import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import pitch_summary_functions as psf import requests import matplotlib from api_scraper import MLB_Scrape from shinywidgets import output_widget, render_widget import shinyswatch colour_palette = ['#FFB000','#648FFF','#785EF0', '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED'] import datasets from datasets import load_dataset ### Import Datasets dataset = load_dataset('nesticot/mlb_data', data_files=['a_pitch_data_2024.csv' ]) dataset_train = dataset['train'] 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') # df_2024.loc[(df_2024['pitcher_id']==804636)&(df_2024['pitch_type'].isin(['FF','FC']),'start_speed'] += 3 # ### Import Datasets # import datasets # from datasets import load_dataset # dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2020.csv' ]) # dataset_train = dataset['train'] # df_2024 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True) ### PITCH COLOURS ### pitch_colours = { 'Four-Seam Fastball':'#FF007D',#BC136F 'Fastball':'#FF007D', 'Sinker':'#98165D',#DC267F 'Cutter':'#BE5FA0', 'Changeup':'#F79E70',#F75233 'Splitter':'#FE6100',#F75233 'Screwball':'#F08223', 'Forkball':'#FFB000', 'Slider':'#67E18D',#1BB999#785EF0 'Sweeper':'#1BB999',#37CD85#904039 'Slurve':'#376748',#785EF0#549C07#BEABD8 'Knuckle Curve':'#311D8B', 'Curveball':'#3025CE', 'Slow Curve':'#274BFC', 'Eephus':'#648FFF', 'Knuckle Ball':'#867A08', 'Pitch Out':'#472C30', 'Other':'#9C8975', } spring_teams = df_2024.groupby(['pitcher_id']).tail(1)[['pitcher_id','pitcher_team']].set_index(['pitcher_id'])['pitcher_team'].to_dict() season_start = '2024-03-20' season_end = '2024-09-29' season_fg=2024 #chad_fg = requests.get(f'https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&qual=0&season={season_fg}&season={season_fg}&month=1000&season1={season_fg}&ind=0&pageitems=2000000000&pagenum=1&ind=0&rost=0&players=&type=36&postseason=&sortdir=default&sortstat=sp_pitching').json() # chadwick_df_small = pd.DataFrame(data={ # 'key_mlbam':[x['xMLBAMID'] for x in chad_fg['data']], # 'key_fangraphs':[x['playerid'] for x in chad_fg['data']], # 'Name':[x['PlayerName'] for x in chad_fg['data']], # }) # mlb_fg_dicts = chadwick_df_small.set_index('key_mlbam')['key_fangraphs'].sort_values().to_dict() statcast_pitch_summary = pd.read_csv('statcast_pitch_summary.csv') cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',]) df_2024_codes = psf.df_update_code(df_2024) df_2024_update = psf.df_clean(df_2024_codes) import joblib model = joblib.load('joblib_model/tjstuff_model_20240428.joblib') # y_pred_mean = 0.0011434511 # y_pred_std = 0.006554768 from stuff_values_20240428 import my_dict y_pred_mean = my_dict['y_pred_mean'] y_pred_std = my_dict['y_pred_std'] xwoba_model = joblib.load('joblib_model/xwoba_model.joblib') features = ['start_speed','spin_rate','log_extension','ivb','hb','x0','z0','primary_mean_velo_diff','primary_mean_ivb_diff','primary_mean_hb_diff'] targets = ['delta_run_exp_mean'] df_2024_update['y_pred'] = model.predict(df_2024_update[features]) df_2024_update['tj_stuff_plus'] = 100 + 10*((-df_2024_update.y_pred +y_pred_mean) / y_pred_std) df_2024_update['woba_pred'] = np.nan 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])] pitcher_dicts = df_2024_update.set_index('pitcher_id')['pitcher_name'].sort_values().to_dict() team_logos = pd.read_csv('team_logos.csv') mlb_stats = MLB_Scrape() teams_df = mlb_stats.get_teams() team_logo_dict = teams_df.set_index(['team_id'])['parent_org_id'].to_dict() font_properties = {'family': 'calibi', 'size': 12} font_properties_titles = {'family': 'calibi', 'size': 20} font_properties_axes = {'family': 'calibi', 'size': 16} df_plot = [] ax2_loc = [] gs = [] fig = [] function_dict={ 'velocity_kde':'Velocity Distributions', 'break_plot':'Pitch Movement', 'rolling_tj_stuff':'Rolling tjStuff+', 'location_lhb':'Locations vs LHB', 'location_rhb':'Locations vs RHB', } split_dict = {'all':'All', 'left':'LHB', 'right':'RHB'} split_dict_hand = {'all':['L','R'], 'left':['L'], 'right':['R']} ball_dict = {'0':'0', '1':'1', '2':'2', '3':'3'} strike_dict = {'0':'0', '1':'1', '2':'2'} # count_dict = {'0_0':'Through 0-0', # '0_1':'Through 0-1', # '0_2':'Through 0-2', # '1_0':'Through 1-0', # '1_1':'Through 1-1', # '1_2':'Through 1-2', # '2_1':'Through 2-1', # '2_0':'Through 2-0', # '3_0':'Through 3-0', # '3_1':'Through 