app.py

import polars as pl
import api_scraper
mlb_scrape = api_scraper.MLB_Scrape()

from stuff_model import *
from shiny import App, reactive, ui, render
from shiny.ui import h2, tags
from api_scraper import MLB_Scrape
import datetime
from stuff_model import feature_engineering as fe
from stuff_model import stuff_apply
from pytabulator import TableOptions, Tabulator, output_tabulator, render_tabulator, theme
theme.tabulator_site()
scraper = MLB_Scrape()

df_year_old_group = pl.read_parquet('pitch_data_agg_2024.parquet')

pitcher_old_dict = dict(zip(df_year_old_group['pitcher_id'],df_year_old_group['pitcher_name']))




app_ui = ui.page_fluid(
    ui.card(
        ui.card_header("2025 Spring Training Pitch Data App"),
        ui.row(
            ui.column(4,
                ui.markdown("""This app generates a table which shows the 2025 Spring Training data.

* Differences are calculated based on 2024 regular season data
* If 2024 data does not exist for pitcher, 2023 Data is used
* If no difference exists, the pitch is labelled as a new pitch"""),
            
            
                ui.input_action_button(
                    "refresh",
                    "Refresh Data",
                    class_="btn-primary",
                    width="100%"
                )
            ),
            ui.column(3,
                ui.div(
                    "By: ",
                    ui.tags.a(
                        "@TJStats",
                        href="https://x.com/TJStats",
                        target="_blank"
                    )
                ),
                ui.tags.p("Data: MLB"),
                ui.tags.p(
                    ui.tags.a(
                        "Support me on Patreon for more baseball content",
                        href="https://www.patreon.com/TJ_Stats",
                        target="_blank"
                    )
                )
            )
        ),
        ui.navset_tab(
            ui.nav("All Pitches",
                ui.row(ui.column(1,ui.download_button("download_all", "Download Data", class_="btn-sm mb-3")),
                       
                    ui.column(2,
                        ui.div(
                            {"class": "input-group"},
                            ui.span("Pitches >=", class_="input-label"),
                            ui.input_numeric(id='pitches_all_min', label='', value=1, min=1, width="100px")
                        )
                    )),

                output_tabulator("table_all")
            ),
            ui.nav("Daily Pitches",
                 ui.row(
                    ui.column(2,
                        ui.div(
                            {"class": "input-group"},
                            ui.span("Pitches >=", class_="input-label"),
                            ui.input_numeric(id='pitches_daily_min', label='', value=1, min=1, width="100px")
                        )
                    )),                  


                output_tabulator("table_daily")
            ),
            ui.nav("tjStuff+",
                   
                 ui.row(
                    ui.column(2,
                        ui.div(
                            {"class": "input-group"},
                            ui.span("Pitches >=", class_="input-label"),
                            ui.input_numeric(id='pitches_tjstuff_min', label='', value=1, min=1, width="100px")
                        )
                    )),           
                output_tabulator("table_tjstuff")
            ),
            ui.nav("tjStuff+ Summary",
                ui.row(ui.column(1,ui.download_button("download_tjsumm", "Download Data", class_="btn-sm mb-3")),
                       
                    ui.column(2,
                        ui.div(
                            {"class": "input-group"},
                            ui.span("Pitches >=", class_="input-label"),
                            ui.input_numeric(id='pitches_tjsumm_min', label='', value=1, min=1, width="100px")
                        )
                    )),

                output_tabulator("table_stuff_all")
                
                ),
            ui.nav("tjStuff+ Team",
                   
                 ui.row(
                    ui.column(2,
                    )),           
                output_tabulator("table_tjstuff_team")
            ),
            
        )
    )
)

def server(input, output, session):


    @reactive.Calc
    def spring_data():
        
        import polars as pl
        df_spring = pl.read_parquet(f"hf://datasets/TJStatsApps/mlb_data/data/mlb_pitch_data_2025_spring.parquet")


        date = (datetime.datetime.now() - datetime.timedelta(hours=8)).date()
        print(datetime.datetime.now())
        date_str = date.strftime('%Y-%m-%d')
        # Initialize the scraper


        game_list_input = (scraper.get_schedule(year_input=[int(date_str[0:4])], sport_id=[1], game_type=['S'])
                    .filter(pl.col('date') == date)['game_id'])

        data = scraper.get_data(game_list_input)
        df = scraper.get_data_df(data)

        df_spring = pl.concat([df_spring, df]).sort('game_date', descending=True)

        return df_spring.filter(pl.col('start_speed')>0)

    @reactive.Calc
    def ts_data():

        df_spring = spring_data()

        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))



        import polars as pl

        # Compute total pitches for each pitcher
        df_pitcher_totals = df_spring_stuff.group_by("pitcher_id").agg(
            pl.col("start_speed").count().alias("pitcher_total")
        )

        df_spring_group = df_spring_stuff.group_by(['pitcher_id', 'pitcher_name', 'pitch_type']).agg([
            pl.col('start_speed').count().alias('count'),
            pl.col('start_speed').mean().alias('start_speed'),
            pl.col('start_speed').max().alias('max_start_speed'),
            pl.col('ivb').mean().alias('ivb'),
            pl.col('hb').mean().alias('hb'),
            pl.col('release_pos_z').mean().alias('release_pos_z'),
            pl.col('release_pos_x').mean().alias('release_pos_x'),
            pl.col('extension').mean().alias('extension'),
            pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus'),
            (pl.col('start_speed').filter(pl.col('batter_hand')=='L').count()).alias('rhh_count'),
            (pl.col('start_speed').filter(pl.col('batter_hand')=='R').count()).alias('lhh_count')
        ])

