TJStatsApps/2025_decision_value
0
1from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui2import datasets3from datasets import load_dataset4import pandas as pd5import numpy as np6import matplotlib.pyplot as plt7import seaborn as sns8import numpy as np9from scipy.stats import gaussian_kde10import matplotlib11from matplotlib.ticker import MaxNLocator12from matplotlib.gridspec import GridSpec13from scipy.stats import zscore14import math15import matplotlib16from adjustText import adjust_text17import matplotlib.ticker as mtick18from shinywidgets import output_widget, render_widget19import pandas as pd20from configure import base_url21import shinyswatch22import inflect23from matplotlib.pyplot import text24 25def percentile(n):26 def percentile_(x):27 return np.nanpercentile(x, n)28 percentile_.__name__ = 'percentile_%s' % n29 return percentile_30 31from matplotlib.colors import Normalize32 33print('Running')34 35cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])36df = pd.read_csv('statcast_20152023.csv')37df['last_name'] = df['last_name, first_name'].str.split(',').str[0]38df['first_name'] = df['last_name, first_name'].str.split(',').str[1].str.strip(' ')39df['name'] = df['first_name'] +' ' +df['last_name']40 41df[[x for x in df if x[-7:] == 'percent']] = df[[x for x in df if x[-7:] == 'percent']]/10042df['barrel_batted_rate'] = df['barrel_batted_rate']/10043 44player_dict = df[['player_id','name']].drop_duplicates().sort_values('name').set_index('player_id').to_dict()['name']45 46df_median = df[df.pa>=400]47 48format_dict = {49 'k_percent':{'format':':.1%','average':df.strikeout.sum()/df.pa.sum(),'a_d_good':False,'tab_name':'K%'},50 'bb_percent':{'format':':.1%','average':df.walk.sum()/df.pa.sum(),'a_d_good':True,'tab_name':'BB%'},51 'batting_avg':{'format':':.3f','average':df.hit.sum()/df.ab.sum(),'a_d_good':True,'tab_name':'AVG'},52 'on_base_plus_slg':{'format':':.3f','average':df.on_base_plus_slg.mean()/df.pa.mean(),'a_d_good':True,'tab_name':'OPS'},53 'isolated_power':{'format':':.3f','average':(df.single.sum() + df.double.sum()*2 + df.triple.sum()*3 + df.home_run.sum()*4)/df.ab.sum() - df.hit.sum()/df.ab.sum(),'a_d_good':True,'tab_name':'ISO'},54 'xba':{'format':':.3f','average':df_median.xba.median(),'a_d_good':True,'tab_name':'xBA'},55 'xslg':{'format':':.3f','average':df_median.xslg.median(),'a_d_good':True,'tab_name':'xSLG'},56 'woba':{'format':':.3f','average':df_median.woba.median(),'a_d_good':True,'tab_name':'wOBA'},57 'xwoba':{'format':':.3f','average':df_median.xwoba.median(),'a_d_good':True,'tab_name':'xwOBA'},58 'xobp':{'format':':.3f','average':df_median.xobp.median(),'a_d_good':True,'tab_name':'xOBP'},59 'xiso':{'format':':.3f','average':df_median.xiso.median(),'a_d_good':True,'tab_name':'xISO'},60 'wobacon':{'format':':.3f','average':df_median.wobacon.median(),'a_d_good':True,'tab_name':'wOBACON'},61 'xwobacon':{'format':':.3f','average':df_median.xwobacon.median(),'a_d_good':True,'tab_name':'xwOBACON'},62 'bacon':{'format':':.1f','average':df_median.bacon.median(),'a_d_good':True,'tab_name':'BACON'},63 'xbacon':{'format':':.1f','average':df_median.xbacon.median(),'a_d_good':True,'tab_name':'xBACON'},64 'xbadiff':{'format':':.3f','average':df_median.xbadiff.median(),'a_d_good':True,'tab_name':'BA-xBA'},65 'xslgdiff':{'format':':.3f','average':df_median.xslgdiff.median(),'a_d_good':True,'tab_name':'SLG-xSLG'},66 'wobadiff':{'format':':.3f','average':df_median.wobadiff.median(),'a_d_good':True,'tab_name':'wOBA-xwOBA'},67 'exit_velocity_avg':{'format':':.1f','average':(df.exit_velocity_avg * df.batted_ball).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'EV'},68 'launch_angle_avg':{'format':':.1f','average':(df.launch_angle_avg * df.batted_ball).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'LA'},69 'barrel':{'format':':.0f','average':df_median.barrel.median(),'a_d_good':True,'tab_name':'Barrel'},70 'barrel_batted_rate':{'format':':.1%','average':(df.barrel).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'Barrel%'},71 'avg_best_speed':{'format':':.1f','average':(df.avg_best_speed * df.batted_ball).