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import polars as pl |
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import api_scraper |
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import pandas as pd |
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scrape = api_scraper.MLB_Scrape() |
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from matplotlib.colors import LinearSegmentedColormap, Normalize |
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import numpy as np |
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import requests |
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from io import BytesIO |
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from PIL import Image |
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from matplotlib.gridspec import GridSpec |
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import matplotlib.pyplot as plt |
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import matplotlib.patches as patches |
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import PIL |
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level_dict = { |
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'11':'AAA', |
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'14':'A',} |
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def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int): |
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""" |
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Display the player's bio information on the given axis. |
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Parameters |
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---------- |
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pitcher_id : str |
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The player's ID. |
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ax : plt.Axes |
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The axis to display the bio information on. |
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sport_id : int |
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The sport ID (1 for MLB, other for minor leagues). |
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year_input : int |
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The season year. |
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""" |
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url = f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}&hydrate=currentTeam" |
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data = requests.get(url).json() |
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player_name = data['people'][0]['fullName'] |
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position = data['people'][0]['primaryPosition']['abbreviation'] |
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pitcher_hand = data['people'][0]['pitchHand']['code'] |
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age = data['people'][0]['currentAge'] |
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height = data['people'][0]['height'] |
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weight = data['people'][0]['weight'] |
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ax.text(0.5, 1, f'{player_name}', va='top', ha='center', fontsize=30) |
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ax.text(0.5, 0.65, f'{position}, Age:{age}, {height}/{weight}', va='top', ha='center', fontsize=20) |
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ax.text(0.5, 0.4, f'Season Batting Percentiles', va='top', ha='center', fontsize=16) |
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response = requests.get(url='https://statsapi.mlb.com/api/v1/sports').json() |
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df_sport_id = pl.DataFrame(response['sports']) |
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abb = df_sport_id.filter(pl.col('id') == sport_id)['abbreviation'][0] |
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ax.text(0.5, 0.20, f'{year_input} {abb} Season', va='top', ha='center', fontsize=14, fontstyle='italic') |
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ax.axis('off') |
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df_teams = scrape.get_teams() |
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team_dict = dict(zip(df_teams['team_id'],df_teams['parent_org_abbreviation'])) |
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mlb_teams = [ |
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{"team": "AZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/ari.png&h=500&w=500"}, |
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{"team": "ATH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"}, |
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{"team": "ATL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/atl.png&h=500&w=500"}, |
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{"team": "BAL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bal.png&h=500&w=500"}, |
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{"team": "BOS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bos.png&h=500&w=500"}, |
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{"team": "CHC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chc.png&h=500&w=500"}, |
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{"team": "CWS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chw.png&h=500&w=500"}, |
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{"team": "CIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cin.png&h=500&w=500"}, |
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{"team": "CLE", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cle.png&h=500&w=500"}, |
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{"team": "COL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/col.png&h=500&w=500"}, |
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{"team": "DET", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/det.png&h=500&w=500"}, |
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{"team": "HOU", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/hou.png&h=500&w=500"}, |
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{"team": "KC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/kc.png&h=500&w=500"}, |
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{"team": "LAA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/laa.png&h=500&w=500"}, |
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{"team": "LAD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/lad.png&h=500&w=500"}, |
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{"team": "MIA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mia.png&h=500&w=500"}, |
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{"team": "MIL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mil.png&h=500&w=500"}, |
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{"team": "MIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/min.png&h=500&w=500"}, |
