Upload 27 files

api_scraper.py

@@ -106,7 +106,9 @@ class MLB_Scrape:
         time_list = [item for sublist in [[y['gameDate'] for y in x['games']] for x in game_call['dates']] for item in sublist]
         date_list = [item for sublist in [[y['officialDate'] for y in x['games']] for x in game_call['dates']] for item in sublist]
         away_team_list = [item for sublist in [[y['teams']['away']['team']['name'] for y in x['games']] for x in game_call['dates']] for item in sublist]
+        away_team_id_list = [item for sublist in [[y['teams']['away']['team']['id'] for y in x['games']] for x in game_call['dates']] for item in sublist]
         home_team_list = [item for sublist in [[y['teams']['home']['team']['name'] for y in x['games']] for x in game_call['dates']] for item in sublist]
+        home_team_id_list = [item for sublist in [[y['teams']['home']['team']['id'] for y in x['games']] for x in game_call['dates']] for item in sublist]
         state_list = [item for sublist in [[y['status']['codedGameState'] for y in x['games']] for x in game_call['dates']] for item in sublist]
         venue_id = [item for sublist in [[y['venue']['id'] for y in x['games']] for x in game_call['dates']] for item in sublist]
         venue_name = [item for sublist in [[y['venue']['name'] for y in x['games']] for x in game_call['dates']] for item in sublist]
@@ -116,7 +118,9 @@ class MLB_Scrape:
                                     'time': time_list,
                                     'date': date_list,
                                     'away': away_team_list,
+                                    'away_id': away_team_id_list,
                                     'home': home_team_list,
+                                    'home_id': home_team_id_list,
                                     'state': state_list,
                                     'venue_id': venue_id,
                                     'venue_name': venue_name})
@@ -161,6 +165,27 @@ class MLB_Scrape:
         
         return data_total
 
+    def get_data_new(self, game_list_input: list):
+        """
+        Retrieves live game data for a list of game IDs in parallel.
+        
+        Parameters:
+        - game_list_input (list): A list of game IDs for which to retrieve live data.
+        
+        Returns:
+        - data_total (list): A list of JSON responses containing live game data for each game ID.
+        """
+        data_total = []
+        print('This May Take a While. Progress Bar shows Completion of Data Retrieval.')
+        
+        def fetch_data(game_id):
+            r = requests.get(f'https://statsapi.mlb.com/api/v1.1/game/{game_id}/feed/live')
+            return r.json()
+        
+        with ThreadPoolExecutor() as executor:
+            futures = {executor.submit(fetch_data, game_id): game_id for game_id in game_list_input}
+            for future in tqdm(as_completed(futures), total=len(futures), desc="Processing", unit="iteration"):
+                data_total.append(future.result())
         