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'Best Speed'},72 'avg_hyper_speed':{'format':':.1f','average':(df.avg_hyper_speed * df.batted_ball).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'Hyper Speed'},73 'out_zone_swing_miss':{'format':':.0f','average':df_median.out_zone_swing_miss.mean(),'a_d_good':True,'tab_name':'O-Whiff%'},74 'out_zone_swing':{'format':':.0f','average':df_median.out_zone_swing.mean(),'a_d_good':True,'tab_name':'O-Swing'},75 'out_zone':{'format':':.0f','average':df_median.out_zone.mean(),'a_d_good':True,'tab_name':'O-Zone'},76 'pitch_count_offspeed':{'format':':.0f','average':df_median.pitch_count_offspeed.mean(),'a_d_good':True,'tab_name':'Pitch Off-Speed'},77 'pitch_count_fastball':{'format':':.0f','average':df_median.pitch_count_fastball.mean(),'a_d_good':True,'tab_name':'Pitch Fastball'},78 'pitch_count_breaking':{'format':':.0f','average':df_median.pitch_count_breaking.mean(),'a_d_good':True,'tab_name':'Pitch Breaking'},79 'pitch_count':{'format':':.0f','average':df_median.pitch_count.mean(),'a_d_good':True,'tab_name':'Pitches'},80 'in_zone_swing_miss':{'format':':.0f','average':df_median.in_zone_swing_miss.mean(),'a_d_good':False,'tab_name':'Z-Whiff'},81 'in_zone_swing':{'format':':.0f','average':df_median.in_zone_swing.mean(),'a_d_good':True,'tab_name':'Z-Swing'},82 'in_zone':{'format':':.0f','average':df_median.in_zone.mean(),'a_d_good':True,'tab_name':'Zone'},83 'edge':{'format':':.0f','average':df_median.edge.mean(),'a_d_good':True,'tab_name':'Edge'},84 'batted_ball':{'format':':.0f','average':df_median.batted_ball.mean(),'a_d_good':True,'tab_name':'Batted Balls'},85 'groundballs':{'format':':.0f','average':df_median.groundballs.mean(),'a_d_good':True,'tab_name':'Groundballs'},86 'flyballs':{'format':':.0f','average':df_median.flyballs.mean(),'a_d_good':True,'tab_name':'Flyballs'},87 'linedrives':{'format':':.0f','average':df_median.linedrives.mean(),'a_d_good':True,'tab_name':'Linedrives'},88 'popups':{'format':':.0f','average':df_median.popups.mean(),'a_d_good':True,'tab_name':'Popups'},89 'n_bolts':{'format':':.0f','average':df_median.n_bolts.mean(),'a_d_good':True,'tab_name':'Bolts'},90 'hp_to_1b':{'format':':.2f','average':df_median.hp_to_1b.mean(),'a_d_good':True,'tab_name':'Home Plate to 1st'},91 'sprint_speed':{'format':':.1f','average':df_median.sprint_speed.mean(),'a_d_good':True,'tab_name':'Sprint Speed'},92 93 'slg_percent':{'format':':.1%','average':(df.single.sum() + df.double.sum()*2 + df.triple.sum()*3 + df.home_run.sum()*4)/df.ab.sum(),'a_d_good':True,'tab_name':'SLG'},94 'on_base_percent':{'format':':.1%','average':df_median.on_base_percent.median(),'a_d_good':True,'tab_name':'OBP'},95 'sweet_spot_percent':{'format':':.1%','average':df_median.sweet_spot_percent.median(),'a_d_good':True,'tab_name':'SweetSpot%'},96 'solidcontact_percent':{'format':':.1%','average':df_median.solidcontact_percent.median(),'a_d_good':True,'tab_name':'Solid%'},97 'flareburner_percent':{'format':':.1%','average':df_median.flareburner_percent.median(),'a_d_good':False,'tab_name':'Flare/Burner%'},98 'poorlyunder_percent':{'format':':.1%','average':df_median.poorlyunder_percent.median(),'a_d_good':False,'tab_name':'Under%'},99 'poorlytopped_percent':{'format':':.1%','average':df_median.poorlytopped_percent.median(),'a_d_good':False,'tab_name':'Topped%'},100 'poorlyweak_percent':{'format':':.1%','average':df_median.poorlyweak_percent.median(),'a_d_good':False,'tab_name':'Weak%'},101 'hard_hit_percent':{'format':':.1%','average':df_median.hard_hit_percent.median(),'a_d_good':True,'tab_name':'HardHit%'},102 'z_swing_percent':{'format':':.1%','average':df.in_zone_swing.sum()/df.in_zone.sum(),'a_d_good':True,'tab_name':'Z-Swing%'},103 'z_swing_miss_percent':{'format':':.1%','average':df.in_zone_swing_miss.sum()/df.in_zone_swing.sum(),'a_d_good':False,'tab_name':'Z-Whiff%'},104 105 'out_zone_percent':{'format':':.1%','average':df.out_zone.sum()/df.pitch_count.sum(),'a_d_good':True,'tab_name':'O-Zone%'},106 