        # Join total pitches per pitcher to the grouped DataFrame on pitcher_id
        df_spring_group = df_spring_group.join(df_pitcher_totals, on="pitcher_id", how="left")

        # Now calculate the pitch percent for each pitcher/pitch_type combination
        df_spring_group = df_spring_group.with_columns(
            (pl.col("count") / pl.col("pitcher_total")).alias("pitch_percent")
        )

        # Optionally, if you want the percentage of left/right-handed batters within the group:
        df_spring_group = df_spring_group.with_columns([
            (pl.col("rhh_count") / pl.col("pitcher_total")).alias("rhh_percent"),
            (pl.col("lhh_count") / pl.col("pitcher_total")).alias("lhh_percent")
        ])

        df_merge = df_spring_group.join(df_year_old_group,on=['pitcher_id','pitch_type'],how='left',suffix='_old')


        df_merge = df_merge.with_columns(
            pl.col('pitcher_id').is_in(df_year_old_group['pitcher_id']).alias('exists_in_old')
        )

        df_merge = df_merge.with_columns(
            pl.when(pl.col('start_speed_old').is_null() & pl.col('exists_in_old'))
            .then(pl.lit(True))
            .otherwise(pl.lit(None))
            .alias("new_pitch")
        )

        df_merge = df_merge.select([
            'pitcher_id',
            'pitcher_name',
            'pitch_type',
            'count',
            'pitch_percent',
            'rhh_percent',
            'lhh_percent',
            'start_speed',
            'max_start_speed',
            'ivb',
            'hb',
            'release_pos_z',
            'release_pos_x',
            'extension',
            'tj_stuff_plus',
        ])

        return df_merge

    @reactive.Calc
    def ts_data():

        df_spring = spring_data()

        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))


        import polars as pl

        # Compute total pitches for each pitcher
        df_pitcher_totals = df_spring_stuff.group_by("pitcher_id").agg(
            pl.col("start_speed").count().alias("pitcher_total")
        )

        df_spring_group = df_spring_stuff.group_by(['pitcher_id', 'pitcher_name', 'pitch_type']).agg([
            pl.col('start_speed').count().alias('count'),
            pl.col('start_speed').mean().alias('start_speed'),
            pl.col('start_speed').max().alias('max_start_speed'),
            pl.col('ivb').mean().alias('ivb'),
            pl.col('hb').mean().alias('hb'),
            pl.col('release_pos_z').mean().alias('release_pos_z'),
            pl.col('release_pos_x').mean().alias('release_pos_x'),
            pl.col('extension').mean().alias('extension'),
            pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus'),
            (pl.col('start_speed').filter(pl.col('batter_hand')=='L').count()).alias('rhh_count'),
            (pl.col('start_speed').filter(pl.col('batter_hand')=='R').count()).alias('lhh_count')
        ])

        # Join total pitches per pitcher to the grouped DataFrame on pitcher_id
        df_spring_group = df_spring_group.join(df_pitcher_totals, on="pitcher_id", how="left")

        # Now calculate the pitch percent for each pitcher/pitch_type combination
        df_spring_group = df_spring_group.with_columns(
            (pl.col("count") / pl.col("pitcher_total")).alias("pitch_percent")
        )

        # Optionally, if you want the percentage of left/right-handed batters within the group:
        df_spring_group = df_spring_group.with_columns([
            (pl.col("rhh_count") / pl.col("pitcher_total")).alias("rhh_percent"),
            (pl.col("lhh_count") / pl.col("pitcher_total")).alias("lhh_percent")
        ])

        df_merge = df_spring_group.join(df_year_old_group,on=['pitcher_id','pitch_type'],how='left',suffix='_old')


        df_merge = df_merge.with_columns(
            pl.col('pitcher_id').is_in(df_year_old_group['pitcher_id']).alias('exists_in_old')
        )

        df_merge = df_merge.with_columns(
            pl.when(pl.col('start_speed_old').is_null() & pl.col('exists_in_old'))
            .then(pl.lit(True))
            .otherwise(pl.lit(None))
            .alias("new_pitch")
        )

        df_merge = df_merge.select([
            'pitcher_id',
            'pitcher_name',
            'pitch_type',
            'count',
            'pitch_percent',
            'rhh_percent',
            'lhh_percent',
            'start_speed',
            'max_start_speed',
            'ivb',
            'hb',
            'release_pos_z',
            'release_pos_x',
            'extension',
            'tj_stuff_plus',
        ])

        return df_merge

    @reactive.Calc
    def ts_data_summ():

        df_spring = spring_data()

        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))







        # Aggregate tj_stuff_plus by pitcher_id and year
        df_agg_2024_pitch = df_spring_stuff.group_by(['pitcher_id','pitcher_name', 'pitch_type']).agg(
            pl.col('tj_stuff_plus').len().alias('count'),
            pl.col('tj_stuff_plus').mean()
        )

