Upload 3 files

app.py

@@ -1,24 +1,172 @@
-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
-
-# Load data
-df = pl.read_csv("tjstuff_plus_pitch_data_2024.csv")
-
-
-column_config_dict = {
-    'pitcher_id': 'Pitcher ID',
-    'pitcher_name': 'Pitcher Name',
-    'pitch_type': 'Pitch Type',
-    'pitches': 'Pitches',
-    'tj_stuff_plus': 'tjStuff+',
-    'pitch_grade': 'Grade'
-}
-
-st.dataframe(df[['pitcher_id', 'pitcher_name', 'pitch_type', 'pitches', 'tj_stuff_plus', 'pitch_grade']],
-                hide_index=True,
-                column_config=column_config_dict,
-                width=1500)
+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
+                            
+"""
+)
+
+
+# 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)
+
+# 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)
+
+# 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)
+

tjstuff_plot.py

@@ -0,0 +1,215 @@
+import polars as pl
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from matplotlib.gridspec import GridSpec
+import streamlit as st
+
+
+# For help with plotting the pitch data, we will use the following dictionary to map pitch types to their corresponding colours
+### PITCH COLOURS ###
+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 a dictionary mapping pitch types to their colors
+dict_colour = dict(zip(pitch_colours.keys(), [pitch_colours[key]['colour'] for key in pitch_colours]))
+dict_colour.update({'All': '#808080'})
+# Create a dictionary mapping pitch types to their colors
+dict_pitch = dict(zip(pitch_colours.keys(), [pitch_colours[key]['name'] for key in pitch_colours]))
+
+# Create a dictionary mapping pitch types to their colors
+dict_pitch_desc_type = dict(zip([pitch_colours[key]['name'] for key in pitch_colours],pitch_colours.keys()))
+
+
+# Create a dictionary mapping pitch types to their colors
+dict_pitch_name = dict(zip([pitch_colours[key]['name'] for key in pitch_colours], 
+                           [pitch_colours[key]['colour'] for key in pitch_colours]))
+
+
+
+required_pitch_types = ['All', 'FF', 'SI', 'FC', 'CH', 'FS','FO','SC','SL', 
+                        'ST','SV' ,'CU', 'KC','KN']
+# Create a mapping dictionary from the list
+custom_order_dict = {pitch: index for index, pitch in enumerate(required_pitch_types)}
+
+def tjstuff_plot(df:pl.DataFrame, 
+                 pitcher_id:int,
+                 position:str,
+                 pitcher_name:str):
+    sns.set_style("ticks")
+    # Create the figure and GridSpec layout
+    fig = plt.figure(figsize=(10, 8), dpi=450)
+    gs = GridSpec(5, 3, height_ratios=[0.1, 10, 10, 2, 0.1], width_ratios=[1, 100, 1])
+    gs.update(hspace=0.4, wspace=0.1)
+
+    # Add subplots to the grid
+    ax0 = fig.add_subplot(gs[1, 1]) 
+    ax1 = fig.add_subplot(gs[2, 1])
+    ax1_left = fig.add_subplot(gs[:, 0]) 
+    ax1_right = fig.add_subplot(gs[:, 2]) 
+    ax1_top = fig.add_subplot(gs[0, :])
+    ax1_bot = fig.add_subplot(gs[4, 1])
+    ax2 = fig.add_subplot(gs[3, 1])
+
+    # Update color dictionary
+    
+
+ 
+    df = df.to_pandas()
+    # Filter data for the specific pitcher
+    pitcher_df = df[(df['pitcher_id'] == pitcher_id) &
+                                    (df['pitches'] >= 10)]
+    
+
+    
+    # Add a new column for the custom order
+    pitcher_df['order'] = pitcher_df['pitch_type'].map(custom_order_dict)
+    pitcher_df = pitcher_df.sort_values('order')
+                         
+    # Get unique pitch types for the pitcher
+    pitcher_pitches = pitcher_df['pitch_type'].unique()
+    pitcher_pitches = [x for x in required_pitch_types if x in pitcher_pitches]
+
+
+                     
+    # Plot tjStuff+ with swarmplot for all players in the same position
+    sns.swarmplot(data=df[(df['pitches'] >= 10) &
+                                            (df['position'] == position)].dropna(subset=['pitch_type']),
+                x='pitch_type',
+                y='tj_stuff_plus',