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{"team": "NYM", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nym.png&h=500&w=500"}, |
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{"team": "NYY", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nyy.png&h=500&w=500"}, |
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{"team": "PHI", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/phi.png&h=500&w=500"}, |
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{"team": "PIT", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/pit.png&h=500&w=500"}, |
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{"team": "SD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sd.png&h=500&w=500"}, |
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{"team": "SF", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sf.png&h=500&w=500"}, |
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{"team": "SEA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sea.png&h=500&w=500"}, |
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{"team": "STL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/stl.png&h=500&w=500"}, |
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{"team": "TB", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tb.png&h=500&w=500"}, |
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{"team": "TEX", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tex.png&h=500&w=500"}, |
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{"team": "TOR", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tor.png&h=500&w=500"}, |
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{"team": "WSH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/wsh.png&h=500&w=500"}, |
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{"team": "ZZZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png&w=500&h=500"} |
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] |
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df_image = pd.DataFrame(mlb_teams) |
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image_dict = df_image.set_index('team')['logo_url'].to_dict() |
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image_dict_flip = df_image.set_index('logo_url')['team'].to_dict() |
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merged_dict = { |
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"woba_percent": { "format": '.3f', "percentile_flip": False, "stat_title": "wOBA" }, |
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"xwoba_percent": { "format": '.3f', "percentile_flip": False, "stat_title": "xwOBA" }, |
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"launch_speed": { "format": '.1f', "percentile_flip": False, "stat_title": "Average EV"}, |
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"launch_speed_90": { "format": '.1f', "percentile_flip": False, "stat_title": "90th% EV"}, |
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"max_launch_speed": { "format": '.1f', "percentile_flip": False, "stat_title": "Max EV"}, |
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"barrel_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Barrel%" }, |
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"hard_hit_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Hard-Hit%" }, |
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"sweet_spot_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "LA Sweet-Spot%" }, |
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"zone_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Zone%" }, |
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"zone_swing_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Z-Swing%" }, |
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"chase_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "O-Swing%" }, |
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"whiff_rate": { "format": '.1%', "percentile_flip": True, "stat_title": "Whiff%" }, |
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"k_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "K%" }, |
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"bb_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "BB%" }, |
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"pull_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Pull%" }, |
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"pulled_fly_ball_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Pull FB%" }, |
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} |
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level_dict = { |
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'11':'AAA', |
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'14':'A (FSL)',} |
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level_dict_file = { |
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'11':'aaa', |
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'14':'a',} |
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year_list = [2024] |
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from shiny import App, reactive, ui, render |
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from shiny.ui import h2, tags |
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app_ui = ui.page_fluid( |
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ui.tags.div( |
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{"style": "width:90%;margin: 0 auto;max-width: 1600px;"}, |
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ui.tags.style( |
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""" |
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h4 { |
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margin-top: 1em;font-size:35px; |
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} |
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h2{ |
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font-size:25px; |
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} |
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""" |
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), |
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ui.tags.h4("TJStats"), |
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ui.tags.i("Baseball Analytics and Visualizations"), |
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ui.markdown("""<a href='https://x.com/TJStats'>Follow me on Twitter</a><sup>1</sup>"""), |
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ui.markdown("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""), |
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ui.markdown("### Statcast Batting Summaries"), |
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ui.markdown("""This Shiny App allows you to generate Baseball Savant-style percentile bars for MiLB players in the 2024 Season. |
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Currently, MiLB Statcast is only available for AAA and and A (Florida State League) levels."""), |
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ui.layout_sidebar( |
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ui.panel_sidebar( |
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ui.row( |