        # Calculate the weighted average of 'tj_stuff_plus' for each pitcher
        df_weighted_avg = df_agg_2024_pitch.with_columns(
            (pl.col('tj_stuff_plus') * pl.col('count')).alias('weighted_tj_stuff_plus')
        ).group_by(['pitcher_id', 'pitcher_name']).agg(
            pl.col('count').sum().alias('total_count'),
            pl.col('weighted_tj_stuff_plus').sum().alias('total_weighted_tj_stuff_plus')
        ).with_columns(
            (pl.col('total_weighted_tj_stuff_plus') / pl.col('total_count')).alias('tj_stuff_plus')
        ).select(['pitcher_id', 'pitcher_name', 'tj_stuff_plus', 'total_count'])

        # Add the 'pitch_type' column with value "All"
        df_weighted_avg = df_weighted_avg.with_columns(
            pl.lit("All").alias('pitch_type')
        )

        # Select and rename columns to match the original DataFrame
        df_weighted_avg = df_weighted_avg.select([
            'pitcher_id',
            'pitcher_name',

            'pitch_type',
            pl.col('total_count').alias('count'),
            'tj_stuff_plus'
        ])

        # Concatenate the new rows with the original DataFrame
        df_small = pl.concat([df_agg_2024_pitch, df_weighted_avg])

        df_game_count = df_spring_stuff.group_by(['pitcher_id']).agg(
            
            (((pl.col('game_id').count())).alias('pitches')/((pl.col('game_id').n_unique()))).alias('pitches_per_game'),
        )



        count_dict = dict(zip(df_small.filter(pl.col('pitch_type')=='All')['pitcher_id'],
                                df_small.filter(pl.col('pitch_type')=='All')['count']))
        # Check if 'FS' column exists, if not create it and fill with None

        df_small_pivot = (df_small.pivot(index=['pitcher_id','pitcher_name'],
                        columns='pitch_type',
                        values='tj_stuff_plus').with_columns(
                        pl.col("pitcher_id").replace_strict(count_dict, default=None).alias("count")))

        # Check if 'FS' column exists, if not create it and fill with None
        for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']:
            if col not in df_small_pivot.columns:
                df_small_pivot = df_small_pivot.with_columns(pl.lit(None).alias(col))

        df_small_pivot.select(['pitcher_id','pitcher_name','count','CH','CU','FC','FF','FS','SI','SL','ST','All']).sort('All',descending=True)#.head(10)#.write_clipboard()
                        
        return df_small_pivot



    @session.download(filename="data.csv")
    def download_all():
        yield ts_data().write_csv()


    @session.download(filename="data_tjstuff.csv")
    def download_tjsumm():
        yield ts_data_summ().write_csv()

    @output
    @render_tabulator
    @reactive.event(input.refresh)
    def table_all():

        df_spring = spring_data()

        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))



        import polars as pl

        # Compute total pitches for each pitcher
        df_pitcher_totals = df_spring_stuff.group_by(["pitcher_id"]).agg(
            pl.col("start_speed").count().alias("pitcher_total")
        )

        df_pitcher_totals_hands = (
            df_spring_stuff
            .group_by(["pitcher_id", "batter_hand"])
            .agg(pl.col("start_speed").count().alias("pitcher_total"))
            .pivot(
                values="pitcher_total",
                index="pitcher_id",
                columns="batter_hand",
                aggregate_function="sum"
            )
            .rename({"L": "pitcher_total_left", "R": "pitcher_total_right"})
            .fill_null(0)  # Fill missing values with 0 if some pitchers don't face both hands
        )
                
        
        df_spring_group = df_spring_stuff.group_by(['pitcher_id', 'pitcher_name', 'pitch_type']).agg([
            pl.col('start_speed').count().alias('count'),
            pl.col('start_speed').mean().alias('start_speed'),
            pl.col('start_speed').max().alias('max_start_speed'),
            pl.col('ivb').mean().alias('ivb'),
            pl.col('hb').mean().alias('hb'),
            pl.col('release_pos_z').mean().alias('release_pos_z'),
            pl.col('release_pos_x').mean().alias('release_pos_x'),
            pl.col('extension').mean().alias('extension'),
            pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus'),
            (pl.col("batter_hand").eq("R").sum()).alias("rhh_count"),  # Corrected: Counts RHH (batter_hand == "R")
            (pl.col("batter_hand").eq("L").sum()).alias("lhh_count")   # Corrected: Counts LHH (batter_hand == "L")
        ])

        # Join total pitches per pitcher to the grouped DataFrame on pitcher_id
        df_spring_group = df_spring_group.join(df_pitcher_totals, on=["pitcher_id"], how="left")
        df_spring_group = df_spring_group.join(df_pitcher_totals_hands, on=["pitcher_id"], how="left")

        # Now calculate the pitch percent for each pitcher/pitch_type combination
        df_spring_group = df_spring_group.with_columns(
            (pl.col("count") / pl.col("pitcher_total")).alias("pitch_percent")
        )