'meatball_swing_percent':{'format':':.1%','average':df_median.meatball_swing_percent.median(),'a_d_good':True,'tab_name':'Meatball Swing%'},107 'meatball_percent':{'format':':.1%','average':df_median.meatball_percent.median(),'a_d_good':True,'tab_name':'Meatball%'},108 'iz_contact_percent':{'format':':.1%','average':1 - df.in_zone_swing_miss.sum()/df.in_zone_swing.sum(),'a_d_good':True,'tab_name':'Z-Contact%'},109 'in_zone_percent':{'format':':.1%','average':df.in_zone.mean()/df.pitch_count.sum(),'a_d_good':True,'tab_name':'Zone%'},110 'oz_swing_percent':{'format':':.1%','average':df.out_zone_swing.sum()/df.out_zone.sum(),'a_d_good':False,'tab_name':'O-Swing%'},111 'oz_swing_miss_percent':{'format':':.1%','average':df.out_zone_swing_miss.sum()/df.out_zone_swing.sum(),'a_d_good':False,'tab_name':'O-Whiff%'},112 'oz_contact_percent':{'format':':.1%','average':1 - df.out_zone_swing_miss.sum()/df.out_zone_swing.sum(),'a_d_good':True,'tab_name':'O-Contact%'},113 'edge_percent':{'format':':.1%','average':df_median.edge_percent.median(),'a_d_good':True,'tab_name':'Edge%'},114 'whiff_percent':{'format':':.1%','average':(df.in_zone_swing_miss.sum() + df.out_zone_swing_miss.sum()) / (df.in_zone_swing.sum() + df.out_zone_swing.sum()),'a_d_good':False,'tab_name':'Whiff%'},115 'swstr_percent':{'format':':.1%','average':(df.in_zone_swing_miss.sum() + df.out_zone_swing_miss.sum()) / (df.pitch_count.sum()),'a_d_good':False,'tab_name':'SwStr%'},116 'swing_percent':{'format':':.1%','average':(df.in_zone_swing.sum() + df.out_zone_swing.sum()) / (df.pitch_count.sum()),'a_d_good':True,'tab_name':'Swing%'},117 'pull_percent':{'format':':.1%','average':df_median.hit.median(),'a_d_good':True,'tab_name':'Pull%'},118 'straightaway_percent':{'format':':.1%','average':df_median.hit.median(),'a_d_good':True,'tab_name':'Straightaway%'},119 'opposite_percent':{'format':':.1%','average':df_median.hit.median(),'a_d_good':True,'tab_name':'Opposite%'},120 'f_strike_percent':{'format':':.1%','average':df_median.hit.median(),'a_d_good':False,'tab_name':'1st Strike%'},121 'groundballs_percent':{'format':':.1%','average':df.groundballs.sum()/df.batted_ball.sum(),'a_d_good':False,'tab_name':'GB%'},122 'flyballs_percent':{'format':':.1%','average':df.flyballs.sum()/df.batted_ball.sum(),'a_d_good':True,'tab_name':'FB%'},123 'linedrives_percent':{'format':':.1%','average':df.linedrives.sum()/df.batted_ball.sum(),'a_d_good':True,'tab_name':'LD%'},124 'popups_percent':{'format':':.1%','average':df.popups.sum()/df.batted_ball.sum(),'a_d_good':False,'tab_name':'PU%'},}125 126column_dict = pd.DataFrame(format_dict.keys(),[format_dict[x]['tab_name'] for x in format_dict.keys()]).reset_index().set_index(0).to_dict()['index']127 128 129def server(input,output,session):130 131 132 @output133 @render.text134 @reactive.event(input.go, ignore_none=False) 135 def txt_title():136 if input.player_id() == '':137 return 'Select a Player'138 139 player_input = int(input.player_id())140 season_1 = max(2015,int(input.season_1()))141 season_2 = min(2023,int(input.season_2()))142 143 if season_1 < season_2:144 season_pick = [season_1,season_2]145 146 elif season_1 > season_2:147 season_pick = [season_2,season_1]148 149 if len(str(input.player_id())) == 0:150 return 'Select a Batter'151 if str(input.season_1()) == str(input.season_2()):152 return 'Select Different Seasons'153 if len(df[(df.player_id == player_input)&(df.year == season_pick[0])] )== 0 or len(df[(df.player_id == player_input)&(df.year == season_pick[1])] )== 0:154 return 'Select Different Seasons'155 return f'{player_dict[int(input.player_id())]} Statcast Season Comparison'156 157 @output158 @render.text159 @reactive.event(input.go, ignore_none=False) 160 def txt_title_compare():161 if type(input.player_id()) is int or input.player_id()=='':162 return163 164 if type(input.player_id_2()) is int or input.player_id_2()=='':165 return 166 167 player_input_1 = int(input.player_id())168 player_input_2 = int(input.player_id_2())169 170 171 # season_pick = [input.season_1(),input.season_2()]172 columns_i_want = list(input.row_select())173 print(columns_i_want)174 season_1 = max(2015,int(input.season_1()))175 season_2 = min(2023,int(input.season_2()))176 177 player_list = [player_input_1,player_input_2]178 name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]179 season_pick_list = [season_1,season_2]180 181 if len(str(input.player_id())) == 0:182 return 'Select a Batter'183 if len(str(input.player_id_2())) == 0:184 return 'Select a Batter'185 186 if str(input.player_id()) == str(input.player_id_2()) and str(input.season_1()) == str(input.season_2()):187 return 'Select Different Seasons'188 if len(df[(df.player_id == player_list[0])&(df.year == season_pick_list[0])] )== 0 or len(df[(df.player_id == player_list[1])&(df.year == season_pick_list[1])])== 0:189 return 'No Data for Specified Batter in Given Season'190 191 return f'Statcast Season Comparison'192 193 194 195 196 @output197 @render.text198 @reactive.event(input.go, ignore_none=False)199 def text_2022():200 if input.player_id() == '':201 return 'Select a Player'202 return f'{int(input.season_1())} Season Results Compares to MLB Average'203 204 205 @output206 @render.text207 @reactive.event(input.go, ignore_none=False)208 def text_2022_1():209 if type(input.player_id()) is int or input.player_id()=='':210 return211 212 if type(input.player_id_2()) is int or input.player_id_2()=='':213 return 214 season_1 = max(2015,int(input.season_1()))215 season_2 = min(2023,int(input.season_2()))216 season_pick_list = [season_1,season_2]217 player_input_1 = int(input.player_id())218 player_input_2 = int(input.player_id_2())219 name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]220 return f"{name_list[0]} '{str(season_pick_list[0])[2:]} Season Results Compares to MLB Average"221 222 @output223 @render.text224 @reactive.event(input.go, ignore_none=False)225 def text_2023():226 if input.player_id() == '':227 return 'Select a Player'228 return f'{int(input.season_2())} Season Results Compares to MLB Average'229 230 @output231 @render.text232 @reactive.event(input.go, ignore_none=False)233 def text_2023_1():234 if type(input.player_id()) is int or input.player_id()=='':235 return236 237 if type(input.player_id_2()) is int or input.player_id_2()=='':238 return 239 season_1 = max(2015,int(input.season_1()))240 season_2 = min(2023,int(input.season_2()))241 season_pick_list = [season_1,season_2]242 player_input_1 = int(input.player_id())243 player_input_2 = int(input.player_id_2())244 name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]245 return f"{name_list[1]} '{str(season_pick_list[1])[2:]} Season Results Compares to MLB Average"246 247 @output248 @render.text249 @reactive.event(input.go, ignore_none=False)250 def text_diff():251 if input.player_id() == '':252 return 'Select a Player'253 return f'Difference Compares {int(input.season_2())} Results to {int(input.season_1())} Results'254 255 @output256 @render.text257 @reactive.event(input.go, ignore_none=False)258 def text_diff_compare():259 if type(input.player_id()) is int or input.player_id()=='':260 return261 262 if type(input.player_id_2()) is int or input.player_id_2()=='':263 return 264 player_input_1 = int(input.player_id())265 player_input_2 = int(input.player_id_2())266 name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]267 return f'Difference Compares {name_list[0]} Results to {name_list[1]} Results'268 269 270 271 @output272 @render.table273 @reactive.event(input.go, ignore_none=False)274 def statcast_compare():275 if input.player_id() == '':276 return277 278 if len(str(input.player_id())) == 0:279 return280 if len(str(input.player_id_2())) == 0:281 return282 283 284 player_input = int(input.player_id())285 286 287 # season_pick = [input.season_1(),input.season_2()]288 columns_i_want = list(input.row_select())289 print(columns_i_want)290 season_1 = max(2015,int(input.season_1()))291 season_2 = min(2023,int(input.season_2()))292 293 294 if season_1 < season_2:295 season_pick = [season_1,season_2]296 297 elif season_1 > season_2:298 season_pick = [season_2,season_1]299 300 else:301 