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from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
import datasets
from datasets import load_dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import gaussian_kde
import matplotlib
from matplotlib.ticker import MaxNLocator
from matplotlib.gridspec import GridSpec
from scipy.stats import zscore
import math
import matplotlib
from adjustText import adjust_text
import matplotlib.ticker as mtick
from shinywidgets import output_widget, render_widget
import pandas as pd
from configure import base_url
import shinyswatch
### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2023.csv' ])
dataset_train = dataset['train']
df_2023 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
print(df_2023)
### Normalize Hit Locations
df_2023['season'] = df_2023['game_date'].str[0:4].astype(int)
# df_2023['hit_x'] = df_2023['hit_x'] - df_2023['hit_x'].median()
# df_2023['hit_y'] = -df_2023['hit_y']+df_2023['hit_y'].quantile(0.9999)
df_2023['hit_x'] = df_2023['hit_x'] - 126#df_2023['hit_x'].median()
df_2023['hit_y'] = -df_2023['hit_y']+204.5#df_2023['hit_y'].quantile(0.9999)
df_2023['hit_x_og'] = df_2023['hit_x']
df_2023.loc[df_2023['batter_hand'] == 'R','hit_x'] = -1*df_2023.loc[df_2023['batter_hand'] == 'R','hit_x']
df_2023['h_la'] = np.arctan(df_2023['hit_x'] / df_2023['hit_y'])*180/np.pi
conditions_ss = [
(df_2023['h_la']<-15),
(df_2023['h_la']<15)&(df_2023['h_la']>=-15),
(df_2023['h_la']>=15)
]
choices_ss = ['Oppo','Straight','Pull']
df_2023['traj'] = np.select(conditions_ss, choices_ss, default=np.nan)
df_2023['bip'] = [1 if x > 0 else np.nan for x in df_2023['launch_speed']]
conditions_woba = [
(df_2023['event_type']=='walk'),
(df_2023['event_type']=='hit_by_pitch'),
(df_2023['event_type']=='single'),
(df_2023['event_type']=='double'),
(df_2023['event_type']=='triple'),
(df_2023['event_type']=='home_run'),
]
choices_woba = [1,
1,
1,
2,
3,
4]
# choices_woba = [0.698,
# 0.728,
# 0.887,
# 1.253,
# 1.583,
# 2.027]
df_2023['woba'] = np.select(conditions_woba, choices_woba, default=0)
choices_woba_train = [1,
1,
1,
2,
3,
4]
df_2023['woba_train'] = np.select(conditions_woba, choices_woba_train, default=0)
df_2023_bip = df_2023[~df_2023['bip'].isnull()].dropna(subset=['h_la','launch_angle'])
df_2023_bip['h_la'] = df_2023_bip['h_la'].round(0)
df_2023_bip['season'] = df_2023_bip['game_date'].str[0:4].astype(int)
df_2023_bip = df_2023[~df_2023['bip'].isnull()].dropna(subset=['launch_angle','bip'])
df_2023_bip_train = df_2023_bip[df_2023_bip['season'] == 2023]
batter_dict = df_2023_bip.sort_values('batter_name').set_index('batter_id')['batter_name'].to_dict()
features = ['launch_angle','launch_speed','h_la']
target = ['woba_train']
df_2023_bip_train = df_2023_bip_train.dropna(subset=features)
import joblib
# # Dump the model to a file named 'model.joblib'
model = joblib.load('xtb_model.joblib')
df_2023_bip_train['y_pred'] = [sum(x) for x in model.predict_proba(df_2023_bip_train[features]) * ([0,1,2,3,4])]
# df_2023_bip_train['y_pred_noh'] = [sum(x) for x in model_noh.predict_proba(df_2023_bip_train[['launch_angle','launch_speed']]) * ([0,0.887,1.253,1.583,2.027])]
df_2023_output = df_2023_bip_train.groupby(['batter_id','batter_name']).agg(
bip = ('y_pred','count'),
y_pred = ('y_pred','sum'),
slgcon = ('woba','mean'),
xslgcon = ('y_pred','mean'),
launch_speed = ('launch_speed','mean'),
launch_angle_std = ('launch_angle','median'),
h_la_std = ('h_la','mean'))
df_2023_output_copy = df_2023_output.copy()
# df_2023_output = df_2023_output[df_2023_output['bip'] > 100]
# df_2023_output[df_2023_output['bip'] > 100].sort_values(by='h_la_std',ascending=True).head(20)
import pandas as pd
