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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import pitch_summary_functions as psf |
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import requests |
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import matplotlib |
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from api_scraper import MLB_Scrape |
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from shinywidgets import output_widget, render_widget |
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import shinyswatch |
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colour_palette = ['#FFB000','#648FFF','#785EF0', |
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'#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED'] |
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import datasets |
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from datasets import load_dataset |
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dataset = load_dataset('nesticot/mlb_data', data_files=['a_pitch_data_2024.csv' ]) |
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dataset_train = dataset['train'] |
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df_2024 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True).drop_duplicates(subset=['play_id'],keep='last') |
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pitch_colours = { |
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'Four-Seam Fastball':'#FF007D', |
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'Fastball':'#FF007D', |
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'Sinker':'#98165D', |
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'Cutter':'#BE5FA0', |
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'Changeup':'#F79E70', |
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'Splitter':'#FE6100', |
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'Screwball':'#F08223', |
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'Forkball':'#FFB000', |
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'Slider':'#67E18D', |
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'Sweeper':'#1BB999', |
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'Slurve':'#376748', |
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'Knuckle Curve':'#311D8B', |
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'Curveball':'#3025CE', |
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'Slow Curve':'#274BFC', |
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'Eephus':'#648FFF', |
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'Knuckle Ball':'#867A08', |
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'Pitch Out':'#472C30', |
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'Other':'#9C8975', |
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} |
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spring_teams = df_2024.groupby(['pitcher_id']).tail(1)[['pitcher_id','pitcher_team']].set_index(['pitcher_id'])['pitcher_team'].to_dict() |
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season_start = '2024-03-20' |
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season_end = '2024-09-29' |
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season_fg=2024 |
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statcast_pitch_summary = pd.read_csv('statcast_pitch_summary.csv') |
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cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',]) |
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df_2024_codes = psf.df_update_code(df_2024) |
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df_2024_update = psf.df_clean(df_2024_codes) |
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import joblib |
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model = joblib.load('joblib_model/tjstuff_model_20240428.joblib') |
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from stuff_values_20240428 import my_dict |
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y_pred_mean = my_dict['y_pred_mean'] |
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y_pred_std = my_dict['y_pred_std'] |
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xwoba_model = joblib.load('joblib_model/xwoba_model.joblib') |
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features = ['start_speed','spin_rate','log_extension','ivb','hb','x0','z0','primary_mean_velo_diff','primary_mean_ivb_diff','primary_mean_hb_diff'] |
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targets = ['delta_run_exp_mean'] |
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df_2024_update['y_pred'] = model.predict(df_2024_update[features]) |
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df_2024_update['tj_stuff_plus'] = 100 + 10*((-df_2024_update.y_pred +y_pred_mean) / y_pred_std) |
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df_2024_update['woba_pred'] = np.nan |
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df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred'] = [sum(x) for x in xwoba_model.predict_proba(df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])] |
