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api_scraper.py

@@ -154,41 +154,30 @@ class MLB_Scrape:
         return game_df
     
 
-    # def get_data(self, game_list_input: list):
-    #     """
-    #     Retrieves live game data for a list of game IDs in parallel.
+    def get_data(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.
+        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.')
+        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()
+        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())
+        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
-
-
-    def get_data(self,game_list_input = [748540]):
-        data_total = []
-        #n_count = 0
-        print('This May Take a While. Progress Bar shows Completion of Data Retrieval.')
-        for i in tqdm(range(len(game_list_input)), desc="Processing", unit="iteration"):
-            r = requests.get(f'https://statsapi.mlb.com/api/v1.1/game/{game_list_input[i]}/feed/live')
-            data_total.append(r.json())
         return data_total
 
-
     def get_data_df(self, data_list):
         """
         Converts a list of game data JSON objects into a Polars DataFrame.

app.py

@@ -6,6 +6,8 @@ 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
@@ -15,10 +17,8 @@ import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 import seaborn as sns
 from functions.pitch_summary_functions import *
-from functions.df_update import *
 from shiny import App, reactive, ui, render
 from shiny.ui import h2, tags
-from functions.heat_map_functions import *
 
 colour_palette = ['#FFB000','#648FFF','#785EF0',
                   '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
@@ -30,8 +30,8 @@ year_list = [2017,2018,2019,2020,2021,2022,2023,2024,2025]
 
 level_dict =  {'1':'MLB',
                '11':'AAA',
-               '12':'AA',
-               '13':'A+',
+              # '12':'AA',
+               #'13':'A+',
                '14':'A',
                '17':'AFL',
                '22':'College',
@@ -61,54 +61,48 @@ type_dict =  {'R':'Regular Season',
                'S':'Spring',
                'P':'Playoffs' }
 
-format_dict = {
-    'pitch_percent': '{:.1%}',
-    'pitches': '{:.0f}',
-    'heart_zone_percent': '{:.1%}',
-    'shadow_zone_percent': '{:.1%}',
-    'chase_zone_percent': '{:.1%}',
-    'waste_zone_percent': '{:.1%}',
-    'csw_percent': '{:.1%}',
-    'whiff_rate': '{:.1%}',
-    'zone_whiff_percent': '{:.1%}',
-    'chase_percent': '{:.1%}',
-    'bip': '{:.0f}',
-    'xwoba_percent_contact': '{:.3f}'
-}
 
-format_dict = {
-    'pitch_percent': '{:.1%}',
-    'pitches': '{:.0f}',
-    'heart_zone_percent': '{:.1%}',
-    'shadow_zone_percent': '{:.1%}',
-    'chase_zone_percent': '{:.1%}',
-    'waste_zone_percent': '{:.1%}',
-    'csw_percent': '{:.1%}',
-    'whiff_rate': '{:.1%}',
-    'zone_whiff_percent': '{:.1%}',
-    'chase_percent': '{:.1%}',
-    'bip': '{:.0f}',
-    'xwoba_percent_contact': '{:.3f}'
-}
-label_translation_dict = {
-    'pitch_percent': 'Pitch%',
-    'pitches': 'Pitches',
-    'heart_zone_percent': 'Heart%',
-    'shadow_zone_percent': 'Shado%',
-    'chase_zone_percent': 'Chas%',
-    'waste_zone_percent': 'Waste%',
-    'csw_percent': 'CSW%',
-    'whiff_rate': 'Whiff%',
-    'zone_whiff_percent': 'Z-Whiff%',
-    'chase_percent': 'O-Swing%',
-    'bip': 'BBE',
-    'xwoba_percent_contact': 'xwOBACON'
-}
 
-cmap_sum22 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFB000',])
-cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
-cmap_sum2 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF','#FFB000','#FE6100'])
-cmap_sum_r = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFB000','#FFFFFF','#648FFF',])
+# 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()
+
 
 
 import requests
@@ -148,24 +142,33 @@ if response.status_code == 200:
             continue
 VALID_PASSWORDS.append(BACKUP_PW)
 VALID_PASSWORDS.append(ADMIN_PW)
-# VALID_PASSWORDS.append('')
+VALID_PASSWORDS.append('')
 
 from shiny import App, reactive, ui, render
 from shiny.ui import h2, tags
 
+from datetime import datetime
+
+def is_valid_date(date_str):
+    try:
+        datetime.strptime(date_str, "%Y-%m-%d")  # Attempt to parse the date
+        return True
+    except ValueError:
+        return False  # If parsing fails, it's not in the correct format
+
 # Define the login UI
 login_ui = ui.page_fluid(
     ui.card(
         ui.h2([
-            "TJStats Pitching Heat Maps App ",
+            "TJStats Pitching Summary App ",
             ui.tags.a("(@TJStats)", href="https://twitter.com/TJStats", target="_blank")
         ]),
         ui.p(
             "This App is available to Superstar Patrons. Please enter your Patreon email address in the box below. If you're having trouble, please refer to the ", 
-            ui.tags.a("Patreon post", href="https://www.patreon.com/posts/117909954", target="_blank"),
+            ui.tags.a("Patreon post", href="https://www.patreon.com/posts/116064432", target="_blank"),
             "."
         ),
-        ui.input_password("password", "Enter Patreon Email (or Password from Link):", width="25%"),
+        ui.input_password("password", "Enter Patreon Email (or Password from Link):", width="50%"),
         ui.tags.input(
             type="checkbox",
             id="authenticated",
@@ -178,6 +181,7 @@ login_ui = ui.page_fluid(
 )
 
 
+# Define the UI layout for the app
 main_ui = ui.page_sidebar(
     ui.sidebar(
         # Row for selecting season and level
@@ -190,32 +194,47 @@ main_ui = ui.page_sidebar(
         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'))),
 