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ui.column(6, ui.input_select('year_input', 'Select Season', year_list, selected=2024)), |
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ui.column(6, ui.input_select('level_input', 'Select Level', level_dict)), |
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), |
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ui.row(ui.input_action_button("player_button", "Get Player List", class_="btn-primary")), |
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ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))), |
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ui.row( |
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ui.column(6, ui.input_switch("switch", "Custom Team?", False)), |
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ui.column(6, ui.input_select('logo_select', 'Select Custom Logo', image_dict_flip, multiple=False)) |
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), |
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ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary")), |
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width=3, |
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), |
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ui.panel_main( |
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ui.navset_tab( |
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ui.nav("Batter Summary", |
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ui.output_text("status"), |
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ui.output_plot('plot', width='1200px', height='1200px') |
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), |
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) |
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) |
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) |
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) |
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) |
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def server(input, output, session): |
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@render.ui |
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@reactive.event(input.player_button, ignore_none=False) |
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def player_select_ui(): |
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df_pitcher_info = scrape.get_players(sport_id=int(input.level_input()), season=int(input.year_input())).filter( |
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~pl.col("position").is_in(['P','TWP'])).sort("name") |
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batter_dict_pos = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['position'])) |
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year = int(input.year_input()) |
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sport_id = int(input.level_input()) |
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batter_summary = pl.read_csv(f'data/statcast/batter_summary_{level_dict_file[str(sport_id)]}_{year}.csv').sort('batter_name',descending=False) |
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batter_summary = batter_summary.with_columns( |
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pl.col("batter_id").map_elements(lambda x: batter_dict_pos.get(x, x)).alias("position") |
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) |
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batter_dict_pos = dict(zip(batter_summary['batter_id'], batter_summary['batter_name'])) |
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batter_dict = dict(zip(batter_summary['batter_id'], batter_summary['batter_name'] + ' - ' + batter_summary['position'])) |
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return ui.input_select("batter_id", "Select Batter", batter_dict, selectize=True) |
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@output |
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@render.plot |
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@reactive.event(input.generate_plot, ignore_none=False) |
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def plot(): |
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with ui.Progress(min=0, max=1) as p: |
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def draw_baseball_savant_percentiles(new_player_metrics, new_player_percentiles, colors=None, |
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sport_id=None, |
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year_input=None): |
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""" |
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Draw Baseball Savant-style percentile bars with proper alignment and scaling. |
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:param new_player_metrics: DataFrame containing new player metrics. |
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:param new_player_percentiles: DataFrame containing new player percentiles. |
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:param colors: List of colors for bars (optional, red/blue default). |
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""" |
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batter_id = new_player_metrics['batter_id'][0] |
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player_name = batter_name_id[batter_id] |
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stats = [merged_dict[x]['stat_title'] for x in merged_dict.keys()] |
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percentiles = [int((1 - x) * 100) if merged_dict[stat]["percentile_flip"] else int(x * 100) for x, stat in zip(new_player_percentiles.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())] |
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percentiles = np.clip(percentiles, 1, 100) |
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values = [str(f'{x:{merged_dict[stat]["format"]}}').strip('%') for x, stat in zip(new_player_metrics.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())] |
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logo_url = image_dict[team_dict[player_team_dict[batter_id]]] |
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color_list = ['#3661AD', '#B4CFD1', '#D82129'] |
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cmap = LinearSegmentedColormap.from_list("custom_cmap", color_list) |
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norm = Normalize(vmin=0.1, vmax=0.9) |
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norm_percentiles = norm(percentiles / 100) |
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colors = [cmap(p) for p in norm_percentiles] |
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num_stats = len(stats) |
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bar_height = 4.5 |
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spacing = 1 |
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fig_height = (bar_height + spacing) * num_stats |
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fig = plt.figure(figsize=(12, 12)) |
