Spaces:
Running
Running
| import seaborn as sns | |
| import streamlit as st | |
| from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode | |
| import PitchPlotFunctions as ppf | |
| import requests | |
| import polars as pl | |
| from datetime import date | |
| import api_scraper | |
| # Display the app title and description | |
| st.markdown(""" | |
| ## MLB & AAA Pitch Plots App | |
| ##### By: Thomas Nestico ([@TJStats](https://x.com/TJStats)) | |
| ##### Code: [GitHub Repo](https://github.com/tnestico/streamlit_pitch_plots) | |
| ##### Data: [MLB](https://baseballsavant.mlb.com/) | |
| #### About | |
| This Streamlit app retrieves MLB and AAA Pitching Data for a selected pitcher from the MLB Stats API and is accessed using my [MLB Stats API Scraper](https://github.com/tnestico/mlb_scraper). | |
| The app outputs the pitcher's data into both a plot and table to illustrate and summarize the data. | |
| It can also display data for games currently in progress. | |
| *More information about the data and plots is shown at the bottom of this page.* | |
| """ | |
| ) | |
| # Initialize the plotter object from PitchPlotFunctions | |
| ploter = ppf.PitchPlotFunctions() | |
| # Initialize the scraper object | |
| scraper = api_scraper.MLB_Scrape() | |
| # Dictionary mapping league names to sport IDs | |
| sport_id_dict = {'MLB': 1, 'AAA': 11} | |
| # Create two columns for league and pitcher selection | |
| st.write("#### Plot") | |
| col_1, col_2 = st.columns(2) | |
| with col_1: | |
| # Select league | |
| selected_league = st.selectbox('##### Select League', list(sport_id_dict.keys())) | |
| selected_sport_id = sport_id_dict[selected_league] | |
| with col_2: | |
| # Get player data and filter for pitchers | |
| df_player = scraper.get_players(sport_id=selected_sport_id) | |
| df_player = df_player.filter(pl.col('position').str.contains('P')) | |
| df_player = df_player.with_columns( | |
| (pl.concat_str(["name", "player_id"], separator=" - ").alias("pitcher_name_id")) | |
| ) | |
| # Select specific columns and convert to dictionary | |
| pitcher_name_id_dict = dict(df_player.select(['pitcher_name_id', 'player_id']).iter_rows()) | |
| # Initialize session state for previous selection | |
| if 'prev_pitcher_id' not in st.session_state: | |
| st.session_state.prev_pitcher_id = None | |
| # Display a selectbox for pitcher selection | |
| selected_pitcher = st.selectbox("##### Select Pitcher", list(pitcher_name_id_dict.keys())) | |
| pitcher_id = pitcher_name_id_dict[selected_pitcher] | |
| # Clear cache if selection changes | |
| if pitcher_id != st.session_state.prev_pitcher_id: | |
| st.cache_data.clear() | |
| st.session_state.prev_pitcher_id = pitcher_id | |
| st.session_state.cache_cleared = False | |
| st.write('Cache cleared!') | |
| # Initialize session state for cache status | |
| if 'cache_cleared' not in st.session_state: | |
| st.session_state.cache_cleared = False | |
| # Dictionary for batter hand selection | |
| batter_hand_picker = { | |
| 'All': ['L', 'R'], | |
| 'LHH': ['L'], | |
| 'RHH': ['R'] | |
| } | |
| # Define date range for the season | |
| min_date = date(2024, 3, 20) | |
| max_date = date(2024, 11, 30) | |
| # Create columns for input widgets | |
| st.write("##### Filters") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| # Selectbox for batter handedness | |
| batter_hand_select = st.selectbox('Batter Handedness:', list(batter_hand_picker.keys())) | |
| batter_hand = batter_hand_picker[batter_hand_select] | |
| with col2: | |
| # Date input for start date | |
| start_date = st.date_input('Start Date:', | |
| value=min_date, | |
| min_value=min_date, | |
| max_value=max_date, | |
| format="YYYY-MM-DD") | |
| with col3: | |
| # Date input for end date | |
| end_date = st.date_input('End Date:', | |
| value="default_value_today", | |
| min_value=min_date, | |
| max_value=max_date, | |
| format="YYYY-MM-DD") | |
| # Dictionary for plot type selection | |
| plot_picker_dict = { | |
| 'Short Form Movement': 'short_form_movement', | |
| 'Long Form Movement': 'long_form_movement', | |
| 'Release Points': 'release_point' | |
| } | |
| # Selectbox for plot type | |
| plot_picker_select = st.selectbox('Select Plot Type:', list(plot_picker_dict.keys())) | |
| plot_picker = plot_picker_dict[plot_picker_select] | |
| # Extract season from start date | |
| season = str(start_date)[0:4] | |
| # Get list of games for the selected player and date range | |
| player_games = scraper.get_player_games_list(player_id=pitcher_id, season=season, | |
| start_date=str(start_date), end_date=str(end_date), | |
| sport_id=selected_sport_id, | |
| game_type = ['R','P']) | |
| # Function to fetch data and cache it | |
| def fetch_data(): | |
| data = scraper.get_data(game_list_input=player_games) | |
| df = scraper.get_data_df(data_list=data) | |
| return df | |
| # Fetch data and manage cache status | |
| if not st.session_state.cache_cleared: | |