         return data_total
 

app.py

@@ -1,400 +1,488 @@
-import polars as pl
-import numpy as np
-import pandas as pd
-import api_scraper
-scrape = api_scraper.MLB_Scrape()
-from functions import df_update
-from functions import pitch_summary_functions
-update = df_update.df_update()
-from stuff_model import feature_engineering as fe
-from stuff_model import stuff_apply
-import requests
-import joblib
-from matplotlib.gridspec import GridSpec
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-import seaborn as sns
-from functions.pitch_summary_functions import *
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-
-colour_palette = ['#FFB000','#648FFF','#785EF0',
-                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
-
-
-year_list = [2017,2018,2019,2020,2021,2022,2023,2024]
-
-
-
-level_dict =  {'1':'MLB',
-               '11':'AAA',
-              # '12':'AA',
-               #'13':'A+',
-               '14':'A',
-               '17':'AFL',
-               '22':'College',
-               '21':'Prospects',
-               '51':'International' }
-
-function_dict={
-    'velocity_kdes':'Velocity Distributions',
-    'break_plot':'Pitch Movement',
-    'tj_stuff_roling':'Rolling tjStuff+ by Pitch',
-    'tj_stuff_roling_game':'Rolling tjStuff+ by Game',
-    'location_plot_lhb':'Locations vs LHB',
-    'location_plot_rhb':'Locations vs RHB',
-}
-
-
-split_dict =  {'all':'All',
-               'left':'LHH',
-               'right':'RHH'}
-
-split_dict_hand =  {'all':['L','R'],
-               'left':['L'],
-               'right':['R']}
-
-
-type_dict =  {'R':'Regular Season',
-               'S':'Spring',
-               'P':'Playoffs' }
-
-
-
-# List of MLB teams and their corresponding ESPN logo URLs
-mlb_teams = [
-    {"team": "AZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/ari.png&h=500&w=500"},
-    {"team": "ATH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
-    {"team": "ATL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/atl.png&h=500&w=500"},
-    {"team": "BAL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bal.png&h=500&w=500"},
-    {"team": "BOS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bos.png&h=500&w=500"},
-    {"team": "CHC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chc.png&h=500&w=500"},
-    {"team": "CWS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chw.png&h=500&w=500"},
-    {"team": "CIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cin.png&h=500&w=500"},
-    {"team": "CLE", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cle.png&h=500&w=500"},
-    {"team": "COL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/col.png&h=500&w=500"},
-    {"team": "DET", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/det.png&h=500&w=500"},
-    {"team": "HOU", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/hou.png&h=500&w=500"},
-    {"team": "KC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/kc.png&h=500&w=500"},
-    {"team": "LAA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/laa.png&h=500&w=500"},
-    {"team": "LAD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/lad.png&h=500&w=500"},
-    {"team": "MIA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mia.png&h=500&w=500"},
-    {"team": "MIL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mil.png&h=500&w=500"},
-    {"team": "MIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/min.png&h=500&w=500"},
-    {"team": "NYM", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nym.png&h=500&w=500"},
-    {"team": "NYY", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nyy.png&h=500&w=500"},
-    {"team": "PHI", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/phi.png&h=500&w=500"},
-    {"team": "PIT", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/pit.png&h=500&w=500"},
-    {"team": "SD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sd.png&h=500&w=500"},
-    {"team": "SF", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sf.png&h=500&w=500"},
-    {"team": "SEA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sea.png&h=500&w=500"},
-    {"team": "STL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/stl.png&h=500&w=500"},
-    {"team": "TB", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tb.png&h=500&w=500"},
-    {"team": "TEX", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tex.png&h=500&w=500"},
-    {"team": "TOR", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tor.png&h=500&w=500"},
-    {"team": "WSH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/wsh.png&h=500&w=500"},
-    {"team": "ZZZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png&w=500&h=500"}   
-]
-
-
-df_image = pd.DataFrame(mlb_teams)
-image_dict = df_image.set_index('team')['logo_url'].to_dict()
-image_dict_flip = df_image.set_index('logo_url')['team'].to_dict()
-
-
-
-# # Define the features to be used for training
-# features_table = ['start_speed',
-#             'spin_rate',
-#             'extension',
-#             'ivb',
-#             'hb',
-#             'x0',
-#             'z0',
-#             'tj_stuff_plus']
-
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-
-# Define the UI layout for the app
-app_ui = ui.page_sidebar(
-    ui.sidebar(
-        # Row for selecting season and level
-        ui.row(
-            ui.column(4, ui.input_select('year_input', 'Select Season', year_list, selected=2024)),
-            ui.column(4, ui.input_select('level_input', 'Select Level', level_dict)),
-            ui.column(4, ui.input_select('type_input', 'Select Type', type_dict,selected='R'))
-        ),
-        # Row for the action button to get player list
-        ui.row(ui.input_action_button("player_button", "Get Player List", class_="btn-primary")),
-        # Row for selecting the player
-        ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))),
-        # Row for selecting the date range
-        ui.row(ui.column(12, ui.output_ui('date_id', 'Select Date'))),
-
-        # Rows for selecting plots and split options
-        ui.row(
-            ui.column(4, ui.input_select('plot_id_1', 'Plot Left', function_dict, multiple=False, selected='velocity_kdes')),
-            ui.column(4, ui.input_select('plot_id_2', 'Plot Middle', function_dict, multiple=False, selected='tj_stuff_roling')),
-            ui.column(4, ui.input_select('plot_id_3', 'Plot Right', function_dict, multiple=False, selected='break_plot'))
-        ),
-        ui.row(
-            ui.column(6, ui.input_select('split_id', 'Select Split', split_dict, multiple=False)),
-            ui.column(6, ui.input_numeric('rolling_window', 'Rolling Window (for tjStuff+ Plot)', min=1, value=50))
-        ),
-        ui.row(
-            ui.column(6, ui.input_switch("switch", "Custom Team?", False)),
-            ui.column(6, ui.input_select('logo_select', 'Select Custom Logo', image_dict_flip, multiple=False))
-        ),
-        
-        # Row for the action button to generate plot
-        ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary")),
-        width="400px"  # Added this parameter to control sidebar width
-    ),
-            
-    # Main content area with tabs (placed directly in page_sidebar)
-    ui.navset_tab(
-        ui.nav_panel("Pitching Summary",
-               ui.output_text("status"),
-               ui.output_plot('plot', width='2100px', height='2100px')
-        ),
-            ui.nav_panel("Summary Table",
-                                   ui.output_data_frame("grid"))
-    )
-)
-
-
-def server(input, output, session):
-
-    @reactive.calc
-    @reactive.event(input.pitcher_id, input.date_id,input.split_id)
-    def cached_data():
-
-        year_input = int(input.year_input())
-        sport_id = int(input.level_input())
-        player_input = int(input.pitcher_id())
-        start_date = str(input.date_id()[0])
-        end_date = str(input.date_id()[1])
-        # Simulate an expensive data operation
-        game_list  = scrape.get_player_games_list(sport_id = sport_id,
-                                                season = year_input, 
-                                                player_id = player_input,
-                                                start_date = start_date,
-                                                end_date = end_date,
-                                                game_type = [input.type_input()])
-
-        data_list = scrape.get_data(game_list_input = game_list[:])
-        df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-                            (pl.col("pitcher_id") == player_input)&
-                            (pl.col("is_pitch") == True)&
-                            (pl.col("start_speed") >= 50)&
-                            (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-
-                            )))).with_columns(
-                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-            ))
-        return df
-
-    @render.ui
-    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
-    def player_select_ui():
-        # Get the list of pitchers for the selected level and season
-        df_pitcher_info = scrape.get_players(sport_id=int(input.level_input()), season=int(input.year_input()), game_type = [input.type_input()]).filter(
-            pl.col("position").is_in(['P','TWP'])).sort("name")
-        
-        # Create a dictionary of pitcher IDs and names
-        pitcher_dict = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['name']))
-        
-        # Return a select input for choosing a pitcher
-        return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict, selectize=True)
-
-    @render.ui
-    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
-    def date_id():
-        # Create a date range input for selecting the date range within the selected year
-        return ui.input_date_range("date_id", "Select Date Range",
-                                   start=f"{int(input.year_input())}-01-01",
-                                   end=f"{int(input.year_input())}-12-31",
-                                   min=f"{int(input.year_input())}-01-01",
-                                    max=f"{int(input.year_input())}-12-31")
-    @output
-    @render.text
-    def status():
-        # Only show status when generating
-        if input.generate == 0:
-            return ""
-        return ""   
-    
-    @output
-    @render.plot
-    @reactive.event(input.generate_plot, ignore_none=False)   
-    def plot():     
-        # Show progress/loading notification
-        with ui.Progress(min=0, max=1) as p:
-            p.set(message="Generating plot", detail="This may take a while...")
-
-            
-            p.set(0.3, "Gathering data...")
-            year_input = int(input.year_input())
-            sport_id = int(input.level_input())
-            player_input = int(input.pitcher_id())
-            start_date = str(input.date_id()[0])
-            end_date = str(input.date_id()[1])
-
-            print(year_input, sport_id, player_input, start_date, end_date)
-
-            df = cached_data()
-            df = df.clone()
-
-            p.set(0.6, "Creating plot...")
-
-  
-            #plt.rcParams["figure.figsize"] = [10,10]
-            fig = plt.figure(figsize=(26,26))
-            plt.rcParams.update({'figure.autolayout': True})
-            fig.set_facecolor('white')
-            sns.set_theme(style="whitegrid", palette=colour_palette)
-            print('this is the one plot')
-
-            gs = gridspec.GridSpec(6, 8,
-                                height_ratios=[5,20,12,36,36,7],
-                                width_ratios=[4,18,18,18,18,18,18,4])
-
-
-            gs.update(hspace=0.2, wspace=0.5)
-
-            # Define the positions of each subplot in the grid
-            ax_headshot = fig.add_subplot(gs[1,1:3])
-            ax_bio = fig.add_subplot(gs[1,3:5])
-            ax_logo = fig.add_subplot(gs[1,5:7])
-
-            ax_season_table = fig.add_subplot(gs[2,1:7])
-
-            ax_plot_1 = fig.add_subplot(gs[3,1:3])
-            ax_plot_2 = fig.add_subplot(gs[3,3:5])
-            ax_plot_3 = fig.add_subplot(gs[3,5:7])
-
-            ax_table = fig.add_subplot(gs[4,1:7])
-
-            ax_footer = fig.add_subplot(gs[-1,1:7])
-            ax_header = fig.add_subplot(gs[0,1:7])
-            ax_left = fig.add_subplot(gs[:,0])
-            ax_right = fig.add_subplot(gs[:,-1])
-
-            # Hide axes for footer, header, left, and right
-            ax_footer.axis('off')
-            ax_header.axis('off')
-            ax_left.axis('off')
-            ax_right.axis('off')
-
-            sns.set_theme(style="whitegrid", palette=colour_palette)
-            fig.set_facecolor('white')
-
-            df_teams = scrape.get_teams()
-
-            player_headshot(player_input=player_input, ax=ax_headshot,sport_id=sport_id,season=year_input)
-            player_bio(pitcher_id=player_input, ax=ax_bio,sport_id=sport_id,year_input=year_input)
-
-            if input.switch():
-
-                # Get the logo URL from the image dictionary using the team abbreviation
-                logo_url = input.logo_select()
-        
-                # Send a GET request to the logo URL
-                response = requests.get(logo_url)
-        
-                # Open the image from the response content
-                img = Image.open(BytesIO(response.content))
-        
-                # Display the image on the axis
-                ax_logo.set_xlim(0, 1.3)
-                ax_logo.set_ylim(0, 1)
-                ax_logo.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper')
-        
-                # Turn off the axis
-                ax_logo.axis('off')
-            
-            else:
-                plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input))
-
-            stat_summary_table(df=df,
-                            ax=ax_season_table,
-                            player_input=player_input,
-                            split=input.split_id(),
-                            sport_id=sport_id,
-                            game_type=[input.type_input()])
-
-            # break_plot(df=df_plot,ax=ax2) 
-            for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax_plot_1,ax_plot_2,ax_plot_3],[1,3,5]):
-                if x == 'velocity_kdes':
-                            velocity_kdes(df,
-                            ax=y,
-                            gs=gs,
-                            gs_x=[3,4],
-                            gs_y=[z,z+2],
-                            fig=fig)
-                if x == 'tj_stuff_roling':
-                    tj_stuff_roling(df=df,
-                                window=int(input.rolling_window()),
-                                ax=y)
-                    
-                if x == 'tj_stuff_roling_game':
-                    tj_stuff_roling_game(df=df, 
-                                        window=int(input.rolling_window()),
-                                        ax=y)
-
-                if x == 'break_plot':
-                    break_plot(df = df,ax=y) 
-
-                if x == 'location_plot_lhb':
-                    location_plot(df = df,ax=y,hand='L')
-
-                if x == 'location_plot_rhb':
-                    location_plot(df = df,ax=y,hand='R')
-
-            summary_table(df=df,
-                        ax=ax_table)
-
-            plot_footer(ax_footer)   
-
-            fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
-
-
-    @output
-    @render.data_frame
-    @reactive.event(input.generate_plot, ignore_none=False)   
-    def grid():
-
-        df = cached_data()
-        df = df.clone()
-        features_table = ['start_speed',
-            'spin_rate',
-            'extension',
-            'ivb',
-            'hb',
-            'x0',
-            'z0']
-        
-        
-        
-        selection = ['game_id','pitcher_id','pitcher_name','batter_id','batter_name','pitcher_hand',
-                      'batter_hand','balls','strikes','play_code','event_type','pitch_type','vaa','haa']+features_table+['tj_stuff_plus']
-
-
-
-        return render.DataGrid(
-                df.select(selection).to_pandas().round(1),
-                row_selection_mode='multiple',
-                 height='700px',
-                width='fit-content',
-                filters=True,
-            )
-    
-
-app = App(app_ui, server)
-
-            
-
-app = App(app_ui, server)
+from shiny import App, ui, render, reactive
+import polars as pl
+import numpy as np
+import pandas as pd
+import api_scraper
+scrape = api_scraper.MLB_Scrape()
+from functions import df_update
+from functions import pitch_summary_functions
+update = df_update.df_update()
+from stuff_model import feature_engineering as fe
+from stuff_model import stuff_apply
+import requests
+import joblib
+from matplotlib.gridspec import GridSpec
+import math
+from pytabulator import TableOptions, Tabulator, output_tabulator, render_tabulator, theme
+theme.tabulator_site()
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+
+# df = pl.read_csv("data.csv")
+# df = pl.read_parquet("data_small.parquet")[:]
+# df = pl.read_parquet("data.parquet")[:]
+# print('df')
+season = 2024
+
+df_mlb = pl.read_parquet("data/data_mlb_2024.parquet")[:]
+df_aaa = pl.read_parquet("data/data_aaa_2024.parquet")[:]
+df_a = pl.read_parquet("data/data_a_2024.parquet")[:]
+
+
+
+def df_final(df:pl.dataframe,year_input:int,sport_id:int):
+
+    df_schedule = scrape.get_schedule(year_input=[year_input],sport_id=[sport_id])
+    df = df.join(df_schedule, on='game_id', how='left')
+
+    df = df.with_columns(
+        pl.when((pl.col('batter_team_id') == pl.col('away_id')))
+        .then(pl.lit('Away'))
+        .when((pl.col('batter_team_id') == pl.col('home_id')))
