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import polars as pl |
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import numpy as np |
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import pandas as pd |
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import api_scraper |
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scrape = api_scraper.MLB_Scrape() |
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from functions import df_update |
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from functions import pitch_summary_functions |
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update = df_update.df_update() |
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from stuff_model import feature_engineering as fe |
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from stuff_model import stuff_apply |
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import requests |
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import joblib |
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from matplotlib.gridspec import GridSpec |
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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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import matplotlib.pyplot as plt |
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import matplotlib.gridspec as gridspec |
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import seaborn as sns |
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from functions.pitch_summary_functions import * |
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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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import datetime |
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import matplotlib.colors |
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import pandas as pd |
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from stuff_model import calculate_arm_angles as caa |
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cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#648FFF","#FFFFFF","#FFB000"]) |
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colour_palette = ['#FFB000','#648FFF','#785EF0', |
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'#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED'] |
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year_list = [2017,2018,2019,2020,2021,2022,2023,2024] |
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level_dict = {'1':'MLB', |
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'11':'AAA', |
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'12':'AA', |
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'13':'A+', |
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'14':'A', |
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'16':'ROK', |
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'17':'AFL', |
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'22':'College', |
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'21':'Prospects', |
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'51':'International' } |
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function_dict={ |
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'velocity_kdes':'Velocity Distributions', |
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'break_plot':'Pitch Movement', |
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'break_plot_rhh':'Pitch Movement LHH', |
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'break_plot_lhh':'Pitch Movement RHH', |
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'tj_stuff_roling':'Rolling tjStuff+ by Pitch', |
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'tj_stuff_roling_game':'Rolling tjStuff+ by Game', |
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'location_plot_lhb':'Locations vs LHB', |
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'location_plot_rhb':'Locations vs RHB', |
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} |
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split_dict = {'all':'All', |
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'left':'LHH', |
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'right':'RHH'} |
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split_dict_hand = {'all':['L','R'], |
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'left':['L'], |
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'right':['R']} |
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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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import requests |
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import requests |
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import os |
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CAMPAIGN_ID = os.getenv("CAMPAIGN_ID") |
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ACCESS_TOKEN = os.getenv("ACCESS_TOKEN") |
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BACKUP_PW = os.getenv("BACKUP_PW") |
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ADMIN_PW = os.getenv("ADMIN_PW") |
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url = f"https://www.patreon.com/api/oauth2/v2/campaigns/{CAMPAIGN_ID}/members" |
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headers = { |
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"Authorization": f"Bearer {ACCESS_TOKEN}" |
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} |
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params = { |
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"fields[member]": "full_name,email", |
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"include": "currently_entitled_tiers", |
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"page[size]": 10000 |
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} |
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response = requests.get(url, headers=headers, params=params) |
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VALID_PASSWORDS = [] |
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if response.status_code == 200: |
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data = response.json() |
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for patron in data['data']: |
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try: |
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tiers = patron['relationships']['currently_entitled_tiers']['data'] |
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if any(tier['id'] == '9078921' for tier in tiers): |
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full_name = patron['attributes']['email'] |
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VALID_PASSWORDS.append(full_name) |
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except KeyError: |
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continue |
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VALID_PASSWORDS.append(BACKUP_PW) |
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VALID_PASSWORDS.append(ADMIN_PW) |
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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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login_ui = ui.page_fluid( |
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ui.card( |
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ui.h2([ |
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"TJStats Daily Pitching Summary App ", |
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ui.tags.a("(@TJStats)", href="https://twitter.com/TJStats", target="_blank") |
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]), |
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ui.p( |
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"This App is available to Superstar Patrons. Please enter your Patreon email address in the box below. If you're having trouble, please refer to the ", |
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ui.tags.a("Patreon post", href="https://www.patreon.com/posts/122860440", target="_blank"), |
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"." |
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), |
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ui.input_password("password", "Enter Patreon Email (or Password from Link):", width="25%"), |
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ui.tags.input( |
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type="checkbox", |
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id="authenticated", |
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value=False, |
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disabled=True |
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), |
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ui.input_action_button("login", "Login", class_="btn-primary"), |
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ui.output_text("login_message"), |
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) |
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) |
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main_ui = ui.page_sidebar( |
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ui.sidebar( |
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ui.row( |
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ui.column(6, ui.input_date('date_input', 'Select Date')), |
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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("game_button", "Get Games", class_="btn-primary")), |
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ui.row( |
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ui.row(ui.column(12, ui.output_ui('game_select_ui', 'Select Game'))), |
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ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))), |
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ui.row(ui.column(12, ui.output_data_frame('season_stats'))), |
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), |
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ui.row( |
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ui.column(4, ui.input_select('plot_id_1', 'Plot Left', function_dict, multiple=False, selected='location_plot_lhb')), |
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ui.column(4, ui.input_select('plot_id_2', 'Plot Middle', function_dict, multiple=False, selected='break_plot')), |
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ui.column(4, ui.input_select('plot_id_3', 'Plot Right', function_dict, multiple=False, selected='location_plot_rhb')) |