        # Optionally, if you want the percentage of left/right-handed batters within the group:
        df_spring_group = df_spring_group.with_columns([
            (pl.col("rhh_count") / pl.col("pitcher_total_right")).alias("rhh_percent"),
            (pl.col("lhh_count") / pl.col("pitcher_total_left")).alias("lhh_percent")
        ])


        df_merge = df_spring_group.join(df_year_old_group,on=['pitcher_id','pitch_type'],how='left',suffix='_old')


        df_merge = df_merge.with_columns(
            pl.col('pitcher_id').is_in(df_year_old_group['pitcher_id']).alias('exists_in_old')
        )

        df_merge = df_merge.with_columns(
            pl.when(pl.col('start_speed_old').is_null() & pl.col('exists_in_old'))
            .then(pl.lit(True))
            .otherwise(pl.lit(None))
            .alias("new_pitch")
        )

        import polars as pl

        # Define the columns to subtract
        cols_to_subtract = [
            ("start_speed", "start_speed_old"),
            ("max_start_speed", "max_start_speed_old"),
            ("ivb", "ivb_old"),
            ("hb", "hb_old"),
            ("release_pos_z", "release_pos_z_old"),
            ("release_pos_x", "release_pos_x_old"),
            ("extension", "extension_old"),
            ("tj_stuff_plus", "tj_stuff_plus_old")
        ]

        df_merge = df_merge.with_columns([
            # Step 1: Create _diff columns with the default value (e.g., 80) if old is null
            pl.when(pl.col(old).is_null())
            .then(pl.lit(10000))  # If old is null, assign 80 as the default
            .otherwise(pl.col(new) - pl.col(old))  # Otherwise subtract old from new
            .alias(new + "_diff")
            for new, old in cols_to_subtract
        ])

        # Step 2: Format the columns with (value (+diff)) - exclude brackets if diff is 80
        df_merge = df_merge.with_columns([
            pl.when(pl.col(new + "_diff").eq(10000))  # If diff is 80, no need to include brackets
            .then(pl.col(new).round(1).cast(pl.Utf8)+'\n\t')  # Just return the new value as string
            .otherwise(
                pl.col(new).round(1).cast(pl.Utf8) + 
                "\n(" + 
                pl.col(new + "_diff").round(1)
                    .map_elements(lambda x: f"{x:+.1f}") + 
                ")"
            ).alias(new + "_formatted")
            for new, _ in cols_to_subtract
        ])






        percent_cols = ['pitch_percent', 'rhh_percent', 'lhh_percent']

        df_merge = df_merge.with_columns([
            (pl.col(col) * 100)  # Convert to percentage
            .round(1)            # Round to 1 decimal
            .map_elements(lambda x: f"{x:.1f}%")  # Format as string with '%'
            .alias(col + "_formatted")
            for col in percent_cols
        ]).sort(['pitcher_id','count'],descending=True)


        columns = [
            { "title": "Pitcher Name", "field": "pitcher_name", "width": 250, "headerFilter":"input" ,"frozen":True,},
            { "title": "Team", "field": "pitcher_team", "width": 100, "headerFilter":"input" ,"frozen":True,},
            { "title": "Pitch Type", "field": "pitch_type", "width": 125, "headerFilter":"input" ,"frozen":True,},
            { "title": "New Pitch?", "field": "new_pitch", "width": 125, "headerFilter":"input" ,"frozen":False,},
            { "title": "Pitches", "field": "count", "width": 100 , "headerFilter":"input","contextMenu":True},
            { "title": "Pitch%", "field": "pitch_percent_formatted", "width": 100, "headerFilter":"input"},
            { "title": "LHH%", "field": "lhh_percent_formatted", "width": 100, "headerFilter":"input"},
            { "title": "RHH%", "field": "rhh_percent_formatted", "width": 100, "headerFilter":"input"},
            { "title": "Velocity", "field": "start_speed_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "Max Velo", "field": "max_start_speed_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "iVB", "field": "ivb_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "HB", "field": "hb_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "RelH", "field": "release_pos_z_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "RelS", "field": "release_pos_x_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "Extension", "field": "extension_formatted", "width": 125, "headerFilter":"input", "formatter":"textarea" },
            { "title": "tjStuff+", "field": "tj_stuff_plus_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" }
        ]



        df_merge = df_merge.filter(pl.col('count')>=int(input.pitches_all_min()))


        df_plot = df_merge.to_pandas()

        team_dict = dict(zip(df_spring['pitcher_id'],df_spring['pitcher_team']))
        df_plot['pitcher_team'] = df_plot['pitcher_id'].map(team_dict)



        return Tabulator(
            df_plot,
            
            table_options=TableOptions(
                height=750,
                
                columns=columns,
            )
        )


    @output
    @render_tabulator
    @reactive.event(input.refresh)
    def table_daily():

        df_spring = spring_data()

        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))



        import polars as pl

        # Compute total pitches for each pitcher
        df_pitcher_totals = df_spring_stuff.group_by(["pitcher_id"]).agg(
            pl.col("start_speed").count().alias("pitcher_total")
        )

        df_pitcher_totals_hands = (
            df_spring_stuff
            .group_by(["pitcher_id", "batter_hand"])
            .agg(pl.col("start_speed").count().alias("pitcher_total"))
            .pivot(
                values="pitcher_total",
                index="pitcher_id",
                columns="batter_hand",
                aggregate_function="sum"
            )
            .rename({"L": "pitcher_total_left", "R": "pitcher_total_right"})
            .fill_null(0)  # Fill missing values with 0 if some pitchers don't face both hands
        )
                
        
        df_spring_group = df_spring_stuff.group_by(['pitcher_id', 'pitcher_name', 'pitch_type']).agg([
            pl.col('start_speed').count().alias('count'),
            pl.col('start_speed').mean().alias('start_speed'),
            pl.col('start_speed').max().alias('max_start_speed'),
            pl.col('ivb').mean().alias('ivb'),
            pl.col('hb').mean().alias('hb'),
            pl.col('release_pos_z').mean().alias('release_pos_z'),
            pl.col('release_pos_x').mean().alias('release_pos_x'),
            pl.col('extension').mean().alias('extension'),
            pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus'),
            (pl.col("batter_hand").eq("R").sum()).alias("rhh_count"),  # Corrected: Counts RHH (batter_hand == "R")
            (pl.col("batter_hand").eq("L").sum()).alias("lhh_count")   # Corrected: Counts LHH (batter_hand == "L")
        ])