-
-        ui.row(ui.input_action_button("get_pitches", "Get Pitch Types", class_="btn-secondary")),
-
-        
         # Rows for selecting plots and split options
-        ui.row(ui.column(12, ui.output_ui('pitch_type_ui', 'Select Pitch Type'))),
-        ui.row(ui.column(12, ui.input_select('plot_type', 'Select Plot', ['Pitch%','Whiff%','xwOBACON']))),
-        ui.row(ui.column(12, ui.output_ui('date_id', 'Select Date'))),
+        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 (former panel_main content)
+            
+    # Main content area with tabs (placed directly in page_sidebar)
     ui.navset_tab(
-        # Tab for game summary plot
-        ui.nav("Pitching Summary",
+        ui.nav_panel("Pitching Summary",
                ui.output_text("status"),
-               ui.output_plot('plot', width='1440px', height=f'{900/1600*1440}px')
+               ui.output_plot('plot', width='2100px', height='2100px')
         ),
+        ui.nav_panel("Game Summary",
+               ui.output_text("status2"),
+               ui.output_plot('game_plot', width='2100px', height='2100px')
+        ),        
+            ui.nav_panel("Table Range",
+                                   ui.output_data_frame("grid")),
+            ui.nav_panel("Table Game",
+                                   ui.output_data_frame("grid_game")),
+        id="tabset"
     )
 )
 
-
 # Combined UI with conditional panel
 app_ui = ui.page_fluid(
     ui.tags.head(
@@ -249,117 +268,147 @@ def server(input, output, session):
     @render.text
     def login_message():
         return ""
-
-    # Instead of using @reactive.calc with @reactive.event
-    cached_data_value = reactive.value(None)  # Initialize with None
-
+    
     @reactive.calc
-    @reactive.event(input.date_id,input.pitcher_id)
+    @reactive.event(input.pitcher_id, input.date_id,input.split_id)
     def cached_data():
 
-        if not hasattr(input, 'pitcher_id') or input.pitcher_id() is None or not hasattr(input, 'date_id') or input.date_id() is None:
-            return  # Exit early if required inputs aren't ready
         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()])
+        
+        # if input.tabset() == 'Game Summary':
+        #     print(year_input, sport_id, player_input, 'yup')
+        #     print(input.date_id())
+        #     game_list = [input.date_id()]
+            
 
         data_list = scrape.get_data(game_list_input = game_list[:])
-        df = (update.update(scrape.get_data_df(data_list = data_list).filter(
-                            (pl.col("pitcher_id") == player_input)&
-                            (pl.col("is_pitch") == True)
-                            
 
-                            ))).with_columns(
-                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-            )
-        return df
+        try:
+            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
+
+            
+        except TypeError:
+            print("NONE")
+            return None
+
+    @reactive.calc
+    @reactive.event(input.pitcher_id, input.date_id,input.split_id,input.tabset)
+    def cached_data_daily():
+
+        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])
+        game_list  = [int(input.date_id())]
+        print(game_list)
+        
+        # if input.tabset() == 'Game Summary':
+        #     print(year_input, sport_id, player_input, 'yup')
+        #     print(input.date_id())
+        #     game_list = 
+            
+
+        data_list = scrape.get_data(game_list_input = game_list[:])
+
+        try:
+            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
+
+            
+        except TypeError:
+            print("NONE")
+            return None
+                
 
     @render.ui
-    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
+    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,input.tabset,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")
+            (pl.col("position").is_in(['P','TWP']))|
+            (pl.col("player_id").is_in([686846]))
+        
+        ).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)
 
-    is_loading = reactive.value(False)
-    data_result = reactive.value(None)
 
-    @reactive.effect
-    @reactive.event(input.get_pitches)
-    def load_data():
-        is_loading.set(True)
-        data_result.set(None)  # Clear any previous data
-        try:
-            # This will fetch the data
-            result = cached_data()
-            data_result.set(result)
-        except Exception as e:
-            # Handle any errors
-            print(f"Error loading data: {e}")
-        finally:
-            is_loading.set(False)
-
-    @output
-    @render.ui
-    def pitch_type_ui():
-        # Make sure to add dependencies on both values
-        input.get_pitches()
-        loading = is_loading()
-        data = data_result()
         
-        # If loading, show spinner
-        if loading:
-            return ui.div(
-                ui.span("Loading pitch types... ", class_="me-2"),
-                ui.tags.div(class_="spinner-border spinner-border-sm text-primary"),
-                style="padding: 10px; background-color: #f8f9fa; border-radius: 5px;"
-            )
         
-        # If data is loaded, show dropdown
-        elif data is not None:
-            df = data
-            df = df.clone() if hasattr(df, 'clone') else df.copy()
-            pitch_dict = dict(zip(df['pitch_type'], df['pitch_description']))
-            return ui.input_select(
-                "pitch_type_input", 
-                "Select Pitch Type", 
-                pitch_dict, 
-                selectize=True
-            )
-        
-        # Initial state or after reset
-        else:
-            return ui.div(
-                ui.p("Click 'Get Pitch Types' to load the dropdown.", class_="text-muted"),
-                style="text-align: center; padding: 10px;"
-            )  # Empty div with instructions
+        # 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)
+    @reactive.event(input.player_button, input.pitcher_id,input.year_input, input.level_input, input.type_input,input.tabset,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")
-    
+        if input.tabset() == 'Pitching Summary' or input.tabset() == 'Table Range':
+            # 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")
+        
+        if input.tabset() == 'Game Summary' or input.tabset() == 'Table Game':
+            year_input = int(input.year_input())
+            sport_id = int(input.level_input())
+            player_input = int(input.pitcher_id())
+            print('game summary')
+            # start_date = str(input.date_id()[0])
+            # end_date = str(input.date_id()[1])
+
+            game_list = scrape.get_player_games_list(player_id = player_input, 
+                              season = year_input, 
+                              sport_id=sport_id, 
+                              game_type=[input.type_input()],
+                              pitching = True)
+            
+            schedule_df = scrape.get_schedule(year_input=[year_input],
+                    sport_id= [sport_id],
+                    game_type = [input.type_input()])
+            
+            player_schedule_df = schedule_df.filter(pl.col('game_id').is_in(game_list)).to_pandas().sort_values('date')
 
