Enhance exposure_spread function by adding salary_max parameter for improved salary filtering and adjusting lineups_to_remove calculation to a 125% factor, refining player selection logic and ensuring existing players are excluded from comparable options.

app.py

@@ -46,6 +46,27 @@ with st.container():
         
     with col4:
         type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
+    
+    if site_var == 'Draftkings':
+        salary_max = 50000
+    elif site_var == 'Fanduel':
+        if type_var == 'Classic':
+            if sport_var == 'MLB':
+                salary_max = 40000
+            elif sport_var == 'WNBA':
+                salary_max = 40000
+            elif sport_var == 'GOLF':
+                salary_max = 60000
+            elif sport_var == 'MMA':
+                salary_max = 100
+            elif sport_var == 'NFL':
+                salary_max = 60000
+            elif sport_var == 'NASCAR':
+                salary_max = 50000
+            else:
+                salary_max = 60000
+        elif type_var == 'Showdown':
+            salary_max = 60000
 
 tab1, tab2 = st.tabs(["Data Load", "Manage Portfolio"])
 with tab1:
@@ -1176,7 +1197,7 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         st.session_state['settings_base'] = False
-                        parsed_frame = exposure_spread(st.session_state['working_frame'], exposure_player, exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var)
+                        parsed_frame = exposure_spread(st.session_state['working_frame'], exposure_player, exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max)
                         st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
         with st.container():

global_func/exposure_spread.py

@@ -209,7 +209,7 @@ 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):
+def exposure_spread(working_frame, exposure_player, exposure_target, exposure_stack_bool, remove_teams, projections_df, sport_var, type_var, salary_max):
     # Find comparable players in the projections
     comparable_players = projections_df[projections_df['player_names'] == exposure_player]
     
@@ -240,7 +240,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
     player_exposure = player_mask.sum() / len(working_frame)
 
     # find the number of lineups that need to be removed to reach the target exposure
-    lineups_to_remove = ((player_exposure - exposure_target) * len(working_frame)) * 1.5
+    lineups_to_remove = ((player_exposure - exposure_target) * len(working_frame)) * 1.25
 
     # isolate the rows that contain the player
     player_rows = working_frame[player_mask]
@@ -257,7 +257,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
         if change_counter < math.ceil(lineups_to_remove):
             comparable_players = projections_df[
                 (projections_df['salary'] >= comp_salary_low) &
-                (projections_df['salary'] <= comp_salary_high + (50000 - working_frame['salary'][row])) &
+                (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
                 (projections_df['median'] >= comp_projection_low) &
                 (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
             ]
@@ -271,24 +271,32 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
                     )
                 comparable_players = comparable_players[remove_mask]
 
+            # Get the current row data to check for existing players
+            current_row_data = working_frame.iloc[row]
+            
+            # Filter out players that are already present in this row
+            existing_players = set(current_row_data.values)
+            comparable_players = comparable_players[~comparable_players['player_names'].isin(existing_players)]
+
             # Create a list of comparable players
             comparable_player_list = comparable_players['player_names'].tolist()
-            insert_player = random.choice(comparable_player_list)
-            # Find which column contains the exposure_player
-            row_data = working_frame.iloc[row]
-            for col in working_frame.columns:
-                if row_data[col] == exposure_player:
-                    # Get the replacement player's positions
-                    replacement_player_positions = projections_df[projections_df['player_names'] == insert_player]['position'].iloc[0].split('/')
-                    
-                    # Check if the replacement player is eligible for this column
-                    if type_var == 'Classic':
-                        if check_position_eligibility(sport_var, col, replacement_player_positions):
+            if comparable_player_list:
+                insert_player = random.choice(comparable_player_list)
+                # Find which column contains the exposure_player
+                row_data = working_frame.iloc[row]
+                for col in working_frame.columns:
+                    if row_data[col] == exposure_player:
+                        # Get the replacement player's positions
+                        replacement_player_positions = projections_df[projections_df['player_names'] == insert_player]['position'].iloc[0].split('/')
+                        
+                        # Check if the replacement player is eligible for this column
+                        if type_var == 'Classic':
+                            if check_position_eligibility(sport_var, col, replacement_player_positions):
+                                working_frame.at[row, col] = insert_player
+                                break
+                        else:
                             working_frame.at[row, col] = insert_player
                             break
-                    else:
-                        working_frame.at[row, col] = insert_player
-                        break
-            change_counter += 1
+                change_counter += 1
     return working_frame