return302 303 print(df[(df.player_id == player_input)&(df.year == season_pick[0])])304 305 306 if len(df[(df.player_id == player_input)&(df.year == season_pick[0])] )== 0 or len(df[(df.player_id == player_input)&(df.year == season_pick[1])] )== 0:307 return308 309 df_compare = pd.concat([df[(df.player_id == player_input)&(df.year == season_pick[0])][[ 'player_age', 'pa']+columns_i_want],310 df[(df.player_id == player_input)&(df.year == season_pick[1])][[ 'player_age', 'pa']+columns_i_want]]).reset_index(drop=True).T311 312 313 print('test')314 print(sum(df.player_id == input.player_id()))315 df_compare.columns = season_pick316 317 df_compare['Difference'] = df_compare.loc[columns_i_want][season_pick[1]] - df_compare.loc[columns_i_want][season_pick[0]]318 319 df_compare_style = df_compare.style.format(320 "{:.0f}")321 df_compare_style = df_compare_style.set_properties(**{'background-color': 'white',322 'color': 'white'},subset=(['player_age', 'pa'],df_compare_style.columns[2])).set_properties(323 **{'min-width':'100px'},overwrite=False).set_table_styles(324 [{'selector': 'th:first-child', 'text-align': 'center','props': [('background-color', 'white')]}],overwrite=False).set_table_styles(325 [{'selector': 'tr:first-child','text-align': 'center', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(326 [{'selector': 'index','text-align': 'center', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(327 [{'selector': 'th', 'text-align': 'center','props': [('line-height', '40px'),('min-width', '30px')]}],overwrite=False).set_properties(328 329 **{'Height': '20px'},**{'text-align': 'center'},overwrite=False).set_table_styles([{330 'selector': 'caption',331 'props': [332 ('color', ''),333 ('fontname', 'Century Gothic'),334 ('font-size', '20px'),335 ('font-style', 'italic'),336 ('font-weight', ''),337 ('text-align', 'centre'),338 ]339 340 },{'selector' :'th', 'props':[('text-align', 'center'),('font-size', '20px'),('Height','20px'),('min-width','200px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '20px'),('min-width','100px')]}],overwrite=False)341 342 343 for r in columns_i_want:344 if format_dict[r]['a_d_good']:345 cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])346 else:347 cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])348 349 350 colormap = plt.get_cmap(cmap)351 norm = Normalize(vmin=0.7, vmax=1.3)352 353 normalized_value = norm(df_compare[df_compare.columns[0]][r]/format_dict[r]['average'])354 df_compare_style.format(355 f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[0])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),356 'color': 'black'},subset=(r,df_compare_style.columns[0]))357 358 norm = Normalize(vmin=0.7, vmax=1.3)359 normalized_value = norm(df_compare[df_compare.columns[1]][r]/format_dict[r]['average'])360 df_compare_style.format(361 f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[1])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),362 'color': 'black'},subset=(r,df_compare_style.columns[1]))363 364 norm = Normalize(vmin=0.7, vmax=1.3)365 normalized_value = norm(df_compare[df_compare.columns[1]][r]/df_compare[df_compare.columns[0]][r])366 df_compare_style.format(367 f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[2])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),368 'color': 'black'},subset=(r,df_compare_style.columns[2]))369 370 371 372 373 df_compare_style.relabel_index(['Age', 'PA']+[format_dict[x]['tab_name'] for x in columns_i_want]).set_properties(374 **{'border': '1px black solid !important'},overwrite=False).set_table_styles(375 [{"selector": "", "props": [("border", "1px solid")]},376 {"selector": "tbody td", "props": [("border", "1px solid")]},377 {"selector": "th", "props": [("border", "1px solid")]}],overwrite=False)378 379 #df_compare = df_compare.fillna(np.nan) 380 381 return df_compare_style382 383 384 385 @output386 @render.table387 @reactive.event(input.go, ignore_none=False)388 def statcast_compare_2():389 390 # if input.player_id() == 0:391 # return392 if type(input.player_id()) is int or input.player_id()=='':393 