+            player_schedule_df['def'] = player_schedule_df['date'].astype(str) + ' - ' + player_schedule_df['away'] + ' @ ' + player_schedule_df['home'] + ' ' 
+
+
+            game_dict = dict(zip(player_schedule_df['game_id'], player_schedule_df['def']))
+            # print(game_dict)
+
+            return ui.input_select("date_id", "Select Game", game_dict)
 
     @output
     @render.text
@@ -368,309 +417,442 @@ def server(input, output, session):
         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])
-            
-
+            if not is_valid_date(start_date):
+                    fig = plt.figure(figsize=(26,26))
+                    fig.text(x=0.1,y=0.9,s='Select Date Range and Generate Plot',fontsize=36,ha='left')
+                    return fig         
             print(year_input, sport_id, player_input, start_date, end_date)
 
             df = cached_data()
-            df = df.clone()
-
-            pitch_input = input.pitch_type_input()
-
-            df_plot = pitch_heat_map(pitch_input, df)
-            pivot_table_l = pitch_prop(df=df_plot, hand = 'L')
-            pivot_table_r = pitch_prop(df=df_plot, hand = 'R')
-
-
-            table_left = df_update().update_summary_select(df=df_plot.filter(pl.col('batter_hand') == 'L'), selection=['pitcher_hand'])
-            table_left = table_left.with_columns(
-                (pl.col('pitches')/len(df.filter(pl.col('batter_hand') == 'L'))).alias('pitch_percent')
-            )
-
-            table_right = df_update().update_summary_select(df=df_plot.filter(pl.col('batter_hand') == 'R'), selection=['pitcher_hand'])
-            table_right = table_right.with_columns(
-                (pl.col('pitches')/len(df.filter(pl.col('batter_hand') == 'R'))).alias('pitch_percent')
-            )
-            try:
-                normalize = mcolors.Normalize(vmin=table_left['pitch_percent']*0.5, 
-                                            vmax=table_left['pitch_percent']*1.5) # Define the range of values
-    
-    
-                df_colour_left = pd.DataFrame(data=[[get_color(x,normalize,cmap_sum2) for x in pivot_table_l[0]],
-                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_l[1]],
-                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_l[2]]])
-                df_colour_left[0] = '#ffffff'
-            except ValueError:
-                normalize = mcolors.Normalize(vmin=0, 
-                                            vmax=1) # Define the range of values
-                df_colour_left = pd.DataFrame(data=[['#ffffff','#ffffff','#ffffff','#ffffff'],
-                            ['#ffffff','#ffffff','#ffffff','#ffffff'],
-                            ['#ffffff','#ffffff','#ffffff','#ffffff']])
-                    
-            try: 
-                normalize = mcolors.Normalize(vmin=table_right['pitch_percent']*0.5, 
-                                            vmax=table_right['pitch_percent']*1.5) # Define the range of values
-    
-    
-                df_colour_right = pd.DataFrame(data=[[get_color(x,normalize,cmap_sum2) for x in pivot_table_r[0]],
-                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_r[1]],
-                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_r[2]]])
-                df_colour_right[0] = '#ffffff'
-                
-            except ValueError:
-                normalize = mcolors.Normalize(vmin=0, 
-                                            vmax=1) # Define the range of values
-                df_colour_right = pd.DataFrame(data=[['#ffffff','#ffffff','#ffffff','#ffffff'],
-                            ['#ffffff','#ffffff','#ffffff','#ffffff'],
-                            ['#ffffff','#ffffff','#ffffff','#ffffff']])
-
-            table_left = table_left.select(
-                'pitch_percent',
-                'pitches',
-                'heart_zone_percent',
-                'shadow_zone_percent',
-                'chase_zone_percent',
-                'waste_zone_percent',
-                'csw_percent',
-                'whiff_rate',
-                'zone_whiff_percent',
-                'chase_percent',
-                'bip',
-                'xwoba_percent_contact').to_pandas().T
-
-            table_right = table_right.select(
-                'pitch_percent',
-                'pitches',
-                'heart_zone_percent',
-                'shadow_zone_percent',
-                'chase_zone_percent',
-                'waste_zone_percent',
-                'csw_percent',
-                'whiff_rate',
-                'zone_whiff_percent',
-                'chase_percent',
-                'bip',
-                'xwoba_percent_contact').to_pandas().T
-
-            table_right = table_right.replace({'nan%':'—'})
-            table_right = table_right.replace({'nan':'—'})
-
-
-
 
+            if df is None:
+                fig = plt.figure(figsize=(26,26))
+                fig.text(x=0.1,y=0.9,s='No Statcast Data For This Pitcher',fontsize=36,ha='left')
+                return fig
+            
+            df = df.clone()
+            
 
             p.set(0.6, "Creating plot...")
 