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), |
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ui.row( |
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ui.column(6, ui.input_select('split_id', 'Select Split', split_dict, multiple=False)), |
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ui.column(6, ui.input_numeric('rolling_window', 'Rolling Window (for tjStuff+ Plot)', min=1, value=50)) |
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), |
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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='500px'), |
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ui.navset_tab( |
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ui.nav_panel("Pitching Summary", |
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ui.output_text("status"), |
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ui.output_plot('plot', width='2100px', height='2100px') |
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), |
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ui.nav_panel("PBP Data", |
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ui.output_data_frame("grid_pbp")), |
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ui.nav_panel("Table Summary", |
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ui.output_data_frame("grid_summary")), |
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ui.nav_panel("Daily Table", |
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ui.output_data_frame("grid")), |
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ui.nav_panel("Daily Table Style", |
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ui.input_numeric('head', 'Table Limit', min=0, value=10), |
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ui.input_numeric('pitch_min', 'Pitch Min.', min=0, value=10), |
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ui.card( |
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{"style": "width: 1560px;"}, |
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ui.head_content( |
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ui.tags.script(src="https://html2canvas.hertzen.com/dist/html2canvas.min.js"), |
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ui.tags.script(""" |
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async function downloadPNG() { |
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const content = document.getElementById('capture-section'); |
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try { |
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// Create a wrapper div with right margin only |
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const wrapper = document.createElement('div'); |
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wrapper.style.paddingRight = '20px'; |
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wrapper.style.paddingLeft = '20px'; |
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wrapper.style.paddingTop = '20px'; |
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wrapper.style.backgroundColor = 'white'; |
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// Clone the content |
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const clonedContent = content.cloneNode(true); |
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wrapper.appendChild(clonedContent); |
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// Add wrapper to document temporarily |
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document.body.appendChild(wrapper); |
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const canvas = await html2canvas(wrapper, { |
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backgroundColor: 'white', |
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scale: 2, |
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useCORS: true, |
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logging: false, |
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width: content.offsetWidth + 20, |
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height: content.offsetHeight + 50 |
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}); |
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// Remove temporary wrapper |
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document.body.removeChild(wrapper); |
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// Convert canvas to blob |
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canvas.toBlob(function(blob) { |
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const url = URL.createObjectURL(blob); |
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const link = document.createElement('a'); |
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link.href = url; |
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link.download = 'stats_card.png'; |
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document.body.appendChild(link); |
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link.click(); |
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document.body.removeChild(link); |
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URL.revokeObjectURL(url); |
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}, 'image/png'); |
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} catch (error) { |
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console.error('Error generating PNG:', error); |
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} |
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} |
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$(document).on('click', '#capture_png_btn', function() { |
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downloadPNG(); |
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}); |
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""") |
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), |
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ui.div( |
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{ |
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"id": "capture-section", |
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"style": "background-color: white; padding: 0; margin-left: 20px; margin-right: 20px; margin-top: 20px; margin-bottom: 20px;" |
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}, |
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ui.div({"style": "font-size:3em;"}, ui.output_text("style_title")), |
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ui.div({"style": "font-size:1.5em;"}, ui.output_text("min_title")), |
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ui.br(), |
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ui.output_table("grid_style"), |
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ui.br(), |
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ui.row( |
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ui.column(8, |
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ui.div( |
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{"style": "text-align: left;"}, |
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ui.markdown("### By: @TJStats"), |
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ui.markdown("### Data: MLB"), |
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) |
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), |
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ui.column(4, |
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ui.div( |
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{"style": "text-align: left; height: 86px; display: flex; justify-content: flex-end;"}, |
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ui.output_image("logo", height="86px") |
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) |
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) |
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), |
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ui.div({"style": "height: 20px;"}) |
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), |
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ui.div( |
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{"style": "display: flex; gap: 10px;"}, |
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ui.input_action_button("capture_png_btn", "Save as PNG", class_="btn-success"), |
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), |
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) |
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), |
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ui.nav_panel("Whiffs Table", |
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ui.output_table("whiff_table")), |
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) |
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) |
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app_ui = ui.page_fluid( |
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ui.tags.head( |
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ui.tags.script(src="script.js") |
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|
), |
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|
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ui.panel_conditional( |
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"!input.authenticated", |
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login_ui |
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), |
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ui.panel_conditional( |
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"input.authenticated", |
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main_ui |
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) |
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) |
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def server(input, output, session): |
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|
|
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@reactive.Effect |
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@reactive.event(input.login) |
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def check_password(): |
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|
if input.password() in VALID_PASSWORDS: |
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|
ui.update_checkbox("authenticated", value=True) |
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|
ui.update_text("login_message", value="") |
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|
else: |
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|
