Enhance reassess_lineup_edge function in reassess_edge.py to incorporate previous finish_percentile and dupes for improved lineup edge calculations. Update the return logic to better reflect the relationship between finish_percentile and lineup edge, ensuring more accurate assessments.

global_func/reassess_edge.py

@@ -85,11 +85,12 @@ def reassess_dupes(row: pd.Series, salary_max: int) -> float:
     
     return math.ceil(final_dupes)
 
-def reassess_lineup_edge(row: pd.Series, Contest_Size: int) -> float:
-    new_win = row['Win%'] * ((.5 - row['Finish_percentile']) * (Contest_Size / 2.5))
-    lineup_edge = new_win / (row['Dupes'] + 1) if row['Dupes'] > 0 else new_win
+def reassess_lineup_edge(row: pd.Series, Contest_Size: int, prev_finish_percentile: float, prev_dupes: int) -> float:
+    fp_rate = row['Finish_percentile'] / prev_finish_percentile
+    dupe_rate = row['Dupes'] / prev_dupes
+    lineup_edge = (fp_rate * dupe_rate)
     
-    return lineup_edge
+    return (row['Finish_percentile'] + (row['Finish_percentile'] * lineup_edge)) / 2
 
 def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame, maps_dict: dict, Contest_Size: int, salary_max: int) -> pd.DataFrame:
     orig_df = original_frame.copy()
@@ -111,10 +112,12 @@ def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame,
         refactored_df[f'player_{col}_own'] = refactored_df.iloc[:,col].map(maps_dict['own_map']).astype('float32') / 100
 
     for lineups in change_mask.index:
+        prev_finish_percentile = refactored_df.loc[lineups, 'Finish_percentile']
+        prev_dupes = refactored_df.loc[lineups, 'Dupes']
         refactored_df.loc[lineups, 'Dupes'] = reassess_dupes(refactored_df.loc[lineups, :], salary_max)
         refactored_df.loc[lineups, 'Finish_percentile'] = reassess_finish_percentile(refactored_df.loc[lineups, :])
         refactored_df.loc[lineups, 'Win%'] = refactored_df.loc[lineups, 'Win%']
-        refactored_df.loc[lineups, 'Lineup Edge'] = reassess_lineup_edge(refactored_df.loc[lineups, :], Contest_Size)
+        refactored_df.loc[lineups, 'Lineup Edge'] = reassess_lineup_edge(refactored_df.loc[lineups, :], Contest_Size, prev_finish_percentile, prev_dupes)
         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))