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pitcher_dicts = df_2024_update.set_index('pitcher_id')['pitcher_name'].sort_values().to_dict() |
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team_logos = pd.read_csv('team_logos.csv') |
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mlb_stats = MLB_Scrape() |
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teams_df = mlb_stats.get_teams() |
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team_logo_dict = teams_df.set_index(['team_id'])['parent_org_id'].to_dict() |
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font_properties = {'family': 'calibi', 'size': 12} |
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font_properties_titles = {'family': 'calibi', 'size': 20} |
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font_properties_axes = {'family': 'calibi', 'size': 16} |
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df_plot = [] |
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ax2_loc = [] |
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gs = [] |
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fig = [] |
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function_dict={ |
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'velocity_kde':'Velocity Distributions', |
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'break_plot':'Pitch Movement', |
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'rolling_tj_stuff':'Rolling tjStuff+', |
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'location_lhb':'Locations vs LHB', |
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'location_rhb':'Locations vs RHB', |
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} |
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split_dict = {'all':'All', |
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'left':'LHB', |
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'right':'RHB'} |
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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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ball_dict = {'0':'0', |
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'1':'1', |
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'2':'2', |
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'3':'3'} |
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strike_dict = {'0':'0', |
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'1':'1', |
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'2':'2'} |
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from urllib.request import Request, urlopen |
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from shiny import App, reactive, ui, render |
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from shiny.ui import h2, tags |
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app_ui = ui.page_fluid( |
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ui.tags.div( |
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{"style": "width:90%;margin: 0 auto;max-width: 1600px;"}, |
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ui.tags.style( |
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""" |
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h4 { |
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margin-top: 1em;font-size:35px; |
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} |
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h2{ |
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font-size:25px; |
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} |
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""" |
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), |
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shinyswatch.theme.simplex(), |
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ui.tags.h4("TJStats"), |
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ui.tags.i("Baseball Analytics and Visualizations"), |
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ui.row( |
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ui.layout_sidebar( |
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ui.panel_sidebar( |
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ui.row( |
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ui.column(6, |
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ui.input_select('player_id','Select Player',pitcher_dicts,selectize=True,multiple=False)), |
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ui.column(6, ui.output_ui('test','Select Game'))), |
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ui.row( |
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ui.column(4, |
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ui.input_select('plot_id_1','Plot Left',function_dict,multiple=False,selected='velocity_kde')), |
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ui.column(4, |
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ui.input_select('plot_id_2','Plot Middle',function_dict,multiple=False,selected='rolling_tj_stuff')), |
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ui.column(4, |
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ui.input_select('plot_id_3','Plot Right',function_dict,multiple=False,selected='break_plot'))), |