+                palette=dict_colour,
+                alpha=0.3,
+                size=3,
+                ax=ax0,
+                order=pitcher_pitches)
+
+    # Overlay swarmplot for the specific pitcher
+    sns.swarmplot(data=df[(df['pitcher_id'] == pitcher_id) &
+                                            (df['pitches'] >= 10)],
+                x='pitch_type',
+                y='tj_stuff_plus',
+                palette=dict_colour,
+                alpha=1,
+                size=16,
+                ax=ax0,
+                order=pitcher_pitches,
+                edgecolor='black',
+                linewidth=1)
+
+    # Annotate the median values on the plot
+    for index, row in pitcher_df.reset_index(drop=True).iterrows():
+        ax0.text(index, 
+                row['tj_stuff_plus'], 
+                f'{row["tj_stuff_plus"]:.0f}', 
+                color='white', 
+                ha="center", 
+                va="center",
+                fontsize=8,
+                weight='bold',
+                clip_on=False)
+
+    # Customize ax0
+    ax0.set_xlabel('')
+    ax0.set_ylabel('tjStuff+')
+    ax0.grid(False)
+    ax0.set_ylim(70, 130)
+    ax0.axhline(y=100, color='black', linestyle='--', alpha=0.2, zorder=0)
+
+    # Plot pitch grade with swarmplot for all players in the same position
+    sns.swarmplot(data=df[(df['pitches'] >= 10) &
+                                            (df['position'] == position)].dropna(subset=['pitch_type']),
+                x='pitch_type',
+                y='pitch_grade',
+                palette=dict_colour,
+                alpha=0.3,
+                size=3,
+                ax=ax1,
+                clip_on=False,
+                order=pitcher_pitches)
+
+    # Overlay swarmplot for the specific pitcher
+    sns.swarmplot(data=df[(df['pitcher_id'] == pitcher_id) &
+                                            (df['pitches'] >= 10)],
+                x='pitch_type',
+                y='pitch_grade',
+                palette=dict_colour,
+                alpha=1,
+                size=16,
+                ax=ax1,
+                order=pitcher_pitches,
+                edgecolor='black',
+                clip_on=False,
+                linewidth=1)
+
+    # Annotate the median values on the plot
+    for index, row in pitcher_df.reset_index(drop=True).iterrows():
+        ax1.text(index, 
+                row['pitch_grade'], 
+                f'{row["pitch_grade"]:.0f}', 
+                color='white', 
+                ha="center", 
+                va="center",
+                fontsize=8,
+                weight='bold',
+                clip_on=False,
+                zorder=1000)
+        
+    # Customize ax1
+    ax1.set_xlabel('Pitch Type')
+    ax1.set_ylabel('Pitch Grade')
+    ax1.grid(False)
+    ax1.set_ylim(20, 80)
+    ax1.axhline(y=50, color='black', linestyle='--', alpha=0.2, zorder=0)
+
+    # Hide axes for additional subplots
+    ax2.axis('off')
+    ax1_left.axis('off')
+    ax1_right.axis('off')
+    ax1_top.axis('off')
+    ax1_bot.axis('off')
+
+    # Add text annotations
+    ax1_bot.text(s='By: @TJStats', x=0, y=1, fontsize=12, ha='left')
+    ax1_bot.text(s='Data: MLB', x=1, y=1, fontsize=12, ha='right')
+
+    ax1_top.text(0.5, 0, f'{pitcher_name} tjStuff+ 2024 Season - {position}',
+                fontsize=24, ha='center', va='top')
+
+    ax2.text(x=0.5, y=0.6, s='tjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\n'
+                            'tjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10\n'
+                            'Pitch Grade is based off tjStuff+ and scales the data to the traditional 20-80 Scouting Scale for a given pitch type',
+                            
+            ha='center', va='top', fontname='Calibri', fontsize=10)
+
+    # Adjust subplot layout
+    fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)
+    # fig.set_facecolor('#e0e0e0')
+    st.pyplot(fig)