return394 395 if type(input.player_id_2()) is int or input.player_id_2()=='':396 return 397 # if len(str(input.player_id())) == 0:398 # return399 400 player_input_1 = int(input.player_id())401 player_input_2 = int(input.player_id_2())402 403 404 # season_pick = [input.season_1(),input.season_2()]405 columns_i_want = list(input.row_select())406 print(columns_i_want)407 season_1 = max(2015,int(input.season_1()))408 season_2 = min(2023,int(input.season_2()))409 410 411 # if season_1 < season_2:412 # season_pick = [season_1,season_2]413 414 # elif season_1 > season_2:415 # season_pick = [season_2,season_1]416 417 # else:418 # return419 420 #print(df[(df.player_id == player_input)&(df.year == season_pick[0])])421 #player_list = ['Elly De La Cruz','Aaron Judge']422 player_list = [player_input_1,player_input_2]423 name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]424 season_pick_list = [season_1,season_2]425 426 if len(df[(df.player_id == player_list[0])&(df.year == season_pick_list[0])] )== 0 or len(df[(df.player_id == player_list[1])&(df.year == season_pick_list[1])] )== 0:427 return428 if str(input.player_id()) == str(input.player_id_2()) and str(input.season_1()) == str(input.season_2()):429 return430 431 432 df_compare = pd.concat([df[(df.player_id == player_list[0])&(df.year == season_pick_list[0])][[ 'year','player_age', 'pa']+columns_i_want],433 df[(df.player_id == player_list[1])&(df.year == season_pick_list[1])][[ 'year','player_age', 'pa']+columns_i_want]]).reset_index(drop=True).T434 435 df_compare.columns = [f"{name_list[0]} '{str(season_pick_list[0])[2:]}",f"{name_list[1]} '{str(season_pick_list[1])[2:]}"]436 df_compare['Difference'] = df_compare.loc[columns_i_want][df_compare.columns [0]] - df_compare.loc[columns_i_want][df_compare.columns[1]]437 #df_compare = df_compare.fillna(np.nan)438 439 df_compare_style = df_compare.style.format(440 "{:.0f}")441 df_compare_style = df_compare_style.set_properties(**{'background-color': 'white',442 'color': 'white'},subset=(['year','player_age', 'pa'],df_compare_style.columns[2])).set_properties(443 **{'min-width':'125px'},overwrite=False).set_table_styles(444 [{'selector': 'th:first-child', 'text-align': 'center','props': [('background-color', 'white')]}],overwrite=False).set_table_styles(445 [{'selector': 'tr:first-child','text-align': 'center', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(446 [{'selector': 'index','text-align': 'center', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(447 [{'selector': 'th', 'text-align': 'center','props': [('line-height', '40px'),('min-width', '30px')]}],overwrite=False).set_properties(448 449 **{'Height': '20px'},**{'text-align': 'center'},overwrite=False).set_table_styles([{450 'selector': 'caption',451 'props': [452 ('color', ''),453 ('fontname', 'Century Gothic'),454 ('font-size', '20px'),455 ('font-style', 'italic'),456 ('font-weight', ''),457 ('text-align', 'centre'),458 ]459 460 },{'selector' :'th', 'props':[('text-align', 'center'),('font-size', '20px'),('Height','20px'),('min-width','200px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '20px'),('min-width','100px')]}],overwrite=False)461 462 463 for r in columns_i_want:464 if format_dict[r]['a_d_good']:465 cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])466 else:467 cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])468 469 470 colormap = plt.get_cmap(cmap)471 norm = Normalize(vmin=0.5, vmax=1.5)472 normalized_value = norm(df_compare[df_compare.columns[0]][r]/format_dict[r]['average'])473 df_compare_style.format(474 f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[0])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),475 'color': 'black'},subset=(r,df_compare_style.columns[0]))476 477 norm = Normalize(vmin=0.5, vmax=1.5)478 normalized_value = norm(df_compare[df_compare.columns[1]][r]/format_dict[r]['average'])479 df_compare_style.format(480 f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[1])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),481 'color': 'black'},subset=(r,df_compare_style.columns[1]))482 