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ui.row( |
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ui.column(6, |
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ui.input_select('ball_id','Balls',ball_dict,multiple=False,selected='0'), |
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ui.input_radio_buttons( |
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"count_id_balls", |
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"Count Filter Balls", |
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{ |
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"exact": "Exact Balls", |
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"greater": ">= Balls", |
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"lesser": "<= Balls", |
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},selected='greater')), |
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ui.column(6, |
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ui.input_select('strike_id','Strikes',strike_dict,multiple=False,selected='0'), |
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ui.input_radio_buttons( |
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"count_id_strikes", |
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"Count Filter Strikes", |
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{ |
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"exact": "Exact Strikes", |
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"greater": ">= Strikes", |
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"lesser": "<= Strikes", |
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},selected='greater'))), |
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ui.row( |
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ui.column(6, |
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ui.input_select('split_id','Select Split',split_dict,multiple=False)), |
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ui.column(6, |
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ui.input_numeric('rolling_window','Rolling Window (for tjStuff+ Plot)',min=1,value=10))), |
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ui.input_action_button("go", "Generate",class_="btn-primary"), |
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width=4) |
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, |
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ui.panel_main( |
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ui.navset_tab( |
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ui.nav("Season Summary", |
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ui.output_plot('plot', |
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width='2000px', |
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height='2000px')), |
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ui.nav("Game Summary", |
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ui.output_plot('plot_game', |
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width='2000px', |
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height='2000px')) |
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,id="my_tabs")))))) |
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def server(input, output, session): |
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@render.ui |
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def test(): |
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if input.my_tabs() == 'Season Summary': |
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return ui.input_date_range("date_range_id", "Date range input",start = df_2024.game_date.min(), |
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end = df_2024.game_date.max(),width=2,min=df_2024.game_date.min(), |
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max=df_2024.game_date.max()), |
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if input.my_tabs() == 'Game Summary': |
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pitcher_id_select = int(input.player_id()) |
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df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)] |
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df_plot['game_opp'] = df_plot['game_date'].astype(str) + ' vs ' + df_plot['batter_team'].astype(str) |
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date_dict = pd.concat([df_plot.drop_duplicates(subset=['pitcher_id','game_id','game_opp'])[['game_id','game_opp']]]).set_index('game_id').to_dict() |
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return ui.input_select("game_id", "Select Game",date_dict,selectize=True) |
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@output |
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@render.plot |
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@reactive.event(input.go, ignore_none=False) |
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def plot(): |
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font_properties = {'family': 'calibi', 'size': 12} |
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font_properties_titles = {'family': 'calibi', 'size': 20} |