-            import matplotlib.pyplot as plt
-            fig = plt.figure(figsize=(16, 9))
+  
+            #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)
-            gs = GridSpec(3, 5,  height_ratios=[2,9,1],width_ratios=[1,9,1,9,1])
-            gs.update(hspace=0.2, wspace=0.3)
+            print('this is the one plot')
 
-            # Add subplots to the grid
-            ax_header = fig.add_subplot(gs[0, :]) 
-            ax_left = fig.add_subplot(gs[1, 1]) 
-            ax_right = fig.add_subplot(gs[1, 3]) 
+            gs = gridspec.GridSpec(6, 8,
+                                height_ratios=[6,20,12,36,36,6],
+                                width_ratios=[4,18,18,18,18,18,18,4])
 
-            axfooter = fig.add_subplot(gs[-1, :])
 
+            gs.update(hspace=0.2, wspace=0.5)
 
-            if input.plot_type() == 'Pitch%':
-                heat_map_plot(df=df_plot, 
-                                ax=ax_left,
-                                cmap=cmap_sum2,
-                                hand='L')
+            # 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])
 
-                heat_map_plot(df=df_plot, 
-                                ax=ax_right,
-                                cmap=cmap_sum2,
-                                hand='R')
+            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])
 
-            if input.plot_type() == 'Whiff%':
-                heat_map_plot_hex_whiff(df=df_plot, 
-                                ax=ax_left,
-                                cmap=cmap_sum,
-                                hand='L')
+            ax_table = fig.add_subplot(gs[4,1:7])
 
-                heat_map_plot_hex_whiff(df=df_plot, 
-                                ax=ax_right,
-                                cmap=cmap_sum,
-                                hand='R')
+            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])
 
-            if input.plot_type() == 'xwOBACON':
-                heat_map_plot_hex_damage(df=df_plot, 
-                                ax=ax_left,
-                                cmap=cmap_sum,
-                                hand='L')
+            # Hide axes for footer, header, left, and right
+            ax_footer.axis('off')
+            ax_header.axis('off')
+            ax_left.axis('off')
+            ax_right.axis('off')
 
-                heat_map_plot_hex_damage(df=df_plot, 
-                                ax=ax_right,
-                                cmap=cmap_sum,
-                                hand='R')
+            sns.set_theme(style="whitegrid", palette=colour_palette)
+            fig.set_facecolor('white')
 
+            df_teams = scrape.get_teams()
 
-            # Load the image
-            img = mpimg.imread('images/left.png')
-            imagebox = OffsetImage(img, zoom=0.58)  # adjust zoom as needed
-            ab = AnnotationBbox(imagebox, (1.25, -0.5), box_alignment=(0, 0), frameon=False)
-            ax_left.add_artist(ab)
+            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():
 
-            # Load the image
-            img = mpimg.imread('images/right.png')
-            imagebox = OffsetImage(img, zoom=0.58)  # adjust zoom as needed
-            # Create an AnnotationBbox
-            ab = AnnotationBbox(imagebox, (-1.25, -0.5), box_alignment=(1, 0), frameon=False)
+                # 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)
 
-            ax_right.add_artist(ab)
+                if x == 'break_plot':
+                    break_plot(df = df,ax=y) 
 
+                if x == 'location_plot_lhb':
+                    location_plot(df = df,ax=y,hand='L')
 
-            table_plot(ax=ax_left,
-                        table=table_left,
-                            hand='L')
+                if x == 'location_plot_rhb':
+                    location_plot(df = df,ax=y,hand='R')
 
-            table_plot_pivot(ax=ax_left,
-                        pivot_table=pivot_table_l,
-                        df_colour=df_colour_left)
+            summary_table(df=df,
+                        ax=ax_table)
 
+            plot_footer(ax_footer)   
 
-            table_plot(ax=ax_right,
-                        table=table_right,
-                            hand='R')
+            ax_watermark = fig.add_subplot(gs[1:-1,1:-1],zorder=-1) 
+            # Hide axes ticks and labels
+            ax_watermark.set_xticks([])
+            ax_watermark.set_yticks([])
+            ax_watermark.set_frame_on(False)  # Optional: Hide border
+            
+            img = Image.open('tj stats circle-01_new.jpg')
+            # Display the image
+            ax_watermark.imshow(img, extent=[0, 1, 0, 1], origin='upper',zorder=-1, alpha=0.1)
+
+
+            ax_watermark2 = fig.add_subplot(gs[-2:,1:4],zorder=1) 
+            ax_watermark2.set_xlim(0,1)
+            ax_watermark2.set_ylim(0,1)
+            # Hide axes ticks and labels
+            ax_watermark2.set_xticks([])
+            ax_watermark2.set_yticks([])
+            ax_watermark2.set_frame_on(False)  # Optional: Hide border
+            
+            # Open the image
+            img = Image.open('tj stats circle-01_new.jpg')
+            # Get the original size
+            width, height = img.size
+            # Calculate the new size (50% larger)
+            new_width = int(width * 0.5)
+            new_height = int(height * 0.5)
+            # Resize the image
+            img_resized = img.resize((new_width, new_height))
+            # Display the image
+            ax_watermark2.imshow(img, extent=[0.26, 0.46, 0.0,0.2], origin='upper',zorder=-1, alpha=1)
 
-            table_plot_pivot(ax=ax_right,
-                        pivot_table=pivot_table_r,
-                        df_colour=df_colour_right)
+            fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
 
 
-            from matplotlib.cm import ScalarMappable
-            from matplotlib.colors import Normalize
-            # Create a ScalarMappable with the same colormap and normalization
-            if input.plot_type() == 'Pitch%':
-                sm = ScalarMappable(cmap=cmap_sum2, norm=Normalize(vmin=0, vmax=1))
 
-                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
-                cbar.set_ticks([])
+    
+    @output
+    @render.plot
+    @reactive.event(input.generate_plot, ignore_none=False)   
+    def game_plot():     
+        # Show progress/loading notification
+        with ui.Progress(min=0, max=1) as p:
+            print(input.date_id(),'TEST')
 
-                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+            if isinstance(input.date_id(), tuple):
+                fig = plt.figure(figsize=(26,26))
+                fig.text(x=0.1,y=0.9,s='Select Game and Generate Plot',fontsize=36,ha='left')
+                return fig                
+                
 
-                cbar.ax.set_xticklabels(['Least', 'Most'])
-                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
-                labels = cbar.ax.get_xticklabels()
+            p.set(message="Generating plot", detail="This may take a while...")
 