ui.update_text("login_message", value="Invalid password!") |
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|
ui.update_text("password", value="") |
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|
|
|
@output |
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|
@render.text |
|
|
def login_message(): |
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return "" |
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|
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|
|
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@render.image |
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|
def logo(): |
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return {"src": "tjstats_logo.jpg"} |
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|
@render.text |
|
|
def style_title(): |
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|
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return f"Daily {level_dict[input.level_input()]} tjStuff+ Leaders - {str(input.date_input())}" |
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|
|
|
|
@render.text |
|
|
def min_title(): |
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|
|
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return f"(Min. {int(input.pitch_min())} Pitches)" |
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|
|
|
|
@render.ui |
|
|
@reactive.event(input.game_button,input.date_input,input.level_input, ignore_none=False) |
|
|
def game_select_ui(): |
|
|
df = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])], |
|
|
sport_id=[int(input.level_input())], |
|
|
game_type=['S','R','P','E','A','I','W','F','L','D']) |
|
|
.filter((pl.col('gameday_type').is_in(['P', 'E'])) | (pl.col('venue_id').is_in([7250,2532]))) |
|
|
.filter(pl.col('state').is_in(['I','M','N','O','F','T','U','Q','R','D'])) |
|
|
.with_columns(pl.col('date').cast(pl.Utf8)) |
|
|
.filter(pl.col('date') == str(input.date_input()))).with_columns( |
|
|
(pl.col('away')+' @ '+pl.col('home')+' - '+pl.col('state')).alias('matchup')).sort('time') |
|
|
game_dict = dict(zip(df['game_id'], df['matchup'])) |
|
|
print('GAMES') |
|
|
print(game_dict) |
|
|
|
|
|
return ui.input_select("game_id", "Select Game", game_dict) |
|
|
|
|
|
|
|
|
@render.ui |
|
|
@reactive.event(input.game_id) |
|
|
def player_select_ui(): |
|
|
try: |
|
|
|
|
|
data_list = scrape.get_data(game_list_input = [int(input.game_id())]) |
|
|
print('DATALIST') |
|
|
print(input.game_id()) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter( |
|
|
(pl.col("is_pitch") == True)& |
|
|
(pl.col('batter_hand').is_in(split_dict_hand[input.split_id()])) |
|
|
|
|
|
)))).with_columns( |
|
|
pl.col('pitch_type').count().over('pitch_type').alias('pitch_count') |
|
|
).with_columns( |
|
|
(pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name')) |
|
|
) |
|
|
|
|
|
|
|
|
pitcher_dict = dict(zip(df['pitcher_id'], df['pitcher_name'])) |
|
|
print('PITCHERS') |
|
|
print(pitcher_dict) |
|
|
return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict) |
|
|
except Exception as e: |
|
|
print(e) |
|
|
return ui.output_text('pitcher_id',"No pitchers available for this game") |
|
|
|
|
|
@output |
|
|
@render.text |
|
|
def status(): |
|
|
|
|
|
if input.generate == 0: |
|
|
return "" |
|
|
return "" |
|
|
|
|
|
@output |
|
|
@render.data_frame |
|
|
def season_stats(): |
|
|
|
|
|
if int(input.level_input()) == 1: |
|
|
season = int(str(input.date_input())[:4]) |
|
|
url_season = url = f""" |
|
|
https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&season={season}&season1={season} |
|
|
&startdate={season}-01-01&enddate={season}-12-01&ind=0&qual=0&type=8&month=33&pageitems=500000 |
|
|
""" |
|
|
|
|
|
url_day = f""" |
|
|
|
|
|
|
|
|
https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&qual=y&season={season}&season1={season} |
|
|
&startdate={season}-03-01&enddate={season}-11-01 |
|
|
&month=34&hand=&team=0&pageitems=500000&ind=0&rost=0&players=&type=8 |
|
|
&sortstat=WAR |
|
|
|
|
|
""" |
|
|
data_season = requests.get(url_season).json() |
|
|
df_season = pl.DataFrame(data=data_season['data'], infer_schema_length=1000) |
|
|
df_season = df_season.with_columns(pl.lit('Season').alias('Time')) |
|
|
|
|
|
data_day = requests.get(url_day).json() |
|
|
|
|
|
if data_day['dateRange'][:10] == str(input.date_input()): |
|
|
df_day = pl.DataFrame(data=data_day['data'], infer_schema_length=1000) |
|
|
df_day = df_day.with_columns(pl.lit('Today').alias('Time')) |
|
|
|
|
|
df_all = pl.DataFrame(pd.concat([df_day.to_pandas(),df_season.to_pandas()])) |
|
|
|
|
|
df_player = df_all.filter(pl.col('xMLBAMID')==int(input.pitcher_id())) |
|
|
|
|
|
else: |
|
|
|
|
|
df_player = df_season.filter(pl.col('xMLBAMID')==int(input.pitcher_id())) |
|
|
|
|
|
|
|
|
return render.DataGrid( |
|
|
df_player.select(['Time','IP','TBF','R','ER','SO','BB','ERA','FIP','WHIP']).to_pandas().round(2), |
|
|
row_selection_mode='multiple', |
|
|
height='700px', |
|
|
width='fit-content', |
|
|
) |
|
|
else: |
|
|
|
|
|
d = scrape.get_data([int(input.game_id())]) |
|
|
|
|
|
player_id = f'ID{int(input.pitcher_id())}' |
|
|
|
|
|
home_players = d[0]['liveData']['boxscore']['teams']['home']['players'] |
|
|
away_players = d[0]['liveData']['boxscore']['teams']['away']['players'] |
|
|
|
|
|
if player_id in home_players: |
|
|
batters_faced = home_players[player_id]['stats']['pitching']['battersFaced'] |
|
|
team_side = 'home' |
|
|
elif player_id in away_players: |
|
|
batters_faced = away_players[player_id]['stats']['pitching']['battersFaced'] |
|
|
team_side = 'away' |
|
|
else: |
|
|
batters_faced = None |
|
|
team_side = None |
|
|
|
|
|
|
|
|
pitches = d[0]['liveData']['boxscore']['teams'][team_side]['players'][player_id]['stats']['pitching']['pitchesThrown'] |
|
|
innings = d[0]['liveData']['boxscore']['teams'][team_side]['players'][player_id]['stats']['pitching']['inningsPitched'] |
|
|
battersFaced = d[0]['liveData']['boxscore']['teams'][team_side]['players'][player_id]['stats']['pitching']['battersFaced'] |
|
|
runs = d[0]['liveData']['boxscore']['teams'][team_side]['players'][player_id]['stats']['pitching']['runs'] |
|
|
earnedRuns = d[0]['liveData']['boxscore']['teams'][team_side]['players'][player_id]['stats']['pitching']['earnedRuns'] |
|
|
strikeOuts = d[0]['liveData']['boxscore']['teams'][team_side]['players'][player_id]['stats']['pitching']['strikeOuts'] |
|
|
baseOnBalls = d[0]['liveData']['boxscore']['teams'][team_side]['players'][player_id]['stats']['pitching']['baseOnBalls'] |
|
|
|
|
|
df_player = pl.DataFrame({ |
|
|
'Pitches': int(pitches), |
|
|
'IP': float(innings), |
|
|
'PA': int(battersFaced), |
|
|
'R': int(runs), |
|
|
'ER': int(earnedRuns), |
|
|
'K': int(strikeOuts), |
|
|
'BB': int(baseOnBalls) |
|
|
}) |
|
|
|
|
|
return render.DataGrid( |
|
|
df_player.to_pandas().round(2), |
|
|
row_selection_mode='multiple', |
|
|
height='700px', |
|
|
width='fit-content', |
|
|
) |
|
|
|
|
|
@output |
|
|
@render.plot |
|
|
@reactive.event(input.generate_plot, ignore_none=False) |
|
|
def plot(): |
|
|
|
|
|
with ui.Progress(min=0, max=1) as p: |
|
|
p.set(message="Generating plot", detail="This may take a while...") |
|
|
|
|
|
|
|
|
p.set(0.3, "Gathering data...") |
|
|
|
|
|
data_list = scrape.get_data(game_list_input = [int(input.game_id())]) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter( |
|
|
(pl.col("pitcher_id") == int(input.pitcher_id()))& |
|
|
(pl.col("is_pitch") == True)& |
|
|
(pl.col('batter_hand').is_in(split_dict_hand[input.split_id()])) |
|
|
|
|
|
))).with_columns( |
|
|
pl.col("extension").fill_null(6.2) |
|
|
)).with_columns( |
|
|
pl.col('pitch_type').count().over('pitch_type').alias('pitch_count') |
|
|
)) |
|
|
|
|
|
|
|
|
df = df.clone() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
p.set(0.6, "Creating plot...") |
|
|
|
|
|
|
|
|
|
|
|
fig = plt.figure(figsize=(26,26)) |
|
|
plt.rcParams.update({'figure.autolayout': True}) |
|
|
fig.set_facecolor('white') |
|
|
sns.set_theme(style="whitegrid", palette=colour_palette) |
|
|
print('this is the one plot') |
|
|
|
|
|
gs = gridspec.GridSpec(6, 8, |
|
|
height_ratios=[6,20,12,36,36,6], |
|
|
width_ratios=[4,18,18,18,18,18,18,4]) |
|
|
|
|
|
|
|
|
gs.update(hspace=0.2, wspace=0.5) |
|
|
|
|
|
|
|
|
ax_headshot = fig.add_subplot(gs[1,1:3]) |
|
|
ax_bio = fig.add_subplot(gs[1,3:5]) |
|
|
ax_logo = fig.add_subplot(gs[1,5:7]) |
|
|
|
|
|
ax_season_table = fig.add_subplot(gs[2,1:7]) |
|
|
|
|
|
ax_plot_1 = fig.add_subplot(gs[3,1:3]) |
|
|
ax_plot_2 = fig.add_subplot(gs[3,3:5]) |
|
|
ax_plot_3 = fig.add_subplot(gs[3,5:7]) |
|
|
|
|
|
ax_table = fig.add_subplot(gs[4,1:7]) |
|
|
|
|
|
|
|
|
ax_footer = fig.add_subplot(gs[-1,1:7]) |
|
|
ax_header = fig.add_subplot(gs[0,1:7]) |
|
|
ax_left = fig.add_subplot(gs[:,0]) |
|
|
ax_right = fig.add_subplot(gs[:,-1]) |
|
|
|
|
|
|
|
|
ax_footer.axis('off') |
|
|
ax_header.axis('off') |
|
|
ax_left.axis('off') |
|
|
ax_right.axis('off') |
|
|
|
|
|
sns.set_theme(style="whitegrid", palette=colour_palette) |
|
|
fig.set_facecolor('white') |
|
|
|
|
|
df_teams = scrape.get_teams() |
|
|
|
|
|
year_input = int(str(input.date_input())[:4]) |
|
|
sport_id = int(input.level_input()) |
|
|
player_input = int(input.pitcher_id()) |
|
|
team_id = df['pitcher_team_id'][0] |
|
|
player_headshot(player_input=player_input, ax=ax_headshot,sport_id=sport_id,season=year_input) |
|
|
player_bio(pitcher_id=player_input, ax=ax_bio,sport_id=sport_id,year_input=year_input) |
|
|
|
|
|
|
|
|
if input.switch(): |
|
|
|
|
|
|
|
|
logo_url = input.logo_select() |
|
|
|
|
|
|
|
|
response = requests.get(logo_url) |
|
|
|
|
|
|
|
|
img = Image.open(BytesIO(response.content)) |
|
|
|
|
|
|
|
|
ax_logo.set_xlim(0, 1.3) |
|
|
ax_logo.set_ylim(0, 1) |
|
|
ax_logo.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper') |
|
|
|
|
|
|
|
|
ax_logo.axis('off') |
|
|
|
|
|
else: |
|
|
plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input),team_id=team_id) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
stat_daily_summary(df=df, |
|
|
data=data_list, |
|
|
player_input=int(input.pitcher_id()), |
|
|
sport_id=int(input.level_input()), |
|
|
ax=ax_season_table) |
|
|
|
|
|
|
|
|
|
|
|
for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax_plot_1,ax_plot_2,ax_plot_3],[1,3,5]): |
|
|
if x == 'velocity_kdes': |
|
|
velocity_kdes(df, |
|
|
ax=y, |
|
|
gs=gs, |
|
|
gs_x=[3,4], |
|
|
gs_y=[z,z+2], |
|
|
fig=fig) |
|
|
if x == 'tj_stuff_roling': |
|
|
tj_stuff_roling(df=df, |
|
|
window=int(input.rolling_window()), |
|
|
ax=y) |
|
|
|
|
|
if x == 'tj_stuff_roling_game': |
|
|
tj_stuff_roling_game(df=df, |
|
|