3-1', # '2_2':'Through 2-2', # '3_2':'Through 3-2'} # count_dict_fg = {'0_0':'', # '0_1':'61', # '0_2':'62', # '1_0':'63', # '1_1':'64', # '1_2':'65', # '2_1':'66', # '2_0':'67', # '3_0':'68', # '3_1':'69', # '2_2':'70', # '3_2':'71'} from urllib.request import Request, urlopen from shiny import App, reactive, ui, render from shiny.ui import h2, tags # importing OpenCV(cv2) module app_ui = ui.page_fluid( ui.tags.div( {"style": "width:90%;margin: 0 auto;max-width: 1600px;"}, ui.tags.style( """ h4 { margin-top: 1em;font-size:35px; } h2{ font-size:25px; } """ ), shinyswatch.theme.simplex(), ui.tags.h4("TJStats"), ui.tags.i("Baseball Analytics and Visualizations"), ui.row( ui.layout_sidebar( ui.panel_sidebar( ui.row( ui.column(6, ui.input_select('player_id','Select Player',pitcher_dicts,selectize=True,multiple=False)), ui.column(6, ui.output_ui('test','Select Game'))), ui.row( ui.column(4, ui.input_select('plot_id_1','Plot Left',function_dict,multiple=False,selected='velocity_kde')), ui.column(4, ui.input_select('plot_id_2','Plot Middle',function_dict,multiple=False,selected='rolling_tj_stuff')), ui.column(4, ui.input_select('plot_id_3','Plot Right',function_dict,multiple=False,selected='break_plot'))), # ui.input_select('count_id','Count',count_dict,multiple=True,selectize=True,selected='0_0'), ui.row( ui.column(6, ui.input_select('ball_id','Balls',ball_dict,multiple=False,selected='0'), ui.input_radio_buttons( "count_id_balls", "Count Filter Balls", { "exact": "Exact Balls", "greater": ">= Balls", "lesser": "<= Balls", },selected='greater')), ui.column(6, ui.input_select('strike_id','Strikes',strike_dict,multiple=False,selected='0'), ui.input_radio_buttons( "count_id_strikes", "Count Filter Strikes", { "exact": "Exact Strikes", "greater": ">= Strikes", "lesser": "<= Strikes", },selected='greater'))), ui.row( ui.column(6, ui.input_select('split_id','Select Split',split_dict,multiple=False)), ui.column(6, ui.input_numeric('rolling_window','Rolling Window (for tjStuff+ Plot)',min=1,value=10))), ui.input_action_button("go", "Generate",class_="btn-primary"), width=4) , ui.panel_main( ui.navset_tab( # ui.nav("Raw Data", # ui.output_data_frame("raw_table")), ui.nav("Season Summary", ui.output_plot('plot', width='2000px', height='2000px')), ui.nav("Game Summary", ui.output_plot('plot_game', width='2000px', height='2000px')) ,id="my_tabs")))))) #print(app_ui) def server(input, output, session): @render.ui def test(): # @reactive.Effect if input.my_tabs() == 'Season Summary': return ui.input_date_range("date_range_id", "Date range input",start = df_2024.game_date.min(), end = df_2024.game_date.max(),width=2,min=df_2024.game_date.min(), max=df_2024.game_date.max()), # @reactive.Effect if input.my_tabs() == 'Game Summary': pitcher_id_select = int(input.player_id()) df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)] # ax0.text(x=0.5,y=0.30,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top') df_plot['game_opp'] = df_plot['game_date'].astype(str) + ' vs ' + df_plot['batter_team'].astype(str) #print(df_plot['game_opp']) 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() return ui.input_select("game_id", "Select Game",date_dict,selectize=True) @output @render.plot @reactive.event(input.go, ignore_none=False) def plot(): #fig, ax = plt.subplots(3, 2, figsize=(9, 9)) 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_plot = df_plot[(pd.to_datetime(df_plot['game_date']).dt.date>=input.date_range_id()[0])& (pd.to_datetime(df_plot['game_date']).dt.date<=input.date_range_id()[1])] 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.merge(df_2024_update[['tj_stuff_plus','play_id']],left_on=['play_id'],right_on=['play_id'],how='left') df_plot = df_plot.sort_values(by=['pitch_description']) df_plot = df_plot.sort_values(by=['start_time']) 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() #plt.rcParams["figure.figsize"] = [10,10] 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(7, 2, width_ratios=[1,1], height_ratios=[1.5,1,1,1,1,1,2.5]) gs = GridSpec(5, 5, height_ratios=[150,75,225,325,50],width_ratios=[1,100,100,100,1]) #### NO FG ####gs = GridSpec(5, 5, height_ratios=[225,0,225,325,50],width_ratios=[1,100,100,100,1]) #gs = GridSpec(4, 1, width_ratios=[1], height_ratios=[1,0.75,7-len(label_labels)/4,1+len(label_labels)/4]) gs.update(hspace=0.2, wspace=0.3) # Add subplots to the grid 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, :]) #axfooter = fig.add_subplot(gs[-1, :]) 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} # ## FANGRAPHS TABLE ### # data_pull = psf.fangraphs_scrape(pitcher_id=pitcher_id_select, # split=input.split_id(), # start_date=input.date_range_id()[0], # end_date=input.date_range_id()[1]) # psf.fangraphs_table(data=data_pull, # stats=['IP','WHIP','ERA','FIP','TBF','K%','BB%','K-BB%'], # ax=ax1_table) start_date = str(pd.to_datetime(input.date_range_id()[0]).strftime('%m/%d/%Y')) end_date = str(pd.to_datetime(input.date_range_id()[1]).