Update reassess_finish_percentile calculation in reassess_edge.py to adjust ownership impact by changing the divisor for own_diff from 2 to 200, improving accuracy in finish_percentile adjustments.

global_func/reassess_edge.py

@@ -47,7 +47,7 @@ def calculate_weighted_ownership_single_row(row_ownerships):
 def reassess_finish_percentile(row: pd.Series) -> float:
     own_diff = float(row['own_diff'])
     median_diff = float(row['median_diff'])
-    finish_percentile = row['Finish_percentile'] + (own_diff / 2) - (median_diff / 100)
+    finish_percentile = row['Finish_percentile'] + (own_diff / 200) - (median_diff / 100)
     
     return finish_percentile
 
@@ -118,6 +118,6 @@ def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame,
         refactored_df.loc[lineups, 'Weighted Own'] = calculate_weighted_ownership_single_row(refactored_df.loc[lineups, own_columns])
         refactored_df.loc[lineups, 'Geomean'] = np.power((refactored_df.loc[lineups, own_columns] * 100).product(), 1 / len(own_columns))
     
-    # refactored_df = refactored_df.drop(columns=['salary_diff', 'median_diff', 'own_diff', 'player_0_own', 'Edge'] + own_columns)
+    refactored_df = refactored_df.drop(columns=['salary_diff', 'median_diff', 'own_diff', 'player_0_own', 'Edge'] + own_columns)
 
     return refactored_df