window=int(input.rolling_window()), |
|
|
ax=y) |
|
|
|
|
|
if x == 'break_plot': |
|
|
break_plot(df = df,ax=y) |
|
|
|
|
|
if x == 'location_plot_lhb': |
|
|
location_plot(df = df,ax=y,hand='L') |
|
|
|
|
|
if x == 'location_plot_rhb': |
|
|
location_plot(df = df,ax=y,hand='R') |
|
|
|
|
|
if x == 'break_plot_rhh': |
|
|
break_plot(df = df.filter(pl.col('batter_hand')=='R'),ax=y) |
|
|
|
|
|
if x == 'break_plot_lhh': |
|
|
break_plot(df = df.filter(pl.col('batter_hand')=='L'),ax=y) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
summary_table(df=df, |
|
|
ax=ax_table) |
|
|
|
|
|
plot_footer(ax_footer) |
|
|
|
|
|
ax_watermark = fig.add_subplot(gs[1:-1,1:-1],zorder=-1) |
|
|
|
|
|
ax_watermark.set_xticks([]) |
|
|
ax_watermark.set_yticks([]) |
|
|
ax_watermark.set_frame_on(False) |
|
|
|
|
|
img = Image.open('tj stats circle-01_new.jpg') |
|
|
img = img.convert("LA") |
|
|
|
|
|
ax_watermark.imshow(img, extent=[0, 1, 0, 1], origin='upper',zorder=-1, alpha=0.06) |
|
|
|
|
|
|
|
|
ax_watermark2 = fig.add_subplot(gs[-2:,1:4],zorder=1) |
|
|
ax_watermark2.set_xlim(0,1) |
|
|
ax_watermark2.set_ylim(0,1) |
|
|
|
|
|
ax_watermark2.set_xticks([]) |
|
|
ax_watermark2.set_yticks([]) |
|
|
ax_watermark2.set_frame_on(False) |
|
|
|
|
|
|
|
|
img = Image.open('tj stats circle-01_new.jpg') |
|
|
|
|
|
width, height = img.size |
|
|
|
|
|
new_width = int(width * 0.5) |
|
|
new_height = int(height * 0.5) |
|
|
|
|
|
img_resized = img.resize((new_width, new_height)) |
|
|
|
|
|
ax_watermark2.imshow(img, extent=[0.26, 0.46, 0.0,0.2], origin='upper',zorder=-1, alpha=1) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01) |
|
|
|
|
|
|
|
|
|
|
|
@output |
|
|
@render.data_frame |
|
|
@reactive.event(input.generate_plot, ignore_none=False) |
|
|
def grid_summary(): |
|
|
|
|
|
data_list = scrape.get_data(game_list_input = [int(input.game_id())]) |
|
|
df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter( |
|
|
(pl.col("pitcher_id") == int(input.pitcher_id()))& |
|
|
(pl.col("is_pitch") == True)& |
|
|
(pl.col('batter_hand').is_in(split_dict_hand[input.split_id()])) |
|
|
|
|
|
))).with_columns( |
|
|
pl.col("extension").fill_null(6.2) |
|
|
)).with_columns( |
|
|
pl.col('pitch_type').count().over('pitch_type').alias('pitch_count') |
|
|
)) |
|
|
|
|
|
|
|
|
df = df.clone() |
|
|
features_table = ['start_speed', |
|
|
'spin_rate', |
|
|
'extension', |
|
|
'ivb', |
|
|
'hb', |
|
|
'x0', |
|
|
'z0'] |
|
|
|
|
|
|
|
|
|
|
|
selection = ['game_id','pitcher_id','pitcher_name','batter_id','batter_name','pitcher_hand', |
|
|
'batter_hand','balls','strikes','play_code','event_type','pitch_type','vaa','haa']+features_table+['tj_stuff_plus','pitch_grade'] |
|
|
|
|
|
|
|
|
|
|
|
return render.DataGrid( |
|
|
df.select(selection).to_pandas().round(1), |
|
|
row_selection_mode='multiple', |
|
|
height='700px', |
|
|
width='fit-content', |
|
|
filters=True, |
|
|
) |
|
|
|
|
|
|
|
|
@output |
|
|
@render.data_frame |
|
|
@reactive.event(input.generate_plot, ignore_none=False) |
|
|
def grid_pbp(): |
|
|
|
|
|
data_list = scrape.get_data(game_list_input = [int(input.game_id())]) |
|
|
df = scrape.get_data_df(data_list = data_list) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
return render.DataGrid( |
|
|
df.to_pandas(), |
|
|
row_selection_mode='multiple', |
|
|
height='700px', |
|
|
width='fit-content', |
|
|
filters=True, |
|
|
) |
|
|
|
|
|
|
|
|
@output |
|
|
@render.data_frame |
|
|
@reactive.event(input.generate_plot, ignore_none=False) |
|
|
def grid(): |
|
|
|
|
|
|
|
|
df_games = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])], |
|
|
sport_id=[int(input.level_input())], |
|
|
game_type=['S','R','P','E','A','I','W','F','L']).with_columns(pl.col('date').cast(pl.Utf8)). |
|
|
filter(pl.col('date') == str(input.date_input()))).with_columns( |
|
|
(pl.col('away')+' @ '+pl.col('home')).alias('matchup')) |
|
|
|
|
|
|
|
|
|
|
|
game_list = df_games['game_id'].unique().to_list() |
|
|
|
|
|
|
|
|
data_list = scrape.get_data(game_list) |
|
|
df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter( |
|
|
(pl.col("is_pitch") == True)& |
|
|
(pl.col('batter_hand').is_in(split_dict_hand[input.split_id()])) |
|
|
|
|
|
)))).with_columns( |
|
|
pl.col('pitch_type').count().over('pitch_type').alias('pitch_count') |
|
|
).with_columns( |
|
|
(pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name')) |
|
|
) |
|
|
|
|
|
|
|
|
data = scrape.get_data(game_list[:]) |
|
|
df = scrape.get_data_df(data) |
|
|
|
|
|
pitcher_team_dict = dict(zip(df['pitcher_id'], df['pitcher_team'])) |
|
|
|
|
|
|
|
|
df_test = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(df).filter( |
|
|
(pl.col("is_pitch") == True))))) |
|
|
|
|
|
df_test = df_test.with_columns( |
|
|
(pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name') |
|
|
) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
df_agg_2024_pitch = df_test.group_by(['pitcher_id','pitcher_name','pitch_type']).agg( |
|
|
pl.col('tj_stuff_plus').len().alias('count'), |
|
|
pl.col('tj_stuff_plus').mean() |
|
|
) |
|
|
|
|
|
|
|
|
df_weighted_avg = df_agg_2024_pitch.with_columns( |
|
|
(pl.col('tj_stuff_plus') * pl.col('count')).alias('weighted_tj_stuff_plus') |
|
|
).group_by(['pitcher_id', 'pitcher_name']).agg( |
|
|
pl.col('count').sum().alias('total_count'), |
|
|
pl.col('weighted_tj_stuff_plus').sum().alias('total_weighted_tj_stuff_plus') |
|
|
).with_columns( |
|
|
(pl.col('total_weighted_tj_stuff_plus') / pl.col('total_count')).alias('tj_stuff_plus') |
|
|
).select(['pitcher_id', 'pitcher_name', 'tj_stuff_plus', 'total_count']) |
|
|
|
|
|
|
|
|
df_weighted_avg = df_weighted_avg.with_columns( |
|
|
pl.lit("All").alias('pitch_type') |
|
|
) |
|
|
|
|
|
|
|
|
df_weighted_avg = df_weighted_avg.select([ |
|
|
'pitcher_id', |
|
|
'pitcher_name', |
|
|
|
|
|
'pitch_type', |
|
|
pl.col('total_count').alias('count'), |
|
|
'tj_stuff_plus' |
|
|
]) |
|
|
|
|
|
|
|
|
df_agg_2024_pitch = pl.concat([df_agg_2024_pitch, df_weighted_avg]) |
|
|
|
|
|
|
|
|
df_small = df_agg_2024_pitch.select(['pitcher_id','pitcher_name','pitch_type','count','tj_stuff_plus']) |
|
|
count_dict = dict(zip(df_small.filter(pl.col('pitch_type')=='All')['pitcher_id'], |
|
|
df_small.filter(pl.col('pitch_type')=='All')['count'])) |
|
|
|
|
|
|
|
|
df_small_pivot = (df_small.pivot(index=['pitcher_id','pitcher_name'], |
|
|
columns='pitch_type', |
|
|
values='tj_stuff_plus').with_columns( |
|
|
pl.col("pitcher_id").replace_strict(count_dict, default=None).alias("count"))) |
|
|
|
|
|
|
|
|
for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']: |
|
|
if col not in df_small_pivot.columns: |
|
|
df_small_pivot = df_small_pivot.with_columns(pl.lit(None).alias(col)) |
|
|
|
|
|
|
|
|
df_small_pivot = df_small_pivot.with_columns( |
|
|
pl.col("pitcher_id").replace_strict(pitcher_team_dict, default=None).alias("pitcher_team")) |
|
|
|
|
|
df_small_pivot = df_small_pivot.select(['pitcher_id','pitcher_name','pitcher_team','count','CH','CU','FC','FF','FS','SI','SL','ST','All']).sort('All',descending=True) |
|
|
|
|
|
df_small_pivot = df_small_pivot.with_columns( |
|
|
pl.col(col).cast(pl.Int32, strict=False) for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All'] |
|
|
) |
|
|
|
|
|
|
|
|
return render.DataGrid( |
|
|
df_small_pivot, |
|
|
row_selection_mode='multiple', |
|
|
height='700px', |
|
|
width='fit-content', |
|
|
filters=True, |
|
|
) |
|
|
|
|
|
|
|
|
@output |
|
|
@render.table |
|
|
@reactive.event(input.generate_plot, input.pitch_min,input.head,ignore_none=False) |
|
|
def grid_style(): |
|
|
|
|
|
row_limit = int(input.head()) |
|
|
pitch_limit = int(input.pitch_min()) |
|
|
df_games = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])], |
|
|
sport_id=[int(input.level_input())], |
|
|
game_type=['S','R','P','E','A','I','W','F','L']).with_columns(pl.col('date').cast(pl.Utf8)). |
|
|
filter(pl.col('date') == str(input.date_input()))).with_columns( |
|
|
(pl.col('away')+' @ '+pl.col('home')).alias('matchup')) |
|
|
|
|
|
|
|
|
|
|
|
game_list = df_games['game_id'].unique().to_list() |
|
|
|
|
|
|
|
|
data_list = scrape.get_data(game_list) |
|
|
df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter( |
|
|
(pl.col("is_pitch") == True)& |
|
|
(pl.col('batter_hand').is_in(split_dict_hand[input.split_id()])) |
|
|
|
|
|
)))).with_columns( |
|
|
pl.col('pitch_type').count().over('pitch_type').alias('pitch_count') |
|
|
).with_columns( |
|
|
(pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name')) |
|
|
) |
|
|
|
|
|
|
|
|
data = scrape.get_data(game_list[:]) |
|
|
df = scrape.get_data_df(data) |
|
|
|
|
|
pitcher_team_dict = dict(zip(df['pitcher_id'], df['pitcher_team'])) |
|
|
|
|
|
|
|
|
df_test = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(df).filter( |
|
|
(pl.col("is_pitch") == True))))) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
df_agg_2024_pitch = df_test.group_by(['pitcher_id','pitcher_name','pitch_type']).agg( |
|
|
pl.col('tj_stuff_plus').len().alias('count'), |
|
|
pl.col('tj_stuff_plus').mean() |
|
|
) |
|
|
|
|
|
|
|
|
df_weighted_avg = df_agg_2024_pitch.with_columns( |
|
|
(pl.col('tj_stuff_plus') * pl.col('count')).alias('weighted_tj_stuff_plus') |
|
|
).group_by(['pitcher_id', 'pitcher_name']).agg( |
|
|
pl.col('count').sum().alias('total_count'), |
|
|
pl.col('weighted_tj_stuff_plus').sum().alias('total_weighted_tj_stuff_plus') |
|
|
).with_columns( |
|
|
(pl.col('total_weighted_tj_stuff_plus') / pl.col('total_count')).alias('tj_stuff_plus') |
|
|
).select(['pitcher_id', 'pitcher_name', 'tj_stuff_plus', 'total_count']) |
|
|
|
|
|
|
|
|
df_weighted_avg = df_weighted_avg.with_columns( |
|
|
pl.lit("All").alias('pitch_type') |
|
|
) |
|
|
|
|
|
|
|
|
df_weighted_avg = df_weighted_avg.select([ |
|
|
'pitcher_id', |
|
|
'pitcher_name', |
|
|
|
|
|
'pitch_type', |
|
|
pl.col('total_count').alias('count'), |
|
|
'tj_stuff_plus' |
|
|
]) |
|
|
|
|
|
|
|
|
df_agg_2024_pitch = pl.concat([df_agg_2024_pitch, df_weighted_avg]) |
|
|
|
|
|
|
|
|
df_small = df_agg_2024_pitch.select(['pitcher_id','pitcher_name','pitch_type','count','tj_stuff_plus']) |
|
|
count_dict = dict(zip(df_small.filter(pl.col('pitch_type')=='All')['pitcher_id'], |
|
|
df_small.filter(pl.col('pitch_type')=='All')['count'])) |
|
|
|
|
|
|
|
|
df_small_pivot = (df_small.pivot(index=['pitcher_id','pitcher_name'], |
|
|
columns='pitch_type', |
|
|
values='tj_stuff_plus').with_columns( |
|
|
pl.col("pitcher_id").replace_strict(count_dict, default=None).alias("count"))) |
|
|
|
|
|
|
|
|
for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']: |
|
|
if col not in df_small_pivot.columns: |
|
|