-                labels[0].set_horizontalalignment('left')
-                labels[-1].set_horizontalalignment('right')
-                labels = cbar.ax.get_xticklabels()
+            
+            p.set(0.3, "Gathering data...")
+            year_input = int(input.year_input())
+            sport_id = int(input.level_input())
+            player_input = int(input.pitcher_id())
+
+            # print(input.game_id())
+            # print(year_input, sport_id, player_input)
 
+            # print(year_input, sport_id, player_input, start_date, end_date)
 
-                cbar.ax.set_xticklabels(labels)
-                cbar.ax.tick_params(length=0)
+            df = cached_data_daily()
 
-            if input.plot_type() == 'Whiff%':
-                sm = ScalarMappable(cmap=cmap_sum, norm=Normalize(vmin=0.15, vmax=0.35))
+            # start_date = str(df['game_date'][0])
+            # end_date = str(df['game_date'][0])
+            
+            if df is None:
+                fig = plt.figure(figsize=(26,26))
+                fig.text(x=0.1,y=0.9,s='No Statcast Data For This Pitcher',fontsize=36,ha='left')
+                return fig
+            
+            df = df.clone()
+            
 
-                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
-                cbar.set_ticks([])
+            p.set(0.6, "Creating plot...")
 
-                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+  
+            #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')
 
-                cbar.ax.set_xticklabels(['15%', '35%'])
-                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
-                labels = cbar.ax.get_xticklabels()
+            gs = gridspec.GridSpec(6, 8,
+                                height_ratios=[6,20,12,36,36,6],
+                                width_ratios=[4,18,18,18,18,18,18,4])
 
-                labels[0].set_horizontalalignment('left')
-                labels[-1].set_horizontalalignment('right')
-                labels = cbar.ax.get_xticklabels()
 
+            gs.update(hspace=0.2, wspace=0.5)
 
-                cbar.ax.set_xticklabels(labels)
-                cbar.ax.tick_params(length=0)
+            # 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])
 
-            if input.plot_type() == 'xwOBACON':
-                sm = ScalarMappable(cmap=cmap_sum_r, norm=Normalize(vmin=0.25, vmax=0.5))
+            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])
 
-                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
-                cbar.set_ticks([])
+            ax_table = fig.add_subplot(gs[4,1:7])
 
-                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+            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])
 
-                cbar.ax.set_xticklabels(['.000', '.500'])
-                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
-                labels = cbar.ax.get_xticklabels()
+            # Hide axes for footer, header, left, and right
+            ax_footer.axis('off')
+            ax_header.axis('off')
+            ax_left.axis('off')
+            ax_right.axis('off')
 
-                labels[0].set_horizontalalignment('left')
-                labels[-1].set_horizontalalignment('right')
-                labels = cbar.ax.get_xticklabels()
+            sns.set_theme(style="whitegrid", palette=colour_palette)
+            fig.set_facecolor('white')
 
+            df_teams = scrape.get_teams()
 
-                cbar.ax.set_xticklabels(labels)
-                cbar.ax.tick_params(length=0)
+            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():
 
-            axfooter.text(x=0.02,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=14,va='top')
-            axfooter.text(x=1-0.02,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=14,va='top')
+                # 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)
 
-            axfooter.axis('off')
+                if x == 'break_plot':
+                    break_plot(df = df,ax=y) 
 
-            # Display the image on the axis
-            ax_header.set_xlim(-12,12)
-            ax_header.set_ylim(0, 2)
+                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')
 
-            if input.plot_type() == 'Pitch%':
-                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} Pitch Frequency",ha='center',fontsize=24,va='top')
-            if input.plot_type() == 'Whiff%':
-                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} Whiff%",ha='center',fontsize=24,va='top')
-            if input.plot_type() == 'xwOBACON':
-                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} xwOBACON",ha='center',fontsize=24,va='top')
+            summary_table(df=df,
+                        ax=ax_table)
 
-            ax_header.text(x=0,y=0.7,s=f"{year_input} {level_dict[str(sport_id)]} Season",ha='center',fontsize=16,va='top')            
-            ax_header.text(x=0,y=0.3,s=f"{df_plot['game_date'][0]} to {df_plot['game_date'][-1]}",ha='center',fontsize=16,va='top',fontstyle='italic')
+            plot_footer(ax_footer)   
 
-            ax_header.axis('off')
+            ax_watermark = fig.add_subplot(gs[1:-1,1:-1],zorder=-1) 
+            # Hide axes ticks and labels
+            ax_watermark.set_xticks([])
+            ax_watermark.set_yticks([])
+            ax_watermark.set_frame_on(False)  # Optional: Hide border
+            
+            img = Image.open('tj stats circle-01_new.jpg')
+            # Display the image
+            ax_watermark.imshow(img, extent=[0, 1, 0, 1], origin='upper',zorder=-1, alpha=0.1)
+
+
+            ax_watermark2 = fig.add_subplot(gs[-2:,1:4],zorder=1) 
+            ax_watermark2.set_xlim(0,1)
+            ax_watermark2.set_ylim(0,1)
+            # Hide axes ticks and labels
+            ax_watermark2.set_xticks([])
+            ax_watermark2.set_yticks([])
+            ax_watermark2.set_frame_on(False)  # Optional: Hide border
+            
+            # Open the image
+            img = Image.open('tj stats circle-01_new.jpg')
+            # Get the original size
+            width, height = img.size
+            # Calculate the new size (50% larger)
+            new_width = int(width * 0.5)
+            new_height = int(height * 0.5)
+            # Resize the image
+            img_resized = img.resize((new_width, new_height))
+            # Display the image
+            ax_watermark2.imshow(img, extent=[0.26, 0.46, 0.0,0.2], origin='upper',zorder=-1, alpha=1)
 
