Refactor large_field_preset function to simplify ranking logic by removing median and finish percentile rankings, and adjust filtering criteria to focus on 'Finish_percentile' for team-based selection, enhancing accuracy in lineup targeting.

global_func/large_field_preset.py

@@ -8,21 +8,17 @@ def large_field_preset(portfolio: pd.DataFrame, lineup_target: int):
         for team in portfolio['Stack'].unique():
             rows_to_drop = []
             working_portfolio = portfolio.copy()
-            working_portfolio['median_rank'] = working_portfolio['median'].rank(method='first', ascending=False)
-            working_portfolio['finish_percentile_rank'] = working_portfolio['Finish_percentile'].rank(method='first', ascending=True)
-            working_portfolio['rank_agg'] = (working_portfolio['median_rank'] + working_portfolio['finish_percentile_rank']) / 2
-            working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='rank_agg', ascending = True)
+            working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='median', ascending = False)
             working_portfolio = working_portfolio.reset_index(drop=True)
-            curr_own_type_max = working_portfolio.loc[0, 'Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Own'])
+            curr_own_type_max = working_portfolio.loc[0, 'Finish_percentile'] + (slack_var / 20 * working_portfolio.loc[0, 'Finish_percentile'])
 
             for i in range(1, len(working_portfolio)):
-                if working_portfolio.loc[i, 'Own'] > curr_own_type_max:
+                if working_portfolio.loc[i, 'Finish_percentile'] < curr_own_type_max:
                     rows_to_drop.append(i)
                 else:
-                    curr_own_type_max = working_portfolio.loc[i, 'Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Own'])
+                    curr_own_type_max = working_portfolio.loc[i, 'Finish_percentile'] + (slack_var / 20 * working_portfolio.loc[i, 'Finish_percentile'])
 
             working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
-            working_portfolio = working_portfolio.drop(columns=['median_rank', 'finish_percentile_rank', 'rank_agg'])
             concat_portfolio = pd.concat([concat_portfolio, working_portfolio])
         if len(concat_portfolio) >= lineup_target:
             return concat_portfolio.sort_values(by='Finish_percentile', ascending=True).head(lineup_target)