+        .then(pl.lit('Home'))
+        .otherwise(None)
+        .alias('home_away')
+    )
+
+    df = df.with_columns(
+        pl.when((pl.col('pitcher_team_id') == pl.col('away_id')))
+        .then(pl.lit('Away'))
+        .when((pl.col('pitcher_team_id') == pl.col('home_id')))
+        .then(pl.lit('Home'))
+        .otherwise(None)
+        .alias('home_away_pitcher')
+    )
+
+
+    print('schedule')
+
+    df_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df))
+    print('stuff')
+    df_up = update.update(df)
+    print('update')
+    df_total = df_up.join(df_stuff[['play_id','tj_stuff_plus']], on='play_id', how='left')
+    print('total')
+    return df_total
+
+
+df_mlb_total = df_final(df=df_mlb,year_input=season,sport_id=1)
+df_aaa_total = df_final(df=df_aaa,year_input=season,sport_id=11)
+df_a_total = df_final(df=df_a.drop_nulls(subset=['start_speed']),year_input=season,sport_id=14)
+
+rounding_dict = {
+    'pa': 0,
+    'bip': 0,
+    'hits': 0,
+    'k': 0,
+    'bb': 0,
+    'max_launch_speed': 1,
+    'launch_speed_90': 1,
+    'launch_speed': 1,
+    'pitches': 0,
+    'tj_stuff_plus_avg': 0,
+    'avg': 3,
+    'obp': 3,
+    'slg': 3,
+    'ops': 3,
+    'k_percent': 3,
+    'bb_percent': 3,
+    'k_minus_bb_percent': 3,
+    'sweet_spot_percent': 3,
+    'woba_percent': 3,
+    'xwoba_percent': 3,
+    'woba_percent_contact': 3,
+    'xwoba_percent_contact': 3,
+    'hard_hit_percent': 3,
+    'barrel_percent': 3,
+    'zone_contact_percent': 3,
+    'zone_swing_percent': 3,
+    'zone_percent': 3,
+    'chase_percent': 3,
+    'chase_contact': 3,
+    'swing_percent': 3,
+    'whiff_rate': 3,
+    'swstr_rate': 3,
+    'ground_ball_percent': 3,
+    'line_drive_percent': 3,
+    'fly_ball_percent': 3,
+    'pop_up_percent': 3,
+    'heart_zone_swing_percent': 3,
+    'shadow_zone_swing_percent': 3,
+    'chase_zone_swing_percent': 3,
+    'waste_zone_swing_percent': 3,
+    'heart_zone_whiff_percent': 3,
+    'shadow_zone_whiff_percent': 3,
+    'chase_zone_whiff_percent': 3,
+    'waste_zone_whiff_percent': 3,
+    'start_speed_avg': 1,
+    'vb_avg': 1,
+    'ivb_avg': 1,
+    'hb_avg': 1,
+    'z0_avg': 1,
+    'x0_avg': 1,
+    'vaa_avg': 1,
+    'haa_avg': 1,
+    'spin_rate_avg': 0,
+    'extension_avg': 1   
+}
+
+columns = [
+    { "title": "PA", "field": "pa", "width": 150},
+    { "title": "BBE", "field": "bip", "width": 150 },
+    { "title": "H", "field": "hits", "width": 150 },
+    { "title": "K", "field": "k", "width": 150 },
+    { "title": "BB", "field": "bb", "width": 150 },
+    { "title": "Max EV", "field": "max_launch_speed", "width": 150 },
+    { "title": "90th% EV", "field": "launch_speed_90", "width": 150 },
+    { "title": "EV", "field": "launch_speed", "width": 150 },
+    { "title": "Pitches", "field": "pitches", "width": 150 },
+    { "title": "AVG", "field": "avg", "width": 150 },
+    { "title": "OBP", "field": "obp", "width": 150 },
+    { "title": "SLG", "field": "slg", "width": 150 },
+    { "title": "OPS", "field": "ops", "width": 150 },
+    { "title": "K%", "field": "k_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "BB%", "field": "bb_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "K-BB%", "field": "k_minus_bb_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "SwSpot%", "field": "sweet_spot_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "wOBA", "field": "woba_percent", "width": 150 },
+    { "title": "xwOBA", "field": "xwoba_percent", "width": 150 },
+    { "title": "wOBACON", "field": "woba_percent_contact", "width": 150 },
+    { "title": "xwOBACON", "field": "xwoba_percent_contact", "width": 150 },
+    { "title": "HardHit%", "field": "hard_hit_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Barrel%", "field": "barrel_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Z-Contact%", "field": "zone_contact_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Z-Swing%", "field": "zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Zone%", "field": "zone_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "O-Swing%", "field": "chase_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "O-Contact%", "field": "chase_contact", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Swing%", "field": "swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Whiff%", "field": "whiff_rate", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "SwStr%", "field": "swstr_rate", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "GB%", "field": "ground_ball_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "LD%", "field": "line_drive_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "FB%", "field": "fly_ball_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "PU%", "field": "pop_up_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Heart Swing%", "field": "heart_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Shadow Swing%", "field": "shadow_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Chase Swing%", "field": "chase_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Waste Swing%", "field": "waste_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Heart Whiff%", "field": "heart_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Shadow Whiff%", "field": "shadow_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Chase Whiff%", "field": "chase_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "Waste Whiff%", "field": "waste_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
+    { "title": "tjStuff+", "field": "tj_stuff_plus_avg", "width": 150 },
+    { "title": "Velocity", "field": "start_speed_avg", "width": 150 },
+    { "title": "Extension", "field": "extension_avg", "width": 150 },
+    { "title": "VB", "field": "vb_avg", "width": 150 },
+    { "title": "iVB", "field": "ivb_avg", "width": 150 },
+    { "title": "HB", "field": "hb_avg", "width": 150 },
+    { "title": "vRel", "field": "z0_avg", "width": 150 },
+    { "title": "hRel", "field": "x0_avg", "width": 150 },
+    { "title": "VAA", "field": "vaa_avg", "width": 150 },
+    { "title": "HAA", "field": "haa_avg", "width": 150 },
+    { "title": "Spin Rate", "field": "spin_rate_avg", "width": 150 },
+    { "title": "Extension", "field": "extension_avg", "width": 150 },
+
+]
+
+stat_titles = dict(zip([col["field"] for col in columns],[col["title"] for col in columns]))
+
+stat_selection = [key for key in stat_titles.keys()]
+
+agg_titles = {'batter_id':'Batter ID',
+    'batter_name':'Batter Name',
+    'batter_team':'Batter Team',
+    'batter_hand':'Batter Hand',
+    'pitcher_id':'Pitcher ID',
+    'pitcher_name':'Pitcher Name',
+    'pitcher_team':'Pitcher Team',
+    'pitcher_hand':'Pitcher Hand',
+    'pitch_type':'Pitch Type',
+    'pitch_group':'Pitch Group',
+    'home_away_batter':'Home/Away Batter',
+    'home_away_pitcher':'Home/Away Pitcher',
+    'is_swing':'Is Swing?',
+    'is_bip':'Is BIP?',
+    'in_zone_final':'In Zone?',
+    'attack_zone_final':'Attack Zone'}
+
+
+columns_group = [
+    { "title": "Batter ID", "field": "batter_id", "width": 150, "headerFilter":"input","frozen":True,},
+    { "title": "Batter Name", "field": "batter_name", "width": 200,"frozen":True, "headerFilter":"input"  },
+    { "title": "Batter Team", "field": "batter_team", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Batter Hand", "field": "batter_hand", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Pitcher ID", "field": "pitcher_id", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Pitcher Name", "field": "pitcher_name", "width": 200,"frozen":True, "headerFilter":"input"  },
+    { "title": "Pitcher Team", "field": "pitcher_team", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Pitcher Hand", "field": "pitcher_hand", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Pitch Type", "field": "pitch_type", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Pitch Group", "field": "pitch_group", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Home/Away Batter", "field": "home_away_batter", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Home/Away Pitcher", "field": "home_away_pitcher", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Is Swing?", "field": "is_swing", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Is BIP?", "field": "is_bip", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "In Zone?", "field": "in_zone_final", "width": 150,"frozen":True, "headerFilter":"input"  },
+    { "title": "Attack Zone", "field": "attack_zone_final", "width": 150,"frozen":True, "headerFilter":"input"  } 
+    ]
+
+
+app_ui = ui.page_sidebar(
+    ui.sidebar(
+        ui.input_selectize(
+            "level_input",
+            "Select Level:",
+            choices=['MLB','AAA','A'],
+            multiple=False,
+            selected=['MLB']
+        ),
+        ui.input_selectize(
+            "list_input",
+            "Select Aggregation:",
+            choices=agg_titles,
+            multiple=True,
+            selected=['batter_id', 'batter_name']
+        ),
+        ui.input_selectize(
+            "list_stats",
+            "Select Stats:",
+            choices=stat_titles,
+            multiple=True,
+            selected=['pa']
+        ),
+        ui.input_date_range(
+            "date_id", 
+            "Select Date Range",
+            start=f'{season}-01-01',
+            end=f'{season}-12-01',
+            min=f'{season}-01-01',
+            max=f'{season}-12-01',
+        ),
+        ui.hr(),
+        ui.h4("Filters"),
+        ui.div(
+            {"id": "filter-container"},
+            ui.div(
+                {"class": "filter-row", "id": "filter_row_1"},  # Add id for deletion
+                ui.row(
+                    ui.column(5,  # Adjusted column widths to make room for delete button
+                        ui.input_select(
+                            "filter_column_1",
+                            "Metric",
+                            choices={}
+                        )
+                    ),
+                    ui.column(3,
+                        ui.input_select(
+                            "filter_operator_1",
+                            "Operator",
+                            choices=[">=", "<="]
+                        ),
+                    ),
+                    ui.column(3,
+                        ui.input_numeric(
+                            "filter_value_1",
+                            "Value",
+                            value=0
+                        )
+                    ),
+                    ui.column(1,
+                        ui.markdown("&nbsp;"),
+                 
+
+                        ui.input_action_button(
+                            f"delete_filter_1",
+                            "",
+                            class_="btn-danger btn-sm",
+                            style="padding: 3px 6px;",
+                            icon='✖'
+                        
+                    )
+                    )
+                )
+            )
+        ),
+        ui.input_action_button(
+            "add_filter",
+            "Add Filter",
+            class_="btn-secondary"
+        ),
+        ui.br(),
+        ui.br(),
+        ui.input_action_button(
+            "generate_table",
+            "Generate Table",
+            class_="btn-primary"
+        ),
+        width="400px"
+    ),
+    ui.navset_tab(
+        ui.nav_panel("Leaderboard",
+            ui.card(
+                #ui.card_header("Leaderboard"),
+                output_tabulator("tabulator")
+            )
+        ),
+
+    )
+)
+
+def server(input, output, session):
+    # Store the number of active filters
+    filter_count = reactive.value(1)
+    # Store active filter IDs
+    active_filters = reactive.value([1])
+
+    @reactive.effect
+    @reactive.event(input.list_stats)
+    def _():
+        stat_choices = {k: k for k in input.list_stats()}
+        filtered_stat_choices = {key: stat_titles[key] for key in stat_choices}   
+        ui.update_select("filter_column_1", choices=filtered_stat_choices)
+
+    @reactive.effect
+    @reactive.event(input.add_filter)
+    def _():
+        current_count = filter_count.get()
+        new_count = current_count + 1
+        
+        stat_choices = {k: k for k in input.list_stats()}
+        filtered_stat_choices = {key: stat_titles[key] for key in stat_choices}   
+        
+        ui.insert_ui(
+            selector="#filter-container",
+            where="beforeEnd",
+            ui=ui.div(
+                {"class": "filter-row", "id": f"filter_row_{new_count}"},
+                ui.row(
+                    ui.column(5,
+                        ui.input_select(
+                            f"filter_column_{new_count}",
+                            "Metric",
+                            choices=filtered_stat_choices
+                        ),
+                    ),
+                    ui.column(3,
+                        ui.input_select(
+                            f"filter_operator_{new_count}",
+                            "Operator",
+                            choices=[">=", "<="]
+                        ),
+                    ),
+                    ui.column(3,
+                        ui.input_numeric(
+                            f"filter_value_{new_count}",
+                            "Value",
+                            value=0
+                        )
+                    ),
+                    ui.column(1,
+                        ui.markdown("&nbsp;"),
+                 
+
+                        ui.input_action_button(
+                            f"delete_filter_{new_count}",
+                            "",
+                            class_="btn-danger btn-sm",
+                            style="padding: 3px 6px;",
+                            icon='✖'
+                        
+                    )
+                    )
+                )
+            )
+        )
+        filter_count.set(new_count)
+        current_filters = active_filters.get()
+        current_filters.append(new_count)
+        active_filters.set(current_filters)
+
+    @reactive.effect
+    def _():
+        # Monitor all possible delete buttons
+        for i in range(1, filter_count.get() + 1):
+            try:
+                if getattr(input, f"delete_filter_{i}")() > 0:
+                    # Remove the filter row
+                    ui.remove_ui(f"#filter_row_{i}")
+                    # Update active filters
+                    current_filters = active_filters.get()
+                    if i in current_filters:
+                        current_filters.remove(i)
+                        active_filters.set(current_filters)
+            except:
+                continue
+
+    @output
+    @render_tabulator
+    @reactive.event(input.generate_table, ignore_none=False) 
+    def tabulator():
+        columns_c = columns.copy()
+        selection_list = list(input.list_input())
+        start_date = str(input.date_id()[0])
+        end_date = str(input.date_id()[1])
+        
+
+        if input.level_input() == "MLB":
+            df_agg = update.update_summary_select(df=df_mlb_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
+                                    selection=selection_list)
+
+        elif input.level_input() == "AAA":
+            df_agg = update.update_summary_select(df=df_aaa_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
+                                    selection=selection_list)
+        
+        elif input.level_input() == "A":
+            df_agg = update.update_summary_select(df=df_a_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
+                                    selection=selection_list)
+                
+        
+        df_agg = df_agg.select(selection_list + list(input.list_stats()))#.sort('pa', descending=True)
+        
+        # Apply filters - only for active filters
+        for i in active_filters.get():
+            try:
+                col_name = getattr(input, f"filter_column_{i}")()
+                if col_name:  # Only apply filter if column is selected
+                    operator = getattr(input, f"filter_operator_{i}")()
+                    if col_name in [col["field"] for col in columns_c if col.get("formatter") == "money"]:
+                        value =  getattr(input, f"filter_value_{i}")()/100
+                    else:
+                        value = getattr(input, f"filter_value_{i}")()
+                
+                    if operator == ">=":
+                        df_agg = df_agg.filter(pl.col(col_name) >= value)
+                    elif operator == "<=":
+                        df_agg = df_agg.filter(pl.col(col_name) <= value)
+            except:
+                continue
+        
+        for col in df_agg.columns[len(selection_list):]:
+            if col in rounding_dict:
+                df_agg = df_agg.with_columns(pl.col(col).round(rounding_dict[col]))
+
+        for column in columns_c:
+            if column.get("formatter") == "money" and column.get("field") in df_agg.columns:
+                df_agg = df_agg.with_columns(pl.col(column.get("field"))*100)
+
+        col_group = []
+        for column in columns_group:
+            if column.get("field") in df_agg.columns:
+                col_group.append(column)
+
+        col_group_stats = []
+        for column in columns_c:
+            if column.get("field") in df_agg.columns:
+                col_group_stats.append(column)
+
+        columns_c = col_group + col_group_stats
+        
+        return Tabulator(
+            df_agg.to_pandas(),
+            table_options=TableOptions(
+                height=800,
+                columns=columns_c,
+            )
+        )
+
+app = App(app_ui, server)

