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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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import df_update
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update = df_update.df_update()
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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.tags.h5("Statcast Batting Summaries"),
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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)
|
|
|
|
|
|
ax_top = fig.add_subplot(gs[0, :])
|
|
ax_bot = fig.add_subplot(gs[-1, :])
|
|
ax_top.axis('off')
|
|
ax_bot.axis('off')
|
|
ax_bot.text(0.05, 2, "By: Thomas Nestico (@TJStats)", ha="left", va="center", fontsize=14)
|
|
ax_bot.text(0.95, 2, "Data: MLB, Fangraphs", ha="right", va="center", fontsize=14)
|
|
fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
|
|
|
|
|
|
ax_headshot = fig.add_subplot(gs[1, 1])
|
|
try:
|
|
url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_640/v1/people/{batter_id}/headshot/milb/current.png'
|
|
response = requests.get(url)
|
|
img = Image.open(BytesIO(response.content))
|
|
ax_headshot.set_xlim(0, 1)
|
|
ax_headshot.set_ylim(0, 1)
|
|
ax_headshot.imshow(img, extent=[1/6, 5/6, 0, 1], origin='upper')
|
|
except PIL.UnidentifiedImageError:
|
|
ax_headshot.axis('off')
|
|
return
|
|
ax_headshot.axis('off')
|
|
ax_table.set_title('Season Summary', style='italic')
|
|
|
|
|
|
print(batter_id)
|
|
ax_fv_table.axis('off')
|
|
if batter_id not in dict_mlb_fg.keys():
|
|
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))
|
|
return
|
|
df_fv_table = df_prospects[(df_prospects['minorMasterId'] == dict_mlb_fg[batter_id])][['cFV', 'Hit', 'Game', 'Raw', 'Spd', 'Fld']].reset_index(drop=True)
|
|
ax_fv_table.axis('off')
|
|
if df_fv_table.empty:
|
|
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))
|
|
return
|
|
df_fv_table.columns = ['FV', 'Hit', 'Game', 'Raw', 'Spd', 'Fld']
|
|
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])
|
|
for key, cell in table_fv.get_celld().items():
|
|
if key[0] == 0:
|
|
cell.set_text_props(fontweight='bold')
|
|
table_fv.auto_set_font_size(False)
|
|
table_fv.set_fontsize(12)
|
|
table_fv.scale(1, 1.5)
|
|
ax_fv_table.set_title('Fangraphs Scouting Grades', style='italic')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def calculate_new_player_percentiles(player_id, new_player_metrics, player_summary_filtered):
|
|
"""
|
|
Calculate percentiles for a new player's metrics.
|
|
|
|
:param player_id: ID of the player.
|
|
:param new_player_metrics: DataFrame containing new player metrics.
|
|
:param player_summary_filtered: Filtered player summary DataFrame.
|
|
:return: DataFrame containing new player percentiles.
|
|
"""
|
|
filtered_summary_clone = player_summary_filtered[['batter_id'] + stat_list].filter(pl.col('batter_id') != player_id).clone()
|
|
combined_data = pl.concat([filtered_summary_clone, new_player_metrics], how="vertical").to_pandas()
|
|
combined_percentiles = pl.DataFrame(pd.concat([combined_data['batter_id'], combined_data[stat_list].rank(pct=True)], axis=1))
|
|
new_player_percentiles = combined_percentiles.filter(pl.col('batter_id') == player_id)
|
|
return new_player_percentiles
|
|
|
|
|
|
|
|
p.set(message="Generating plot", detail="This may take a while...")
|
|
|
|
|
|
p.set(0.3, "Gathering data...")
|
|
|
|
|
|
year = int(input.year_input())
|
|
sport_id = int(input.level_input())
|
|
batter_id = int(input.batter_id())
|
|
|
|
|
|
df_player = scrape.get_players(sport_id=sport_id,season=year)
|
|
batter_name_id = dict(zip(df_player['player_id'],df_player['name']))
|
|
player_team_dict = dict(zip(df_player['player_id'],df_player['team']))
|
|
player_position_dict = dict(zip(df_player['player_id'],df_player['position']))
|
|
|
|
|
|
batter_summary = pl.read_csv(f'data/statcast/batter_summary_{level_dict_file[str(sport_id)]}_{year}.csv')
|
|
df_prospects = pd.read_csv(f'data/prospects/prospects_{year}.csv')
|
|
df_rosters = pd.read_csv(f'data/rosters/fangraphs_rosters_{year}.csv')
|
|
df_small = df_rosters[['minorbamid','minormasterid']].dropna()
|
|
dict_mlb_fg=dict(zip(df_small['minorbamid'].astype(int),df_small['minormasterid']))
|
|
|
|
|
|
|
|
|
|
batter_summary_filter = batter_summary.filter((pl.col('pa') >= 300) & (pl.col('launch_speed') >= 0))
|
|
stat_list = batter_summary.columns[2:]
|
|
batter_summary_filter_pd = batter_summary_filter.to_pandas()
|
|
new_player_metrics = batter_summary.filter(pl.col('batter_id') == batter_id)[['batter_id'] + stat_list]
|
|
|
|
|
|
new_player_percentiles = calculate_new_player_percentiles(batter_id, new_player_metrics, batter_summary_filter)
|
|
|
|
p.set(0.6, "Creating plot...")
|
|
|
|
draw_baseball_savant_percentiles(new_player_metrics=new_player_metrics,
|
|
new_player_percentiles=new_player_percentiles,
|
|
sport_id=sport_id,
|
|
year_input=year)
|
|
|
|
app = App(app_ui, server) |