+            fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
 
-            import urllib
-            import urllib.request
-            import urllib.error
-            from urllib.error import HTTPError
 
 
-            plot_header(pitcher_id=player_input,
-                    ax=ax_header, 
-                    df_team=scrape.get_teams(), 
-                    df_players=scrape.get_players(sport_id,year_input),
-                    sport_id=sport_id,)
+    @output
+    @render.data_frame
+    @reactive.event(input.generate_plot, ignore_none=False)   
+    def grid():
 
+        start_date = str(input.date_id()[0])
+        if not is_valid_date(start_date):
+             return pd.DataFrame({"Message": ["Select range to generate table"]})
+        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,
+            )
 
 
-            fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)
+    @output
+    @render.data_frame
+    @reactive.event(input.generate_plot, ignore_none=False)   
+    def grid_game():
+        if isinstance(input.date_id(), tuple):
+                return pd.DataFrame({"Message": ["Select game to generate table"]})
+                     
+        df = cached_data_daily()
+        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']
 
 
-    
 
-app = App(app_ui, server)
+        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)

functions/df_update.py

@@ -323,7 +323,6 @@ class df_update:
             (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'),
-            (1 - (pl.col('zone_contact') / pl.col('zone_swing'))).alias('zone_whiff_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'),
@@ -443,7 +442,6 @@ class df_update:
             (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'),
-            (1 - (pl.col('zone_contact') / pl.col('zone_swing'))).alias('zone_whiff_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'),

functions/pitch_summary_functions.py

@@ -19,6 +19,7 @@ from matplotlib.offsetbox import OffsetImage, AnnotationBbox
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 import PIL
+from stuff_model import calculate_arm_angles as caa
 
 
 ### PITCH COLOURS ###
@@ -50,6 +51,7 @@ pitch_colours = {
 
     ## Others ##
     'KN': {'colour': '#867A08', 'name': 'Knuckleball'},
+    'KN': {'colour': '#867A08', 'name': 'Knuckle Ball'},
     'PO': {'colour': '#472C30', 'name': 'Pitch Out'},
     'UN': {'colour': '#9C8975', 'name': 'Unknown'},
 }
@@ -393,6 +395,17 @@ def break_plot(df: pl.DataFrame, ax: plt.Axes):
     ax.set_xlim((-25, 25))
     ax.set_ylim((-25, 25))
 
+
+    df_aa = caa.calculate_arm_angles(df,df['pitcher_id'][0])['arm_angle']
+
+    # Plot average arm angle
+    mean_arm_angle = df_aa.mean()
+    x_end = 30
+    y_end = x_end * np.tan(np.radians(mean_arm_angle))
+    ax.plot([0, x_end], [0, y_end], color='grey', linestyle='--', linewidth=2,zorder=0)
+
+    
+
     # Add horizontal and vertical lines
     ax.hlines(y=0, xmin=-50, xmax=50, color=colour_palette[8], alpha=0.5, linestyles='--', zorder=1)
     ax.vlines(x=0, ymin=-50, ymax=50, color=colour_palette[8], alpha=0.5, linestyles='--', zorder=1)
@@ -400,7 +413,7 @@ def break_plot(df: pl.DataFrame, ax: plt.Axes):
     # Set axis labels and title
     ax.set_xlabel('Horizontal Break (in)', fontdict=font_properties_axes)
     ax.set_ylabel('Induced Vertical Break (in)', fontdict=font_properties_axes)
-    ax.set_title("Pitch Breaks", fontdict=font_properties_titles)
+    ax.set_title(f"Pitch Breaks - Arm Angle: {mean_arm_angle:.0f}°", fontdict=font_properties_titles)
 
     # Remove legend
     ax.get_legend().remove()
@@ -543,8 +556,8 @@ def summary_table(df: pl.DataFrame, ax: plt.Axes):
         pl.col('spin_rate').mean().alias('spin_rate'),
         pl.col('vaa').mean().alias('vaa'),
         pl.col('haa').mean().alias('haa'),
-        pl.col('z0').mean().alias('z0'),
-        pl.col('x0').mean().alias('x0'),
+        pl.col('release_pos_z').mean().alias('z0'),
+        pl.col('release_pos_x').mean().alias('x0'),
         pl.col('extension').mean().alias('extension'),
         (((pl.col('spin_direction').mean() + 180) % 360 // 30) + 
         (((pl.col('spin_direction').mean() + 180) % 360 % 30 / 30 / 100 * 60).round(2) * 10).round(0) // 1.5 / 4)
@@ -624,7 +637,7 @@ def summary_table(df: pl.DataFrame, ax: plt.Axes):
         # Apply color to specific columns based on normalized values
         columns_to_color = [(3, 'release_speed', 0.95, 1.05), (11, 'release_extension', 0.9, 1.1), (13, None, 80, 120), 
                             (14, None, 30, 70), (15, 'in_zone_rate', 0.7, 1.3), (16, 'chase_rate', 0.7, 1.3), 
-                            (17, 'whiff_rate', 0.7, 1.3), (18, 'xwoba', 0.7, 1.3)]
+                            (17, 'whiff_rate', 0.7, 1.3), (18, 'xwobacon', 0.7, 1.3)]
 
         for col, stat, vmin_factor, vmax_factor in columns_to_color:
             cell_value = table.get_celld()[(i + 1, col)].get_text().get_text()
@@ -764,7 +777,7 @@ def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int):
 
     # 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, 0.7, 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)
 
     # Make API call to retrieve sports information
@@ -1055,7 +1068,8 @@ def stat_summary_table(df: pl.DataFrame,
                         player_input: int, 
                         sport_id: int, 
                         ax: plt.Axes,
-                        split: str = 'All'):
+                        split: str = 'All',
+                        game_type: list = ['R']):
     """
     Create a summary table of player statistics.
 