data/data_a_2024.parquet

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+size 44964738

data/data_aaa_2024.parquet

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+oid sha256:cf48b06263a3b0a25b701e98c589f7e11bdf0ec3665562082ea209a61b55468e
+size 117704536

data/data_mlb_2024.parquet

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+version https://git-lfs.github.com/spec/v1
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+size 128295721

functions/df_update.py

@@ -1,475 +1,579 @@
-import polars as pl
-import numpy as np
-import joblib
-
-loaded_model = joblib.load('joblib_model/barrel_model.joblib')
-in_zone_model = joblib.load('joblib_model/in_zone_model_knn_20240410.joblib')
-attack_zone_model = joblib.load('joblib_model/model_attack_zone.joblib')
-xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
-px_model = joblib.load('joblib_model/linear_reg_model_x.joblib')
-pz_model = joblib.load('joblib_model/linear_reg_model_z.joblib')
-
-
-class df_update:
-    def __init__(self):
-        pass
-
-    def update(self, df_clone: pl.DataFrame):
-
-        df = df_clone.clone()
-        # Assuming px_model is defined and df is your DataFrame
-        hit_codes = ['single',
-            'double','home_run', 'triple']
-
-        ab_codes = ['single', 'strikeout', 'field_out',
-            'grounded_into_double_play', 'fielders_choice', 'force_out',
-            'double', 'field_error', 'home_run', 'triple',
-            'double_play',
-            'fielders_choice_out', 'strikeout_double_play',
-            'other_out','triple_play']
-
-
-        obp_true_codes = ['single', 'walk',
-            'double','home_run', 'triple',
-            'hit_by_pitch', 'intent_walk']
-
-        obp_codes = ['single', 'strikeout', 'walk', 'field_out',
-            'grounded_into_double_play', 'fielders_choice', 'force_out',
-            'double', 'sac_fly', 'field_error', 'home_run', 'triple',
-            'hit_by_pitch', 'double_play', 'intent_walk',
-            'fielders_choice_out', 'strikeout_double_play',
-            'sac_fly_double_play',
-            'other_out','triple_play']
-
-
-        contact_codes = ['In play, no out',
-                'Foul', 'In play, out(s)',
-            'In play, run(s)',
-            'Foul Bunt']
-
-        bip_codes = ['In play, no out', 'In play, run(s)','In play, out(s)']
-
-
-        conditions_barrel = [
-            df['launch_speed'].is_null(),
-            (df['launch_speed'] * 1.5 - df['launch_angle'] >= 117) & 
-            (df['launch_speed'] + df['launch_angle'] >= 124) & 
-            (df['launch_speed'] >= 98) & 
-            (df['launch_angle'] >= 4) & (df['launch_angle'] <= 50)
-        ]
-        choices_barrel = [False, True]
-
-        conditions_tb = [
-            (df['event_type'] == 'single'),
-            (df['event_type'] == 'double'),
-            (df['event_type'] == 'triple'),
-            (df['event_type'] == 'home_run')
-        ]
-        choices_tb = [1, 2, 3, 4]
-
-
-        conditions_woba = [
-            df['event_type'].is_in(['strikeout', 'field_out', 'sac_fly', 'force_out', 'grounded_into_double_play', 'fielders_choice', 'field_error', 'sac_bunt', 'double_play', 'fielders_choice_out', 'strikeout_double_play', 'sac_fly_double_play', 'other_out']),
-            df['event_type'] == 'walk',
-            df['event_type'] == 'hit_by_pitch',
-            df['event_type'] == 'single',
-            df['event_type'] == 'double',
-            df['event_type'] == 'triple',
-            df['event_type'] == 'home_run'
-        ]
-        choices_woba = [0, 0.689, 0.720, 0.881, 1.254, 1.589, 2.048]
-
-        woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch', 'double', 'sac_fly', 'force_out', 'home_run', 'grounded_into_double_play', 'fielders_choice', 'field_error', 'triple', 'sac_bunt', 'double_play', 'fielders_choice_out', 'strikeout_double_play', 'sac_fly_double_play', 'other_out']
-
-        pitch_cat = {'FA': 'Fastball',
-                    'FF': 'Fastball',
-                    'FT': 'Fastball',
-                    'FC': 'Fastball',
-                    'FS': 'Off-Speed',
-                    'FO': 'Off-Speed',
-                    'SI': 'Fastball',
-                    'ST': 'Breaking',
-                    'SL': 'Breaking',
-                    'CU': 'Breaking',
-                    'KC': 'Breaking',
-                    'SC': 'Off-Speed',
-                    'GY': 'Off-Speed',
-                    'SV': 'Breaking',
-                    'CS': 'Breaking',
-                    'CH': 'Off-Speed',
-                    'KN': 'Off-Speed',
-                    'EP': 'Breaking',
-                    'UN': None,
-                    'IN': None,
-                    'PO': None,
-                    'AB': None,
-                    'AS': None,
-                    'NP': None}
-
-
-        df = df.with_columns([
-            pl.when(df['type_ab'].is_not_null()).then(1).otherwise(0).alias('pa'),
-            pl.when(df['is_pitch']).then(1).otherwise(0).alias('pitches'),
-            pl.when(df['sz_top'] == 0).then(None).otherwise(df['sz_top']).alias('sz_top'),
-            pl.when(df['sz_bot'] == 0).then(None).otherwise(df['sz_bot']).alias('sz_bot'),
-            pl.when(df['zone'] > 0).then(df['zone'] < 10).otherwise(None).alias('in_zone'),
-            pl.Series(px_model.predict(df[['x']].fill_null(0).to_numpy())[:, 0]).alias('px_predict'),
-            pl.Series(pz_model.predict(df[['y']].fill_null(0).to_numpy())[:, 0] + 3.2).alias('pz_predict'),
-            pl.Series(in_zone_model.predict(df[['px','pz','sz_top','sz_bot']].fill_null(0).to_numpy())[:]).alias('in_zone_predict'),
-            pl.Series(attack_zone_model.predict(df[['px','pz','sz_top','sz_bot']].fill_null(0).to_numpy())[:]).alias('attack_zone_predict'),
-            pl.when(df['event_type'].is_in(hit_codes)).then(True).otherwise(False).alias('hits'),
-            pl.when(df['event_type'].is_in(ab_codes)).then(True).otherwise(False).alias('ab'),
-            pl.when(df['event_type'].is_in(obp_true_codes)).then(True).otherwise(False).alias('on_base'),
-            pl.when(df['event_type'].is_in(obp_codes)).then(True).otherwise(False).alias('obp'),
-            pl.when(df['play_description'].is_in(bip_codes)).then(True).otherwise(False).alias('bip'),
-            pl.when(conditions_barrel[0]).then(choices_barrel[0]).when(conditions_barrel[1]).then(choices_barrel[1]).otherwise(None).alias('barrel'),
-            pl.when(df['launch_angle'].is_null()).then(False).when((df['launch_angle'] >= 8) & (df['launch_angle'] <= 32)).then(True).otherwise(None).alias('sweet_spot'),
-            pl.when(df['launch_speed'].is_null()).then(False).when(df['launch_speed'] >= 94.5).then(True).otherwise(None).alias('hard_hit'),
-            pl.when(conditions_tb[0]).then(choices_tb[0]).when(conditions_tb[1]).then(choices_tb[1]).when(conditions_tb[2]).then(choices_tb[2]).when(conditions_tb[3]).then(choices_tb[3]).otherwise(None).alias('tb'),
-            pl.when(conditions_woba[0]).then(choices_woba[0]).when(conditions_woba[1]).then(choices_woba[1]).when(conditions_woba[2]).then(choices_woba[2]).when(conditions_woba[3]).then(choices_woba[3]).when(conditions_woba[4]).then(choices_woba[4]).when(conditions_woba[5]).then(choices_woba[5]).when(conditions_woba[6]).then(choices_woba[6]).otherwise(None).alias('woba'),
-            pl.when((df['play_code'] == 'S') | (df['play_code'] == 'W') | (df['play_code'] == 'T')).then(1).otherwise(0).alias('whiffs'),
-            pl.when((df['play_code'] == 'S') | (df['play_code'] == 'W') | (df['play_code'] == 'T') | (df['play_code'] == 'C')).then(1).otherwise(0).alias('csw'),
-            pl.when(pl.col('is_swing').cast(pl.Boolean)).then(1).otherwise(0).alias('swings'),
-            pl.col('event_type').is_in(['strikeout','strikeout_double_play']).alias('k'),
-            pl.col('event_type').is_in(['walk', 'intent_walk']).alias('bb'),
-            pl.lit(None).alias('attack_zone'),
-            pl.lit(None).alias('woba_pred'),
-            pl.lit(None).alias('woba_pred_contact')
-
-        ])
-
-        df = df.with_columns([
-            pl.when(df['event_type'].is_in(woba_codes)).then(1).otherwise(None).alias('woba_codes'),
-            pl.when(df['event_type'].is_in(woba_codes)).then(1).otherwise(None).alias('xwoba_codes'),
-            pl.when((pl.col('tb') >= 0)).then(df['woba']).otherwise(None).alias('woba_contact'),
-            pl.when(pl.col('px').is_null()).then(pl.col('px_predict')).otherwise(pl.col('px')).alias('px'),
-            pl.when(pl.col('pz').is_null()).then(pl.col('pz_predict')).otherwise(pl.col('pz')).alias('pz'),
-            pl.when(pl.col('in_zone').is_null()).then(pl.col('in_zone_predict')).otherwise(pl.col('in_zone')).alias('in_zone'),
-            pl.when(df['launch_speed'].is_null()).then(None).otherwise(df['barrel']).alias('barrel'),
-            pl.lit('average').alias('average'),
-                pl.when(pl.col('in_zone') == False).then(True).otherwise(False).alias('out_zone'),
-            pl.when((pl.col('in_zone') == True) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('zone_swing'),
-            pl.when((pl.col('in_zone') == True) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('zone_contact'),
-            pl.when((pl.col('in_zone') == False) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('ozone_swing'),
-            pl.when((pl.col('in_zone') == False) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('ozone_contact'),
-            pl.when(pl.col('event_type').str.contains('strikeout')).then(True).otherwise(False).alias('k'),
-            pl.when(pl.col('event_type').is_in(['walk', 'intent_walk'])).then(True).otherwise(False).alias('bb'),
-            pl.when(pl.col('attack_zone').is_null()).then(pl.col('attack_zone_predict')).otherwise(pl.col('attack_zone')).alias('attack_zone'),
-            
-
-        ])
-
-        df = df.with_columns([
-            (df['k'].cast(pl.Float32) - df['bb'].cast(pl.Float32)).alias('k_minus_bb'),
-            (df['bb'].cast(pl.Float32) - df['k'].cast(pl.Float32)).alias('bb_minus_k'),
-            (df['launch_speed'] > 0).alias('bip_div'),
-            (df['attack_zone'] == 0).alias('heart'),
-            (df['attack_zone'] == 1).alias('shadow'),
-            (df['attack_zone'] == 2).alias('chase'),
-            (df['attack_zone'] == 3).alias('waste'),
-            ((df['attack_zone'] == 0) & (df['swings'] == 1)).alias('heart_swing'),
-            ((df['attack_zone'] == 1) & (df['swings'] == 1)).alias('shadow_swing'),
-            ((df['attack_zone'] == 2) & (df['swings'] == 1)).alias('chase_swing'),
-            ((df['attack_zone'] == 3) & (df['swings'] == 1)).alias('waste_swing'),
-            ((df['attack_zone'] == 0) & (df['whiffs'] == 1)).alias('heart_whiff'),
-            ((df['attack_zone'] == 1) & (df['whiffs'] == 1)).alias('shadow_whiff'),
-            ((df['attack_zone'] == 2) & (df['whiffs'] == 1)).alias('chase_whiff'),
-            ((df['attack_zone'] == 3) & (df['whiffs'] == 1)).alias('waste_whiff')
-        ])
-
-
-        [0, 0.689, 0.720, 0.881, 1.254, 1.589, 2.048]
-
-        df = df.with_columns([
-                pl.Series(
-                    [sum(x) for x in xwoba_model.predict_proba(df[['launch_angle', 'launch_speed']].fill_null(0).to_numpy()[:]) * ([0, 0.881, 1.254, 1.589, 2.048])]
-                ).alias('woba_pred_predict')
-            ])
-
-        df = df.with_columns([
-            pl.when(pl.col('event_type').is_in(['walk'])).then(0.689)
-            .when(pl.col('event_type').is_in(['hit_by_pitch'])).then(0.720)
-            .when(pl.col('event_type').is_in(['strikeout', 'strikeout_double_play'])).then(0)
-            .otherwise(pl.col('woba_pred_predict')).alias('woba_pred_predict')
-        ])
-
-        df = df.with_columns([
-            pl.when(pl.col('woba_codes').is_null()).then(None).otherwise(pl.col('woba_pred_predict')).alias('woba_pred'),
-            pl.when(pl.col('bip')!=1).then(None).otherwise(pl.col('woba_pred_predict')).alias('woba_pred_contact'),
-        ])
-
-        df = df.with_columns([
-            pl.when(pl.col('trajectory').is_in(['bunt_popup'])).then(pl.lit('popup'))
-            .when(pl.col('trajectory').is_in(['bunt_grounder'])).then(pl.lit('ground_ball'))
-            .when(pl.col('trajectory').is_in(['bunt_line_drive'])).then(pl.lit('line_drive'))
-            .when(pl.col('trajectory').is_in([''])).then(pl.lit(None))
-            .otherwise(pl.col('trajectory')).alias('trajectory')
-        ])
-
-
-        # Create one-hot encoded columns for the trajectory column
-        dummy_df = df.select(pl.col('trajectory')).to_dummies()
-
-        # Rename the one-hot encoded columns
-        dummy_df = dummy_df.rename({
-            'trajectory_fly_ball': 'trajectory_fly_ball',
-            'trajectory_ground_ball': 'trajectory_ground_ball',
-            'trajectory_line_drive': 'trajectory_line_drive',
-            'trajectory_popup': 'trajectory_popup'
-        })
-
-        # Ensure the columns are present in the DataFrame
-        for col in ['trajectory_fly_ball', 'trajectory_ground_ball', 'trajectory_line_drive', 'trajectory_popup']:
-            if col not in dummy_df.columns:
-                dummy_df = dummy_df.with_columns(pl.lit(0).alias(col))
-
-        # Join the one-hot encoded columns back to the original DataFrame
-        df = df.hstack(dummy_df)
-
-        # Check if 'trajectory_null' column exists and drop it
-        if 'trajectory_null' in df.columns:
-            df = df.drop('trajectory_null')
-            
-        return df
-
-    # Assuming df is your Polars DataFrame
-    def update_summary(self, df: pl.DataFrame, pitcher: bool = True) -> pl.DataFrame:
-        """
-        Update summary statistics for pitchers or batters.
-
-        Parameters:
-        df (pl.DataFrame): The input Polars DataFrame containing player statistics.
-        pitcher (bool): A flag indicating whether to calculate statistics for pitchers (True) or batters (False).
-
-        Returns:
-        pl.DataFrame: A Polars DataFrame with aggregated and calculated summary statistics.
-        """
-
-        # Determine the position based on the pitcher flag
-        if pitcher:
-            position = 'pitcher'
-        else:
-            position = 'batter'
-
-        # Group by position_id and position_name, then aggregate various statistics
-        df_summ = df.group_by([f'{position}_id', f'{position}_name']).agg([
-            pl.col('pa').sum().alias('pa'),
-            pl.col('ab').sum().alias('ab'),
-            pl.col('obp').sum().alias('obp_pa'),
-            pl.col('hits').sum().alias('hits'),
-            pl.col('on_base').sum().alias('on_base'),
-            pl.col('k').sum().alias('k'),
-            pl.col('bb').sum().alias('bb'),
-            pl.col('bb_minus_k').sum().alias('bb_minus_k'),
-            pl.col('csw').sum().alias('csw'),
-            pl.col('bip').sum().alias('bip'),
-            pl.col('bip_div').sum().alias('bip_div'),
