Add support for LOL in app.py and related functions, updating sport selection and eligibility checks across multiple modules to include League of Legends, enhancing overall functionality and user experience.

app.py

@@ -28,7 +28,7 @@ from global_func.stratification_function import stratification_function
 from global_func.exposure_spread import exposure_spread
 
 freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'}
-stacking_sports = ['MLB', 'NHL', 'NFL']
+stacking_sports = ['MLB', 'NHL', 'NFL', 'LOL']
 player_wrong_names_mlb = ['Enrique Hernandez', 'Joseph Cantillo', 'Mike Soroka', 'Jakob Bauers', 'Temi Fágbénlé']
 player_right_names_mlb = ['Kike Hernandez', 'Joey Cantillo', 'Michael Soroka', 'Jake Bauers', 'Temi Fagbenle']
 
@@ -42,7 +42,7 @@ with st.container():
         site_var = st.selectbox("Select Site", ['Draftkings', 'Fanduel'])
 
     with col3:
-        sport_var = st.selectbox("Select Sport", ['NFL', 'MLB', 'NBA', 'NHL', 'MMA', 'CS2', 'TENNIS', 'NASCAR', 'GOLF', 'WNBA'])
+        sport_var = st.selectbox("Select Sport", ['NFL', 'MLB', 'NBA', 'NHL', 'MMA', 'CS2', 'LOL', 'TENNIS', 'NASCAR', 'GOLF', 'WNBA'])
         
     with col4:
         type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
@@ -296,7 +296,7 @@ with tab1:
             if 'map_dict' not in st.session_state:
                 if site_var == 'Draftkings':
                     if type_var == 'Classic':
-                        if sport_var == 'CS2':
+                        if sport_var == 'CS2' or sport_var == 'LOL':
                             st.session_state['map_dict'] = {
                                 'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
                                 'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
@@ -308,7 +308,7 @@ with tab1:
                                 'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
                                 'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
                             }
-                        elif sport_var != 'CS2':
+                        elif sport_var != 'CS2' and sport_var != 'LOL':
                             st.session_state['map_dict'] = {
                                 'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
                                 'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
@@ -903,7 +903,7 @@ with tab2:
             st.session_state['working_frame'] = pd.read_parquet(io.BytesIO(st.session_state['origin_portfolio']))
             
             if type_var == 'Classic':
-                if sport_var == 'CS2':
+                if sport_var == 'CS2' or sport_var == 'LOL':
                     # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
                     st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(
                         lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
@@ -925,7 +925,7 @@ with tab2:
                         axis=1
                     )
 
-                elif sport_var != 'CS2':
+                elif sport_var != 'CS2' and sport_var != 'LOL':
                     st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
                     st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row), axis=1)
                     st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row), axis=1)
@@ -989,7 +989,7 @@ with tab2:
                     max_salary = st.number_input("Max acceptable salary?", value=100000, min_value=1000, step=100)
                     max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=.50, min_value=0.005, step=.001)
                     min_lineup_edge = st.number_input("Min acceptable Lineup Edge?", value=-.5, min_value=-1.00, step=.001)
-                    if sport_var in ['NFL', 'MLB', 'NHL']:
+                    if sport_var in ['NFL', 'MLB', 'NHL', 'LOL']:
                         stack_include_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0)
                         stack_selections = st.multiselect("If Specific Stacks, Which to include?", options=sorted(list(set(st.session_state['stack_dict'].values()))), default=[])
 
@@ -1028,7 +1028,7 @@ with tab2:
                     player_remove = st.multiselect("Remove players?", options=sorted(list(player_names)), default=[])
                     team_include = st.multiselect("Include teams?", options=sorted(list(set(st.session_state['projections_df']['team'].unique()))), default=[])
                     team_remove = st.multiselect("Remove teams?", options=sorted(list(set(st.session_state['projections_df']['team'].unique()))), default=[])
-                    if sport_var in ['NFL', 'MLB', 'NHL']:
+                    if sport_var in ['NFL', 'MLB', 'NHL', 'LOL']:
                         size_include = st.multiselect("Include sizes?", options=sorted(list(set(st.session_state['working_frame']['Size'].unique()))), default=[])
                     else:
                         size_include = []
@@ -1329,7 +1329,7 @@ with tab2:
                                         'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
                                     })
                     else:
-                        if sport_var == 'CS2':
+                        if sport_var == 'CS2' or sport_var == 'LOL':
                             # Handle Captain positions
                             for player in player_names:
                                 # Create mask for lineups where this player is Captain (first column)
@@ -1363,7 +1363,7 @@ with tab2:
                                         'Avg Finish %': display_frame[flex_mask]['Finish_percentile'].mean(),
                                         'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
                                     })
-                        elif sport_var != 'CS2':
+                        elif sport_var != 'CS2' and sport_var != 'LOL':
                             # Original Classic format processing
                             for player in player_names:
                                 player_mask = display_frame[player_columns].apply(