@@ -1072,6 +1086,18 @@ def stat_summary_table(df: pl.DataFrame,
     split : str, optional
         The split type (default is 'All').
     """
+
+    type_dict =  {'R':'Regular Season',
+                   'S':'Spring',
+                   'P':'Playoffs' }
+
+    split_title = {
+        'all':'',
+        'right':' vs RHH',
+        'left':' vs LHH'
+    }
+
+    
     # Format start and end dates
     start_date_format = str(pd.to_datetime(df['game_date'][0]).strftime('%m/%d/%Y'))
     end_date_format = str(pd.to_datetime(df['game_date'][-1]).strftime('%m/%d/%Y'))
@@ -1079,11 +1105,14 @@ def stat_summary_table(df: pl.DataFrame,
     # Determine app context based on sport ID
     appContext = 'majorLeague' if sport_id == 1 else 'minorLeague'
 
+    game_type_str = ','.join([str(x) for x in game_type])
+
     # Fetch player stats from MLB API
     pitcher_stats_call = requests.get(
-        f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format})'
+        f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format},gameType=[{game_type_str}])'
     ).json()
-
+    print('HERE')
+    print(f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format},gameType=[{game_type_str}])')
     # Extract stats and create DataFrame
     pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat']]
     pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat']]
@@ -1103,11 +1132,11 @@ def stat_summary_table(df: pl.DataFrame,
     if df['game_id'][0] == df['game_id'][-1]:
         pitcher_stats_call_df_small = pitcher_stats_call_df.select(['inningsPitched', 'battersFaced', 'earnedRuns', 'hits', 'strikeOuts', 'baseOnBalls', 'hitByPitch', 'homeRuns', 'strikePercentage', 'whiffs'])
         new_column_names = ['$\\bf{IP}$', '$\\bf{PA}$', '$\\bf{ER}$', '$\\bf{H}$', '$\\bf{K}$', '$\\bf{BB}$', '$\\bf{HBP}$', '$\\bf{HR}$', '$\\bf{Strike\%}$', '$\\bf{Whiffs}$']
-        title = f'{df["game_date"][0]} vs {df["batter_team"][0]}'
-    elif sport_id != 1:
+        title = f'{df["game_date"][0]} vs {df["batter_team"][0]} ({type_dict[game_type[0]]}){split_title[split]}'
+    elif sport_id != 1 or game_type[0] in ['S','P']:
         pitcher_stats_call_df_small = pitcher_stats_call_df.select(['inningsPitched', 'battersFaced', 'whip', 'era', 'fip', 'k_percent', 'bb_percent', 'k_bb_percent', 'strikePercentage'])
         new_column_names = ['$\\bf{IP}$', '$\\bf{PA}$', '$\\bf{WHIP}$', '$\\bf{ERA}$', '$\\bf{FIP}$', '$\\bf{K\%}$', '$\\bf{BB\%}$', '$\\bf{K-BB\%}$', '$\\bf{Strike\%}$']
-        title = f'{df["game_date"][0]} to {df["game_date"][-1]}'
+        title = f'{df["game_date"][0]} to {df["game_date"][-1]} ({type_dict[game_type[0]]}{split_title[split]})'
     else:
         fangraphs_table(df=df, ax=ax, player_input=player_input, season=int(df['game_date'][0][0:4]), split=split)
         return
@@ -1121,149 +1150,3 @@ def stat_summary_table(df: pl.DataFrame,
     # Add title to the plot
     ax.text(0.5, 0.9, title, va='bottom', ha='center', fontsize=36, fontstyle='italic')
     ax.axis('off')
-
-
-
-def stat_daily_summary(df: pl.DataFrame,
-                        data: list, 
-                        player_input: int, 
-                        sport_id: int, 
-                        ax: plt.Axes):
- 
-        
-        pk_list = []
-        pitcher_id_list = []
-        summary_list = []
-        ip_list = []
-        pa_list = []
-        er_list = []
-        hit_list = []
-        k_list = []
-        bb_list = []
-        hbp_list = []
-        strikes_list = []
-        hr_list = []
-        test_list = []
-        game_pk_list = []
-        pitches_list = []
-
-
-        # 'inningsPitched','battersFaced','earnedRuns','hits','strikeOuts','baseOnBalls','hitByPitch'
-
-        for y in range(0,len(data)):
-                
-            pk_list.append([data[y]['gameData']['game']['pk'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            pk_list.append([data[y]['gameData']['game']['pk'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            pitcher_id_list.append([x for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            pitcher_id_list.append([x for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-
-            ip_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['inningsPitched'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            ip_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['inningsPitched'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            pa_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['battersFaced'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            pa_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['battersFaced'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            er_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['earnedRuns'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            er_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['earnedRuns'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            hit_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['hits'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            hit_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['hits'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            k_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['strikeOuts'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            k_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['strikeOuts'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            bb_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['baseOnBalls'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            bb_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['baseOnBalls'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            hbp_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['hitByPitch'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            hbp_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['hitByPitch'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            strikes_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['strikes'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            strikes_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['strikes'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            pitches_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['pitchesThrown'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-            pitches_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['pitchesThrown'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-                        
-            
-            hr_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['homeRuns'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            hr_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['homeRuns'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-            
-            summary_list.append([data[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['summary'] for x in data[y]['liveData']['boxscore']['teams']['away']['pitchers']])
-            summary_list.append([data[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['summary'] for x in data[y]['liveData']['boxscore']['teams']['home']['pitchers']])
-
-            test_list.append([x for x in data[y]['liveData']['plays']['allPlays']])
-            game_pk_list.append([data[y]['gameData']['game']['pk'] for x in data[y]['liveData']['plays']['allPlays']])
-
-        flat_list_pk = [item for sublist in pk_list for item in sublist]
-        flat_list_pitcher_id = [item for sublist in pitcher_id_list for item in sublist]
-        flat_list_summary = [item for sublist in summary_list for item in sublist]
-        flat_list_hits = [item for sublist in hit_list for item in sublist]
-        flat_list_k = [item for sublist in k_list for item in sublist]
-        flat_list_bb = [item for sublist in bb_list for item in sublist]
-        flat_list_pa = [item for sublist in pa_list for item in sublist]
-        flat_list_ip = [item for sublist in ip_list for item in sublist]
-        flat_list_hbp= [item for sublist in hbp_list for item in sublist]
-        flat_list_strikes = [item for sublist in strikes_list for item in sublist]
-        flat_list_hr= [item for sublist in hr_list for item in sublist]
-        flat_list_er= [item for sublist in er_list for item in sublist]
-        flat_list_pitches= [item for sublist in pitches_list for item in sublist]
-
-
-
-        pitcher_summary_df = pl.DataFrame(data={'game_id':flat_list_pk,
-                                                'pitcher_id':flat_list_pitcher_id,
-                                                'summary':flat_list_summary,
-                                                'hits':flat_list_hits,
-                                                'k':flat_list_k,
-                                                'bb':flat_list_bb,
-                                                'pa':flat_list_pa,
-                                                'hbp':flat_list_hbp,
-                                                'strikes':flat_list_strikes,
-                                                'hr':flat_list_hr,
-                                                'ip':flat_list_ip,
-                                                'er':flat_list_er,
-                                                'pitches':flat_list_pitches})
-
-
-
-        # Add additional calculated columns
-        pitcher_summary_df = pitcher_summary_df.filter(pl.col('pitcher_id')==player_input).with_columns(
-            pl.lit(df['is_whiff'].sum()).alias('whiffs'),
-            ((pl.col('strikes'))/(pl.col('pitches'))*100).round(1).cast(pl.Utf8).str.concat('%').alias('strikePercentage')
-        )
-
-        # Determine columns and title based on game count and sport ID
-        
-        pitcher_stats_call_df_small = pitcher_summary_df.select(['ip', 
-                                                                    'pa', 
-                                                                    'er', 
-                                                                    'hits', 
-                                                                    'k', 
-                                                                    'bb', 
-                                                                    'hbp', 
-                                                                    'hr', 
-                                                                    'strikePercentage', 
-                                                                    'whiffs'])
-        
-        new_column_names = ['$\\bf{IP}$', '$\\bf{PA}$', '$\\bf{ER}$', '$\\bf{H}$', '$\\bf{K}$', '$\\bf{BB}$', '$\\bf{HBP}$', '$\\bf{HR}$', '$\\bf{Strike\%}$', '$\\bf{Whiffs}$']
-        title = f'{df["game_date"][0]} vs {df["batter_team"][0]}'
-
-        table_fg = ax.table(cellText=pitcher_stats_call_df_small.to_numpy(), colLabels=pitcher_stats_call_df_small.columns, cellLoc='center',
-                        bbox=[0.0, 0.1, 1, 0.7])
-        
-        min_font_size = 20
-        table_fg.set_fontsize(min_font_size)
-
-
-        new_column_names = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ER}$','$\\bf{H}$','$\\bf{K}$','$\\bf{BB}$','$\\bf{HBP}$','$\\bf{HR}$','$\\bf{Strike\%}$','$\\bf{Whiffs}$']
-        # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%']
-        for i, col_name in enumerate(new_column_names):
-            table_fg.get_celld()[(0, i)].get_text().set_text(col_name)
-
-        ax.axis('off')
-
-        # Add title to the plot
-        ax.text(0.5, 0.9, title, va='bottom', ha='center', fontsize=36, fontstyle='italic')
-        ax.axis('off')