-            pl.col('tb').sum().alias('tb'),
-            pl.col('woba').sum().alias('woba'),
-            pl.col('woba_contact').sum().alias('woba_contact'),
-            pl.col('woba_pred').sum().alias('xwoba'),
-            pl.col('woba_pred_contact').sum().alias('xwoba_contact'),
-            pl.col('woba_codes').sum().alias('woba_codes'),
-            pl.col('xwoba_codes').sum().alias('xwoba_codes'),
-            pl.col('hard_hit').sum().alias('hard_hit'),
-            pl.col('barrel').sum().alias('barrel'),
-            pl.col('sweet_spot').sum().alias('sweet_spot'),
-            pl.col('launch_speed').max().alias('max_launch_speed'),
-            pl.col('launch_speed').quantile(0.90).alias('launch_speed_90'),
-            pl.col('launch_speed').mean().alias('launch_speed'),
-            pl.col('launch_angle').mean().alias('launch_angle'),
-            pl.col('is_pitch').sum().alias('pitches'),
-            pl.col('swings').sum().alias('swings'),
-            pl.col('in_zone').sum().alias('in_zone'),
-            pl.col('out_zone').sum().alias('out_zone'),
-            pl.col('whiffs').sum().alias('whiffs'),
-            pl.col('zone_swing').sum().alias('zone_swing'),
-            pl.col('zone_contact').sum().alias('zone_contact'),
-            pl.col('ozone_swing').sum().alias('ozone_swing'),
-            pl.col('ozone_contact').sum().alias('ozone_contact'),
-            pl.col('trajectory_ground_ball').sum().alias('ground_ball'),
-            pl.col('trajectory_line_drive').sum().alias('line_drive'),
-            pl.col('trajectory_fly_ball').sum().alias('fly_ball'),
-            pl.col('trajectory_popup').sum().alias('pop_up'),
-            pl.col('attack_zone').count().alias('attack_zone'),
-            pl.col('heart').sum().alias('heart'),
-            pl.col('shadow').sum().alias('shadow'),
-            pl.col('chase').sum().alias('chase'),
-            pl.col('waste').sum().alias('waste'),
-            pl.col('heart_swing').sum().alias('heart_swing'),
-            pl.col('shadow_swing').sum().alias('shadow_swing'),
-            pl.col('chase_swing').sum().alias('chase_swing'),
-            pl.col('waste_swing').sum().alias('waste_swing'),
-            pl.col('heart_whiff').sum().alias('heart_whiff'),
-            pl.col('shadow_whiff').sum().alias('shadow_whiff'),
-            pl.col('chase_whiff').sum().alias('chase_whiff'),
-            pl.col('waste_whiff').sum().alias('waste_whiff')
-        ])
-
-        # Add calculated columns to the summary DataFrame
-        df_summ = df_summ.with_columns([
-            (pl.col('hits') / pl.col('ab')).alias('avg'),
-            (pl.col('on_base') / pl.col('obp_pa')).alias('obp'),
-            (pl.col('tb') / pl.col('ab')).alias('slg'),
-            (pl.col('on_base') / pl.col('obp_pa') + pl.col('tb') / pl.col('ab')).alias('ops'),
-            (pl.col('k') / pl.col('pa')).alias('k_percent'),
-            (pl.col('bb') / pl.col('pa')).alias('bb_percent'),
-            (pl.col('bb_minus_k') / pl.col('pa')).alias('bb_minus_k_percent'),
-            (pl.col('bb') / pl.col('k')).alias('bb_over_k_percent'),
-            (pl.col('csw') / pl.col('pitches')).alias('csw_percent'),
-            (pl.col('sweet_spot') / pl.col('bip_div')).alias('sweet_spot_percent'),
-            (pl.col('woba') / pl.col('woba_codes')).alias('woba_percent'),
-            (pl.col('woba_contact') / pl.col('bip')).alias('woba_percent_contact'),
-            (pl.col('hard_hit') / pl.col('bip_div')).alias('hard_hit_percent'),
-            (pl.col('barrel') / pl.col('bip_div')).alias('barrel_percent'),
-            (pl.col('zone_contact') / pl.col('zone_swing')).alias('zone_contact_percent'),
-            (pl.col('zone_swing') / pl.col('in_zone')).alias('zone_swing_percent'),
-            (pl.col('in_zone') / pl.col('pitches')).alias('zone_percent'),
-            (pl.col('ozone_swing') / (pl.col('pitches') - pl.col('in_zone'))).alias('chase_percent'),
-            (pl.col('ozone_contact') / pl.col('ozone_swing')).alias('chase_contact'),
-            (pl.col('swings') / pl.col('pitches')).alias('swing_percent'),
-            (pl.col('whiffs') / pl.col('swings')).alias('whiff_rate'),
-            (pl.col('whiffs') / pl.col('pitches')).alias('swstr_rate'),
-            (pl.col('ground_ball') / pl.col('bip')).alias('ground_ball_percent'),
-            (pl.col('line_drive') / pl.col('bip')).alias('line_drive_percent'),
-            (pl.col('fly_ball') / pl.col('bip')).alias('fly_ball_percent'),
-            (pl.col('pop_up') / pl.col('bip')).alias('pop_up_percent'),
-            (pl.col('heart') / pl.col('attack_zone')).alias('heart_zone_percent'),
-            (pl.col('shadow') / pl.col('attack_zone')).alias('shadow_zone_percent'),
-            (pl.col('chase') / pl.col('attack_zone')).alias('chase_zone_percent'),
-            (pl.col('waste') / pl.col('attack_zone')).alias('waste_zone_percent'),
-            (pl.col('heart_swing') / pl.col('heart')).alias('heart_zone_swing_percent'),
-            (pl.col('shadow_swing') / pl.col('shadow')).alias('shadow_zone_swing_percent'),
-            (pl.col('chase_swing') / pl.col('chase')).alias('chase_zone_swing_percent'),
-            (pl.col('waste_swing') / pl.col('waste')).alias('waste_zone_swing_percent'),
-            (pl.col('heart_whiff') / pl.col('heart_swing')).alias('heart_zone_whiff_percent'),
-            (pl.col('shadow_whiff') / pl.col('shadow_swing')).alias('shadow_zone_whiff_percent'),
-            (pl.col('chase_whiff') / pl.col('chase_swing')).alias('chase_zone_whiff_percent'),
-            (pl.col('waste_whiff') / pl.col('waste_swing')).alias('waste_zone_whiff_percent'),
-            (pl.col('xwoba') / pl.col('xwoba_codes')).alias('xwoba_percent'),
-            (pl.col('xwoba_contact') / pl.col('bip')).alias('xwoba_percent_contact')
-        ])
-
-        return df_summ
-    
-
-
-
-
-    
-    # Assuming df is your Polars DataFrame
-    def update_summary_select(self, df: pl.DataFrame, selection: list) -> pl.DataFrame:
-        """
-        Update summary statistics for pitchers or batters.
-
-        Parameters:
-        df (pl.DataFrame): The input Polars DataFrame containing player statistics.
-        pitcher (bool): A flag indicating whether to calculate statistics for pitchers (True) or batters (False).
-
-        Returns:
-        pl.DataFrame: A Polars DataFrame with aggregated and calculated summary statistics.
-        """
-
-        # Group by position_id and position_name, then aggregate various statistics
-        df_summ = df.group_by(selection).agg([
-            pl.col('pa').sum().alias('pa'),
-            pl.col('ab').sum().alias('ab'),
-            pl.col('obp').sum().alias('obp_pa'),
-            pl.col('hits').sum().alias('hits'),
-            pl.col('on_base').sum().alias('on_base'),
-            pl.col('k').sum().alias('k'),
-            pl.col('bb').sum().alias('bb'),
-            pl.col('bb_minus_k').sum().alias('bb_minus_k'),
-            pl.col('csw').sum().alias('csw'),
-            pl.col('bip').sum().alias('bip'),
-            pl.col('bip_div').sum().alias('bip_div'),
-            pl.col('tb').sum().alias('tb'),
-            pl.col('woba').sum().alias('woba'),
-            pl.col('woba_contact').sum().alias('woba_contact'),
-            pl.col('woba_pred').sum().alias('xwoba'),
-            pl.col('woba_pred_contact').sum().alias('xwoba_contact'),
-            pl.col('woba_codes').sum().alias('woba_codes'),
-            pl.col('xwoba_codes').sum().alias('xwoba_codes'),
-            pl.col('hard_hit').sum().alias('hard_hit'),
-            pl.col('barrel').sum().alias('barrel'),
-            pl.col('sweet_spot').sum().alias('sweet_spot'),
-            pl.col('launch_speed').max().alias('max_launch_speed'),
-            pl.col('launch_speed').quantile(0.90).alias('launch_speed_90'),
-            pl.col('launch_speed').mean().alias('launch_speed'),
-            pl.col('launch_angle').mean().alias('launch_angle'),
-            pl.col('is_pitch').sum().alias('pitches'),
-            pl.col('swings').sum().alias('swings'),
-            pl.col('in_zone').sum().alias('in_zone'),
-            pl.col('out_zone').sum().alias('out_zone'),
-            pl.col('whiffs').sum().alias('whiffs'),
-            pl.col('zone_swing').sum().alias('zone_swing'),
-            pl.col('zone_contact').sum().alias('zone_contact'),
-            pl.col('ozone_swing').sum().alias('ozone_swing'),
-            pl.col('ozone_contact').sum().alias('ozone_contact'),
-            pl.col('trajectory_ground_ball').sum().alias('ground_ball'),
-            pl.col('trajectory_line_drive').sum().alias('line_drive'),
-            pl.col('trajectory_fly_ball').sum().alias('fly_ball'),
-            pl.col('trajectory_popup').sum().alias('pop_up'),
-            pl.col('attack_zone').count().alias('attack_zone'),
-            pl.col('heart').sum().alias('heart'),
-            pl.col('shadow').sum().alias('shadow'),
-            pl.col('chase').sum().alias('chase'),
-            pl.col('waste').sum().alias('waste'),
-            pl.col('heart_swing').sum().alias('heart_swing'),
-            pl.col('shadow_swing').sum().alias('shadow_swing'),
-            pl.col('chase_swing').sum().alias('chase_swing'),
-            pl.col('waste_swing').sum().alias('waste_swing'),
-            pl.col('heart_whiff').sum().alias('heart_whiff'),
-            pl.col('shadow_whiff').sum().alias('shadow_whiff'),
-            pl.col('chase_whiff').sum().alias('chase_whiff'),
-            pl.col('waste_whiff').sum().alias('waste_whiff'),
-            pl.col('tj_stuff_plus').sum().alias('tj_stuff_plus')
-        ])
-
-        # Add calculated columns to the summary DataFrame
-        df_summ = df_summ.with_columns([
-            (pl.col('hits') / pl.col('ab')).alias('avg'),
-            (pl.col('on_base') / pl.col('obp_pa')).alias('obp'),
-            (pl.col('tb') / pl.col('ab')).alias('slg'),
-            (pl.col('on_base') / pl.col('obp_pa') + pl.col('tb') / pl.col('ab')).alias('ops'),
-            (pl.col('k') / pl.col('pa')).alias('k_percent'),
-            (pl.col('bb') / pl.col('pa')).alias('bb_percent'),
-            (pl.col('bb_minus_k') / pl.col('pa')).alias('bb_minus_k_percent'),
-            (pl.col('bb') / pl.col('k')).alias('bb_over_k_percent'),
-            (pl.col('csw') / pl.col('pitches')).alias('csw_percent'),
-            (pl.col('sweet_spot') / pl.col('bip_div')).alias('sweet_spot_percent'),
-            (pl.col('woba') / pl.col('woba_codes')).alias('woba_percent'),
-            (pl.col('woba_contact') / pl.col('bip')).alias('woba_percent_contact'),
-            (pl.col('hard_hit') / pl.col('bip_div')).alias('hard_hit_percent'),
-            (pl.col('barrel') / pl.col('bip_div')).alias('barrel_percent'),
-            (pl.col('zone_contact') / pl.col('zone_swing')).alias('zone_contact_percent'),
-            (pl.col('zone_swing') / pl.col('in_zone')).alias('zone_swing_percent'),
-            (pl.col('in_zone') / pl.col('pitches')).alias('zone_percent'),
-            (pl.col('ozone_swing') / (pl.col('pitches') - pl.col('in_zone'))).alias('chase_percent'),
-            (pl.col('ozone_contact') / pl.col('ozone_swing')).alias('chase_contact'),
-            (pl.col('swings') / pl.col('pitches')).alias('swing_percent'),
-            (pl.col('whiffs') / pl.col('swings')).alias('whiff_rate'),
-            (pl.col('whiffs') / pl.col('pitches')).alias('swstr_rate'),
-            (pl.col('ground_ball') / pl.col('bip')).alias('ground_ball_percent'),
-            (pl.col('line_drive') / pl.col('bip')).alias('line_drive_percent'),
-            (pl.col('fly_ball') / pl.col('bip')).alias('fly_ball_percent'),
-            (pl.col('pop_up') / pl.col('bip')).alias('pop_up_percent'),
-            (pl.col('heart') / pl.col('attack_zone')).alias('heart_zone_percent'),
-            (pl.col('shadow') / pl.col('attack_zone')).alias('shadow_zone_percent'),
-            (pl.col('chase') / pl.col('attack_zone')).alias('chase_zone_percent'),
-            (pl.col('waste') / pl.col('attack_zone')).alias('waste_zone_percent'),
-            (pl.col('heart_swing') / pl.col('heart')).alias('heart_zone_swing_percent'),
-            (pl.col('shadow_swing') / pl.col('shadow')).alias('shadow_zone_swing_percent'),
-            (pl.col('chase_swing') / pl.col('chase')).alias('chase_zone_swing_percent'),
-            (pl.col('waste_swing') / pl.col('waste')).alias('waste_zone_swing_percent'),
-            (pl.col('heart_whiff') / pl.col('heart_swing')).alias('heart_zone_whiff_percent'),
-            (pl.col('shadow_whiff') / pl.col('shadow_swing')).alias('shadow_zone_whiff_percent'),
-            (pl.col('chase_whiff') / pl.col('chase_swing')).alias('chase_zone_whiff_percent'),
-            (pl.col('waste_whiff') / pl.col('waste_swing')).alias('waste_zone_whiff_percent'),
-            (pl.col('xwoba') / pl.col('xwoba_codes')).alias('xwoba_percent'),
-            (pl.col('xwoba_contact') / pl.col('bip')).alias('xwoba_percent_contact'),
-            (pl.col('tj_stuff_plus') / pl.col('pitches')).alias('tj_stuff_plus_avg'),
-            
-        ])
-
+import polars as pl
+import numpy as np
+import joblib
+
+loaded_model = joblib.load('joblib_model/barrel_model.joblib')
+in_zone_model = joblib.load('joblib_model/in_zone_model_knn_20240410.joblib')
+attack_zone_model = joblib.load('joblib_model/model_attack_zone.joblib')
+xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
+px_model = joblib.load('joblib_model/linear_reg_model_x.joblib')
+pz_model = joblib.load('joblib_model/linear_reg_model_z.joblib')
+
+
+class df_update:
+    def __init__(self):
+        pass
+
+    def update(self, df_clone: pl.DataFrame):
+
+        df = df_clone.clone()
+        # Assuming px_model is defined and df is your DataFrame
+        hit_codes = ['single',
+            'double','home_run', 'triple']
+
+        ab_codes = ['single', 'strikeout', 'field_out',
+            'grounded_into_double_play', 'fielders_choice', 'force_out',
+            'double', 'field_error', 'home_run', 'triple',
+            'double_play',
+            'fielders_choice_out', 'strikeout_double_play',
+            'other_out','triple_play']
+
+
+        obp_true_codes = ['single', 'walk',
+            'double','home_run', 'triple',
+            'hit_by_pitch', 'intent_walk']
+
+        obp_codes = ['single', 'strikeout', 'walk', 'field_out',
+            'grounded_into_double_play', 'fielders_choice', 'force_out',
+            'double', 'sac_fly', 'field_error', 'home_run', 'triple',
+            'hit_by_pitch', 'double_play', 'intent_walk',
+            'fielders_choice_out', 'strikeout_double_play',
+            'sac_fly_double_play',
+            'other_out','triple_play']
+
+
+        contact_codes = ['In play, no out',
+                'Foul', 'In play, out(s)',
+            'In play, run(s)',
+            'Foul Bunt']
+
+        bip_codes = ['In play, no out', 'In play, run(s)','In play, out(s)']
+
+
+        conditions_barrel = [