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font_properties_axes = {'family': 'calibi', 'size': 16} |
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if len((input.player_id()))<1: |
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fig, ax = plt.subplots(1, 1, figsize=(9, 9)) |
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ax.text(x=0.5,y=0.5,s='Please Select\nA Player',fontsize=150,ha='center') |
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ax.grid('off') |
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return |
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pitcher_id_select = int(input.player_id()) |
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df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)] |
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df_plot = df_plot[(pd.to_datetime(df_plot['game_date']).dt.date>=input.date_range_id()[0])& |
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(pd.to_datetime(df_plot['game_date']).dt.date<=input.date_range_id()[1])] |
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df_plot = df_plot[df_plot['batter_hand'].isin(split_dict_hand[input.split_id()])] |
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if input.count_id_balls()=='greater' and input.count_id_strikes()=='greater' and int(input.ball_id())==0 and int(input.strike_id())==0: |
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ball_title = '' |
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strike_title = '' |
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else: |
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if input.count_id_balls()=='exact': |
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df_plot = df_plot[df_plot['balls']==int(input.ball_id())] |
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ball_title = str(f'{(input.ball_id())} Ball Count; ') |
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elif input.count_id_balls()=='greater': |
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df_plot = df_plot[df_plot['balls']>=int(input.ball_id())] |
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ball_title = str(f'At Least {(input.ball_id())} Ball Count; ') |
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elif input.count_id_balls()=='lesser': |
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df_plot = df_plot[df_plot['balls']<=int(input.ball_id())] |
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ball_title = str(f'At Most {(input.ball_id())} Ball Count; ') |
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if input.count_id_strikes()=='exact': |
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df_plot = df_plot[df_plot['strikes']==int(input.strike_id())] |
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strike_title = str(f'{(input.strike_id())} Strike Count; ') |
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elif input.count_id_strikes()=='greater': |
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df_plot = df_plot[df_plot['strikes']>=int(input.strike_id())] |
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strike_title = str(f'At Least {(input.strike_id())} Strike Count; ') |
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elif input.count_id_strikes()=='lesser': |
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df_plot = df_plot[df_plot['strikes']<=int(input.strike_id())] |
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strike_title = str(f'At Most {(input.strike_id())} Strike Count; ') |
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if input.split_id() == 'all': |
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split_title = '' |
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elif input.split_id() == 'left': |
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split_title = 'vs. LHH' |
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elif input.split_id() == 'right': |
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split_title = 'vs. RHH' |
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if len(df_plot)<1: |
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fig, ax = plt.subplots(1, 1, figsize=(9, 9)) |
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ax.text(x=0.5,y=0.5,s='Please Select\nOther Parameters',fontsize=150,ha='center') |
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ax.grid('off') |
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return |
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df_plot['pitch_type_count'] = df_plot.groupby(['pitcher_id'])['pitch_type'].cumcount()+1 |
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df_plot['pitch_type_count_each'] = df_plot.groupby(['pitch_type'])['pitch_type'].cumcount()+1 |
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df_plot = df_plot.sort_values(by=['pitch_description']) |
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df_plot = df_plot.sort_values(by=['start_time']) |
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grouped_ivb = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand','pitch_type','pitch_description']) |
