app.py

import seaborn as sns
import streamlit as st
from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
import requests
import polars as pl
from datetime import date
import pandas as pd
import matplotlib



# Display the app title and description
st.markdown("""
## tjStuff+ App

##### By: Thomas Nestico ([@TJStats](https://x.com/TJStats))
##### Code: [GitHub Repo](https://github.com/tnestico/streamlit_tjstuff)
##### Data: [MLB](https://baseballsavant.mlb.com/) ([Gathered from my MLB Scraper](https://github.com/tnestico/mlb_scraper))

#### About
This Streamlit app tabulates and plots my pitching metric, tjStuff+, for all MLB players during the 2024 MLB Season

About tjStuff+:
* tjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type
* tjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10
* Pitch Grade is based off tjStuff+ and scales the data to the traditional 20-80 Scouting Scale for a given pitch type

[Learn More about tjStuff+ here](https://github.com/tnestico/tjstuff_plus/tree/main)
                            
"""
)


# Dictionary to map pitch types to their corresponding colors and names
pitch_colours = {
    ## Fastballs ##
    'FF': {'colour': '#FF007D', 'name': '4-Seam Fastball'},
    'FA': {'colour': '#FF007D', 'name': 'Fastball'},
    'SI': {'colour': '#98165D', 'name': 'Sinker'},
    'FC': {'colour': '#BE5FA0', 'name': 'Cutter'},

    ## Offspeed ##
    'CH': {'colour': '#F79E70', 'name': 'Changeup'},
    'FS': {'colour': '#FE6100', 'name': 'Splitter'},
    'SC': {'colour': '#F08223', 'name': 'Screwball'},
    'FO': {'colour': '#FFB000', 'name': 'Forkball'},

    ## Sliders ##
    'SL': {'colour': '#67E18D', 'name': 'Slider'},
    'ST': {'colour': '#1BB999', 'name': 'Sweeper'},
    'SV': {'colour': '#376748', 'name': 'Slurve'},

    ## Curveballs ##
    'KC': {'colour': '#311D8B', 'name': 'Knuckle Curve'},
    'CU': {'colour': '#3025CE', 'name': 'Curveball'},
    'CS': {'colour': '#274BFC', 'name': 'Slow Curve'},
    'EP': {'colour': '#648FFF', 'name': 'Eephus'},

    ## Others ##
    'KN': {'colour': '#867A08', 'name': 'Knuckleball'},
    'PO': {'colour': '#472C30', 'name': 'Pitch Out'},
    'UN': {'colour': '#9C8975', 'name': 'Unknown'},
}

# Create dictionaries for pitch types and their attributes
dict_colour = {key: value['colour'] for key, value in pitch_colours.items()}
dict_pitch = {key: value['name'] for key, value in pitch_colours.items()}
dict_pitch_desc_type = {value['name']: key for key, value in pitch_colours.items()}
dict_pitch_name = {value['name']: value['colour'] for key, value in pitch_colours.items()}

# Define a custom colormap for styling
cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF', '#FFFFFF', '#FFB000'])

# Initialize session state for cache status
if 'cache_cleared' not in st.session_state:
    st.session_state.cache_cleared = False

# Function to fetch data and cache it
@st.cache_data
def fetch_data():
    df = pl.read_csv("tjstuff_plus_pitch_data_2024.csv").fill_nan(None)
    return df

# Fetch and preprocess data
df = fetch_data()
df_plot = df.clone()
df = df.filter(df['pitches'] >= 10).drop_nulls(subset=['pitch_grade', 'tj_stuff_plus'])
df = df.sort(['pitcher_name', 'pitch_type'], descending=[False, False])

# Cast columns to appropriate data types
df = df.with_columns([
    pl.col('tj_stuff_plus').cast(pl.Int64).alias('tj_stuff_plus'),
    pl.col('pitches').cast(pl.Int64).alias('pitches'),
    pl.col('pitcher_id').cast(pl.Int64).alias('pitcher_id'),
    pl.col('pitch_grade').cast(pl.Int64).alias('pitch_grade')
])