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['k_percent'] = pitcher_stats_call_df['strikeOuts']/pitcher_stats_call_df['battersFaced'] pitcher_stats_call_df['bb_percent'] = pitcher_stats_call_df['baseOnBalls']/pitcher_stats_call_df['battersFaced'] pitcher_stats_call_df['k_bb_percent'] = pitcher_stats_call_df['k_percent']-pitcher_stats_call_df['bb_percent'] pitcher_stats_call_df_small = pitcher_stats_call_df[['inningsPitched','battersFaced','era','whip','k_percent','bb_percent','k_bb_percent']] pitcher_stats_call_df_small['k_percent'] = pitcher_stats_call_df_small['k_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) pitcher_stats_call_df_small['bb_percent'] = pitcher_stats_call_df_small['bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) pitcher_stats_call_df_small['k_bb_percent'] = pitcher_stats_call_df_small['k_bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) 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 = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ERA}$','$\\bf{WHIP}$','$\\bf{K\%}$','$\\bf{BB\%}$','$\\bf{K-BB\%}$'] # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%'] 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, labels = ax2_right.get_legend_handles_labels() # # Manually create handles and labels for each pitch-color pair handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches] labels = pitches_list ### FANGRAPHS TABLE ### 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()) # ############ FOOTER ################ # #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10) # 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, Fangraphs',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.tight_layout() # Get value counts of the column and sort in descending order sorted_value_counts = df_plot['pitch_description'].value_counts().sort_values(ascending=False) # Get the list of items ordered from most to least frequent items_in_order = sorted_value_counts.index.tolist() # Create a dictionary to map names to colors name_to_color = dict(zip(labels, handles)) # Order the colors based on the correct order of names 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 ########## 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.text(x=0.5,y=0.05,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top') 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=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top') # ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top') # ax0.axis('off') 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) #player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json() 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) # response = requests.get(url) # im = Image.open(BytesIO(response.content)) # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) imagebox = OffsetImage(im, zoom = 0.4) ab = AnnotationBbox(imagebox, (0.875, 0.40), frameon = False) ax0.add_artist(ab) except IndexError: print() ############ FOOTER ################ #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10) 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.tight_layout() 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(): #fig, ax = plt.subplots(3, 2, figsize=(9, 9)) 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.merge(df_2024_update[['tj_stuff_plus','play_id']],left_on=['play_id'],right_on=['play_id'],how='left') df_plot = df_plot.sort_values(by=['pitch_description']) df_plot = df_plot.sort_values(by=['start_time']) # ax0.text(x=0.5,y=0.30,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top') df_plot['game_opp'] = df_plot['game_date'].astype(str) + ' vs ' + df_plot['batter_team'].astype(str) #print(df_plot['game_opp']) #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() 