Add position eligibility check in exposure_spread function to filter comparable players based on overlapping positions, improving accuracy in player comparisons.

global_func/exposure_spread.py

@@ -15,12 +15,23 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
     comp_salary_low = comparable_players['salary'][0] - 500
     comp_projection_high = comparable_players['median'][0]
     comp_projection_low = comparable_players['median'][0] - (comparable_players['median'][0] * .75)
+    # players can be eligible at multiple positions, so we need to find all the positions the player is eligible at
+    # the position column can have positions designated as 1B/OF which means they are eligible at 1B and OF
+    comp_player_position = comparable_players['position'].tolist()
+    comp_player_position = [pos.split('/') for pos in comp_player_position]
+    comp_player_position = [item for sublist in comp_player_position for item in sublist]
+    comp_player_position = list(set(comp_player_position))
+
+    def has_position_overlap(player_positions, target_positions):
+        player_pos_list = player_positions.split('/')
+        return any(pos in target_positions for pos in player_pos_list)
 
     comparable_players = projections_df[
         (projections_df['salary'] >= comp_salary_low) &
         (projections_df['salary'] <= comp_salary_high) &
         (projections_df['median'] >= comp_projection_low) &
-        (projections_df['median'] <= comp_projection_high)
+        (projections_df['median'] <= comp_projection_high) &
+        (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
     ]
     
     # Create a list of comparable players