# Define column configuration for Streamlit
column_config_dict = {
    'pitcher_id': 'Pitcher ID',
    'pitcher_name': 'Pitcher Name',
    'pitch_type': 'Pitch Type',
    'pitches': 'Pitches',
    'tj_stuff_plus': st.column_config.NumberColumn("tjStuff+", format="%.0f"),
    'pitch_grade': st.column_config.NumberColumn("Pitch Grade", format="%.0f")
}

# Get unique pitch types for selection
unique_pitch_types = [''] + sorted(df['pitch_type'].unique().to_list())
unique_pitch_types = [dict_pitch.get(x, x) for x in unique_pitch_types]


st.markdown("""
#### tjStuff+ Table

Filter and sort tjStuff+ Data for all MLB Pitchers
"""
           )
# Create a selectbox widget for pitch types
selected_pitch_types = st.selectbox('Select Pitch Types *(leave blank for all pitch types)*', unique_pitch_types)

# Create a selectbox widget for position
selected_position = st.selectbox('Select Position *(leave blank for all Pitchers)*', ['','SP','RP'])


# Filter the DataFrame based on selected pitch types
if selected_pitch_types == 'All':
    df = df.filter(pl.col('pitch_type') == 'All').sort('tj_stuff_plus', descending=True)
elif selected_pitch_types != '':
    df = df.filter(pl.col('pitch_type') == dict_pitch_desc_type[selected_pitch_types]).sort('tj_stuff_plus', descending=True)

if selected_position != '':
    df = df.filter(pl.col('position') == selected_position).sort('tj_stuff_plus', descending=True)


# Convert Polars DataFrame to Pandas DataFrame and apply styling
styled_df = df[['pitcher_id', 'pitcher_name', 'pitch_type', 'pitches', 'tj_stuff_plus', 'pitch_grade']].to_pandas().style

# Apply background gradient styling to specific columns
styled_df = styled_df.background_gradient(subset=['tj_stuff_plus'], cmap=cmap_sum, vmin=80, vmax=120)
styled_df = styled_df.background_gradient(subset=['pitch_grade'], cmap=cmap_sum, vmin=20, vmax=80)

# Display the styled DataFrame in Streamlit
st.dataframe(styled_df, hide_index=True, column_config=column_config_dict, width=1500)

# Create dictionaries for pitcher information
pitcher_id_name = dict(zip(df_plot['pitcher_id'], df_plot['pitcher_name']))
pitcher_id_name_id = dict(zip(df_plot['pitcher_id'], df_plot['pitcher_name'] + ' - ' + df_plot['pitcher_id']))
pitcher_name_id_id = dict(zip(df_plot['pitcher_name'] + ' - ' + df_plot['pitcher_id'], df_plot['pitcher_id']))
pitcher_id_position = dict(zip(df_plot['pitcher_id'], df_plot.drop_nulls(subset=['position'])['position']))


st.markdown("""
#### tjStuff+ Plot

Visualize tjStuff+ and Pitching Grade by Pitcher
"""
           )

# Create a selectbox widget for pitchers
pitcher_id_name_select = st.selectbox('Select Pitcher', sorted(pitcher_name_id_id.keys()))

# Get selected pitcher information
pitcher_id = pitcher_name_id_id[pitcher_id_name_select]
position = pitcher_id_position[pitcher_id]
pitcher_name = pitcher_id_name[pitcher_id]

import tjstuff_plot
# Button to update plot

# Get selected pitcher information
pitcher_id = pitcher_name_id_id[pitcher_id_name_select]
position = pitcher_id_position[pitcher_id]
pitcher_name = pitcher_id_name[pitcher_id]

import tjstuff_plot

# Button to update plot
if st.button('Update Plot'):
    st.session_state.update_plot = True
    tjstuff_plot.tjstuff_plot(df_plot, pitcher_id, position, pitcher_name)