Enhance preset functions in app.py and related modules to include 'sport' parameter for improved flexibility in lineup management. This update modifies the small_field_preset, large_field_preset, volatility_preset, hedging_preset, and reduce_volatility_preset functions to accommodate sport-specific logic, enhancing user experience and accuracy in lineup strategies.

app.py

@@ -1118,15 +1118,15 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         if preset_choice == 'Small Field (Heavy Own)':
-                            parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
+                            parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
                         elif preset_choice == 'Large Field (Manage Diversity)':
-                            parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
+                            parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
                         elif preset_choice == 'Volatility (Heavy Lineup Edge)':
-                            parsed_frame = volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
+                            parsed_frame = volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
                         elif preset_choice == 'Hedge Chalk (Manage Leverage)':
-                            parsed_frame = hedging_preset(st.session_state['working_frame'], lineup_target, st.session_state['projections_df'])
+                            parsed_frame = hedging_preset(st.session_state['working_frame'], lineup_target, st.session_state['projections_df'], sport_var)
                         elif preset_choice == 'Reduce Volatility (Manage Own)':
-                            parsed_frame = reduce_volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols)
+                            parsed_frame = reduce_volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
                         st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
 

global_func/hedging_preset.py

@@ -3,10 +3,13 @@ import math
 from global_func.small_field_preset import small_field_preset
 from global_func.large_field_preset import large_field_preset
 
-def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file: pd.DataFrame):
+def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file: pd.DataFrame, sport: str):
     
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
-    list_size = 3
+    if sport == 'MLB':
+        list_size = 3
+    else:
+        list_size = 5
 
     check_own_df = projections_file.copy()
     check_own_df = check_own_df.sort_values(by='ownership', ascending=False)
@@ -31,8 +34,10 @@ def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file
     
         return team_ownership
 
-    team_ownership = get_team_hitter_ownership(projections_file)
-    top_owned_teams = team_ownership.head(list_size).index.tolist()
+    if sport == 'MLB':
+        team_ownership = get_team_hitter_ownership(projections_file)
+        top_owned_teams = team_ownership.head(list_size).index.tolist()
+    
     init_counter = 6
 
     for runs in range(1, 5):
@@ -66,20 +71,21 @@ def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file
                 print(f"No lineups found containing {player}")
                 concat_portfolio = pd.concat([concat_portfolio, removed_lineups])
         
-        for team in top_owned_teams:
-            working_df = portfolio.copy()
-            removed_df = working_df[working_df['Stack'] != team]
-            teams_df = working_df[working_df['Stack'] == team]
+        if sport == 'MLB':
+            for team in top_owned_teams:
+                working_df = portfolio.copy()
+                removed_df = working_df[working_df['Stack'] != team]
+                teams_df = working_df[working_df['Stack'] == team]
 
-            removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / (list_size * init_counter)), excluded_cols)
-            # Check if teams_df is empty before calling large_field_preset
-            if not teams_df.empty:
-                team_lineups = large_field_preset(teams_df, math.ceil(lineup_target / (list_size * init_counter)), excluded_cols)
-                concat_portfolio = pd.concat([concat_portfolio, removed_lineups, team_lineups])
-            else:
-                # If no lineups have this team stacked, just add the removed lineups
-                print(f"No lineups found with {team} stacked")
-                concat_portfolio = pd.concat([concat_portfolio, removed_lineups])
+                removed_lineups = small_field_preset(removed_df, math.ceil(lineup_target / (list_size * init_counter)), excluded_cols)
+                # Check if teams_df is empty before calling large_field_preset
+                if not teams_df.empty:
+                    team_lineups = large_field_preset(teams_df, math.ceil(lineup_target / (list_size * init_counter)), excluded_cols)
+                    concat_portfolio = pd.concat([concat_portfolio, removed_lineups, team_lineups])
+                else:
+                    # If no lineups have this team stacked, just add the removed lineups
+                    print(f"No lineups found with {team} stacked")
+                    concat_portfolio = pd.concat([concat_portfolio, removed_lineups])
 
         concat_portfolio = concat_portfolio.drop_duplicates(subset=['median', 'Own', 'Lineup Edge', 'Diversity'])
 

global_func/large_field_preset.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 
-def large_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
+def large_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str):
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
     player_columns = [col for col in portfolio.columns if col not in excluded_cols]
     

global_func/reduce_volatility_preset.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 
-def reduce_volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
+def reduce_volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str):
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
     player_columns = [col for col in portfolio.columns if col not in excluded_cols]
 

global_func/small_field_preset.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-def small_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
+def small_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str):
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
     player_columns = [col for col in portfolio.columns if col not in excluded_cols]
 

global_func/volatility_preset.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 
-def volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list):
+def volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str):
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
     player_columns = [col for col in portfolio.columns if col not in excluded_cols]