import numpy as np
# Create grid coordinates
x = np.arange(30, 121,1 )
y = np.arange(-30, 61,1 )
z = np.arange(-45, 46,1 )
# Create a meshgrid
X, Y, Z = np.meshgrid(x, y, z, indexing='ij')
# Flatten the meshgrid to get x and y coordinates
x_flat = X.flatten()
y_flat = Y.flatten()
z_flat = Z.flatten()
# Create a DataFrame
df = pd.DataFrame({'launch_speed': x_flat, 'launch_angle': y_flat,'h_la':z_flat})
df['y_pred'] = [sum(x) for x in model.predict_proba(df[features]) * ([0,1,2,3,4])]
import matplotlib
colour_palette = ['#FFB000','#648FFF','#785EF0',
'#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[1],'#ffffff',colour_palette[0]])
cmap_hue2 = matplotlib.colors.LinearSegmentedColormap.from_list("",['#ffffff',colour_palette[0]])
from matplotlib.pyplot import text
import inflect
from scipy.stats import percentileofscore
p = inflect.engine()
def server(input,output,session):
@output
@render.plot(alt="hex_plot")
@reactive.event(input.go, ignore_none=False)
def hex_plot():
if input.batter_id() is "":
fig = plt.figure(figsize=(12, 12))
fig.text(s='Please Select a Batter',x=0.5,y=0.5)
return
batter_select_id = int(input.batter_id())
# batter_select_name = 'Edouard Julien'
quant = int(input.quant())/100
df_batter_og = df_2023_bip_train[df_2023_bip_train['batter_id']==batter_select_id]
# df_batter_og = df_2023_bip_train[df_2023_bip_train['batter_name']==batter_select_name]
df_batter = df_batter_og[df_batter_og['launch_speed'] >= df_batter_og['launch_speed'].quantile(quant)]
# df_batter_best_speed = df_batter['launch_speed'].mean().round()
# df_bip_league = df_2023_bip_train[df_2023_bip_train['launch_speed'] >= df_2023_bip_train['launch_speed'].quantile(quant)]
import pandas as pd
import numpy as np
# Create grid coordinates
#x = np.arange(30, 121,1 )
y_b = np.arange(df_batter['launch_angle'].median()-df_batter['launch_angle'].std(),
df_batter['launch_angle'].median()+df_batter['launch_angle'].std(),1 )
z_b = np.arange(df_batter['h_la'].median()-df_batter['h_la'].std(),
df_batter['h_la'].median()+df_batter['h_la'].std(),1 )
# Create a meshgrid
Y_b, Z_b = np.meshgrid( y_b,z_b, indexing='ij')
# Flatten the meshgrid to get x and y coordinates
y_flat_b = Y_b.flatten()
z_flat_b = Z_b.flatten()
# Create a DataFrame
df_batter_base = pd.DataFrame({'launch_angle': y_flat_b,'h_la':z_flat_b,'c':[0]*len(y_flat_b)})
# df_batter_base['y_pred'] = [sum(x) for x in model.predict_proba(df_batter_base[features]) * ([0,1,2,3,4])]
from matplotlib.gridspec import GridSpec
# fig,ax = plt.subplots(figsize=(12, 12),dpi=150)
fig = plt.figure(figsize=(12,12))
gs = GridSpec(4, 3, height_ratios=[0.5,10,1.5,0.2], width_ratios=[0.05,0.9,0.05])
axheader = fig.add_subplot(gs[0, :])
ax10 = fig.add_subplot(gs[1, 0])
ax = fig.add_subplot(gs[1, 1]) # Subplot at the top-right position
ax12 = fig.add_subplot(gs[1, 2])
ax2_ = fig.add_subplot(gs[2, :])
axfooter1 = fig.add_subplot(gs[-1, :])
axheader.axis('off')
ax10.axis('off')
ax12.axis('off')
ax2_.axis('off')
axfooter1.axis('off')
extents = [-45,45,-30,60]
def hexLines(a=None,i=None,off=[0,0]):
'''regular hexagon segment lines as `(xy1,xy2)` in clockwise
order with points in line sorted top to bottom
for irregular hexagon pass both `a` (vertical) and `i` (horizontal)'''
if a is None: a = 2 / np.sqrt(3) * i;
if i is None: i = np.sqrt(3) / 2 * a;
h = a / 2
xy = np.array([ [ [ 0, a], [ i, h] ],
[ [ i, h], [ i,-h] ],
[ [ i,-h], [ 0,-a] ],
[ [-i,-h], [ 0,-a] ], #flipped
[ [-i, h], [-i,-h] ], #flipped
[ [ 0, a], [-i, h] ] #flipped
])
return xy+off;
h = ax.hexbin(x=df_batter_base['h_la'],
y=df_batter_base['launch_angle'],
gridsize=25,