        # Join total pitches per pitcher to the grouped DataFrame on pitcher_id
        df_spring_group = df_spring_group.join(df_pitcher_totals, on=["pitcher_id"], how="left")
        df_spring_group = df_spring_group.join(df_pitcher_totals_hands, on=["pitcher_id"], how="left")

        # Now calculate the pitch percent for each pitcher/pitch_type combination
        df_spring_group = df_spring_group.with_columns(
            (pl.col("count") / pl.col("pitcher_total")).alias("pitch_percent")
        )

        # Optionally, if you want the percentage of left/right-handed batters within the group:
        df_spring_group = df_spring_group.with_columns([
            (pl.col("rhh_count") / pl.col("pitcher_total_right")).alias("rhh_percent"),
            (pl.col("lhh_count") / pl.col("pitcher_total_left")).alias("lhh_percent")
        ])


        df_merge = df_spring_group.join(df_year_old_group,on=['pitcher_id','pitch_type'],how='left',suffix='_old')


        df_merge = df_merge.with_columns(
            pl.col('pitcher_id').is_in(df_year_old_group['pitcher_id']).alias('exists_in_old')
        )

        df_merge = df_merge.with_columns(
            pl.when(pl.col('start_speed_old').is_null() & pl.col('exists_in_old'))
            .then(pl.lit(True))
            .otherwise(pl.lit(None))
            .alias("new_pitch")
        )

        import polars as pl

        # Define the columns to subtract
        cols_to_subtract = [
            ("start_speed", "start_speed_old"),
            ("max_start_speed", "max_start_speed_old"),
            ("ivb", "ivb_old"),
            ("hb", "hb_old"),
            ("release_pos_z", "release_pos_z_old"),
            ("release_pos_x", "release_pos_x_old"),
            ("extension", "extension_old"),
            ("tj_stuff_plus", "tj_stuff_plus_old")
        ]

        df_merge = df_merge.with_columns([
            # Step 1: Create _diff columns with the default value (e.g., 80) if old is null
            pl.when(pl.col(old).is_null())
            .then(pl.lit(10000))  # If old is null, assign 80 as the default
            .otherwise(pl.col(new) - pl.col(old))  # Otherwise subtract old from new
            .alias(new + "_diff")
            for new, old in cols_to_subtract
        ])

        # Step 2: Format the columns with (value (+diff)) - exclude brackets if diff is 80
        df_merge = df_merge.with_columns([
            pl.when(pl.col(new + "_diff").eq(10000))  # If diff is 80, no need to include brackets
            .then(pl.col(new).round(1).cast(pl.Utf8)+'\n\t')  # Just return the new value as string
            .otherwise(
                pl.col(new).round(1).cast(pl.Utf8) + 
                "\n(" + 
                pl.col(new + "_diff").round(1)
                    .map_elements(lambda x: f"{x:+.1f}") + 
                ")"
            ).alias(new + "_formatted")
            for new, _ in cols_to_subtract
        ])






        percent_cols = ['pitch_percent', 'rhh_percent', 'lhh_percent']

        df_merge = df_merge.with_columns([
            (pl.col(col) * 100)  # Convert to percentage
            .round(1)            # Round to 1 decimal
            .map_elements(lambda x: f"{x:.1f}%")  # Format as string with '%'
            .alias(col + "_formatted")
            for col in percent_cols
        ]).sort(['pitcher_id','count'],descending=True)


        columns = [
            { "title": "Pitcher Name", "field": "pitcher_name", "width": 250, "headerFilter":"input" ,"frozen":True,},
            { "title": "Team", "field": "pitcher_team", "width": 100, "headerFilter":"input" ,"frozen":True,},
            { "title": "Pitch Type", "field": "pitch_type", "width": 125, "headerFilter":"input" ,"frozen":True,},
            { "title": "New Pitch?", "field": "new_pitch", "width": 125, "headerFilter":"input" ,"frozen":False,},
            { "title": "Date", "field": "game_date", "width": 100, "headerFilter":"input" ,"frozen":True,},
            { "title": "Pitches", "field": "count", "width": 100 , "headerFilter":"input"},
            { "title": "Pitch%", "field": "pitch_percent_formatted", "width": 100, "headerFilter":"input"},
            { "title": "LHH%", "field": "lhh_percent_formatted", "width": 100, "headerFilter":"input"},
            { "title": "RHH%", "field": "rhh_percent_formatted", "width": 100, "headerFilter":"input"},
            { "title": "Velocity", "field": "start_speed_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "Max Velo", "field": "max_start_speed_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },            
            { "title": "iVB", "field": "ivb_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "HB", "field": "hb_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "RelH", "field": "release_pos_z_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "RelS", "field": "release_pos_x_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "Extension", "field": "extension_formatted", "width": 125, "headerFilter":"input", "formatter":"textarea" },
            { "title": "tjStuff+", "field": "tj_stuff_plus_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" }
        ]


        df_merge = df_merge.filter(pl.col('count')>=int(input.pitches_daily_min()))

        df_plot = df_merge.to_pandas()

        team_dict = dict(zip(df_spring['pitcher_id'],df_spring['pitcher_team']))
        df_plot['pitcher_team'] = df_plot['pitcher_id'].map(team_dict)



        return Tabulator(
            df_plot,
            
            table_options=TableOptions(
                height=750,
                
                columns=columns,
            )
        )