global_func/exposure_spread.py

@@ -32,6 +32,25 @@ def check_nba_position_eligibility(column_name, player_positions):
         return True  # UTIL can be any position
     return False
 
+def check_lol_position_eligibility(column_name, player_positions):
+    """
+    Check if a player is eligible for a specific LOL column position.
+    
+    Args:
+        column_name (str): The column name (TOP, JNG, MID, ADC, SUP, UTIL)
+        player_positions (list): List of positions the player is eligible for
+        
+    Returns:
+        bool: True if player is eligible for the column
+    """
+    if any(pos in column_name for pos in ['TOP', 'JNG', 'MID', 'ADC', 'SUP', 'Team']):
+        # Extract the base position from the column name
+        base_position = next(pos for pos in ['TOP', 'JNG', 'MID', 'ADC', 'SUP', 'Team'] if pos in column_name)
+        return base_position in player_positions
+    elif 'CPT' in column_name:
+        return any(pos in ['TOP', 'JNG', 'MID', 'ADC', 'SUP'] for pos in player_positions)
+    return False
+
 def check_mlb_position_eligibility(column_name, player_positions):
     """
     Check if a player is eligible for a specific MLB column position.
@@ -184,6 +203,8 @@ def check_position_eligibility(sport, column_name, player_positions):
         return check_golf_position_eligibility(column_name, player_positions)
     elif sport == 'Tennis':
         return check_tennis_position_eligibility(column_name, player_positions)
+    elif sport == 'LOL':
+        return check_lol_position_eligibility(column_name, player_positions)
     else:
         # Default fallback - assume exact position match
         return column_name in player_positions

global_func/hedging_preset.py

@@ -35,7 +35,7 @@ def hedging_preset(portfolio: pd.DataFrame, lineup_target: int, projections_file
     
         return team_ownership
 
-    if sport == 'MLB':
+    if sport == 'MLB' or sport == 'LOL' or sport == 'NHL':
         team_ownership = get_team_hitter_ownership(projections_file)
         top_owned_teams = team_ownership.head(list_size).index.tolist()
     
@@ -72,7 +72,7 @@ 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])
         
-        if sport == 'MLB':
+        if sport == 'MLB' or sport == 'LOL' or sport == 'NHL':
             for team in top_owned_teams:
                 working_df = portfolio.copy()
                 removed_df = working_df[working_df['Stack'] != team]

global_func/predict_dupes.py

@@ -208,7 +208,7 @@ def predict_dupes_vectorized(portfolio, maps_dict, site_var, type_var, Contest_S
     percentile_cut_scalar = portfolio_values[:, portfolio.columns.get_loc('median')].max()
     
     if type_var == 'Classic':
-        own_ratio_nerf = 2 if sport_var == 'CS2' else 1.5
+        own_ratio_nerf = 2 if sport_var == 'CS2' or sport_var == 'LOL' else 1.5
     elif type_var == 'Showdown':
         own_ratio_nerf = 1.5
     

global_func/small_field_preset.py

@@ -6,7 +6,7 @@ def small_field_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols
 
     for slack_var in range(1, 20):
         concat_portfolio = pd.DataFrame(columns=portfolio.columns)
-        if sport == 'MLB':
+        if sport == 'MLB' or sport == 'LOL' or sport == 'NHL':
             for team in portfolio['Stack'].unique():
                 rows_to_drop = []
                 working_portfolio = portfolio.copy()

global_func/volatility_preset.py

@@ -6,7 +6,7 @@ def volatility_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols:
 
     for slack_var in range(1, 20):
         concat_portfolio = pd.DataFrame(columns=portfolio.columns)
-        if sport == 'MLB':
+        if sport == 'MLB' or sport == 'LOL' or sport == 'NHL':
             for team in portfolio['Stack'].unique():
                 rows_to_drop = []
                 working_portfolio = portfolio.copy()