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grouped_ivb_all = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand']) |
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from matplotlib.gridspec import GridSpec |
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plt.rcParams['font.family'] = 'Calibri' |
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df_plot['prop'] = df_plot.groupby("pitch_type")["is_pitch"].transform("sum") |
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label_labels = df_plot.sort_values(by=['prop','pitch_type'],ascending=[False,True]).pitch_description.unique() |
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fig = plt.figure(figsize=(20, 20)) |
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plt.rcParams.update({'figure.autolayout': True}) |
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fig.set_facecolor('white') |
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sns.set_theme(style="whitegrid", palette=colour_palette) |
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print('this is the one plot') |
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gs = GridSpec(5, 5, height_ratios=[150,75,225,325,50],width_ratios=[1,100,100,100,1]) |
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gs.update(hspace=0.2, wspace=0.3) |
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ax0 = fig.add_subplot(gs[0, :]) |
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ax1_table = fig.add_subplot(gs[1, :]) |
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ax2_left = fig.add_subplot(gs[2, 1]) |
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ax2_middle = fig.add_subplot(gs[2, 2]) |
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ax2_right = fig.add_subplot(gs[2, 3]) |
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ax3 = fig.add_subplot(gs[-2, :]) |
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ax1_table.axis('off') |
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sns.set_theme(style="whitegrid", palette=colour_palette) |
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fig.set_facecolor('white') |
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font_properties = {'family': 'calibi', 'size': 12} |
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font_properties_titles = {'family': 'calibi', 'size': 20} |
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font_properties_axes = {'family': 'calibi', 'size': 16} |
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start_date = str(pd.to_datetime(input.date_range_id()[0]).strftime('%m/%d/%Y')) |
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end_date = str(pd.to_datetime(input.date_range_id()[1]).strftime('%m/%d/%Y')) |
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pitcher_stats_call = requests.get(f'https://statsapi.mlb.com/api/v1/people/{pitcher_id_select}?appContext=minorLeague&hydrate=stats(group=[pitching],type=[byDateRange],sportId=14,startDate={start_date},endDate={end_date})').json() |
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pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']] |
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pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']] |
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pitcher_stats_call_df = pd.DataFrame(data=dict(zip(pitcher_stats_call_header,pitcher_stats_call_values)),index=[0]) |
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pitcher_stats_call_df['k_percent'] = pitcher_stats_call_df['strikeOuts']/pitcher_stats_call_df['battersFaced'] |
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pitcher_stats_call_df['bb_percent'] = pitcher_stats_call_df['baseOnBalls']/pitcher_stats_call_df['battersFaced'] |
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pitcher_stats_call_df['k_bb_percent'] = pitcher_stats_call_df['k_percent']-pitcher_stats_call_df['bb_percent'] |
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pitcher_stats_call_df_small = pitcher_stats_call_df[['inningsPitched','battersFaced','era','whip','k_percent','bb_percent','k_bb_percent']] |
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pitcher_stats_call_df_small['k_percent'] = pitcher_stats_call_df_small['k_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) |
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pitcher_stats_call_df_small['bb_percent'] = pitcher_stats_call_df_small['bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) |
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pitcher_stats_call_df_small['k_bb_percent'] = pitcher_stats_call_df_small['k_bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) |
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table_fg = ax1_table.table(cellText=pitcher_stats_call_df_small.values, colLabels=pitcher_stats_call_df_small.columns, cellLoc='center', |
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bbox=[0.04, 0.2, 0.92, 0.8]) |
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min_font_size = 20 |
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table_fg.set_fontsize(min_font_size) |