+            df['launch_speed'].is_null(),
+            (df['launch_speed'] * 1.5 - df['launch_angle'] >= 117) & 
+            (df['launch_speed'] + df['launch_angle'] >= 124) & 
+            (df['launch_speed'] >= 98) & 
+            (df['launch_angle'] >= 4) & (df['launch_angle'] <= 50)
+        ]
+        choices_barrel = [False, True]
+
+        conditions_tb = [
+            (df['event_type'] == 'single'),
+            (df['event_type'] == 'double'),
+            (df['event_type'] == 'triple'),
+            (df['event_type'] == 'home_run')
+        ]
+        choices_tb = [1, 2, 3, 4]
+
+
+        conditions_woba = [
+            df['event_type'].is_in(['strikeout', 'field_out', 'sac_fly', 'force_out', 'grounded_into_double_play', 'fielders_choice', 'field_error', 'sac_bunt', 'double_play', 'fielders_choice_out', 'strikeout_double_play', 'sac_fly_double_play', 'other_out']),
+            df['event_type'] == 'walk',
+            df['event_type'] == 'hit_by_pitch',
+            df['event_type'] == 'single',
+            df['event_type'] == 'double',
+            df['event_type'] == 'triple',
+            df['event_type'] == 'home_run'
+        ]
+        choices_woba = [0, 0.689, 0.720, 0.881, 1.254, 1.589, 2.048]
+
+        woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch', 'double', 'sac_fly', 'force_out', 'home_run', 'grounded_into_double_play', 'fielders_choice', 'field_error', 'triple', 'sac_bunt', 'double_play', 'fielders_choice_out', 'strikeout_double_play', 'sac_fly_double_play', 'other_out']
+
+        pitch_cat = {'FA': 'Fastball',
+                    'FF': 'Fastball',
+                    'FT': 'Fastball',
+                    'FC': 'Fastball',
+                    'FS': 'Off-Speed',
+                    'FO': 'Off-Speed',
+                    'SI': 'Fastball',
+                    'ST': 'Breaking',
+                    'SL': 'Breaking',
+                    'CU': 'Breaking',
+                    'KC': 'Breaking',
+                    'SC': 'Off-Speed',
+                    'GY': 'Off-Speed',
+                    'SV': 'Breaking',
+                    'CS': 'Breaking',
+                    'CH': 'Off-Speed',
+                    'KN': 'Off-Speed',
+                    'EP': 'Breaking',
+                    'UN': None,
+                    'IN': None,
+                    'PO': None,
+                    'AB': None,
+                    'AS': None,
+                    'NP': None}
+
+
+        df = df.with_columns([
+            pl.when(df['type_ab'].is_not_null()).then(1).otherwise(0).alias('pa'),
+            pl.when(df['is_pitch']).then(1).otherwise(0).alias('pitches'),
+            pl.when(df['sz_top'] == 0).then(None).otherwise(df['sz_top']).alias('sz_top'),
+            pl.when(df['sz_bot'] == 0).then(None).otherwise(df['sz_bot']).alias('sz_bot'),
+            pl.when(df['zone'] > 0).then(df['zone'] < 10).otherwise(None).alias('in_zone'),
+            pl.Series(px_model.predict(df[['x']].fill_null(0).to_numpy())[:, 0]).alias('px_predict'),
+            pl.Series(pz_model.predict(df[['y']].fill_null(0).to_numpy())[:, 0] + 3.2).alias('pz_predict'),
+            pl.Series(in_zone_model.predict(df[['px','pz','sz_top','sz_bot']].fill_null(0).to_numpy())[:]).alias('in_zone_predict'),
+            pl.Series(attack_zone_model.predict(df[['px','pz','sz_top','sz_bot']].fill_null(0).to_numpy())[:]).alias('attack_zone_predict'),
+            pl.when(df['event_type'].is_in(hit_codes)).then(True).otherwise(False).alias('hits'),
+            pl.when(df['event_type'].is_in(ab_codes)).then(True).otherwise(False).alias('ab'),
+            pl.when(df['event_type'].is_in(obp_true_codes)).then(True).otherwise(False).alias('on_base'),
+            pl.when(df['event_type'].is_in(obp_codes)).then(True).otherwise(False).alias('obp'),
+            pl.when(df['play_description'].is_in(bip_codes)).then(True).otherwise(False).alias('bip'),
+            pl.when(conditions_barrel[0]).then(choices_barrel[0]).when(conditions_barrel[1]).then(choices_barrel[1]).otherwise(None).alias('barrel'),
+            pl.when(df['launch_angle'].is_null()).then(False).when((df['launch_angle'] >= 8) & (df['launch_angle'] <= 32)).then(True).otherwise(None).alias('sweet_spot'),
+            pl.when(df['launch_speed'].is_null()).then(False).when(df['launch_speed'] >= 94.5).then(True).otherwise(None).alias('hard_hit'),
+            pl.when(conditions_tb[0]).then(choices_tb[0]).when(conditions_tb[1]).then(choices_tb[1]).when(conditions_tb[2]).then(choices_tb[2]).when(conditions_tb[3]).then(choices_tb[3]).otherwise(None).alias('tb'),
+            pl.when(conditions_woba[0]).then(choices_woba[0]).when(conditions_woba[1]).then(choices_woba[1]).when(conditions_woba[2]).then(choices_woba[2]).when(conditions_woba[3]).then(choices_woba[3]).when(conditions_woba[4]).then(choices_woba[4]).when(conditions_woba[5]).then(choices_woba[5]).when(conditions_woba[6]).then(choices_woba[6]).otherwise(None).alias('woba'),
+            pl.when((df['play_code'] == 'S') | (df['play_code'] == 'W') | (df['play_code'] == 'T')).then(1).otherwise(0).alias('whiffs'),
+            pl.when((df['play_code'] == 'S') | (df['play_code'] == 'W') | (df['play_code'] == 'T') | (df['play_code'] == 'C')).then(1).otherwise(0).alias('csw'),
+            pl.when(pl.col('is_swing').cast(pl.Boolean)).then(1).otherwise(0).alias('swings'),
+            pl.col('event_type').is_in(['strikeout','strikeout_double_play']).alias('k'),
+            pl.col('event_type').is_in(['walk', 'intent_walk']).alias('bb'),
+            pl.lit(None).alias('attack_zone'),
+            pl.lit(None).alias('woba_pred'),
+            pl.lit(None).alias('woba_pred_contact')
+
+        ])
+
+
+        df = df.with_columns([
+            pl.when(df['event_type'].is_in(woba_codes)).then(1).otherwise(None).alias('woba_codes'),
+            pl.when(df['event_type'].is_in(woba_codes)).then(1).otherwise(None).alias('xwoba_codes'),
+            pl.when((pl.col('tb') >= 0)).then(df['woba']).otherwise(None).alias('woba_contact'),
+            pl.when(pl.col('px').is_null()).then(pl.col('px_predict')).otherwise(pl.col('px')).alias('px'),
+            pl.when(pl.col('pz').is_null()).then(pl.col('pz_predict')).otherwise(pl.col('pz')).alias('pz'),
+            pl.when(pl.col('in_zone').is_null()).then(pl.col('in_zone_predict')).otherwise(pl.col('in_zone')).alias('in_zone_final'),
+
+        ])
+
+        df = df.with_columns([
+            pl.when(df['launch_speed'].is_null()).then(None).otherwise(df['barrel']).alias('barrel'),
+            pl.lit('average').alias('average'),
+            pl.when(pl.col('in_zone_final') == False).then(True).otherwise(False).alias('out_zone'),
+            pl.when((pl.col('in_zone_final') == True) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('zone_swing'),
+            pl.when((pl.col('in_zone_final') == True) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('zone_contact'),
+            pl.when((pl.col('in_zone_final') == False) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('ozone_swing'),
+            pl.when((pl.col('in_zone_final') == False) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('ozone_contact'),
+            pl.when(pl.col('event_type').str.contains('strikeout')).then(True).otherwise(False).alias('k'),
+            pl.when(pl.col('event_type').is_in(['walk', 'intent_walk'])).then(True).otherwise(False).alias('bb'),
+            pl.when(pl.col('attack_zone').is_null()).then(pl.col('attack_zone_predict')).otherwise(pl.col('attack_zone')).alias('attack_zone_final'),
+            
+
+        ])
+
+        df = df.with_columns([
+            (df['k'].cast(pl.Float32) - df['bb'].cast(pl.Float32)).alias('k_minus_bb'),
+            (df['bb'].cast(pl.Float32) - df['k'].cast(pl.Float32)).alias('bb_minus_k'),
+            (df['launch_speed'] > 0).alias('bip_div'),
+            (df['attack_zone_final'] == 0).alias('heart'),
+            (df['attack_zone_final'] == 1).alias('shadow'),
+            (df['attack_zone_final'] == 2).alias('chase'),
+            (df['attack_zone_final'] == 3).alias('waste'),
+            ((df['attack_zone_final'] == 0) & (df['swings'] == 1)).alias('heart_swing'),
+            ((df['attack_zone_final'] == 1) & (df['swings'] == 1)).alias('shadow_swing'),
+            ((df['attack_zone_final'] == 2) & (df['swings'] == 1)).alias('chase_swing'),
+            ((df['attack_zone_final'] == 3) & (df['swings'] == 1)).alias('waste_swing'),
+            ((df['attack_zone_final'] == 0) & (df['whiffs'] == 1)).alias('heart_whiff'),
+            ((df['attack_zone_final'] == 1) & (df['whiffs'] == 1)).alias('shadow_whiff'),
+            ((df['attack_zone_final'] == 2) & (df['whiffs'] == 1)).alias('chase_whiff'),
+            ((df['attack_zone_final'] == 3) & (df['whiffs'] == 1)).alias('waste_whiff')
+        ])
+
+
+        [0, 0.689, 0.720, 0.881, 1.254, 1.589, 2.048]
+
+        df = df.with_columns([
+                pl.Series(
+                    [sum(x) for x in xwoba_model.predict_proba(df[['launch_angle', 'launch_speed']].fill_null(0).to_numpy()[:]) * ([0, 0.881, 1.254, 1.589, 2.048])]
+                ).alias('woba_pred_predict')
+            ])
+
+        df = df.with_columns([
+            pl.when(pl.col('event_type').is_in(['walk'])).then(0.689)
+            .when(pl.col('event_type').is_in(['hit_by_pitch'])).then(0.720)
+            .when(pl.col('event_type').is_in(['strikeout', 'strikeout_double_play'])).then(0)
+            .otherwise(pl.col('woba_pred_predict')).alias('woba_pred_predict')
+        ])
+
+        df = df.with_columns([
+            pl.when(pl.col('woba_codes').is_null()).then(None).otherwise(pl.col('woba_pred_predict')).alias('woba_pred'),
+            pl.when(pl.col('bip')!=1).then(None).otherwise(pl.col('woba_pred_predict')).alias('woba_pred_contact'),
+        ])
+
+        df = df.with_columns([
+            pl.when(pl.col('trajectory').is_in(['bunt_popup'])).then(pl.lit('popup'))
+            .when(pl.col('trajectory').is_in(['bunt_grounder'])).then(pl.lit('ground_ball'))
+            .when(pl.col('trajectory').is_in(['bunt_line_drive'])).then(pl.lit('line_drive'))
+            .when(pl.col('trajectory').is_in([''])).then(pl.lit(None))
+            .otherwise(pl.col('trajectory')).alias('trajectory')
+        ])
+
+
+        # Create one-hot encoded columns for the trajectory column
+        dummy_df = df.select(pl.col('trajectory')).to_dummies()
+
+        # Rename the one-hot encoded columns
+        dummy_df = dummy_df.rename({
+            'trajectory_fly_ball': 'trajectory_fly_ball',
+            'trajectory_ground_ball': 'trajectory_ground_ball',
+            'trajectory_line_drive': 'trajectory_line_drive',
+            'trajectory_popup': 'trajectory_popup'
+        })
+
+        # Ensure the columns are present in the DataFrame
+        for col in ['trajectory_fly_ball', 'trajectory_ground_ball', 'trajectory_line_drive', 'trajectory_popup']:
+            if col not in dummy_df.columns:
+                dummy_df = dummy_df.with_columns(pl.lit(0).alias(col))
+
+        # Join the one-hot encoded columns back to the original DataFrame
+        df = df.hstack(dummy_df)
+
+        # Check if 'trajectory_null' column exists and drop it
+        if 'trajectory_null' in df.columns:
+            df = df.drop('trajectory_null')
+
+
+        pitch_cat = {'FA': None,
+                            'FF': 'Fastball',
+                            'FT': 'Fastball',
+                            'FC': 'Fastball',
+                            'FS': 'Off-Speed',
+                            'FO': 'Off-Speed',
+                            'SI': 'Fastball',
+                            'ST': 'Breaking',
+                            'SL': 'Breaking',
+                            'CU': 'Breaking',
+                            'KC': 'Breaking',
+                            'SC': 'Off-Speed',
+                            'GY': 'Off-Speed',
+                            'SV': 'Breaking',
+                            'CS': 'Breaking',
+                            'CH': 'Off-Speed',
+                            'KN': 'Off-Speed',
+                            'EP': 'Breaking',
+                            'UN': None,
+                            'IN': None,
+                            'PO': None,
+                            'AB': None,
+                            'AS': None,
+                            'NP': None}
+        df = df.with_columns(
+            df["pitch_type"].map_elements(lambda x: pitch_cat.get(x, x)).alias("pitch_group")
+        )
+
+        df = df.with_columns([
+            
+            (-(pl.col('vy0')**2 - (2 * pl.col('ay') * (pl.col('y0') - 17/12)))**0.5).alias('vy_f'),
+            ])
+
+        df = df.with_columns([
+            ((pl.col('vy_f') - pl.col('vy0')) / pl.col('ay')).alias('t'),
+            ])
+
+        df = df.with_columns([
+            (pl.col('vz0') + (pl.col('az') * pl.col('t'))).alias('vz_f'),
+            (pl.col('vx0') + (pl.col('ax') * pl.col('t'))).alias('vx_f')
+            ])
+
+        df = df.with_columns([
+                (-np.arctan(pl.col('vz_f') / pl.col('vy_f')) * (180 / np.pi)).alias('vaa'),
+                (-np.arctan(pl.col('vx_f') / pl.col('vy_f')) * (180 / np.pi)).alias('haa')
+            ])
+
+        # Mirror horizontal break for left-handed pitchers
+        df = df.with_columns(
+            pl.when(pl.col('pitcher_hand') == 'L')
+            .then(-pl.col('ax'))
+            .otherwise(pl.col('ax'))
+            .alias('ax')
+        )
+
+        # Mirror horizontal break for left-handed pitchers
+        df = df.with_columns(
+            pl.when(pl.col('pitcher_hand') == 'L')
+            .then(-pl.col('hb'))
+            .otherwise(pl.col('hb'))
+            .alias('hb')
+        )
+
+        # Mirror horizontal release point for left-handed pitchers
+        df = df.with_columns(
+            pl.when(pl.col('pitcher_hand') == 'L')
+            .then(pl.col('x0'))
+            .otherwise(-pl.col('x0'))
+            .alias('x0')
+        )
+
+        df = df.with_columns([
+            pl.when(df['swings'].is_null()).then(None).otherwise(df['swings']).alias('is_swing'),
+            pl.when(df['bip'].is_null()).then(None).otherwise(df['bip']).alias('is_bip')])
+
+
+        return df
+
+    # Assuming df is your Polars DataFrame
+    def update_summary(self, df: pl.DataFrame, pitcher: bool = True) -> pl.DataFrame:
+        """
+        Update summary statistics for pitchers or batters.
+
+        Parameters:
+        df (pl.DataFrame): The input Polars DataFrame containing player statistics.
+        pitcher (bool): A flag indicating whether to calculate statistics for pitchers (True) or batters (False).
+
+        Returns:
+        pl.DataFrame: A Polars DataFrame with aggregated and calculated summary statistics.
+        """
+
+        # Determine the position based on the pitcher flag
+        if pitcher:
+            position = 'pitcher'
+        else:
+            position = 'batter'
+
