Enhance exposure_spread function to include stacking_sports parameter, improving player selection logic for size 5 lineups in app.py and exposure_spread.py.

app.py

@@ -26,7 +26,6 @@ from global_func.reduce_volatility_preset import reduce_volatility_preset
 from global_func.analyze_player_combos import analyze_player_combos
 from global_func.stratification_function import stratification_function
 from global_func.exposure_spread import exposure_spread
-from global_func.reassess_edge import reassess_edge
 
 freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'}
 stacking_sports = ['MLB', 'NHL', 'NFL', 'LOL']
@@ -1348,7 +1347,7 @@ with tab2:
                     if reg_submitted:
                         st.session_state['settings_base'] = False
                         prior_frame = st.session_state['working_frame'].copy()
-                        parsed_frame = exposure_spread(st.session_state['working_frame'], st.session_state['exposure_player'], exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max)
+                        parsed_frame = exposure_spread(st.session_state['working_frame'], st.session_state['exposure_player'], exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
                         
                         if type_var == 'Classic':
                             if sport_var == 'CS2' or sport_var == 'LOL':
@@ -1441,7 +1440,7 @@ with tab2:
                     elif exp_submitted:
                         st.session_state['settings_base'] = False
                         prior_frame = st.session_state['export_base'].copy()
-                        parsed_frame = exposure_spread(st.session_state['export_base'], st.session_state['exposure_player'], exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max)
+                        parsed_frame = exposure_spread(st.session_state['export_base'], st.session_state['exposure_player'], exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
                         
                         if type_var == 'Classic':
                             if sport_var == 'CS2' or sport_var == 'LOL':

global_func/exposure_spread.py

@@ -148,7 +148,7 @@ def check_position_eligibility(sport, column_name, player_positions):
         # Default fallback - assume exact position match
         return column_name in player_positions
 
-def exposure_spread(working_frame, exposure_player, exposure_target, exposure_stack_bool, remove_teams, projections_df, sport_var, type_var, salary_max):
+def exposure_spread(working_frame, exposure_player, exposure_target, exposure_stack_bool, remove_teams, projections_df, sport_var, type_var, salary_max, stacking_sports):
     comparable_players = projections_df[projections_df['player_names'] == exposure_player]
     
     comparable_players = comparable_players.reset_index(drop=True)
@@ -266,12 +266,12 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
                     (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
                     (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
                 ]
-
-                if row['Size'] == 5 and comp_team != row['Stack']:
-                    remove_mask = comparable_players.apply(
-                        lambda row: not any(team in list(row) for team in working_frame['Stack'][row]), axis=1
-                    )
-                    comparable_players = comparable_players[remove_mask]
+                if sport_var in stacking_sports:
+                    if row['Size'] == 5 and comp_team != row['Stack']:
+                        remove_mask = comparable_players.apply(
+                            lambda row: not any(team in list(row) for team in working_frame['Stack'][row]), axis=1
+                        )
+                        comparable_players = comparable_players[remove_mask]
                 
                 if remove_teams is not None:
                     remove_mask = comparable_players.apply(