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new_column_names = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ERA}$','$\\bf{WHIP}$','$\\bf{K\%}$','$\\bf{BB\%}$','$\\bf{K-BB\%}$'] |
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for i, col_name in enumerate(new_column_names): |
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table_fg.get_celld()[(0, i)].get_text().set_text(col_name) |
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ax1_table.axis('off') |
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for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax2_left,ax2_middle,ax2_right],[1,2,3]): |
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if x == 'velocity_kde': |
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psf.velocity_kdes(df=df_plot,ax=y,gs=gs,gs_list=z,fig=fig) |
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if x == 'rolling_tj_stuff': |
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psf.tj_stuff_roling(df = df_plot,window = int(input.rolling_window()),ax=y) |
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if x == 'break_plot': |
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psf.break_plot(df=df_plot,ax=y) |
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if x == 'location_lhb': |
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psf.location_plot(df=df_plot,ax=y,hand='L') |
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if x == 'location_rhb': |
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psf.location_plot(df=df_plot,ax=y,hand='R') |
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pitches_list = df_plot['pitch_description'].unique() |
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colour_pitches = [pitch_colours[x] for x in pitches_list] |
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handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches] |
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labels = pitches_list |
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psf.table_summary(df=df_plot.copy(), |
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pitcher_id=pitcher_id_select, |
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ax=ax3, |
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df_group=grouped_ivb.copy(), |
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df_group_all=grouped_ivb_all.copy(), |
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statcast_pitch_summary=statcast_pitch_summary.copy()) |
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sorted_value_counts = df_plot['pitch_description'].value_counts().sort_values(ascending=False) |
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items_in_order = sorted_value_counts.index.tolist() |
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name_to_color = dict(zip(labels, handles)) |
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ordered_colors = [name_to_color[name] for name in items_in_order] |
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ax3.legend(ordered_colors, items_in_order, bbox_to_anchor=(0.1, 0.81, 0.8, 0.2), ncol=5, |
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fancybox=True,loc='lower center',fontsize=20,framealpha=1.0, markerscale=2,prop={'family': 'calibi', 'size': 20}) |
|
|
|
|
|
|
|
title_spot = f'{df_plot.pitcher_name.values[0]}' |
|
|
|
|
|
ax0.text(x=0.5,y=0.8,s=title_spot,fontname='Calibri',ha='center',fontsize=56,va='top') |
|
ax0.text(x=0.5,y=0.35,s='A Season Pitching Summary',fontname='Calibri',ha='center',fontsize=40,va='top',fontstyle='italic') |
|
|
|
player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json() |
|
|
|
ax0.axis('off') |
|
ax0.text(x=0.5,y=0.5,s=f"{ player_bio['people'][0]['pitchHand']['code']}HP, Age: {player_bio['people'][0]['currentAge']}, {player_bio['people'][0]['height']}/{player_bio['people'][0]['weight']}",fontname='Calibri',ha='center',fontsize=24,va='top') |
|
|
|
|
|
|
|
|
|
|
|
|
|
ax0.text(x=0.5,y=0.15,s=f'{input.date_range_id()[0]} to {input.date_range_id()[1]}',fontname='Calibri',ha='center',fontsize=30,va='top',fontstyle='italic') |
|
|
|
ax0.text(x=0.5,y=0.0,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top') |
|
ax0.axis('off') |
|
|
|
from matplotlib.offsetbox import (OffsetImage, AnnotationBbox) |
|
import urllib |
|
import urllib.request |
|
import urllib.error |
|
from urllib.error import HTTPError |
|
|
|
try: |
|
url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{pitcher_id_select}/headshot/milb/current.png' |
|
test_mage = plt.imread(url) |
|
except urllib.error.HTTPError as err: |
|
url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' |
|
test_mage = plt.imread(url) |
|
imagebox = OffsetImage(test_mage, zoom = 0.5) |
|
ab = AnnotationBbox(imagebox, (0.125, 0.4), frameon = False) |
|
ax0.add_artist(ab) |
|
|
|
|
|
|
|
|
|
|
|
|
|
if 'currentTeam' in player_bio['people'][0]: |
|
try: |
|
url = team_logos[team_logos['id'] == team_logo_dict[player_bio['people'][0]['currentTeam']['id']]]['imageLink'].values[0] |
|
|
|
im = plt.imread(url) |
|
|
|
|
|
|
|
|
|
imagebox = OffsetImage(im, zoom = 0.4) |
|
ab = AnnotationBbox(imagebox, (0.875, 0.40), frameon = False) |
|
ax0.add_artist(ab) |
|
except IndexError: |
|
print() |
|
|
|
|
|
|
|
|
|
axfooter = fig.add_subplot(gs[-1, :]) |
