Update reassess_finish_percentile calculation in reassess_edge.py to use max() instead of min(), ensuring that the finish_percentile does not fall below the sum of a minimum threshold and 'Win%', thereby enhancing the accuracy of lineup assessments.

global_func/reassess_edge.py

@@ -118,7 +118,7 @@ def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame,
         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'] = min(reassess_finish_percentile(refactored_df.loc[lineups, :]), .005 + refactored_df.loc[lineups, 'Win%'])
+        refactored_df.loc[lineups, 'Finish_percentile'] = max(reassess_finish_percentile(refactored_df.loc[lineups, :]), .005 + refactored_df.loc[lineups, 'Win%'])
         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, prev_finish_percentile, prev_dupes)
         refactored_df.loc[lineups, 'Weighted Own'] = calculate_weighted_ownership_single_row(refactored_df.loc[lineups, own_columns])