df_small_pivot = df_small_pivot.with_columns(pl.lit(None).alias(col)) |
|
|
|
|
|
|
|
|
df_small_pivot = df_small_pivot.with_columns( |
|
|
pl.col("pitcher_id").replace_strict(pitcher_team_dict, default=None).alias("pitcher_team")) |
|
|
|
|
|
df_small_pivot = df_small_pivot.select(['pitcher_name','pitcher_team','count','CH','CU','FC','FF','FS','SI','SL','ST','All']).sort('All',descending=True) |
|
|
|
|
|
df_small_pivot = df_small_pivot.with_columns( |
|
|
pl.col(col).cast(pl.Int32, strict=False) for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All'] |
|
|
) |
|
|
|
|
|
df_export = df_small_pivot.filter(pl.col('count')>=pitch_limit).to_pandas().head(row_limit) |
|
|
df_export.columns = ['Name', 'Team', 'Pitches', 'CH', 'CU', 'FC', |
|
|
'FF', 'FS', 'SI', 'SL', 'ST', 'All'] |
|
|
df_style = df_export.style |
|
|
|
|
|
df_style = df_style.set_properties(**{'border': '1.0 px'},overwrite=False).set_table_styles([{'selector' :'th', |
|
|
'props':[('text-align', 'center'),('font-size', '22px'),('Height','30px'),('border', '1px black solid !important')]}, |
|
|
{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '22px')]}],overwrite=False).set_table_styles( |
|
|
[{'selector': 'tr', 'props': [('line-height', '1px')]}],overwrite=False).set_properties( |
|
|
**{'Height': '60px'},**{'text-align': 'center'},overwrite=False).hide_index() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
df_style = df_style.format('{:.0f}',subset=df_export.columns[3:], na_rep='') |
|
|
|
|
|
|
|
|
|
|
|
df_style = df_style.background_gradient(cmap=cmap_sum,subset = ((list(df_export.index[:]),df_export.columns[3:])),vmin=80,vmax=120) |
|
|
|
|
|
|
|
|
|
|
|
df_style = df_style.applymap(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else '') |
|
|
|
|
|
|
|
|
|
|
|
df_style = df_style.set_properties( |
|
|
**{'border': '1px black solid !important'},subset = ((list(df_style.index[:-1]),df_style.columns[:]))).set_properties( |
|
|
**{'min-width':'325px'},subset = ((list(df_style.index[:-1]),df_style.columns[0])),overwrite=False).set_properties( |
|
|
**{'min-width':'100px'},subset = ((list(df_style.index[:-1]),df_style.columns[1:3])),overwrite=False).set_properties( |
|
|
**{'min-width':'100px'},subset = ((list(df_style.index[:-1]),df_style.columns[3:])),overwrite=False).set_properties( |
|
|
|
|
|
**{'border': '1px black solid !important'},subset = ((list(df_style.index[:]),df_style.columns[:]))) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
return df_style |
|
|
|
|
|
@output |
|
|
@render.table |
|
|
@reactive.event(input.generate_plot, ignore_none=False) |
|
|
def whiff_table(): |
|
|
|
|
|
time_delta_fill = 8 |
|
|
teams = requests.get(url='https://statsapi.mlb.com/api/v1/teams/').json() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlb_teams_city = [x['franchiseName'] if 'franchiseName' in x else None for x in teams['teams']] |
|
|
mlb_teams_name = [x['teamName'] if 'franchiseName' in x else None for x in teams['teams']] |
|
|
mlb_teams_franchise = [x['name'] if 'franchiseName' in x else None for x in teams['teams']] |
|
|
mlb_teams_id = [x['id'] if 'franchiseName' in x else None for x in teams['teams']] |
|
|
mlb_teams_abb = [x['abbreviation'] if 'franchiseName' in x else None for x in teams['teams']] |
|
|
mlb_teams_parent = [x['parentOrgName'] if 'parentOrgName' in x else None for x in teams['teams']] |
|
|
|
|
|
|
|
|
|
|
|
mlb_teams_df = pd.DataFrame(data={'team_id':mlb_teams_id,'city':mlb_teams_franchise,'name':mlb_teams_name,'franchise':mlb_teams_franchise,'abbreviation':mlb_teams_abb,'parent_org':mlb_teams_parent}).drop_duplicates() |
|
|
|
|
|
|
|
|
player_data = requests.get(url=f'https://statsapi.mlb.com/api/v1/sports/{int(input.level_input())}/players').json() |
|
|
|
|
|
|
|
|
fullName_list = [x['fullName'] for x in player_data['people']] |
|
|
id_list = [x['id'] for x in player_data['people']] |
|
|
position_list = [x['primaryPosition']['abbreviation'] for x in player_data['people']] |
|
|
team_list = [x['currentTeam']['id']for x in player_data['people']] |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
df_games = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])], |
|
|
sport_id=[int(input.level_input())], |
|
|
game_type=['S','R','P','E','A','I','W','F','L']).with_columns(pl.col('date').cast(pl.Utf8)). |
|
|
filter(pl.col('date') == str(input.date_input()))).with_columns( |
|
|
(pl.col('away')+' @ '+pl.col('home')).alias('matchup')) |
|
|
|
|
|
|
|
|
|
|
|
game_list = df_games['game_id'].unique().to_list() |
|
|
|
|
|
|
|
|
data_list = scrape.get_data(game_list) |
|
|
df = scrape.get_data_df(data_list = data_list).filter( |
|
|
(pl.col("is_pitch") == True)& |
|
|
(pl.col('batter_hand').is_in(split_dict_hand[input.split_id()])) |
|
|
|
|
|
).with_columns( |
|
|
pl.col('pitch_type').count().over('pitch_type').alias('pitch_count') |
|
|
).with_columns( |
|
|
(pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name')) |
|
|
|
|
|
|
|
|
|
|
|
data = scrape.get_data(game_list[:]) |
|
|
df = scrape.get_data_df(data) |
|
|
|
|
|
player_id = [] |
|
|
|
|
|
team_id = [] |
|
|
|
|
|
for y in range(0,len(data_list)): |
|
|
|
|
|
player_id.append([data_list[y]['liveData']['boxscore']['teams']['away']['players'][x]['person']['id'] if 'parentTeamId' in data_list[y]['liveData']['boxscore']['teams']['away']['players'][x] else None for x in data_list[y]['liveData']['boxscore']['teams']['away']['players']]) |
|
|
player_id.append([data_list[y]['liveData']['boxscore']['teams']['home']['players'][x]['person']['id'] if 'parentTeamId' in data_list[y]['liveData']['boxscore']['teams']['home']['players'][x] else None for x in data_list[y]['liveData']['boxscore']['teams']['home']['players']]) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
team_id.append([data_list[y]['liveData']['boxscore']['teams']['away']['players'][x]['parentTeamId'] if 'parentTeamId' in data_list[y]['liveData']['boxscore']['teams']['away']['players'][x] else None for x in data_list[y]['liveData']['boxscore']['teams']['away']['players']]) |
|
|
team_id.append([data_list[y]['liveData']['boxscore']['teams']['home']['players'][x]['parentTeamId'] if 'parentTeamId' in data_list[y]['liveData']['boxscore']['teams']['home']['players'][x] else None for x in data_list[y]['liveData']['boxscore']['teams']['home']['players']]) |
|
|
|
|
|
|
|
|
player_id_final = [item for sublist in player_id for item in sublist] |
|
|
|
|
|
team_id_final = [item for sublist in team_id for item in sublist] |
|
|
|
|
|
player_list = pd.DataFrame(data={'player_id':player_id_final,'team_id':team_id_final}) |
|
|
|
|
|
player_list = player_list.drop_duplicates(subset=['player_id'],keep='last') |
|
|
player_df_all = player_list.merge(right=mlb_teams_df[['team_id','abbreviation']],left_on = 'team_id',right_on='team_id',how='left').drop_duplicates(keep='last') |
|
|
|
|
|
|
|
|
mlb_teams_df = mlb_teams_df.merge(right=mlb_teams_df[['abbreviation','franchise']],left_on='parent_org',right_on='franchise',how='left').drop_duplicates().reset_index(drop=True) |
|
|
mlb_teams_df = mlb_teams_df[mlb_teams_df.columns[:-1]] |
|
|
|
|
|
mlb_teams_df.columns = ['team_id', 'city', 'name', 'franchise', 'abbreviation', |
|
|
'parent_org', 'parent_org_abb'] |
|
|
|
|
|
|
|
|
pk_list = [] |
|
|
pitcher_id_list = [] |
|
|
summary_list = [] |
|
|
hit_list = [] |
|
|
k_list = [] |
|
|
bb_list = [] |
|
|
pa_list = [] |
|
|
test_list = [] |
|
|
game_pk_list = [] |
|
|
for y in range(0,len(data_list)): |
|
|
|
|
|
pk_list.append([data_list[y]['gameData']['game']['pk'] for x in data_list[y]['liveData']['boxscore']['teams']['away']['pitchers']]) |
|
|
pk_list.append([data_list[y]['gameData']['game']['pk'] for x in data_list[y]['liveData']['boxscore']['teams']['home']['pitchers']]) |
|
|
|
|
|
pitcher_id_list.append([x for x in data_list[y]['liveData']['boxscore']['teams']['away']['pitchers']]) |
|
|
pitcher_id_list.append([x for x in data_list[y]['liveData']['boxscore']['teams']['home']['pitchers']]) |
|
|
|
|
|
|
|
|
|
|
|
summary_list.append([data_list[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['summary'] for x in data_list[y]['liveData']['boxscore']['teams']['away']['pitchers']]) |
|
|
summary_list.append([data_list[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['summary'] for x in data_list[y]['liveData']['boxscore']['teams']['home']['pitchers']]) |
|
|
|
|
|
|
|
|
|
|
|
hit_list.append([data_list[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['hits'] for x in data_list[y]['liveData']['boxscore']['teams']['away']['pitchers']]) |
|
|
hit_list.append([data_list[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['hits'] for x in data_list[y]['liveData']['boxscore']['teams']['home']['pitchers']]) |
|
|
|
|
|
k_list.append([data_list[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['strikeOuts'] for x in data_list[y]['liveData']['boxscore']['teams']['away']['pitchers']]) |
|
|
k_list.append([data_list[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['strikeOuts'] for x in data_list[y]['liveData']['boxscore']['teams']['home']['pitchers']]) |
|
|
|
|
|
bb_list.append([data_list[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['baseOnBalls'] for x in data_list[y]['liveData']['boxscore']['teams']['away']['pitchers']]) |
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bb_list.append([data_list[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['baseOnBalls'] for x in data_list[y]['liveData']['boxscore']['teams']['home']['pitchers']]) |
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pa_list.append([data_list[y]['liveData']['boxscore']['teams']['away']['players']['ID'+str(x)]['stats']['pitching']['battersFaced'] for x in data_list[y]['liveData']['boxscore']['teams']['away']['pitchers']]) |
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pa_list.append([data_list[y]['liveData']['boxscore']['teams']['home']['players']['ID'+str(x)]['stats']['pitching']['battersFaced'] for x in data_list[y]['liveData']['boxscore']['teams']['home']['pitchers']]) |
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test_list.append([x for x in data_list[y]['liveData']['plays']['allPlays']]) |
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game_pk_list.append([data_list[y]['gameData']['game']['pk'] for x in data_list[y]['liveData']['plays']['allPlays']]) |
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flat_list = [item for sublist in test_list for item in sublist] |
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flat_list_games = [item for sublist in game_pk_list for item in sublist] |
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test_list_2 = [x['playEvents'] for x in flat_list] |