|
axfooter.text(x=0.05,y=1,s='By: Thomas Nestico\n @TJStats',fontname='Calibri',ha='left',fontsize=24,va='top') |
|
axfooter.text(x=1-0.05,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=24,va='top') |
|
|
|
|
|
axfooter.text(x=0.5,y=0.8,s='Colour Coding Compares to League Average By Pitch\ntjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\ntjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10', |
|
ha='center',va='center',fontname='Calibri',fontsize=16) |
|
axfooter.axis('off') |
|
|
|
|
|
fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03) |
|
|
|
|
|
@output |
|
@render.plot |
|
@reactive.event(input.go, ignore_none=False) |
|
def plot_game(): |
|
|
|
|
|
font_properties = {'family': 'calibi', 'size': 12} |
|
font_properties_titles = {'family': 'calibi', 'size': 20} |
|
font_properties_axes = {'family': 'calibi', 'size': 16} |
|
|
|
if len((input.player_id()))<1: |
|
fig, ax = plt.subplots(1, 1, figsize=(9, 9)) |
|
ax.text(x=0.5,y=0.5,s='Please Select\nA Player',fontsize=150,ha='center') |
|
ax.grid('off') |
|
return |
|
|
|
pitcher_id_select = int(input.player_id()) |
|
|
|
|
|
|
|
|
|
df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)&(df_2024_update['game_id']==int(input.game_id()))] |
|
df_plot = df_plot[df_plot['batter_hand'].isin(split_dict_hand[input.split_id()])] |
|
|
|
if input.count_id_balls()=='greater' and input.count_id_strikes()=='greater' and int(input.ball_id())==0 and int(input.strike_id())==0: |
|
ball_title = '' |
|
strike_title = '' |
|
else: |
|
if input.count_id_balls()=='exact': |
|
df_plot = df_plot[df_plot['balls']==int(input.ball_id())] |
|
ball_title = str(f'{(input.ball_id())} Ball Count; ') |
|
elif input.count_id_balls()=='greater': |
|
df_plot = df_plot[df_plot['balls']>=int(input.ball_id())] |
|
ball_title = str(f'At Least {(input.ball_id())} Ball Count; ') |
|
elif input.count_id_balls()=='lesser': |
|
df_plot = df_plot[df_plot['balls']<=int(input.ball_id())] |
|
ball_title = str(f'At Most {(input.ball_id())} Ball Count; ') |
|
|
|
if input.count_id_strikes()=='exact': |
|
df_plot = df_plot[df_plot['strikes']==int(input.strike_id())] |
|
strike_title = str(f'{(input.strike_id())} Strike Count; ') |
|
elif input.count_id_strikes()=='greater': |
|
df_plot = df_plot[df_plot['strikes']>=int(input.strike_id())] |
|
strike_title = str(f'At Least {(input.strike_id())} Strike Count; ') |
|
elif input.count_id_strikes()=='lesser': |
|
df_plot = df_plot[df_plot['strikes']<=int(input.strike_id())] |
|
strike_title = str(f'At Most {(input.strike_id())} Strike Count; ') |
|
|
|
|
|
|
|
|
|
|
|
if input.split_id() == 'all': |
|
split_title = '' |
|
|
|
elif input.split_id() == 'left': |
|
split_title = 'vs. LHH' |
|
|
|
elif input.split_id() == 'right': |
|
split_title = 'vs. RHH' |
|
|
|
if len(df_plot)<1: |
|
fig, ax = plt.subplots(1, 1, figsize=(9, 9)) |
|
ax.text(x=0.5,y=0.5,s='Please Select\nOther Parameters',fontsize=150,ha='center') |
|
ax.grid('off') |
|
return |
|
|
|
|
|
df_plot['pitch_type_count'] = df_plot.groupby(['pitcher_id'])['pitch_type'].cumcount()+1 |
|
df_plot['pitch_type_count_each'] = df_plot.groupby(['pitch_type'])['pitch_type'].cumcount()+1 |
|
|
|
df_plot = df_plot.sort_values(by=['pitch_description']) |
|
df_plot = df_plot.sort_values(by=['start_time']) |
|
|
|
|
|
df_plot['game_opp'] = df_plot['game_date'].astype(str) + ' vs ' + df_plot['batter_team'].astype(str) |
|
|
|
|
|
|
|
|
|
grouped_ivb = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand','pitch_type','pitch_description']) |
|
grouped_ivb_all = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand']) |
|
|
|
|
|
|
|
from matplotlib.gridspec import GridSpec |
|
plt.rcParams['font.family'] = 'Calibri' |
|
df_plot['prop'] = df_plot.groupby("pitch_type")["is_pitch"].transform("sum") |
|
label_labels = df_plot.sort_values(by=['prop','pitch_type'],ascending=[False,True]).pitch_description.unique() |
|
|
|
|
|
fig = plt.figure(figsize=(20, 20)) |
|
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(5, 5, height_ratios=[150,75,225,325,50],width_ratios=[1,100,100,100,1]) |
|
|
|
|
|
|
|
|
|
gs.update(hspace=0.2, wspace=0.3) |
|
|
|
|
|
ax0 = fig.add_subplot(gs[0, :]) |
|
ax1_table = fig.add_subplot(gs[1, :]) |
|
ax2_left = fig.add_subplot(gs[2, 1]) |
|
ax2_middle = fig.add_subplot(gs[2, 2]) |
|
ax2_right = fig.add_subplot(gs[2, 3]) |
|
ax3 = fig.add_subplot(gs[-2, :]) |
|
|
|
|
|
ax1_table.axis('off') |
|
|
|
sns.set_theme(style="whitegrid", palette=colour_palette) |
|
fig.set_facecolor('white') |
|
|
|
font_properties = {'family': 'calibi', 'size': 12} |
|
font_properties_titles = {'family': 'calibi', 'size': 20} |
|
font_properties_axes = {'family': 'calibi', 'size': 16} |
|
|
|
print(df_2024_update['game_date'].values[0]) |
|
|
|
|
|
|
|
|
|
|
|
|
|
start_date = str(pd.to_datetime(df_plot['game_date'].values[0]).strftime('%m/%d/%Y')) |
|
end_date = str(pd.to_datetime(df_plot['game_date'].values[0]).strftime('%m/%d/%Y')) |
|
|
|
|
|
pitcher_stats_call = requests.get(f'https://statsapi.mlb.com/api/v1/people/{pitcher_id_select}?appContext=minorLeague&hydrate=stats(group=[pitching],type=[byDateRange],sportId=14,startDate={start_date},endDate={end_date})').json() |
