Enhanced ownership projection calculations by applying a conditional scaling factor for values exceeding the 90th percentile.

src/sports/nascar_functions.py

@@ -3,6 +3,7 @@ from numpy import random as np_random
 from numpy import array as np_array
 from numpy import zeros as np_zeros
 from numpy import nan as np_nan
+from numpy import where as np_where
 
 # Pandas
 from pandas import DataFrame
@@ -338,6 +339,7 @@ def player_level_classic_roo(working_proj: DataFrame, stat_dicts: dict, working_
         final_Proj['Own'] = final_Proj['Own'].astype('float')
         power_scale = 1.33
         final_Proj['Own'] = final_Proj['Own'] ** power_scale
+        final_Proj['Own'] = np_where(final_Proj['Own'] > final_Proj['Own'].quantile(0.90), final_Proj['Own'] * 1.5, final_Proj['Own'])
         own_norm = 600 / final_Proj['Own'].sum()
         final_Proj['Own'] = final_Proj['Own'] * own_norm
         final_Proj['Small_Own'] = final_Proj['Own'] + (.2 * (final_Proj['Own'] - final_Proj['Own'].mean()))