edgecolors='k',
extent=extents,mincnt=1,lw=2,zorder=-3,)
# cfg = {**cfg,'vmin':h.get_clim()[0], 'vmax':h.get_clim()[1]}
# plt.hexbin( ec="black" ,lw=6,zorder=4,mincnt=2,**cfg,alpha=0.1)
# plt.hexbin( ec="#ffffff",lw=1,zorder=5,mincnt=2,**cfg,alpha=0.1)
ax.hexbin(x=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['h_la'],
y=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['launch_angle'],
C=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['y_pred'],
gridsize=25,
vmin=0,
vmax=4,
cmap=cmap_hue2,
extent=extents,zorder=-3)
# Get the counts and centers of the hexagons
counts = ax.hexbin(x=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['h_la'],
y=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['launch_angle'],
C=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['y_pred'],
gridsize=25,
vmin=0,
vmax=4,
cmap=cmap_hue2,
extent=extents).get_array()
bin_centers = ax.hexbin(x=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['h_la'],
y=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['launch_angle'],
C=df[(df['launch_angle']>=-30)&(df['launch_angle']<=60)&(df['launch_speed']>=df_batter['launch_speed'].median())&(df['launch_speed']<=df_batter['launch_speed'].max())]['y_pred'],
gridsize=25,
vmin=0,
vmax=4,
cmap=cmap_hue2,
extent=extents).get_offsets()
# Add text with the values of "C" to each hexagon
for count, (x, y) in zip(counts, bin_centers):
if count >= 1:
ax.text(x, y, f'{count:.1f}', color='black', ha='center', va='center',fontsize=7)
#get hexagon centers that should be highlighted
verts = h.get_offsets()
cnts = h.get_array()
highl = verts[cnts > .5*cnts.max()]
#create hexagon lines
a = ((verts[0,1]-verts[1,1])/3).round(6)
i = ((verts[1:,0]-verts[:-1,0])/2).round(6)
i = i[i>0][0]
lines = np.concatenate([hexLines(a,i,off) for off in highl])
#select contour lines and draw
uls,c = np.unique(lines.round(4),axis=0,return_counts=True)
for l in uls[c==1]: ax.plot(*l.transpose(),'w-',lw=2,scalex=False,scaley=False,color=colour_palette[1],zorder=100)
# Plot filled hexagons
for hc in highl:
hx = hc[0] + np.array([0, i, i, 0, -i, -i])
hy = hc[1] + np.array([a, a/2, -a/2, -a, -a/2, a/2])
ax.fill(hx, hy, color=colour_palette[1], alpha=0.15, edgecolor=None) # Adjust color and alpha as needed
# # Create grid coordinates
# #x = np.arange(30, 121,1 )
# y_b = np.arange(df_bip_league['launch_angle'].median()-df_bip_league['launch_angle'].std(),
# df_bip_league['launch_angle'].median()+df_bip_league['launch_angle'].std(),1 )
# z_b = np.arange(df_bip_league['h_la'].median()-df_bip_league['h_la'].std(),
# df_bip_league['h_la'].median()+df_bip_league['h_la'].std(),1 )
# # Create a meshgrid
# Y_b, Z_b = np.meshgrid( y_b,z_b, indexing='ij')
# # Flatten the meshgrid to get x and y coordinates
# y_flat_b = Y_b.flatten()
# z_flat_b = Z_b.flatten()
# # Create a DataFrame
# df_league_base = pd.DataFrame({'launch_angle': y_flat_b,'h_la':z_flat_b,'c':[0]*len(y_flat_b)})
# h_league = ax.hexbin(x=df_league_base['h_la'],
# y=df_league_base['launch_angle'],
# gridsize=25,
# edgecolors=colour_palette[1],
# extent=extents,mincnt=1,lw=2,zorder=-3,)
# #get hexagon centers that should be highlighted
# verts = h_league.get_offsets()
# cnts = h_league.get_array()
# highl = verts[cnts > .5*cnts.max()]
# #create hexagon lines
# a = ((verts[0,1]-verts[1,1])/3).round(6)
# i = ((verts[1:,0]-verts[:-1,0])/2).round(6)
# i = i[i>0][0]
# lines = np.concatenate([hexLines(a,i,off) for off in highl])
# #select contour lines and draw
# uls,c = np.unique(lines.round(4),axis=0,return_counts=True)
# for l in uls[c==1]: ax.plot(*l.transpose(),'w-',lw=2,scalex=False,scaley=False,color=colour_palette[3],zorder=99)
axheader.text(s=f"{df_batter['batter_name'].values[0]} - {int(quant*100)}th% EV and Greater Batted Ball Tendencies",x=0.5,y=0.2,fontsize=20,ha='center',va='bottom')
