Refactor position eligibility checks in exposure_spread.py to simplify logic for player positions, enhancing readability and maintainability. Update reassess_dupes function in reassess_edge.py to streamline salary difference calculations for improved performance.

global_func/exposure_spread.py

@@ -34,8 +34,18 @@ def check_lol_position_eligibility(column_name, player_positions):
 def check_mlb_position_eligibility(column_name, player_positions):
     if any(pos in column_name for pos in ['P', 'SP', 'RP']):
         return any(pos in ['P', 'SP', 'RP'] for pos in player_positions)
-    elif any(pos in column_name for pos in ['C', '1B', '2B', '3B', 'SS', 'OF']):
-        return any(pos in ['C', '1B', '2B', '3B', 'SS', 'OF'] for pos in player_positions)
+    elif 'C' in column_name:
+        return 'C' in player_positions
+    elif '1B' in column_name:
+        return '1B' in player_positions
+    elif '2B' in column_name:
+        return '2B' in player_positions
+    elif '3B' in column_name:
+        return '3B' in player_positions
+    elif 'SS' in column_name:
+        return 'SS' in player_positions
+    elif 'OF' in column_name:
+        return 'OF' in player_positions
     return False
 
 def check_nfl_position_eligibility(column_name, player_positions):

global_func/reassess_edge.py

@@ -10,12 +10,7 @@ import numpy as np
 import math
 
 def reassess_dupes(row: pd.Series, salary_max: int) -> float:
-    if row['salary'] == salary_max:
-        return math.ceil(row['Dupes'] + ((row['salary_diff'] / 100) * (row['own_diff'] / 100))).clip(lower=0)
-    elif row['salary'] != salary_max:
-        return math.ceil(row['Dupes'] + ((row['salary_diff'] / 100) * (row['own_diff'] / 100))).clip(lower=0)
-    else:
-        return row['Dupes']
+    return math.ceil(row['Dupes'] + ((row['salary_diff'] / 100) + ((salary_max + (salary_max - row['salary'])) / 100)) * (1 - (row['own_diff'] / 100))).clip(lower=0)
 
 def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame, maps_dict: dict, site_var: str, type_var: str, Contest_Size: int, strength_var: str, sport_var: str, salary_max: int) -> pd.DataFrame:
     orig_df = original_frame.copy()