|
|
|
pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']] |
|
pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']] |
|
pitcher_stats_call_df = pd.DataFrame(data=dict(zip(pitcher_stats_call_header,pitcher_stats_call_values)),index=[0]) |
|
|
|
|
|
|
|
pitcher_stats_call_df_small = pitcher_stats_call_df[['inningsPitched','battersFaced','earnedRuns','hits','strikeOuts','baseOnBalls','hitByPitch','homeRuns']] |
|
pitcher_stats_call_df_small['whiffs'] = int(df_plot['is_whiff'].sum()) |
|
|
|
|
|
|
|
|
|
table_fg = ax1_table.table(cellText=pitcher_stats_call_df_small.values, colLabels=pitcher_stats_call_df_small.columns, cellLoc='center', |
|
bbox=[0.04, 0.2, 0.92, 0.8]) |
|
|
|
min_font_size = 20 |
|
table_fg.set_fontsize(min_font_size) |
|
|
|
|
|
new_column_names = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ER}$','$\\bf{H}$','$\\bf{K}$','$\\bf{BB}$','$\\bf{HBP}$','$\\bf{HR}$','$\\bf{Whiffs}$'] |
|
|
|
for i, col_name in enumerate(new_column_names): |
|
table_fg.get_celld()[(0, i)].get_text().set_text(col_name) |
|
|
|
ax1_table.axis('off') |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax2_left,ax2_middle,ax2_right],[1,2,3]): |
|
if x == 'velocity_kde': |
|
psf.velocity_kdes(df=df_plot,ax=y,gs=gs,gs_list=z,fig=fig) |
|
if x == 'rolling_tj_stuff': |
|
psf.tj_stuff_roling(df = df_plot,window = int(input.rolling_window()),ax=y) |
|
if x == 'break_plot': |
|
psf.break_plot(df=df_plot,ax=y) |
|
if x == 'location_lhb': |
|
psf.location_plot(df=df_plot,ax=y,hand='L') |
|
if x == 'location_rhb': |
|
psf.location_plot(df=df_plot,ax=y,hand='R') |
|
|
|
pitches_list = df_plot['pitch_description'].unique() |
|
colour_pitches = [pitch_colours[x] for x in pitches_list] |
|
|
|
|
|
|
|
|
|
handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches] |
|
labels = pitches_list |
|
|
|
|
|
|
|
psf.table_summary(df=df_plot.copy(), |
|
pitcher_id=pitcher_id_select, |
|
ax=ax3, |
|
df_group=grouped_ivb.copy(), |
|
df_group_all=grouped_ivb_all.copy(), |
|
statcast_pitch_summary=statcast_pitch_summary.copy()) |
|
|
|
|
|
|
|
|
|
|
|
|
|
sorted_value_counts = df_plot['pitch_description'].value_counts().sort_values(ascending=False) |
|
|
|
|
|
items_in_order = sorted_value_counts.index.tolist() |
|
|
|
name_to_color = dict(zip(labels, handles)) |
|
|
|
|
|
ordered_colors = [name_to_color[name] for name in items_in_order] |
|
|
|
|
|
ax3.legend(ordered_colors, items_in_order, bbox_to_anchor=(0.1, 0.81, 0.8, 0.2), ncol=5, |
|
fancybox=True,loc='lower center',fontsize=20,framealpha=1.0, markerscale=2,prop={'family': 'calibi', 'size': 20}) |
|
|
|
|
|
|
|
title_spot = f'{df_plot.pitcher_name.values[0]}' |
|
|
|
|
|
ax0.text(x=0.5,y=0.8,s=title_spot,fontname='Calibri',ha='center',fontsize=56,va='top') |
|
ax0.text(x=0.5,y=0.35,s='A Game Pitching Summary',fontname='Calibri',ha='center',fontsize=40,va='top',fontstyle='italic') |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ax0.text(x=0.5,y=0.15,s= df_plot['game_opp'].values[0],fontname='Calibri',ha='center',fontstyle='italic',fontsize=30,va='top') |
|
|
|
ax0.text(x=0.5,y=0.00,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top') |
|
ax0.axis('off') |
|
|
|
|
|
from matplotlib.offsetbox import (OffsetImage, AnnotationBbox) |
|
import urllib |
|
import urllib.request |
|
import urllib.error |
|
from urllib.error import HTTPError |
|
|
|
try: |
|
url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{pitcher_id_select}/headshot/milb/current.png' |
|
test_mage = plt.imread(url) |
|
except urllib.error.HTTPError as err: |
|
url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' |
|
test_mage = plt.imread(url) |
|
imagebox = OffsetImage(test_mage, zoom = 0.5) |
|
ab = AnnotationBbox(imagebox, (0.125, 0.4), frameon = False) |
|
ax0.add_artist(ab) |
|
|
|
|
|
|
|
player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json() |
|
|
|
ax0.axis('off') |
|
ax0.text(x=0.5,y=0.5,s=f"{ player_bio['people'][0]['pitchHand']['code']}HP, Age: {player_bio['people'][0]['currentAge']}, {player_bio['people'][0]['height']}/{player_bio['people'][0]['weight']}",fontname='Calibri',ha='center',fontsize=24,va='top') |
|
|
|
|
|
|
|
if 'currentTeam' in player_bio['people'][0]: |
|
try: |
|
url = team_logos[team_logos['id'] == team_logo_dict[player_bio['people'][0]['currentTeam']['id']]]['imageLink'].values[0] |
|
|
|
im = plt.imread(url) |
|
|
|
|
|
|
|
|
|
imagebox = OffsetImage(im, zoom = 0.4) |
|
ab = AnnotationBbox(imagebox, (0.875, 0.40), frameon = False) |
|
ax0.add_artist(ab) |
|
except IndexError: |
|
print() |
|
|
|
|
|
|
|
axfooter = fig.add_subplot(gs[-1, :]) |
|
axfooter.text(x=0.05,y=1,s='By: Thomas Nestico\n @TJStats',fontname='Calibri',ha='left',fontsize=24,va='top') |
|
axfooter.text(x=1-0.05,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=24,va='top') |
|
|
|
|
|
axfooter.text(x=0.5,y=0.8,s='Colour Coding Compares to League Average By Pitch\ntjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\ntjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10', |
|
ha='center',va='center',fontname='Calibri',fontsize=16) |
|
axfooter.axis('off') |
|
|
|
|
|
|
|
fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03) |
|
|
|
app = App(app_ui, server) |