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test_list_3 = [x['matchup'] for x in flat_list] |
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flat_list_pk = [item for sublist in pk_list for item in sublist] |
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flat_list_pitcher_id = [item for sublist in pitcher_id_list for item in sublist] |
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flat_list_summary = [item for sublist in summary_list for item in sublist] |
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flat_list_hits = [item for sublist in hit_list for item in sublist] |
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flat_list_k = [item for sublist in k_list for item in sublist] |
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flat_list_bb = [item for sublist in bb_list for item in sublist] |
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flat_list_pa = [item for sublist in pa_list for item in sublist] |
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pitcher_summary_df = pd.DataFrame(data={'game_id':flat_list_pk,'pitcher_id':flat_list_pitcher_id,'summary':flat_list_summary,'hits':flat_list_hits,'k':flat_list_k,'bb':flat_list_bb,'pa':flat_list_pa}) |
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pitcher_summary_df.summary = pitcher_summary_df.summary + ', ' + pitcher_summary_df.hits.astype(str) + ' H' |
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pitcher_summary_df['k_bb_percent'] = (pitcher_summary_df.k - pitcher_summary_df.bb) / (pitcher_summary_df.pa) |
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game_id_list = [] |
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bat_play_id = [] |
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batter_id_list = [] |
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batter_name_list = [] |
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date_list = [] |
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desc_list = [] |
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code_list = [] |
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pitch_list = [] |
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pitch_type_code_list = [] |
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pitch_type_list = [] |
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pitch_velo_list = [] |
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pitch_break_length_list = [] |
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pitch_break_angle_list = [] |
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pitch_ivb_list = [] |
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pitch_spin_list = [] |
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pitch_spin_direction_list = [] |
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launchSpeed_list = [] |
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launchAngle_list = [] |
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trajectory_list = [] |
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hardness_list = [] |
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pitcher_id_list = [] |
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pitcher_name_list = [] |
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k_zone_top = [] |
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k_zone_bottom = [] |
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pitch_x = [] |
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pitch_z = [] |
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zone_list = [] |
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pitch_x_what = [] |
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pitch_y_what = [] |
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from datetime import datetime |
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from datetime import timedelta |
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for i in range(0,len(test_list_2)): |
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for j in range(0,len(test_list_2[i])): |
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if 'playId' in test_list_2[i][j]: |
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game_id_list.append(flat_list_games[i]) |
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bat_play_id.append(str(flat_list_games[i])+str(1000+flat_list[i]['about']['atBatIndex'])+str(1000+test_list_2[i][j]['index'])) |
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batter_id_list.append(test_list_3[i]['batter']['id']) |
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batter_name_list.append(test_list_3[i]['batter']['fullName']) |
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pitcher_id_list.append(test_list_3[i]['pitcher']['id']) |
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pitcher_name_list.append(test_list_3[i]['pitcher']['fullName']) |
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date_list.append((datetime.strptime(test_list_2[i][j]['startTime'][0:16], '%Y-%m-%dT%H:%M') - timedelta(hours=8)).date()) |
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desc_list.append(test_list_2[i][j]['details']['description'] if 'description' in test_list_2[i][j]['details'] else np.nan) |
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code_list.append(test_list_2[i][j]['details']['code'] if 'code' in test_list_2[i][j]['details'] else np.nan) |
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if 'pitchData' in test_list_2[i][j]: |
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pitch_type_code_list.append(test_list_2[i][j]['details']['type']['description'] if 'type' in test_list_2[i][j]['details'] else np.nan) |
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pitch_type_list.append(test_list_2[i][j]['details']['type']['code'] if 'type' in test_list_2[i][j]['details'] else np.nan) |
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pitch_velo_list.append(test_list_2[i][j]['pitchData']['startSpeed'] if 'startSpeed' in test_list_2[i][j]['pitchData'] else np.nan) |
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pitch_break_length_list.append(test_list_2[i][j]['pitchData']['coordinates']['pfxX'] if 'pfxX' in test_list_2[i][j]['pitchData']['coordinates'] else np.nan) |
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pitch_break_angle_list.append(test_list_2[i][j]['pitchData']['breaks']['breakAngle'] if 'breakAngle' in test_list_2[i][j]['pitchData']['breaks'] else np.nan) |
|
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pitch_ivb_list.append(test_list_2[i][j]['pitchData']['coordinates']['pfxZ'] if 'pfxZ' in test_list_2[i][j]['pitchData']['coordinates'] else np.nan) |
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pitch_spin_list.append(test_list_2[i][j]['pitchData']['breaks']['spinRate'] if 'spinRate' in test_list_2[i][j]['pitchData']['breaks'] else np.nan) |
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pitch_spin_direction_list.append(test_list_2[i][j]['pitchData']['breaks']['spinDirection'] if 'spinDirection' in test_list_2[i][j]['pitchData']['breaks'] else np.nan) |
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k_zone_top.append(test_list_2[i][j]['pitchData']['strikeZoneTop'] if 'strikeZoneTop' in test_list_2[i][j]['pitchData'] else np.nan) |
|
|
k_zone_bottom.append(test_list_2[i][j]['pitchData']['strikeZoneBottom'] if 'strikeZoneBottom' in test_list_2[i][j]['pitchData'] else np.nan) |
|
|
pitch_x.append(test_list_2[i][j]['pitchData']['coordinates']['pX'] if 'pX' in test_list_2[i][j]['pitchData']['coordinates'] else np.nan) |
|
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pitch_z.append(test_list_2[i][j]['pitchData']['coordinates']['pZ'] if 'pZ' in test_list_2[i][j]['pitchData']['coordinates'] else np.nan) |
|
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zone_list.append(test_list_2[i][j]['pitchData']['zone'] if 'zone' in test_list_2[i][j]['pitchData'] else np.nan) |
|
|
pitch_x_what.append(test_list_2[i][j]['pitchData']['coordinates']['x'] if 'x' in test_list_2[i][j]['pitchData']['coordinates'] else np.nan) |
|
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pitch_y_what.append(test_list_2[i][j]['pitchData']['coordinates']['y'] if 'y' in test_list_2[i][j]['pitchData']['coordinates'] else np.nan) |
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else: |
|
|
pitch_velo_list.append(np.nan) |
|
|
pitch_type_code_list.append(np.nan) |
|
|
pitch_type_list.append(np.nan) |
|
|
pitch_break_length_list.append(np.nan) |
|
|
pitch_break_angle_list.append(np.nan) |
|
|
pitch_ivb_list.append(np.nan) |
|
|
pitch_spin_list.append(np.nan) |
|
|
pitch_spin_direction_list.append(np.nan) |
|
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|
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k_zone_top.append(np.nan) |
|
|
k_zone_bottom.append(np.nan) |
|
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pitch_x.append(np.nan) |
|
|
pitch_z.append(np.nan) |
|
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zone_list.append(np.nan) |
|
|
pitch_x_what.append(np.nan) |
|
|
pitch_y_what.append(np.nan) |
|
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|
|
if 'hitData' in test_list_2[i][j]: |
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launchSpeed_list.append(test_list_2[i][j]['hitData']['launchSpeed'] if 'launchSpeed' in test_list_2[i][j]['hitData'] else np.nan) |
|
|
launchAngle_list.append(test_list_2[i][j]['hitData']['launchAngle'] if 'launchAngle' in test_list_2[i][j]['hitData'] else np.nan) |
|
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else: |
|
|
launchSpeed_list.append(np.nan) |
|
|
launchAngle_list.append(np.nan) |
|
|
|
|
|
exit_velo_df = pd.DataFrame(data={'play_id':bat_play_id, |
|
|
'game_id':game_id_list, |
|
|
'date':date_list, |
|
|
'pitcher_id':pitcher_id_list, |
|
|
'pitcher':pitcher_name_list, |
|
|
'pitch_code':pitch_type_code_list, |
|
|
'pitch_type':pitch_type_list, |
|
|
'pitch_velocity':pitch_velo_list, |
|
|
'break_length':pitch_break_length_list, |
|
|
'break_angle':pitch_break_angle_list, |
|
|
'break_ivb':pitch_ivb_list, |
|
|
'spin_rate':pitch_spin_list, |
|
|
'spin_direction':pitch_spin_direction_list, |
|
|
'batter_id':batter_id_list, |
|
|
'batter':batter_name_list, |
|
|
'code':code_list, |
|
|
'description':desc_list, |
|
|
'launch_speed':launchSpeed_list, |
|
|
'launch_angle':launchAngle_list, |
|
|
|
|
|
'k_zone_top':k_zone_top, |
|
|
'k_zone_bottom':k_zone_bottom, |
|
|
'pitch_x':pitch_x, |
|
|
'pitch_z':pitch_z, |
|
|
'zone':zone_list, |
|
|
'pitch_x_what':pitch_x_what, |
|
|
'pitch_y_what':pitch_y_what, |
|
|
}) |
|
|
|
|
|