    @output
    @render_tabulator
    @reactive.event(input.refresh)
    def table_tjstuff():

        df_spring = spring_data()

        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))



        import polars as pl

        # Compute total pitches for each pitcher
        df_pitcher_totals = df_spring_stuff.group_by(["pitcher_id"]).agg(
            pl.col("start_speed").count().alias("pitcher_total")
        )

        df_pitcher_totals_hands = (
            df_spring_stuff
            .group_by(["pitcher_id", "batter_hand"])
            .agg(pl.col("start_speed").count().alias("pitcher_total"))
            .pivot(
                values="pitcher_total",
                index="pitcher_id",
                columns="batter_hand",
                aggregate_function="sum"
            )
            .rename({"L": "pitcher_total_left", "R": "pitcher_total_right"})
            .fill_null(0)  # Fill missing values with 0 if some pitchers don't face both hands
        )
                
        
        df_spring_group = df_spring_stuff.group_by(['pitcher_id', 'pitcher_name', 'pitch_type']).agg([
            pl.col('start_speed').count().alias('count'),
            pl.col('start_speed').mean().alias('start_speed'),
            pl.col('start_speed').max().alias('max_start_speed'),
            pl.col('ivb').mean().alias('ivb'),
            pl.col('hb').mean().alias('hb'),
            pl.col('release_pos_z').mean().alias('release_pos_z'),
            pl.col('release_pos_x').mean().alias('release_pos_x'),
            pl.col('extension').mean().alias('extension'),
            pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus'),
            (pl.col("batter_hand").eq("R").sum()).alias("rhh_count"),  # Corrected: Counts RHH (batter_hand == "R")
            (pl.col("batter_hand").eq("L").sum()).alias("lhh_count")   # Corrected: Counts LHH (batter_hand == "L")
        ])

        # Join total pitches per pitcher to the grouped DataFrame on pitcher_id
        df_spring_group = df_spring_group.join(df_pitcher_totals, on=["pitcher_id"], how="left")
        df_spring_group = df_spring_group.join(df_pitcher_totals_hands, on=["pitcher_id"], how="left")

        # Now calculate the pitch percent for each pitcher/pitch_type combination
        df_spring_group = df_spring_group.with_columns(
            (pl.col("count") / pl.col("pitcher_total")).alias("pitch_percent")
        )

        # Optionally, if you want the percentage of left/right-handed batters within the group:
        df_spring_group = df_spring_group.with_columns([
            (pl.col("rhh_count") / pl.col("pitcher_total_right")).alias("rhh_percent"),
            (pl.col("lhh_count") / pl.col("pitcher_total_left")).alias("lhh_percent")
        ])

        df_merge = df_spring_group.join(df_year_old_group,on=['pitcher_id','pitch_type'],how='left',suffix='_old')


        df_merge = df_merge.with_columns(
            pl.col('pitcher_id').is_in(df_year_old_group['pitcher_id']).alias('exists_in_old')
        )

        df_merge = df_merge.with_columns(
            pl.when(pl.col('start_speed_old').is_null() & pl.col('exists_in_old'))
            .then(pl.lit(True))
            .otherwise(pl.lit(None))
            .alias("new_pitch")
        )

        import polars as pl

        # Define the columns to subtract
        cols_to_subtract = [
            ("start_speed", "start_speed_old"),
            ("max_start_speed", "max_start_speed_old"),
            ("ivb", "ivb_old"),
            ("hb", "hb_old"),
            ("release_pos_z", "release_pos_z_old"),
            ("release_pos_x", "release_pos_x_old"),
            ("extension", "extension_old"),
            ("tj_stuff_plus", "tj_stuff_plus_old")
        ]

        df_merge = df_merge.with_columns([
            # Step 1: Create _diff columns with the default value (e.g., 80) if old is null
            pl.when(pl.col(old).is_null())
            .then(pl.lit(None))  # If old is null, assign 80 as the default
            .otherwise(pl.col(new) - pl.col(old))  # Otherwise subtract old from new
            .alias(new + "_diff")
            for new, old in cols_to_subtract
        ])

        # Step 2: Format the columns with (value (+diff)) - exclude brackets if diff is 80
        # Step 2: Format the columns with (value (+diff)) - exclude brackets if diff is 80
        df_merge = df_merge.with_columns([
 
                pl.col(new).round(1).cast(pl.Utf8).alias(new + "_formatted")
            for new, _ in cols_to_subtract
        ])



        df_merge = df_merge.with_columns([
            pl.col("tj_stuff_plus_old").round(1).cast(pl.Utf8).alias("tj_stuff_plus_old"),
            pl.col("tj_stuff_plus_diff").round(1).map_elements(lambda x: f"{x:+.1f}").alias("tj_stuff_plus_diff")
        ])



        percent_cols = ['pitch_percent', 'rhh_percent', 'lhh_percent']

        df_merge = df_merge.with_columns([
            (pl.col(col) * 100)  # Convert to percentage
            .round(1)            # Round to 1 decimal
            .map_elements(lambda x: f"{x:.1f}%")  # Format as string with '%'
            .alias(col + "_formatted")
            for col in percent_cols
        ]).sort(['pitcher_id','count'],descending=True)