+        # Group by position_id and position_name, then aggregate various statistics
+        df_summ = df.group_by([f'{position}_id', f'{position}_name']).agg([
+            pl.col('pa').sum().alias('pa'),
+            pl.col('ab').sum().alias('ab'),
+            pl.col('obp').sum().alias('obp_pa'),
+            pl.col('hits').sum().alias('hits'),
+            pl.col('on_base').sum().alias('on_base'),
+            pl.col('k').sum().alias('k'),
+            pl.col('bb').sum().alias('bb'),
+            pl.col('bb_minus_k').sum().alias('bb_minus_k'),
+            pl.col('csw').sum().alias('csw'),
+            pl.col('bip').sum().alias('bip'),
+            pl.col('bip_div').sum().alias('bip_div'),
+            pl.col('tb').sum().alias('tb'),
+            pl.col('woba').sum().alias('woba'),
+            pl.col('woba_contact').sum().alias('woba_contact'),
+            pl.col('woba_pred').sum().alias('xwoba'),
+            pl.col('woba_pred_contact').sum().alias('xwoba_contact'),
+            pl.col('woba_codes').sum().alias('woba_codes'),
+            pl.col('xwoba_codes').sum().alias('xwoba_codes'),
+            pl.col('hard_hit').sum().alias('hard_hit'),
+            pl.col('barrel').sum().alias('barrel'),
+            pl.col('sweet_spot').sum().alias('sweet_spot'),
+            pl.col('launch_speed').max().alias('max_launch_speed'),
+            pl.col('launch_speed').quantile(0.90).alias('launch_speed_90'),
+            pl.col('launch_speed').mean().alias('launch_speed'),
+            pl.col('launch_angle').mean().alias('launch_angle'),
+            pl.col('is_pitch').sum().alias('pitches'),
+            pl.col('swings').sum().alias('swings'),
+            pl.col('in_zone').sum().alias('in_zone'),
+            pl.col('out_zone').sum().alias('out_zone'),
+            pl.col('whiffs').sum().alias('whiffs'),
+            pl.col('zone_swing').sum().alias('zone_swing'),
+            pl.col('zone_contact').sum().alias('zone_contact'),
+            pl.col('ozone_swing').sum().alias('ozone_swing'),
+            pl.col('ozone_contact').sum().alias('ozone_contact'),
+            pl.col('trajectory_ground_ball').sum().alias('ground_ball'),
+            pl.col('trajectory_line_drive').sum().alias('line_drive'),
+            pl.col('trajectory_fly_ball').sum().alias('fly_ball'),
+            pl.col('trajectory_popup').sum().alias('pop_up'),
+            pl.col('attack_zone').count().alias('attack_zone'),
+            pl.col('heart').sum().alias('heart'),
+            pl.col('shadow').sum().alias('shadow'),
+            pl.col('chase').sum().alias('chase'),
+            pl.col('waste').sum().alias('waste'),
+            pl.col('heart_swing').sum().alias('heart_swing'),
+            pl.col('shadow_swing').sum().alias('shadow_swing'),
+            pl.col('chase_swing').sum().alias('chase_swing'),
+            pl.col('waste_swing').sum().alias('waste_swing'),
+            pl.col('heart_whiff').sum().alias('heart_whiff'),
+            pl.col('shadow_whiff').sum().alias('shadow_whiff'),
+            pl.col('chase_whiff').sum().alias('chase_whiff'),
+            pl.col('waste_whiff').sum().alias('waste_whiff')
+        ])
+
+        # Add calculated columns to the summary DataFrame
+        df_summ = df_summ.with_columns([
+            (pl.col('hits') / pl.col('ab')).alias('avg'),
+            (pl.col('on_base') / pl.col('obp_pa')).alias('obp'),
+            (pl.col('tb') / pl.col('ab')).alias('slg'),
+            (pl.col('on_base') / pl.col('obp_pa') + pl.col('tb') / pl.col('ab')).alias('ops'),
+            (pl.col('k') / pl.col('pa')).alias('k_percent'),
+            (pl.col('bb') / pl.col('pa')).alias('bb_percent'),
+            (pl.col('bb_minus_k') / pl.col('pa')).alias('bb_minus_k_percent'),
+            (pl.col('bb') / pl.col('k')).alias('bb_over_k_percent'),
+            (pl.col('csw') / pl.col('pitches')).alias('csw_percent'),
+            (pl.col('sweet_spot') / pl.col('bip_div')).alias('sweet_spot_percent'),
+            (pl.col('woba') / pl.col('woba_codes')).alias('woba_percent'),
+            (pl.col('woba_contact') / pl.col('bip')).alias('woba_percent_contact'),
+            (pl.col('hard_hit') / pl.col('bip_div')).alias('hard_hit_percent'),
+            (pl.col('barrel') / pl.col('bip_div')).alias('barrel_percent'),
+            (pl.col('zone_contact') / pl.col('zone_swing')).alias('zone_contact_percent'),
+            (pl.col('zone_swing') / pl.col('in_zone')).alias('zone_swing_percent'),
+            (pl.col('in_zone') / pl.col('pitches')).alias('zone_percent'),
+            (pl.col('ozone_swing') / (pl.col('pitches') - pl.col('in_zone'))).alias('chase_percent'),
+            (pl.col('ozone_contact') / pl.col('ozone_swing')).alias('chase_contact'),
+            (pl.col('swings') / pl.col('pitches')).alias('swing_percent'),
+            (pl.col('whiffs') / pl.col('swings')).alias('whiff_rate'),
+            (pl.col('whiffs') / pl.col('pitches')).alias('swstr_rate'),
+            (pl.col('ground_ball') / pl.col('bip')).alias('ground_ball_percent'),
+            (pl.col('line_drive') / pl.col('bip')).alias('line_drive_percent'),
+            (pl.col('fly_ball') / pl.col('bip')).alias('fly_ball_percent'),
+            (pl.col('pop_up') / pl.col('bip')).alias('pop_up_percent'),
+            (pl.col('heart') / pl.col('attack_zone')).alias('heart_zone_percent'),
+            (pl.col('shadow') / pl.col('attack_zone')).alias('shadow_zone_percent'),
+            (pl.col('chase') / pl.col('attack_zone')).alias('chase_zone_percent'),
+            (pl.col('waste') / pl.col('attack_zone')).alias('waste_zone_percent'),
+            (pl.col('heart_swing') / pl.col('heart')).alias('heart_zone_swing_percent'),
+            (pl.col('shadow_swing') / pl.col('shadow')).alias('shadow_zone_swing_percent'),
+            (pl.col('chase_swing') / pl.col('chase')).alias('chase_zone_swing_percent'),
+            (pl.col('waste_swing') / pl.col('waste')).alias('waste_zone_swing_percent'),
+            (pl.col('heart_whiff') / pl.col('heart_swing')).alias('heart_zone_whiff_percent'),
+            (pl.col('shadow_whiff') / pl.col('shadow_swing')).alias('shadow_zone_whiff_percent'),
+            (pl.col('chase_whiff') / pl.col('chase_swing')).alias('chase_zone_whiff_percent'),
+            (pl.col('waste_whiff') / pl.col('waste_swing')).alias('waste_zone_whiff_percent'),
+            (pl.col('xwoba') / pl.col('xwoba_codes')).alias('xwoba_percent'),
+            (pl.col('xwoba_contact') / pl.col('bip')).alias('xwoba_percent_contact')
+        ])
+
+        return df_summ
+    
+
+
+
+
+    
+    # Assuming df is your Polars DataFrame
+    def update_summary_select(self, df: pl.DataFrame, selection: list) -> pl.DataFrame:
+        """
+        Update summary statistics for pitchers or batters.
+
+        Parameters:
+        df (pl.DataFrame): The input Polars DataFrame containing player statistics.
+        pitcher (bool): A flag indicating whether to calculate statistics for pitchers (True) or batters (False).
+
+        Returns:
+        pl.DataFrame: A Polars DataFrame with aggregated and calculated summary statistics.
+        """
+
+        # Group by position_id and position_name, then aggregate various statistics
+        df_summ = df.group_by(selection).agg([
+            pl.col('pa').sum().alias('pa'),
+            pl.col('ab').sum().alias('ab'),
+            pl.col('obp').sum().alias('obp_pa'),
+            pl.col('hits').sum().alias('hits'),
+            pl.col('on_base').sum().alias('on_base'),
+            pl.col('k').sum().alias('k'),
+            pl.col('bb').sum().alias('bb'),
+            pl.col('bb_minus_k').sum().alias('bb_minus_k'),
+            pl.col('k_minus_bb').sum().alias('k_minus_bb'),
+            pl.col('csw').sum().alias('csw'),
+            pl.col('bip').sum().alias('bip'),
+            pl.col('bip_div').sum().alias('bip_div'),
+            pl.col('tb').sum().alias('tb'),
+            pl.col('woba').sum().alias('woba'),
+            pl.col('woba_contact').sum().alias('woba_contact'),
+            pl.col('woba_pred').sum().alias('xwoba'),
+            pl.col('woba_pred_contact').sum().alias('xwoba_contact'),
+            pl.col('woba_codes').sum().alias('woba_codes'),
+            pl.col('xwoba_codes').sum().alias('xwoba_codes'),
+            pl.col('hard_hit').sum().alias('hard_hit'),
+            pl.col('barrel').sum().alias('barrel'),
+            pl.col('sweet_spot').sum().alias('sweet_spot'),
+            pl.col('launch_speed').max().alias('max_launch_speed'),
+            pl.col('launch_speed').quantile(0.90).alias('launch_speed_90'),
+            pl.col('launch_speed').mean().alias('launch_speed'),
+            pl.col('launch_angle').mean().alias('launch_angle'),
+            pl.col('is_pitch').sum().alias('pitches'),
+            pl.col('swings').sum().alias('swings'),
+            pl.col('in_zone').sum().alias('in_zone'),
+            pl.col('out_zone').sum().alias('out_zone'),
+            pl.col('whiffs').sum().alias('whiffs'),
+            pl.col('zone_swing').sum().alias('zone_swing'),
+            pl.col('zone_contact').sum().alias('zone_contact'),
+            pl.col('ozone_swing').sum().alias('ozone_swing'),
+            pl.col('ozone_contact').sum().alias('ozone_contact'),
+            pl.col('trajectory_ground_ball').sum().alias('ground_ball'),
+            pl.col('trajectory_line_drive').sum().alias('line_drive'),
+            pl.col('trajectory_fly_ball').sum().alias('fly_ball'),
+            pl.col('trajectory_popup').sum().alias('pop_up'),
+            pl.col('attack_zone').count().alias('attack_zone'),
+            pl.col('heart').sum().alias('heart'),
+            pl.col('shadow').sum().alias('shadow'),
+            pl.col('chase').sum().alias('chase'),
+            pl.col('waste').sum().alias('waste'),
+            pl.col('heart_swing').sum().alias('heart_swing'),
+            pl.col('shadow_swing').sum().alias('shadow_swing'),
+            pl.col('chase_swing').sum().alias('chase_swing'),
+            pl.col('waste_swing').sum().alias('waste_swing'),
+            pl.col('heart_whiff').sum().alias('heart_whiff'),
+            pl.col('shadow_whiff').sum().alias('shadow_whiff'),
+            pl.col('chase_whiff').sum().alias('chase_whiff'),
+            pl.col('waste_whiff').sum().alias('waste_whiff'),
+            pl.col('tj_stuff_plus').sum().alias('tj_stuff_plus'),
+            pl.col('start_speed').sum(),
+            pl.col('vb').sum(),
+            pl.col('ivb').sum(),
+            pl.col('hb').sum(),
+            pl.col('x0').sum(),
+            pl.col('z0').sum(),
+            pl.col('vaa').sum(),
+            pl.col('haa').sum(),
+            pl.col('spin_rate').sum(),
+            pl.col('extension').sum(),
+        ])
+
+        # Add calculated columns to the summary DataFrame
+        df_summ = df_summ.with_columns([
+            (pl.col('hits') / pl.col('ab')).alias('avg'),
+            (pl.col('on_base') / pl.col('obp_pa')).alias('obp'),
+            (pl.col('tb') / pl.col('ab')).alias('slg'),
+            (pl.col('on_base') / pl.col('obp_pa') + pl.col('tb') / pl.col('ab')).alias('ops'),
+            (pl.col('k') / pl.col('pa')).alias('k_percent'),
+            (pl.col('bb') / pl.col('pa')).alias('bb_percent'),
+            (pl.col('bb_minus_k') / pl.col('pa')).alias('bb_minus_k_percent'),
+            (pl.col('k_minus_bb') / pl.col('pa')).alias('k_minus_bb_percent'),
+            (pl.col('bb') / pl.col('k')).alias('bb_over_k_percent'),
+            (pl.col('csw') / pl.col('pitches')).alias('csw_percent'),
+            (pl.col('sweet_spot') / pl.col('bip_div')).alias('sweet_spot_percent'),
+            (pl.col('woba') / pl.col('woba_codes')).alias('woba_percent'),
+            (pl.col('woba_contact') / pl.col('bip')).alias('woba_percent_contact'),
+            (pl.col('hard_hit') / pl.col('bip_div')).alias('hard_hit_percent'),
+            (pl.col('barrel') / pl.col('bip_div')).alias('barrel_percent'),
+            (pl.col('zone_contact') / pl.col('zone_swing')).alias('zone_contact_percent'),
+            (pl.col('zone_swing') / pl.col('in_zone')).alias('zone_swing_percent'),
+            (pl.col('in_zone') / pl.col('pitches')).alias('zone_percent'),
+            (pl.col('ozone_swing') / (pl.col('pitches') - pl.col('in_zone'))).alias('chase_percent'),
+            (pl.col('ozone_contact') / pl.col('ozone_swing')).alias('chase_contact'),
+            (pl.col('swings') / pl.col('pitches')).alias('swing_percent'),
+            (pl.col('whiffs') / pl.col('swings')).alias('whiff_rate'),
+            (pl.col('whiffs') / pl.col('pitches')).alias('swstr_rate'),
+            (pl.col('ground_ball') / pl.col('bip')).alias('ground_ball_percent'),
+            (pl.col('line_drive') / pl.col('bip')).alias('line_drive_percent'),
+            (pl.col('fly_ball') / pl.col('bip')).alias('fly_ball_percent'),
+            (pl.col('pop_up') / pl.col('bip')).alias('pop_up_percent'),
+            (pl.col('heart') / pl.col('attack_zone')).alias('heart_zone_percent'),
+            (pl.col('shadow') / pl.col('attack_zone')).alias('shadow_zone_percent'),
+            (pl.col('chase') / pl.col('attack_zone')).alias('chase_zone_percent'),
+            (pl.col('waste') / pl.col('attack_zone')).alias('waste_zone_percent'),
+            (pl.col('heart_swing') / pl.col('heart')).alias('heart_zone_swing_percent'),
+            (pl.col('shadow_swing') / pl.col('shadow')).alias('shadow_zone_swing_percent'),
+            (pl.col('chase_swing') / pl.col('chase')).alias('chase_zone_swing_percent'),
+            (pl.col('waste_swing') / pl.col('waste')).alias('waste_zone_swing_percent'),
+            (pl.col('heart_whiff') / pl.col('heart_swing')).alias('heart_zone_whiff_percent'),
+            (pl.col('shadow_whiff') / pl.col('shadow_swing')).alias('shadow_zone_whiff_percent'),
+            (pl.col('chase_whiff') / pl.col('chase_swing')).alias('chase_zone_whiff_percent'),
+            (pl.col('waste_whiff') / pl.col('waste_swing')).alias('waste_zone_whiff_percent'),
+            (pl.col('xwoba') / pl.col('xwoba_codes')).alias('xwoba_percent'),
+            (pl.col('xwoba_contact') / pl.col('bip')).alias('xwoba_percent_contact'),
+            (pl.col('tj_stuff_plus') / pl.col('pitches')).alias('tj_stuff_plus_avg'),
+            (pl.col('start_speed')/ pl.col('pitches')).alias('start_speed_avg'),
+            (pl.col('vb')/ pl.col('pitches')).alias('vb_avg'),
+            (pl.col('ivb')/ pl.col('pitches')).alias('ivb_avg'),
+            (pl.col('hb')/ pl.col('pitches')).alias('hb_avg'),
+            (pl.col('x0')/ pl.col('pitches')).alias('x0_avg'),
+            (pl.col('z0')/ pl.col('pitches')).alias('z0_avg'),
+            (pl.col('vaa')/ pl.col('pitches')).alias('vaa_avg'),
+            (pl.col('haa')/ pl.col('pitches')).alias('haa_avg'),
+            (pl.col('spin_rate')/ pl.col('pitches')).alias('spin_rate_avg'),
+            (pl.col('extension')/ pl.col('pitches')).alias('extension_avg'),
+            
+        ])
+
         return df_summ

