math
 
+def calculate_weighted_ownership(row_ownerships):
+    """
+    Calculate weighted ownership based on the formula:
+    (AVERAGE of (each value's average with overall average)) * count - (max - min)
+    
+    Args:
+        row_ownerships: Series containing ownership values for a row
+        
+    Returns:
+        float: Calculated weighted ownership value
+    """
+    # Get the mean of all ownership values
+    row_mean = row_ownerships.mean()
+    
+    # Calculate average of each value with the overall mean
+    value_means = [(val + row_mean) / 2 for val in row_ownerships]
+    
+    # Take average of all those means
+    avg_of_means = sum(value_means) / len(value_means)
+    
+    # Multiply by count of values
+    weighted = avg_of_means * len(row_ownerships)
+    
+    # Subtract (max - min)
+    weighted = weighted - (row_ownerships.max() - row_ownerships.min())
+    
+    return weighted
+
 def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, strength_var, sport_var):
     if strength_var == 'Weak':
         dupes_multiplier = .75
@@ -229,7 +257,7 @@ 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) - portfolio[own_columns].min(axis=1)) / 100)) * 200
+    portfolio['Weighted Own'] = portfolio[own_columns].apply(calculate_weighted_ownership, axis=1)
     portfolio['Geomean'] = np.power((portfolio[own_columns] * 100).product(axis=1), 1 / len(own_columns))
     portfolio = portfolio.drop(columns=dup_count_columns)
     portfolio = portfolio.drop(columns=own_columns)