483 norm = Normalize(vmin=0.8, vmax=1.2)484 normalized_value = norm(df_compare[df_compare.columns[0]][r]/df_compare[df_compare.columns[1]][r])485 df_compare_style.format(486 f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[2])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),487 'color': 'black'},subset=(r,df_compare_style.columns[2]))488 489 df_compare_style = df_compare_style490 491 492 df_compare_style.relabel_index(['Year','Age', 'PA']+[format_dict[x]['tab_name'] for x in columns_i_want]).set_properties(493 **{'border': '1px black solid !important'},overwrite=False).set_table_styles(494 [{"selector": "", "props": [("border", "1px solid")]},495 {"selector": "tbody td", "props": [("border", "1px solid")]},496 {"selector": "th", "props": [("border", "1px solid")]}],overwrite=False)497 498 #df_compare = df_compare.fillna(np.nan) 499 500 return df_compare_style501 502 @output503 @render.table504 @reactive.event(input.go, ignore_none=False)505 def colour_scale():506 off_b2b_df = pd.DataFrame(data={'one':-0.30,'two':0,'three':0.30},index=[0])507 off_b2b_df_style = off_b2b_df.style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{508 'selector': 'caption',509 'props': [510 ('color', ''),511 ('fontname', 'Century Gothic'),512 ('font-size', '20px'),513 ('font-style', 'italic'),514 ('font-weight', ''),515 ('text-align', 'centre'),516 ]517 518 },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),(519 'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(520 **{'background-color':'White','index':'White','min-width':'150px'},overwrite=False).set_table_styles(521 [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(522 [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(523 [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(524 **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).set_properties(525 **{'background-color':'#4285F4'},subset=off_b2b_df.columns[0]).set_properties(526 **{'background-color':'white'},subset=off_b2b_df.columns[1]).set_properties(527 **{'background-color':'#FBBC04'},subset=off_b2b_df.columns[2]).set_properties(528 **{'color':'black'},subset=off_b2b_df.columns[:]).hide_index().set_table_styles([529 {'selector': 'thead', 'props': [('display', 'none')]}530 ]).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(531 **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))).set_properties(532 **{'min-width':'130'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])),overwrite=False).set_properties(**{533 'color': 'black'},overwrite=False).set_properties(534 **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))) .format(535 "{:+.0%}")536 return off_b2b_df_style537 538 @output539 @render.table540 @reactive.event(input.go, ignore_none=False)541 def colour_scale_2():542 off_b2b_df = pd.DataFrame(data={'one':-0.30,'two':0,'three':0.30},index=[0])543 off_b2b_df_style = off_b2b_df.style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{544 'selector': 'caption',545 'props': [546 ('color', ''),547 ('fontname', 'Century Gothic'),548 ('font-size', '20px'),549 ('font-style', 'italic'),550 ('font-weight', ''),551 ('text-align', 'centre'),552 ]553 554 },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),(555 'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(556 **{'background-color':'White','index':'White','min-width':'150px'},overwrite=False).set_table_styles(557 [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(558 [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(559 [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(560 **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).set_properties(561 **{'background-color':'#4285F4'},subset=off_b2b_df.columns[0]).set_properties(562 **{'background-color':'white'},subset=off_b2b_df.columns[1]).set_properties(563 **{'background-color':'#FBBC04'},subset=off_b2b_df.columns[2]).set_properties(564 **{'color':'black'},subset=off_b2b_df.columns[:]).hide_index().set_table_styles([565 {'selector': 'thead', 'props': [('display', 'none')]}566 ]).