exit_velo_df['plate_negative'] = -17/12/2 |
|
|
exit_velo_df['plate_positive'] = 17/12/2 |
|
|
|
|
|
exit_velo_df[['k_zone_top','k_zone_bottom']] = exit_velo_df.groupby('batter_id')[['k_zone_top','k_zone_bottom']].transform('mean') |
|
|
exit_velo_df.play_id = exit_velo_df.play_id.astype(float) |
|
|
exit_velo_df = exit_velo_df.drop_duplicates(subset=['play_id'],keep='last').reset_index(drop=True) |
|
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|
|
exit_velo_df = exit_velo_df.drop_duplicates(subset='play_id',keep='last') |
|
|
print('check the length') |
|
|
print(len(exit_velo_df)) |
|
|
|
|
|
|
|
|
player_id = [] |
|
|
|
|
|
team_id = [] |
|
|
|
|
|
for y in range(0,len(data_list)): |
|
|
|
|
|
player_id.append([data_list[y]['liveData']['boxscore']['teams']['away']['players'][x]['person']['id'] if 'parentTeamId' in data_list[y]['liveData']['boxscore']['teams']['away']['players'][x] else None for x in data_list[y]['liveData']['boxscore']['teams']['away']['players']]) |
|
|
player_id.append([data_list[y]['liveData']['boxscore']['teams']['home']['players'][x]['person']['id'] if 'parentTeamId' in data_list[y]['liveData']['boxscore']['teams']['home']['players'][x] else None for x in data_list[y]['liveData']['boxscore']['teams']['home']['players']]) |
|
|
|
|
|
|
|
|
|
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|
|
team_id.append([data_list[y]['liveData']['boxscore']['teams']['away']['players'][x]['parentTeamId'] if 'parentTeamId' in data_list[y]['liveData']['boxscore']['teams']['away']['players'][x] else None for x in data_list[y]['liveData']['boxscore']['teams']['away']['players']]) |
|
|
team_id.append([data_list[y]['liveData']['boxscore']['teams']['home']['players'][x]['parentTeamId'] if 'parentTeamId' in data_list[y]['liveData']['boxscore']['teams']['home']['players'][x] else None for x in data_list[y]['liveData']['boxscore']['teams']['home']['players']]) |
|
|
|
|
|
game_id_pk_list = flat_list_games |
|
|
play_id_list_full = [str(game_id_pk_list[i])+str(1000+flat_list[i]['about']['atBatIndex'])+str(1000+flat_list[i]['playEvents'][-1]['index']) if (len(flat_list[i]['playEvents'])>0 and 'playEvents' in flat_list[i] and 'startTime' in flat_list[i]['playEvents'][-1]) else np.nan for i in range(len(flat_list))] |
|
|
date_list = [(datetime.strptime(flat_list[i]['about']['startTime'][0:16], '%Y-%m-%dT%H:%M') - timedelta(hours=8)).date() if 'startTime' in flat_list[i]['about'] else np.nan for i in range(len(flat_list))] |
|
|
time_list = [flat_list[i]['playEvents'][-1]['startTime'] if (len(flat_list[i]['playEvents'])>0 and 'playEvents' in flat_list[i] and 'startTime' in flat_list[i]['playEvents'][-1]) else np.nan for i in range(len(flat_list))] |
|
|
type_list = [flat_list[i]['result']['type'] if 'type' in flat_list[i]['result'] else np.nan for i in range(len(flat_list))] |
|
|
eventType_list = [flat_list[i]['result']['eventType'] if 'eventType' in flat_list[i]['result'] else np.nan for i in range(len(flat_list))] |
|
|
batter_id_type_list = [flat_list[i]['matchup']['batter']['id'] if 'id' in flat_list[i]['matchup']['batter'] else np.nan for i in range(len(flat_list))] |
|
|
batter_name_type_list = [flat_list[i]['matchup']['batter']['fullName'] if 'fullName' in flat_list[i]['matchup']['batter'] else np.nan for i in range(len(flat_list))] |
|
|
pitcher_id_type_list = [flat_list[i]['matchup']['pitcher']['id'] if 'id' in flat_list[i]['matchup']['pitcher'] else np.nan for i in range(len(flat_list))] |
|
|
pitcher_name_type_list = [flat_list[i]['matchup']['pitcher']['fullName'] if 'fullName' in flat_list[i]['matchup']['pitcher'] else np.nan for i in range(len(flat_list))] |
|
|
|
|
|
is_out_id_type_list = [flat_list[i]['playEvents'][-1]['details']['isInPlay'] if (len(flat_list[i]['playEvents'])>0 and 'playEvents' in flat_list[i] and 'isInPlay' in flat_list[i]['playEvents'][-1]['details']) else np.nan for i in range(len(flat_list))] |
|
|
is_in_play_id_type_list = [flat_list[i]['playEvents'][-1]['details']['isOut'] if (len(flat_list[i]['playEvents'])>0 and 'playEvents' in flat_list[i] and 'isInPlay' in flat_list[i]['playEvents'][-1]['details']) else np.nan for i in range(len(flat_list))] |
|
|
complete_id_type_list = [flat_list[i]['about']['isComplete'] if 'isComplete' in flat_list[i]['about'] else np.nan for i in range(len(flat_list))] |
|
|
|
|
|
pa_df = pd.DataFrame(data={'game_id':game_id_pk_list,'play_id':play_id_list_full,'date':date_list,'start_time':time_list,'type':type_list,'event_type':eventType_list,'batter_id':batter_id_type_list, |
|
|
'batter_name':batter_name_type_list,'pitcher_id':pitcher_id_type_list,'pitcher_name':pitcher_name_type_list, |
|
|
'is_out':is_out_id_type_list,'is_in_play':is_in_play_id_type_list,'complete_id':complete_id_type_list}) |
|
|
|
|
|
pa_df = pa_df.dropna(subset=['play_id']).drop_duplicates(subset='play_id',keep='last').reset_index(drop=True) |
|
|
|
|
|
|
|
|
if os.path.isfile('pa_df.csv'): |
|
|
pa_df_full = pd.read_csv('pa_df.csv',index_col=[0]) |
|
|
pa_df = pd.concat([pa_df_full,pa_df]).dropna(subset=['play_id']).drop_duplicates(subset=['play_id'],keep='last').reset_index(drop=True) |
|
|
|
|
|
|
|
|
player_id_final = [item for sublist in player_id for item in sublist] |
|
|
|
|
|
team_id_final = [item for sublist in team_id for item in sublist] |
|
|
|
|
|
player_list = pd.DataFrame(data={'player_id':player_id_final,'team_id':team_id_final}) |
|
|
|
|
|
player_list = player_list.drop_duplicates(subset=['player_id'],keep='last') |
|
|
player_df_all = player_list.merge(right=mlb_teams_df[['team_id','abbreviation']],left_on = 'team_id',right_on='team_id',how='left').drop_duplicates(keep='last') |
|
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|
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|
|
|
codes_in = ['In play, out(s)', |
|
|
'Swinging Strike', |
|
|
'Ball', |
|
|
'Foul', |
|
|
'In play, no out', |
|
|
'Called Strike', |
|
|
'Foul Tip', |
|
|
'In play, run(s)', |
|
|
'Hit By Pitch', |
|
|
'Ball In Dirt', |
|
|
'Pitchout', |
|
|
'Swinging Strike (Blocked)', |
|
|
'Foul Bunt', |
|
|
'Missed Bunt', |
|
|
'Foul Pitchout', |
|
|
'Intent Ball', |
|
|
'Swinging Pitchout'] |
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
exit_velo_df_copy = exit_velo_df.copy() |
|
|
|
|
|
exit_velo_df_copy = exit_velo_df_copy[exit_velo_df_copy['description'].isin(codes_in)] |
|
|
exit_velo_df_copy['pitch'] = exit_velo_df_copy.groupby('pitcher_id').cumcount() + 1 |
|
|
|
|
|
exit_velo_df_copy['pitch_type_count'] = exit_velo_df_copy.groupby(['pitcher_id','pitch_type']).cumcount() + 1 |
|
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|
|
exit_velo_df_batter = exit_velo_df.copy() |
|
|
exit_velo_df_batter = exit_velo_df_batter.merge(right=player_df_all,left_on='batter_id',right_on='player_id',how='left',suffixes=('','_y')) |
|
|
|
|
|
exit_velo_df = exit_velo_df.merge(right=player_df_all,left_on='pitcher_id',right_on='player_id',how='left',suffixes=('','_y')).merge(right=player_df_all,left_on='batter_id',right_on='player_id',how='left',suffixes=('','_batter')) |
|
|
|
|
|
codes_in = ['In play, out(s)', |
|
|
'Swinging Strike', |
|
|
'Ball', |
|
|
'Foul', |
|
|
'In play, no out', |
|
|
'Called Strike', |
|
|
'Foul Tip', |
|
|
'In play, run(s)', |
|
|
'Hit By Pitch', |
|
|
'Ball In Dirt', |
|
|
'Pitchout', |
|
|
'Swinging Strike (Blocked)', |
|
|
'Foul Bunt', |
|
|
'Missed Bunt'] |
|
|
|
|
|
exit_velo_df.date = pd.to_datetime(exit_velo_df.date).dt.date |
|
|
|
|
|
pitch_df = exit_velo_df[exit_velo_df['description'].isin(codes_in)].groupby(['pitcher_id','pitcher','game_id','abbreviation','abbreviation_batter','date']).agg( |
|
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pitches = ('pitcher_id','count'), |
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).reset_index().sort_values(by='pitches',ascending=False) |
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whiff_df = exit_velo_df[((exit_velo_df.code == 'S')|(exit_velo_df.code == 'W')|(exit_velo_df.code =='T'))].groupby(['pitcher_id','pitcher','game_id','abbreviation','abbreviation_batter','date']).agg( |
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whiffs = ('pitcher_id','count'), |
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).reset_index().sort_values(by='whiffs',ascending=False) |
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csw_df = exit_velo_df[((exit_velo_df.code == 'S')|(exit_velo_df.code == 'W')|(exit_velo_df.code =='T')|(exit_velo_df.code =='C'))].groupby(['pitcher_id','pitcher','game_id','abbreviation','abbreviation_batter','date']).agg( |
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csw = ('pitcher_id','count'), |
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).reset_index().sort_values(by='csw',ascending=False) |
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pitch_df_batter = exit_velo_df[exit_velo_df['description'].isin(codes_in)].groupby(['abbreviation_batter']).agg( |
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pitches = ('pitcher_id','count'), |
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).reset_index().sort_values(by='abbreviation_batter',ascending=True) |
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whiff_df_batter = exit_velo_df[((exit_velo_df.code == 'S')|(exit_velo_df.code == 'W')|(exit_velo_df.code =='T'))].groupby(['abbreviation_batter']).agg( |
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whiffs = ('pitcher_id','count'), |
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).reset_index().sort_values(by='abbreviation_batter',ascending=True) |
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csw_df_batter = exit_velo_df[((exit_velo_df.code == 'S')|(exit_velo_df.code == 'W')|(exit_velo_df.code =='T')|(exit_velo_df.code =='C'))].groupby(['abbreviation_batter']).agg( |
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csw = ('pitcher_id','count'), |
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).reset_index().sort_values(by='abbreviation_batter',ascending=True) |
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pitch_df_batter = pitch_df_batter.merge(whiff_df_batter,how='left').merge(csw_df_batter,how='left').fillna(0) |
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pitch_df_batter['whiffs'] = pitch_df_batter['whiffs'].astype(int) |
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pitch_df_batter['csw'] = pitch_df_batter['csw'].astype(int) |
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pitch_df_batter['whiff_rate'] = pitch_df_batter.whiffs/pitch_df_batter.pitches |
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pitch_df_batter['csw_rate'] = pitch_df_batter.csw/pitch_df_batter.pitches |