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gs = GridSpec(6, 5, height_ratios=[0.1, 1.5, 0.9, 0.9, 7.6, 0.1], width_ratios=[0.2, 1.5, 7, 1.5, 0.2]) |
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ax_title = fig.add_subplot(gs[1, 2]) |
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ax_table = fig.add_subplot(gs[2, :]) |
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ax_fv_table = fig.add_subplot(gs[3, :]) |
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ax = fig.add_subplot(gs[4, :]) |
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ax_logo = fig.add_subplot(gs[1, 3]) |
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ax.set_xlim(-1, 99) |
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ax.set_ylim(-1, 99) |
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ax.set_aspect("equal") |
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ax.axis("off") |
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for i, (stat, percentile, value, color) in enumerate(zip(stats, percentiles, values, colors)): |
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y = fig_height - (i + 1) * (bar_height + spacing) |
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ax.add_patch(patches.Rectangle((0, y + bar_height / 4), 100, bar_height / 2, color="#C7DCDC", lw=0)) |
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ax.add_patch(patches.Rectangle((0, y), percentile, bar_height, color=color, lw=0)) |
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circle_y = y + bar_height - bar_height / 2 |
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circle = plt.Circle((percentile, circle_y), bar_height / 2, color=color, ec='white', lw=1.5, zorder=10) |
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ax.add_patch(circle) |
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fs = 14 |
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ax.text(percentile, circle_y, f"{percentile}", ha="center", va="center", fontsize=10, color='white', zorder=10, fontweight='bold') |
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ax.text(-5, y + bar_height / 2, stat, ha="right", va="center", fontsize=fs) |
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ax.text(115, y + bar_height / 2, str(value), ha="right", va="center", fontsize=fs, zorder=5) |
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if i < len(stats) and i > 0: |
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ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=-33, xmax=0) |
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ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=100, xmax=115) |
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for x, label, align, color in zip([10, 50, 90], ["Poor", "Average", "Great"], ['center', 'center', 'center'], color_list): |
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ax.axvline(x=x, ymin=0, ymax=1, color='#FFF', linestyle='-', lw=1, zorder=1, alpha=0.5) |
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ax.text(x, fig_height + 4, label, ha=align, va='center', fontsize=12, fontweight='bold', color=color) |
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triangle = patches.RegularPolygon((x, fig_height + 1), 3, radius=1, orientation=0, color=color, zorder=2) |
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ax.add_patch(triangle) |
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player_bio(batter_id, ax=ax_title, sport_id=sport_id, year_input=year_input) |
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if input.switch(): |
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response = requests.get(input.logo_select()) |
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else: |
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response = requests.get(logo_url) |
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img = Image.open(BytesIO(response.content)) |
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ax_logo.imshow(img) |
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ax_logo.axis("off") |
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ax.axis('equal') |
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metrics_data = { |
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"Pitches": new_player_metrics['pitches'][0], |
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"PA": new_player_metrics['pa'][0], |
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"BIP": new_player_metrics['bip'][0], |
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"HR": f"{new_player_metrics['home_run'][0]:.0f}", |
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"AVG": f"{new_player_metrics['avg'][0]:.3f}", |
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"OBP": f"{new_player_metrics['obp'][0]:.3f}", |
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"SLG": f"{new_player_metrics['slg'][0]:.3f}", |
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"OPS": f"{new_player_metrics['obp'][0] + new_player_metrics['slg'][0]:.3f}", |
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} |
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df_table = pd.DataFrame(metrics_data, index=[0]) |
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ax_table.axis('off') |
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table = ax_table.table(cellText=df_table.values, colLabels=df_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1]) |
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for key, cell in table.get_celld().items(): |
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if key[0] == 0: |
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cell.set_text_props(fontweight='bold') |
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table.auto_set_font_size(False) |
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table.set_fontsize(12) |
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table.scale(1, 1.5) |
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ax_top = fig.add_subplot(gs[0, :]) |
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ax_bot = fig.add_subplot(gs[-1, :]) |
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ax_top.axis('off') |
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ax_bot.axis('off') |
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ax_bot.text(0.05, 2, "By: Thomas Nestico (@TJStats)", ha="left", va="center", fontsize=14) |
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ax_bot.text(0.95, 2, "Data: MLB, Fangraphs", ha="right", va="center", fontsize=14) |
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fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01) |
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ax_headshot = fig.add_subplot(gs[1, 1]) |
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try: |
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url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_640/v1/people/{batter_id}/headshot/milb/current.png' |
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response = requests.get(url) |
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img = Image.open(BytesIO(response.content)) |