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(567 **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))).set_properties(568 **{'min-width':'130'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])),overwrite=False).set_properties(**{569 'color': 'black'},overwrite=False).set_properties(570 **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))) .format(571 "{:+.0%}")572 573 574 return off_b2b_df_style575# test = test.fillna(0)576#test['PP TOI'] = ["%d:%02d" % (int(x),(x*60)%60) if x>0 else '0:00' for x in test['PP TOI']]577 578 579statcast_compare = App(ui.page_fluid(580 ui.tags.base(href=base_url), 581 ui.tags.div(582 {"style": "width:90%;margin: 0 auto;max-width: 1600px;"},583 ui.tags.style(584 """585 h4 {586 margin-top: 1em;font-size:35px;587 }588 h2{589 font-size:25px;590 }591 """592 ),593 shinyswatch.theme.simplex(),594 ui.tags.h4("TJStats"),595 ui.tags.i("Baseball Analytics and Visualizations"),596 ui.markdown("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""),597 ui.navset_tab(598 ui.nav_control(599 ui.a(600 "Home",601 href="home/"602 ),603 ),604 ui.nav_menu(605 "Batter Charts",606 ui.nav_control(607 ui.a(608 "Batting Rolling",609 href="rolling_batter/"610 ),611 ui.a(612 "Spray & Damage",613 href="spray/"614 ),615 ui.a(616 "Decision Value",617 href="decision_value/"618 ),619 # ui.a(620 # "Damage Model",621 # href="damage_model/"622 # ),623 ui.a(624 "Batter Scatter",625 href="batter_scatter/"626 ),627 # ui.a(628 # "EV vs LA Plot",629 # href="ev_angle/"630 # ),631 ui.a(632 "Statcast Compare",633 href="statcast_compare/"634 )635 ),636 ),637 ui.nav_menu(638 "Pitcher Charts",639 ui.nav_control(640 ui.a(641 "Pitcher Rolling",642 href="rolling_pitcher/"643 ),644 ui.a(645 "Pitcher Summary",646 href="pitching_summary_graphic_new/"647 ),648 ui.a(649 "Pitcher Scatter",650 href="pitcher_scatter/"651 )652 ),653 )),ui.row(654 ui.layout_sidebar(655 656 657 658 ui.panel_sidebar(659 #ui.input_date_range("date_range_id", "Date range input",start = statcast_df.game_date.min(), end = statcast_df.game_date.max()),660 ui.input_select("player_id", "Select Player 1",player_dict,width=1,size=1,selectize=True,multiple=False,selected=592450),661 ui.input_select("player_id_2", "Select Player 2 (For Player Compare Tab)",player_dict,width=1,size=1,selectize=True,multiple=False,selected=592450),662 ui.input_numeric("season_1", "Season 1", value=2022,min=2015,max=2023),663 ui.input_numeric("season_2", "Season 2", value=2023,min=2015,max=2023),664 ui.input_select("row_select", "Select Stats",665 column_dict,width=1,size=1,selectize=True,666 multiple=True,667 selected=['k_percent','bb_percent','woba','xwoba','iz_contact_percent','oz_swing_percent','whiff_percent']),668 ui.input_action_button("go", "Generate",class_="btn-primary",669 )),670 671 ui.panel_main(ui.tags.h3(""),672 ui.navset_tab(673 ui.nav("Single Player",674 ui.div({"style": "font-size:2.1em;"},ui.output_text("txt_title")),675 #ui.tags.h2("Fantasy Hockey Schedule Summary"),676 ui.tags.h5("Created By: @TJStats, Data: MLB"),677 #ui.div({"style": "font-size:1.6em;"},ui.output_text("txt")),678 ui.output_table("statcast_compare"),679 #ui.tags.h5('Legend'),680 ui.tags.h3(""),681 ui.tags.h5('Colour Scale:'),682 ui.output_table("colour_scale"),683 ui.div({"style": "font-size:1em;"},ui.output_text("text_2022")),684 ui.div({"style": "font-size:1em;"},ui.output_text("text_2023")),685 ui.div({"style": "font-size:1em;"},ui.output_text("text_diff"))),686 687 ui.nav("Player Compare",688 ui.div({"style": "font-size:2.1em;"},ui.output_text("txt_title_compare")),689 #ui.tags.h2("Fantasy Hockey Schedule Summary"),690 ui.tags.h5("Created By: @TJStats, Data: MLB"),691 #ui.div({"style": "font-size:1.6em;"},ui.output_text("txt")),692 ui.output_table("statcast_compare_2"),693 ui.tags.h3(""),694 ui.tags.h5('Colour Scale:'),695 ui.output_table("colour_scale_2"),696 ui.div({"style": "font-size:1em;"},ui.output_text("text_2022_1")),697 ui.div({"style": "font-size:1em;"},ui.output_text("text_2023_1")),698 ui.div({"style": "font-size:1em;"},ui.output_text("text_diff_compare")))699 )700 ),701 )),)),server)