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exit_velo_df.date = pd.to_datetime(exit_velo_df.date).dt.date |
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starter_list = exit_velo_df[['pitcher_id','pitcher','game_id','abbreviation']].drop_duplicates(subset=['game_id','abbreviation']).reset_index(drop=True) |
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print('Creating df:') |
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print('Creating df:') |
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pitch_whiff_df = pitch_df.merge(whiff_df,how='left').merge(csw_df,how='left').fillna(0) |
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pitch_whiff_df.date = pd.to_datetime(pitch_whiff_df.date).dt.date |
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pitch_whiff_df['whiffs'] = pitch_whiff_df['whiffs'].astype(int) |
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pitch_whiff_df['csw'] = pitch_whiff_df['csw'].astype(int) |
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pitch_whiff_df['whiff_rate'] = pitch_whiff_df.whiffs/pitch_whiff_df.pitches |
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pitch_whiff_df['csw_rate'] = pitch_whiff_df.csw/pitch_whiff_df.pitches |
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pitch_whiff_df = pitch_whiff_df.sort_values(by='whiffs',ascending=False).reset_index(drop=True) |
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pitch_whiff_df['rank'] = pitch_whiff_df['whiffs'].rank(ascending=False,method='min').astype(int) |
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pitch_whiff_df = pitch_whiff_df.merge(right=pitcher_summary_df,left_on=['pitcher_id','game_id'],right_on=['pitcher_id','game_id'],how='left') |
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pitch_whiff_df = pitch_whiff_df[['rank','pitcher','abbreviation','abbreviation_batter','pitches','whiffs','whiff_rate','csw','csw_rate']] |
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pitch_whiff_df.columns = ['Rank','Pitcher','Team','Opp.','Pitches','Whiffs','SwStr%','CSW','CSW%'] |
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print(pitch_whiff_df) |
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top_d_score = pitch_df.merge(whiff_df,how='left').merge(csw_df,how='left').fillna(0) |
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top_d_score = pitch_df.merge(whiff_df,how='left').merge(csw_df,how='left').fillna(0) |
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top_d_score.date = pd.to_datetime(top_d_score.date).dt.date |
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top_d_score['whiffs'] = top_d_score['whiffs'].astype(int) |
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top_d_score['csw'] = top_d_score['csw'].astype(int) |
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top_d_score['whiff_rate'] = top_d_score.whiffs/top_d_score.pitches |
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top_d_score['csw_rate'] = top_d_score.csw/top_d_score.pitches |
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top_d_score = top_d_score.merge(right=pitcher_summary_df,left_on=['pitcher_id','game_id'],right_on=['pitcher_id','game_id']) |
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top_d_score.summary = top_d_score.summary.str.replace(', ER', ', 1 ER') |
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top_d_score.summary = top_d_score.summary.str.replace(', K', ', 1 K') |
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top_d_score.summary = top_d_score.summary.str.replace(', BB', ', 1 BB') |
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top_d_score = top_d_score.merge(right=pitch_df_batter[['abbreviation_batter','csw_rate']],left_on='abbreviation_batter',right_on='abbreviation_batter',how='left',suffixes=['','_opp']) |
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top_d_score['diff'] = top_d_score.csw_rate - top_d_score.csw_rate_opp |
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top_d_score = top_d_score.sort_values(by=['whiffs','csw_rate'],ascending=False).reset_index(drop=True) |
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top_d_score['rank'] = top_d_score['whiffs'].rank(ascending=False,method='min').astype(int) |
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pitcher_to_select = top_d_score['pitcher_id'].values[0] |
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top_d_score = top_d_score[['rank','pitcher','abbreviation','abbreviation_batter','pitches','whiffs','whiff_rate','csw','csw_rate','k_bb_percent','summary']] |
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top_d_score.columns = ['Rank','Pitcher','Team','Opp.','Pitches','Whiffs','SwStr%','CSW','CSW%','K-BB%','Summary'] |
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cols = top_d_score.columns.tolist(); |
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df_combined_t_style = top_d_score.style.set_properties(**{'border': '0.4 px'},overwrite=False).set_caption('MLB Daily Whiff Leaders - ' + str((datetime.today()-timedelta(hours=time_delta_fill)).date())).set_table_styles([{ |
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'selector': 'caption', |
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'props': [ |
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('color', ''), |
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('fontname', 'Century Gothic'), |
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('font-size', '24px'), |
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('font-style', 'italic'), |
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('font-weight', ''), |
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('text-align', 'centre'), |
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] |
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},{'selector' :'th', 'props':[('text-align', 'center'),('Height','24px'),('border', '0.4px black solid !important'),('font-size', '16px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '16px')]}],overwrite=False).set_table_styles( |
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[{'selector': 'tr', 'props': [('line-height', '0.4px')]}],overwrite=False).set_properties( |
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**{'Height': '24px'},**{'text-align': 'center'},overwrite=False).hide_index() |
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cmap_sum_2 = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"]) |
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df_combined_t_style = df_combined_t_style.background_gradient(cmap=cmap_sum_2,subset = ((list(top_d_score.index[:]),top_d_score.columns[5]))) |
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df_combined_t_style = df_combined_t_style.background_gradient(cmap=cmap_sum,subset = ((list(top_d_score.index[:]),top_d_score.columns[8])),vmax=.40,vmin=.20) |
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df_combined_t_style = df_combined_t_style.background_gradient(cmap=cmap_sum,subset = ((list(top_d_score.index[:]),top_d_score.columns[6])),vmax=.20,vmin=.05) |
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df_combined_t_style = df_combined_t_style.background_gradient(cmap=cmap_sum,subset = ((list(top_d_score.index[:]),top_d_score.columns[-2])),vmax=0.4,vmin=-.1) |
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def cond_formatting_csw(x): |
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if x > 0.45: |
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return 'background-color: #d4af37' |
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else: |
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return None |
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def cond_formatting_whiff(x): |
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if x > 0.25: |
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return 'background-color: #d4af37' |
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else: |
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return None |
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def cond_formatting_diff(x): |
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if x > 0.15: |
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return 'background-color: #d4af37' |
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else: |
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return None |
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df_combined_t_style = df_combined_t_style.applymap(cond_formatting_csw,subset=((list(top_d_score.index[:]),top_d_score.columns[8]))) |
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df_combined_t_style = df_combined_t_style.applymap(cond_formatting_whiff,subset=((list(top_d_score.index[:]),top_d_score.columns[6]))) |
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df_combined_t_style = df_combined_t_style.format( |
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{df_combined_t_style.columns[6]: '{:,.1%}'.format, |
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df_combined_t_style.columns[5]: '{:,.0f}'.format, |
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df_combined_t_style.columns[8]: '{:,.1%}'.format, |
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df_combined_t_style.columns[7]: '{:,.0f}'.format, |
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df_combined_t_style.columns[-2]:'{0:.1%}'.format, |
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}) |
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df_combined_t_style = df_combined_t_style.set_properties( |
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**{'border': '0.4px black solid !important'},subset = ((list(top_d_score.index[:-1]),top_d_score.columns[:]))).set_properties( |
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**{'min-width':'75px'},subset = ((list(top_d_score.index[:-1]),top_d_score.columns[1])),overwrite=False).set_properties( |
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**{'min-width':'50px'},subset = ((list(top_d_score.index[:-1]),top_d_score.columns[2:])),overwrite=False).set_properties( |
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**{'min-width':'75px'},subset = ((list(top_d_score.index[:-1]),top_d_score.columns[-1])),overwrite=False).set_properties( |
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**{'border': '0.4px black solid !important'},subset = ((list(top_d_score.index[:]),top_d_score.columns[:]))) |
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return df_combined_t_style |
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app = App(app_ui, server) |