functions/pitch_summary_functions.py

@@ -662,18 +662,21 @@ def summary_table(df: pl.DataFrame, ax: plt.Axes):
     format_cells(['count_percent', 'zone_percent', 'chase_percent', 'whiff_percent'], '{:,.1%}')
     format_cells(['tj_stuff_plus', 'pitch_grade', 'spin_rate'], '{:,.0f}')
 
-    # Create legend for pitch types
-    items_in_order = (df.sort("pitch_count", descending=True)['pitch_type'].unique(maintain_order=True).to_numpy())
-    colour_pitches = [dict_colour[x] for x in items_in_order]
-    label = [dict_pitch[x] for x in items_in_order]
-    handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches]
-    if len(label) > 5:
-        ax.legend(handles, label, bbox_to_anchor=(0.1, 0.81, 0.8, 0.14), ncol=5,
-                  fancybox=True, loc='lower center', fontsize=16, framealpha=1.0, markerscale=1.7, prop={'family': 'calibi', 'size': 16})
-    else:
-        ax.legend(handles, label, bbox_to_anchor=(0.1, 0.81, 0.8, 0.14), ncol=5,
-                  fancybox=True, loc='lower center', fontsize=20, framealpha=1.0, markerscale=2, prop={'family': 'calibi', 'size': 20})
+    # # Create legend for pitch types
+    # items_in_order = (df.sort("pitch_count", descending=True)['pitch_type'].unique(maintain_order=True).to_numpy())
+    # colour_pitches = [dict_colour[x] for x in items_in_order]
+    # label = [dict_pitch[x] for x in items_in_order]
+    # handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches]
+    # if len(label) > 5:
+    #     ax.legend(handles, label, bbox_to_anchor=(0.1, 0.81, 0.8, 0.14), ncol=5,
+    #               fancybox=True, loc='lower center', fontsize=16, framealpha=1.0, markerscale=1.7, prop={'family': 'calibi', 'size': 16})
+    # else:
+    #     ax.legend(handles, label, bbox_to_anchor=(0.1, 0.81, 0.8, 0.14), ncol=5,
+    #               fancybox=True, loc='lower center', fontsize=20, framealpha=1.0, markerscale=2, prop={'family': 'calibi', 'size': 20})
     ax.axis('off')
+    # ax.set_title(f'{df["pitcher_name"][0]}', fontdict=font_properties_titles)
+    # ax.text(x=0.5,y=0.90,s=f'{df["pitcher_name"][0]}', 
+    #         fontsize=30, ha='center', va='bottom',)
 
 def plot_footer(ax: plt.Axes):
     """
@@ -685,8 +688,8 @@ def plot_footer(ax: plt.Axes):
         The axis to add the footer text to.
     """
     # Add footer text
-    ax.text(0, 1, 'By: @TJStats', ha='left', va='top', fontsize=24)
-    ax.text(0.5, 0.25, 
+    ax.text(0, 1, 'By: Thomas Nestico\n      @TJStats', ha='left', va='top', fontsize=24)
+    ax.text(0.5, 0.15, 
             '''
             Colour Coding Compares to League Average By Pitch
             tjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type
@@ -694,7 +697,31 @@ def plot_footer(ax: plt.Axes):
             Pitch Grade scales tjStuff+ to the traditional 20-80 Scouting Scale for a given pitch type
             ''', 
             ha='center', va='bottom', fontsize=12)
-    ax.text(1, 1, 'Data: MLB, Fangraphs\nImages: MLB, ESPN', ha='right', va='top', fontsize=24)
+    ax.text(1, 1, 'Data: MLB\nImages: MLB, ESPN', ha='right', va='top', fontsize=24)
+    ax.axis('off')
+
+
+
+def plot_footer_break(ax: plt.Axes):
+    """
+    Add footer text to the plot.
+
+    Parameters
+    ----------
+    ax : plt.Axes
+        The axis to add the footer text to.
+    """
+    # Add footer text
+    ax.text(0, 0.25, 'By: Thomas Nestico\n      @TJStats', ha='left', va='bottom', fontsize=24)
+    # ax.text(0.5, 0.15, 
+    #         '''
+    #         Colour Coding Compares to League Average By Pitch
+    #         tjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type
+    #         tjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10
+    #         Pitch Grade scales tjStuff+ to the traditional 20-80 Scouting Scale for a given pitch type
+    #         ''', 
+    #         ha='center', va='bottom', fontsize=12)
+    ax.text(1, 0.25, 'Data: MLB\nImages: MLB, ESPN', ha='right', va='bottom', fontsize=24)
     ax.axis('off')
 
 # Function to get an image from a URL and display it on the given axis
@@ -725,7 +752,7 @@ def player_headshot(player_input: str, ax: plt.Axes, sport_id: int, season: int)
         img = Image.open(BytesIO(response.content))
 
         # Display the image on the axis
-        ax.set_xlim(0, 1.3)
+        ax.set_xlim(0, 1)
         ax.set_ylim(0, 1)
         ax.imshow(img, extent=[0, 1, 0, 1] if sport_id == 1 else [1/6, 5/6, 0, 1], origin='upper')
     except PIL.UnidentifiedImageError:
@@ -735,7 +762,18 @@ def player_headshot(player_input: str, ax: plt.Axes, sport_id: int, season: int)
     # Turn off the axis
     ax.axis('off')
 
-def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int):
+splits_title = {
+
+    'all':'',
+    'left':' vs LHH',
+    'right':' vs RHH',
+
+}
+
+type_dict =  {'R':'Regular Season',
+                   'S':'Spring',
+                   'P':'Playoffs' }
+def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int,game_type: str, year_input: int,split: str,df: pl.DataFrame):
     """
     Display the player's bio information on the given axis.
 
@@ -750,6 +788,9 @@ def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int):
     year_input : int
         The season year.
     """
+    start_date = df['game_date'][0]
+    end_date = df['game_date'][-1]
+
     # Construct the URL to fetch player data
     url = f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}&hydrate=currentTeam"
 
@@ -764,19 +805,24 @@ def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int):
     weight = data['people'][0]['weight']
 
     # Display the player's name, handedness, age, height, and weight on the axis
-    ax.text(0.5, 1, f'{player_name}', va='top', ha='center', fontsize=56)
-    ax.text(0.5, 0.7, f'{pitcher_hand}HP, Age: {age}, {height}/{weight}', va='top', ha='center', fontsize=30)
-    ax.text(0.5, 0.45, f'Season Pitching Summary', va='top', ha='center', fontsize=40)
+    ax.text(0.5, 1, f'{player_name}', va='top', ha='center', fontsize=36)
+    ax.text(0.5, 0.68, f'{pitcher_hand}HP, Age: {age}, {height}/{weight}', va='top', ha='center', fontsize=24)
 
+    # Convert the JSON response into a Polars DataFrame
     # Make API call to retrieve sports information
     response = requests.get(url='https://statsapi.mlb.com/api/v1/sports').json()
-    
-    # Convert the JSON response into a Polars DataFrame
+        
     df_sport_id = pl.DataFrame(response['sports'])    
     abb = df_sport_id.filter(pl.col('id') == sport_id)['abbreviation'][0]
+    # title = f'{df["game_date"][0]} vs {df["batter_team"][0]} ({type_dict[game_type[0]]}){split_title[split]}'
+    ax.text(0.5, 0.45, f'{year_input} {abb} {type_dict[game_type]}', va='top', ha='center', fontsize=24)
+
+
+
 
     # Display the season and sport abbreviation
-    ax.text(0.5, 0.20, f'{year_input} {abb} Season', va='top', ha='center', fontsize=30, fontstyle='italic')
+    ax.text(0.5, 0.20, f'{start_date} to {end_date}{splits_title[split]}', va='top', ha='center', fontsize=24, fontstyle='italic')
+
 
     # Turn off the axis
     ax.axis('off')
@@ -861,9 +907,9 @@ def plot_logo(pitcher_id: str, ax: plt.Axes, df_team: pl.DataFrame, df_players:
         img = Image.open(BytesIO(response.content))
 
         # Display the image on the axis
-        ax.set_xlim(0, 1.3)
+        ax.set_xlim(0, 1)
         ax.set_ylim(0, 1)
-        ax.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper')
+        ax.imshow(img, extent=[0, 1, 0, 1], origin='upper')
 
         # Turn off the axis
         ax.axis('off')