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() #plt.rcParams["figure.figsize"] = [10,10] 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(7, 2, width_ratios=[1,1], height_ratios=[1.5,1,1,1,1,1,2.5]) gs = GridSpec(5, 5, height_ratios=[150,75,225,325,50],width_ratios=[1,100,100,100,1]) #### NO FG ####gs = GridSpec(5, 5, height_ratios=[225,0,225,325,50],width_ratios=[1,100,100,100,1]) #gs = GridSpec(4, 1, width_ratios=[1], height_ratios=[1,0.75,7-len(label_labels)/4,1+len(label_labels)/4]) gs.update(hspace=0.2, wspace=0.3) # Add subplots to the grid 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, :]) # axfooter = fig.add_subplot(gs[-1, :]) 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]) # ## FANGRAPHS TABLE ### # data_pull = psf.fangraphs_scrape(pitcher_id=pitcher_id_select, # split=input.split_id(), # start_date=df_plot['game_date'].values[0], # end_date=df_plot['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['k_percent'] = pitcher_stats_call_df['strikeOuts']/pitcher_stats_call_df['battersFaced'] # pitcher_stats_call_df['bb_percent'] = pitcher_stats_call_df['baseOnBalls']/pitcher_stats_call_df['battersFaced'] # pitcher_stats_call_df['k_bb_percent'] = pitcher_stats_call_df['k_percent']-pitcher_stats_call_df['bb_percent'] 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()) # pitcher_stats_call_df_small['k_percent'] = pitcher_stats_call_df_small['k_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) # pitcher_stats_call_df_small['bb_percent'] = pitcher_stats_call_df_small['bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) # pitcher_stats_call_df_small['k_bb_percent'] = pitcher_stats_call_df_small['k_bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) 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 = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ER}$','$\\bf{H}$','$\\bf{K}$','$\\bf{BB}$','$\\bf{HBP}$','$\\bf{HR}$','$\\bf{Whiffs}$'] # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%'] 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') # psf.fangraphs_table(data=data_pull, # stats=['IP','WHIP','ERA','FIP','TBF','K%','BB%','K-BB%'], # ax=ax1_table) # psf.velocity_kdes(df=df_plot, # ax=ax2_loc, # gs=gs, # fig=fig) # # psf.tj_stuff_roling(df = df_plot, # # window = 5, # # ax=ax2_velo) # psf.location_plot(df=df_plot,ax=ax2_velo,hand='L') # psf.location_plot(df=df_plot,ax=ax2_loc,hand='R') # # # ## Break Plot # psf.break_plot(df=df_plot,ax=ax2) 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, labels = ax2_right.get_legend_handles_labels() # # Manually create handles and labels for each pitch-color pair handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches] labels = pitches_list ### FANGRAPHS TABLE ### 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()) # Get value counts of the column and sort in descending order sorted_value_counts = df_plot['pitch_description'].value_counts().sort_values(ascending=False) # Get the list of items ordered from most to least frequent items_in_order = sorted_value_counts.index.tolist() # Create a dictionary to map names to colors name_to_color = dict(zip(labels, handles)) # Order the colors based on the correct order of names 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 ########## 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=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top') #ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top') #ax0.text(x=0.5,y=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top') # ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top') 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() player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json() #ax0.text(x=0.5,y=0.05,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top') 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) # response = requests.get(url) # im = Image.open(BytesIO(response.content)) # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) imagebox = OffsetImage(im, zoom = 0.4) ab = AnnotationBbox(imagebox, (0.875, 0.40), frameon = False) ax0.add_artist(ab) except IndexError: print() ############ FOOTER ################ #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10) 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.tight_layout() fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03) app = App(app_ui, server)