| df_original = fetch_data() | |
| st.session_state.cache_cleared = True | |
| else: | |
| df_original = fetch_data() | |
| # Button to generate plot | |
| if st.button('Generate Plot'): | |
| try: | |
| # Convert dataframe to polars and filter based on inputs | |
| df = ploter.df_to_polars(df_original=df_original, | |
| pitcher_id=pitcher_id, | |
| start_date=str(start_date), | |
| end_date=str(end_date), | |
| batter_hand=batter_hand) | |
| print(df) | |
| if len(df) == 0: | |
| st.write('Please select different parameters.') | |
| else: | |
| # Generate the final plot | |
| ploter.final_plot( | |
| df=df, | |
| pitcher_id=pitcher_id, | |
| plot_picker=plot_picker, | |
| sport_id=selected_sport_id) | |
| # Use a container to control the width of the AgGrid display | |
| with st.container(): | |
| # Group the data by pitch type | |
| grouped_df = ( | |
| df.group_by(['pitcher_id', 'pitch_description']) | |
| .agg([ | |
| pl.col('is_pitch').drop_nans().count().alias('pitches'), | |
| pl.col('start_speed').drop_nans().mean().round(1).alias('start_speed'), | |
| pl.col('vb').drop_nans().mean().round(1).alias('vb'), | |
| pl.col('ivb').drop_nans().mean().round(1).alias('ivb'), | |
| pl.col('hb').drop_nans().mean().round(1).alias('hb'), | |
| pl.col('spin_rate').drop_nans().mean().round(0).alias('spin_rate'), | |
| pl.col('x0').drop_nans().mean().round(1).alias('x0'), | |
| pl.col('z0').drop_nans().mean().round(1).alias('z0'), | |
| ]) | |
| .with_columns( | |
| (pl.col('pitches') / pl.col('pitches').sum().over('pitcher_id') * 100).round(3).alias('proportion') | |
| )).sort('proportion', descending=True).select(["pitch_description", "pitches", "proportion", "start_speed", "vb", "ivb", "hb", | |
| "spin_rate", "x0", "z0"]) | |
| st.write("#### Pitching Data") | |
| column_config_dict = { | |
| 'pitcher_id': 'Pitcher ID', | |
| 'pitch_description': 'Pitch Type', | |
| 'pitches': 'Pitches', | |
| 'start_speed': 'Velocity', | |
| 'vb': 'VB', | |
| 'ivb': 'iVB', | |
| 'hb': 'HB', | |
| 'spin_rate': 'Spin Rate', | |
| 'proportion': st.column_config.NumberColumn("Pitch%", format="%.1f%%"), | |
| 'x0': 'hRel', | |
| 'z0': 'vRel', | |
| } | |
| st.markdown(f"""##### {selected_pitcher.split('-')[0]} {selected_league} Pitch Data""") | |
| st.dataframe(grouped_df, | |
| hide_index=True, | |
| column_config=column_config_dict, | |
| width=1500) | |
| # Configure the AgGrid options | |
| # gb = GridOptionsBuilder.from_dataframe(grouped_df) | |
| # # Set display names for columns | |
| # for col, display_name in zip(grouped_df.columns, grouped_df.columns): | |
| # gb.configure_column(col, headerName=display_name) | |
| # grid_options = gb.build() | |
| # # Display the dataframe using AgGrid | |
| # grid_response = AgGrid( | |
| # grouped_df, | |
| # gridOptions=grid_options, | |
| # height=300, | |
| # allow_unsafe_jscode=True, | |
| # ) | |
| except IndexError: | |
| st.write('Please select different parameters.') | |
| # Display column and plot descriptions | |
| st.markdown(""" | |
| #### Column Descriptions | |
| - **`Pitch Type`**: Describes the type of pitch thrown (e.g., 4-Seam Fastball, Curveball, Slider). | |
| - **`Pitches`**: The total number of pitches thrown by the pitcher. | |
| - **`Pitch%`**: Proportion of pitch thrown. | |
| - **`Velocity`**: The initial velocity of the pitch as it leaves the pitcher's hand, measured in miles per hour (mph). | |
| - **`VB`**: Vertical Break (VB), representing the amount movement of a pitch due to spin and gravity, measured in inches (in). | |
| - **`iVB`**: Induced Vertical Break (iVB), representing the amount movement of a pitch strictly due to the spin imparted on the ball, measured in inches (in). | |
| - **`HB`**: Horizontal Break (HB), indicating the amount of horizontal movement of a pitch, measured in inches (in). | |
| - **`Spin Rate`**: The rate of spin of the pitch as it is released, measured in revolutions per minute (rpm). | |
| - **`hRel`**: The horizontal release point of the pitch, measured in feet from the center of the pitcher's mound (ft). | |
| - **`vRel`**: The vertical release point of the pitch, measured in feet above the ground (ft). | |
| #### Plot Descriptions | |
| - **`Short Form Movement`**: Illustrates the movement of the pitch due to spin, where (0,0) indicates a pitch with perfect gyro-spin (e.g. Like a Football). | |
| - **`Long Form Movement`**: Illustrates the movement of the pitch due to spin and gravity. | |
| - **`Release Points`**: Illustrates a pitchers release points from the catcher's perspective. | |
| #### Acknowledgements | |
| Big thanks to [Michael Rosen](https://twitter.com/bymichaelrosen) and [Jeremy Maschino](https://twitter.com/pitchprofiler) for inspiration for this project | |
| Check Out Michael's [Pitch Plotting App](https://pitchplotgenerator.streamlit.app/) | |
| Check Out Jeremy's Website [Pitch Profiler](http://www.mlbpitchprofiler.com/) | |
| """ | |
| ) | |