functions/statcast_2024_grouped.csv

@@ -1,19 +1,19 @@
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-SC,159,81.02264150943397,-3.1056603773584905,-8.001509433962264,2050.5974842767296,-1.0535849056603774,6.110377358490566,6.064150943396227,4.623,58,13,63,96,20,0.35349463636363637,0.0002191081608832677,0.22413793103448276,0.39622641509433965,0.20833333333333334,-2.9075471698113207,
-SI,116002,93.34805382235511,7.567078832293412,-6.148476070311284,2147.3631502060834,-0.7671983511070397,5.622119363257688,6.435364206296976,-32.837000000000025,53318,7390,65492,50222,12474,0.3501967420378125,0.15985525080994228,0.13860234817510034,0.5645764728194341,0.2483772052088726,0.028307270564300636,
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-SV,2702,81.67483345669874,-4.788941524796447,7.356861584011844,2470.624858757062,-0.5779570688378979,5.420762398223538,6.227296392711045,0.19299999999999926,1117,339,1138,1564,479,0.2907683709923664,0.0037234606962678577,0.3034914950760967,0.42116950407105846,0.3062659846547315,-0.007142857142857115,
-All,725669,89.1521052747817,7.058379139422499,-1.2140087540219224,2255.6768252515376,-0.8282529777063689,5.758824349487279,6.456550518555369,-20.178000000000118,352163,89742,359413,365054,104080,0.3147037524825,1.0,0.25483085957354973,0.4952850404247667,0.28510850449522535,0.002780606585095976,all
+pitch_type,pitch,release_speed,pfx_z,pfx_x,release_spin_rate,release_pos_x,release_pos_z,release_extension,delta_run_exp,swing,whiff,in_zone,out_zone,chase,xwoba,xwobacon,pitch_usage,whiff_rate,in_zone_rate,chase_rate,delta_run_exp_per_100,all
+CH,74155,85.46226726,5.247514143,-3.974501168,1803.342541,-0.507762986,5.740925968,6.449406057,204.631,37385,11538,28912,45151,15250,0.289735649,0.341580895,0.102188463,0.308626454,0.389886049,0.337755531,-0.275950374,
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