        columns = [
            { "title": "Pitcher Name", "field": "pitcher_name", "width": 250, "headerFilter":"input" ,"frozen":True,},
            { "title": "Team", "field": "pitcher_team", "width": 90, "headerFilter":"input" ,"frozen":True,},
            { "title": "Pitch Type", "field": "pitch_type", "width": 125, "headerFilter":"input" ,"frozen":True,},
            { "title": "New?", "field": "new_pitch", "width": 125, "headerFilter":"input" ,"frozen":False,},
            { "title": "Pitches", "field": "count", "width": 100 , "headerFilter":"input"},
            { "title": "Pitch%", "field": "pitch_percent_formatted", "width": 100, "headerFilter":"input"},+
            { "title": "LHH%", "field": "lhh_percent_formatted", "width": 90, "headerFilter":"input"},
            { "title": "RHH%", "field": "rhh_percent_formatted", "width": 90, "headerFilter":"input"},
            { "title": "Velocity", "field": "start_speed_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "Max Velo", "field": "max_start_speed_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "iVB", "field": "ivb_formatted", "width": 80, "headerFilter":"input", "formatter":"textarea" },
            { "title": "HB", "field": "hb_formatted", "width": 80, "headerFilter":"input", "formatter":"textarea" },
            { "title": "RelH", "field": "release_pos_z_formatted", "width": 80, "headerFilter":"input", "formatter":"textarea" },
            { "title": "RelS", "field": "release_pos_x_formatted", "width": 80, "headerFilter":"input", "formatter":"textarea" },
            { "title": "Extension", "field": "extension_formatted", "width": 125, "headerFilter":"input", "formatter":"textarea" },
            { "title": "tjStuff+", "field": "tj_stuff_plus_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "2024 tjStuff+", "field": "tj_stuff_plus_old", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            { "title": "Δ", "field": "tj_stuff_plus_diff", "width": 100, "headerFilter":"input", "formatter":"textarea" }
        ]

        df_merge = df_merge.filter(pl.col('count')>=int(input.pitches_tjstuff_min()))

        df_plot = df_merge.sort(['pitcher_id','count'],descending=True).to_pandas()

        team_dict = dict(zip(df_spring['pitcher_id'],df_spring['pitcher_team']))
        df_plot['pitcher_team'] = df_plot['pitcher_id'].map(team_dict)



        return Tabulator(
            df_plot,
            
            table_options=TableOptions(
                height=750,
                
                columns=columns,
            )
        )

    @output
    @render_tabulator
    @reactive.event(input.refresh)
    def table_stuff_all():

        df_spring = spring_data()

        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))







        # Aggregate tj_stuff_plus by pitcher_id and year
        df_agg_2024_pitch = df_spring_stuff.group_by(['pitcher_id','pitcher_name', 'pitch_type']).agg(
            pl.col('tj_stuff_plus').len().alias('count'),
            pl.col('tj_stuff_plus').mean()
        )

        # Calculate the weighted average of 'tj_stuff_plus' for each pitcher
        df_weighted_avg = df_agg_2024_pitch.with_columns(
            (pl.col('tj_stuff_plus') * pl.col('count')).alias('weighted_tj_stuff_plus')
        ).group_by(['pitcher_id', 'pitcher_name']).agg(
            pl.col('count').sum().alias('total_count'),
            pl.col('weighted_tj_stuff_plus').sum().alias('total_weighted_tj_stuff_plus')
        ).with_columns(
            (pl.col('total_weighted_tj_stuff_plus') / pl.col('total_count')).alias('tj_stuff_plus')
        ).select(['pitcher_id', 'pitcher_name', 'tj_stuff_plus', 'total_count'])

        # Add the 'pitch_type' column with value "All"
        df_weighted_avg = df_weighted_avg.with_columns(
            pl.lit("All").alias('pitch_type')
        )

        # Select and rename columns to match the original DataFrame
        df_weighted_avg = df_weighted_avg.select([
            'pitcher_id',
            'pitcher_name',

            'pitch_type',
            pl.col('total_count').alias('count'),
            'tj_stuff_plus'
        ])

        # Concatenate the new rows with the original DataFrame
        df_small = pl.concat([df_agg_2024_pitch, df_weighted_avg])

        df_game_count = df_spring_stuff.group_by(['pitcher_id']).agg(
            
            (((pl.col('game_id').count())).alias('pitches')/((pl.col('game_id').n_unique()))).alias('pitches_per_game'),
        )



        count_dict = dict(zip(df_small.filter(pl.col('pitch_type')=='All')['pitcher_id'],
                                df_small.filter(pl.col('pitch_type')=='All')['count']))
        # Check if 'FS' column exists, if not create it and fill with None

        df_small_pivot = (df_small.pivot(index=['pitcher_id','pitcher_name'],
                        columns='pitch_type',
                        values='tj_stuff_plus').with_columns(
                        pl.col("pitcher_id").replace_strict(count_dict, default=None).alias("count")))