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ax_headshot.set_xlim(0, 1) |
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ax_headshot.set_ylim(0, 1) |
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ax_headshot.imshow(img, extent=[1/6, 5/6, 0, 1], origin='upper') |
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except PIL.UnidentifiedImageError: |
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ax_headshot.axis('off') |
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return |
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ax_headshot.axis('off') |
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ax_table.set_title('Season Summary', style='italic') |
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print(batter_id) |
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ax_fv_table.axis('off') |
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if batter_id not in dict_mlb_fg.keys(): |
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ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10)) |
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return |
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df_fv_table = df_prospects[(df_prospects['minorMasterId'] == dict_mlb_fg[batter_id])][['cFV', 'Hit', 'Game', 'Raw', 'Spd', 'Fld']].reset_index(drop=True) |
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ax_fv_table.axis('off') |
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if df_fv_table.empty: |
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ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10)) |
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return |
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df_fv_table.columns = ['FV', 'Hit', 'Game', 'Raw', 'Spd', 'Fld'] |
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table_fv = ax_fv_table.table(cellText=df_fv_table.values, colLabels=df_fv_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1]) |
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for key, cell in table_fv.get_celld().items(): |
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if key[0] == 0: |
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cell.set_text_props(fontweight='bold') |
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table_fv.auto_set_font_size(False) |
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table_fv.set_fontsize(12) |
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table_fv.scale(1, 1.5) |
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ax_fv_table.set_title('Fangraphs Scouting Grades', style='italic') |
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def calculate_new_player_percentiles(player_id, new_player_metrics, player_summary_filtered): |
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""" |
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Calculate percentiles for a new player's metrics. |
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|
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:param player_id: ID of the player. |
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:param new_player_metrics: DataFrame containing new player metrics. |
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:param player_summary_filtered: Filtered player summary DataFrame. |
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:return: DataFrame containing new player percentiles. |
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""" |
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filtered_summary_clone = player_summary_filtered[['batter_id'] + stat_list].filter(pl.col('batter_id') != player_id).clone() |
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combined_data = pl.concat([filtered_summary_clone, new_player_metrics], how="vertical").to_pandas() |
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combined_percentiles = pl.DataFrame(pd.concat([combined_data['batter_id'], combined_data[stat_list].rank(pct=True)], axis=1)) |
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new_player_percentiles = combined_percentiles.filter(pl.col('batter_id') == player_id) |
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return new_player_percentiles |
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p.set(message="Generating plot", detail="This may take a while...") |
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p.set(0.3, "Gathering data...") |
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year = int(input.year_input()) |
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sport_id = int(input.level_input()) |
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batter_id = int(input.batter_id()) |
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df_player = scrape.get_players(sport_id=sport_id,season=year) |
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batter_name_id = dict(zip(df_player['player_id'],df_player['name'])) |
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player_team_dict = dict(zip(df_player['player_id'],df_player['team'])) |
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player_position_dict = dict(zip(df_player['player_id'],df_player['position'])) |
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batter_summary = pl.read_csv(f'data/statcast/batter_summary_{level_dict_file[str(sport_id)]}_{year}.csv') |
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df_prospects = pd.read_csv(f'data/prospects/prospects_{year}.csv') |
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df_rosters = pd.read_csv(f'data/rosters/fangraphs_rosters_{year}.csv') |
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df_small = df_rosters[['minorbamid','minormasterid']].dropna() |
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dict_mlb_fg=dict(zip(df_small['minorbamid'].astype(int),df_small['minormasterid'])) |
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batter_summary_filter = batter_summary.filter((pl.col('pa') >= 300) & (pl.col('launch_speed') >= 0)) |
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stat_list = batter_summary.columns[2:] |
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batter_summary_filter_pd = batter_summary_filter.to_pandas() |
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new_player_metrics = batter_summary.filter(pl.col('batter_id') == batter_id)[['batter_id'] + stat_list] |
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new_player_percentiles = calculate_new_player_percentiles(batter_id, new_player_metrics, batter_summary_filter) |
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p.set(0.6, "Creating plot...") |
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|
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draw_baseball_savant_percentiles(new_player_metrics=new_player_metrics, |
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new_player_percentiles=new_player_percentiles, |
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sport_id=sport_id, |
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year_input=year) |
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|
|
app = App(app_ui, server) |
