Refactor large_field_preset function to adjust filtering criteria, replacing 'Finish_percentile' with 'Own' for team-based selection, enhancing accuracy in lineup targeting and improving the logic for dropping rows based on ownership thresholds.

global_func/large_field_preset.py

@@ -8,15 +8,15 @@ 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 = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='median', ascending = False)
+            working_portfolio = working_portfolio[working_portfolio['Stack'] == team].sort_values(by='Finish_percentile', ascending = True)
             working_portfolio = working_portfolio.reset_index(drop=True)
-            curr_own_type_max = working_portfolio.loc[0, 'Finish_percentile'] + (slack_var / 20 * working_portfolio.loc[0, 'Finish_percentile'])
+            curr_own_type_max = working_portfolio.loc[0, 'Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Own'])
 
             for i in range(1, len(working_portfolio)):
-                if working_portfolio.loc[i, 'Finish_percentile'] < curr_own_type_max:
+                if working_portfolio.loc[i, 'Own'] > curr_own_type_max:
                     rows_to_drop.append(i)
                 else:
-                    curr_own_type_max = working_portfolio.loc[i, 'Finish_percentile'] + (slack_var / 20 * working_portfolio.loc[i, 'Finish_percentile'])
+                    curr_own_type_max = working_portfolio.loc[i, 'Own'] + (slack_var / 20 * working_portfolio.loc[i, 'Own'])
 
             working_portfolio = working_portfolio.drop(rows_to_drop).reset_index(drop=True)
             concat_portfolio = pd.concat([concat_portfolio, working_portfolio])