        # Check if 'FS' column exists, if not create it and fill with None
        for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']:
            if col not in df_small_pivot.columns:
                df_small_pivot = df_small_pivot.with_columns(pl.lit(None).alias(col))

        df_small_pivot.select(['pitcher_id','pitcher_name','count','CH','CU','FC','FF','FS','SI','SL','ST','All']).sort('All',descending=True)#.head(10)#.write_clipboard()
                        

        df_small_pivot = df_small_pivot.with_columns([
            pl.col(col).round(0).alias(col) for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']
        ])


        df_small_pivot = df_small_pivot.filter(pl.col('count')>=int(input.pitches_tjsumm_min()))

        df_plot = df_small_pivot.sort(['pitcher_id','count'],descending=True).to_pandas()

        team_dict = dict(zip(df_spring['pitcher_id'],df_spring['pitcher_team']))
        df_plot['pitcher_team'] = df_plot['pitcher_id'].map(team_dict)


        columns = [
            { "title": "Pitcher Name", "field": "pitcher_name", "width": 250, "headerFilter":"input" ,"frozen":True,},
            { "title": "Team", "field": "pitcher_team", "width": 90, "headerFilter":"input" ,"frozen":True,},
            { "title": "Pitches", "field": "count", "width": 100 , "headerFilter":"input"},
            { "title": "CH", "field": "CH", "width": 80, "formatter":"textarea" },
            { "title": "CU", "field": "CU", "width": 80, "formatter":"textarea" },
            { "title": "FC", "field": "FC", "width": 80, "formatter":"textarea" },
            { "title": "FF", "field": "FF", "width": 80,  "formatter":"textarea" },
            { "title": "FS", "field": "FS", "width": 80, "formatter":"textarea" },
            { "title": "SI", "field": "SI", "width": 80,  "formatter":"textarea" },
            { "title": "SL", "field": "SL", "width": 80,  "formatter":"textarea" },
            { "title": "ST", "field": "ST", "width": 80, "formatter":"textarea" },
            { "title": "All", "field": "All", "width": 80,  "formatter":"textarea" }

        ]




        return Tabulator(
            df_plot,
            
            table_options=TableOptions(
                height=750,
                
                
                columns=columns,
            ),
        )


    @output
    @render_tabulator
    @reactive.event(input.refresh)
    def table_tjstuff_team():

        df_spring = spring_data()

        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))



        import polars as pl



        df_spring_group = df_spring_stuff.group_by(['pitcher_team']).agg([
            pl.col('start_speed').count().alias('count'),
            pl.col('start_speed').mean().alias('start_speed'),
            pl.col('start_speed').max().alias('max_start_speed'),
            pl.col('ivb').mean().alias('ivb'),
            pl.col('hb').mean().alias('hb'),
            pl.col('release_pos_z').mean().alias('release_pos_z'),
            pl.col('release_pos_x').mean().alias('release_pos_x'),
            pl.col('extension').mean().alias('extension'),
            pl.col('tj_stuff_plus').mean().round(0).alias('tj_stuff_plus'),
            (pl.col('start_speed').filter(pl.col('batter_hand')=='L').count()).alias('rhh_count'),
            (pl.col('start_speed').filter(pl.col('batter_hand')=='R').count()).alias('lhh_count')
        ])



        columns = [
            # { "title": "Pitcher Name", "field": "pitcher_name", "width": 250, "headerFilter":"input" ,"frozen":True,},
            { "title": "Team", "field": "pitcher_team", "width": 250, "headerFilter":"input" ,"frozen":True,},
            # { "title": "Pitch Type", "field": "pitch_type", "width": 125, "headerFilter":"input" ,"frozen":True,},
            # { "title": "New?", "field": "new_pitch", "width": 125, "headerFilter":"input" ,"frozen":False,},
            { "title": "Pitches", "field": "count", "width": 250 , "headerFilter":"input"},
            # { "title": "Pitch%", "field": "pitch_percent_formatted", "width": 100, "headerFilter":"input"},
            # { "title": "RHH%", "field": "rhh_percent_formatted", "width": 90, "headerFilter":"input"},
            # { "title": "LHH%", "field": "lhh_percent_formatted", "width": 90, "headerFilter":"input"},
            # { "title": "Velocity", "field": "start_speed_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            # { "title": "Max Velo", "field": "max_start_speed_formatted", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            # { "title": "iVB", "field": "ivb_formatted", "width": 80, "headerFilter":"input", "formatter":"textarea" },
            # { "title": "HB", "field": "hb_formatted", "width": 80, "headerFilter":"input", "formatter":"textarea" },
            # { "title": "RelH", "field": "release_pos_z_formatted", "width": 80, "headerFilter":"input", "formatter":"textarea" },
            # { "title": "RelS", "field": "release_pos_x_formatted", "width": 80, "headerFilter":"input", "formatter":"textarea" },
            # { "title": "Extension", "field": "extension_formatted", "width": 125, "headerFilter":"input", "formatter":"textarea" },
            { "title": "tjStuff+", "field": "tj_stuff_plus", "width": 250, "headerFilter":"input", "formatter":"textarea" },
            # { "title": "2024 tjStuff+", "field": "tj_stuff_plus_old", "width": 100, "headerFilter":"input", "formatter":"textarea" },
            # { "title": "Δ", "field": "tj_stuff_plus_diff", "width": 100, "headerFilter":"input", "formatter":"textarea" }
        ]

        df_merge = df_spring_group.clone()

        df_plot = df_merge.sort(['pitcher_team','count'],descending=True).to_pandas()

        # team_dict = dict(zip(df_spring['pitcher_id'],df_spring['pitcher_team']))
        # df_plot['pitcher_team'] = df_plot['pitcher_id'].map(team_dict)



        return Tabulator(
            df_plot,
            
            table_options=TableOptions(
                height=750,
                
                columns=columns,
            )
        )




app = App(app_ui, server)