axheader.text(s=f"2023 Season",x=0.5,y=-0.1,fontsize=14,ha='center',va='top')
ax.set_xlabel(f"Horizontal Spray Angle (°)",fontsize=12)
ax.set_ylabel(f"Vertical Launch Angle (°)",fontsize=12)
ax2_.text(x=0.5,
y=0.0,
s="Notes:\n" \
f"- {int(quant*100)}th% EV and Greater BBE is defined as a batter's top {100 - int(quant*100)}% hardest hit BBE\n" \
f"- Colour Scale and Number Labels Represents the Expected Total Bases for a batter's range of Best Speeds\n" \
f"- Shaded Area Represents the 2-D Region bounded by ±1σ Launch Angle and Horizontal Spray Angle on batter's Best Speed BBE\n"\
f"- {df_batter['batter_name'].values[0]} {int(quant*100)}th% EV and Greater BBE Range from {df_batter['launch_speed'].min():.0f} to {df_batter['launch_speed'].max():.0f} mph ({len(df_batter)} BBE)\n"\
f"- Positive Horizontal Spray Angle Represents a BBE hit in same direction as batter handedness (i.e. Pulled)" ,
fontsize=11,
fontstyle='oblique',
va='bottom',
ha='center',
bbox=dict(facecolor='white', edgecolor='black'),ma='left')
axfooter1.text(0.05, 0.5, "By: Thomas Nestico\n @TJStats",ha='left', va='bottom',fontsize=12)
axfooter1.text(0.95, 0.5, "Data: MLB",ha='right', va='bottom',fontsize=12)
if df_batter['batter_hand'].values[0] == 'R':
ax.invert_xaxis()
ax.grid(False)
ax.axis('equal')
# Adjusting subplot to center it within the figure
fig.subplots_adjust(left=0.01, right=0.99, top=0.975, bottom=0.025)
#ax.text(f"Vertical Spray Angle (°)")
@output
@render.plot(alt="roll_plot")
@reactive.event(input.go, ignore_none=False)
def roll_plot():
# player_select = 'Nolan Gorman'
# player_select_full =player_select
if input.batter_id() is "":
fig = plt.figure(figsize=(12, 12))
fig.text(s='Please Select a Batter',x=0.5,y=0.5)
return
# df_will = df_model_2023[df_model_2023.batter_name == player_select].sort_values(by=['game_date','start_time'])
# df_will = df_will[df_will['is_swing'] != 1]
batter_select_id = int(input.batter_id())
# batter_select_name = 'Edouard Julien'
df_batter_og = df_2023_bip_train[df_2023_bip_train['batter_id']==batter_select_id]
batter_select_name = df_batter_og['batter_name'].values[0]
win = min(int(input.rolling_window()),len(df_batter_og))
df_2023_output = df_2023_output_copy[df_2023_output_copy['bip'] >= win]
sns.set_theme(style="whitegrid", palette="pastel")
#fig, ax = plt.subplots(1, 1, figsize=(10, 10),dpi=300)
from matplotlib.gridspec import GridSpec
# fig,ax = plt.subplots(figsize=(12, 12),dpi=150)
fig = plt.figure(figsize=(12,12))
gs = GridSpec(3, 3, height_ratios=[0.3,10,0.2], width_ratios=[0.01,2,0.01])
axheader = fig.add_subplot(gs[0, :])
ax10 = fig.add_subplot(gs[1, 0])
ax = fig.add_subplot(gs[1, 1]) # Subplot at the top-right position
ax12 = fig.add_subplot(gs[1, 2])
axfooter1 = fig.add_subplot(gs[-1, :])
axheader.axis('off')
ax10.axis('off')
ax12.axis('off')
axfooter1.axis('off')
sns.lineplot( x= range(win,len(df_batter_og.y_pred.rolling(window=win).mean())+1),
y= df_batter_og.y_pred.rolling(window=win).mean().dropna(),
color=colour_palette[0],linewidth=2,ax=ax)
ax.hlines(y=df_batter_og.y_pred.mean(),xmin=win,xmax=len(df_batter_og),color=colour_palette[0],linestyle='--',
label=f'{batter_select_name} Average: {df_batter_og.y_pred.mean():.3f} xSLGCON ({p.ordinal(int(np.around(percentileofscore(df_2023_output["xslgcon"],df_batter_og.y_pred.mean(), kind="strict"))))} Percentile)')
# ax.hlines(y=df_model_2023.y_pred_no_swing.std()*100,xmin=win,xmax=len(df_will))
# sns.scatterplot( x= [976],
# y= df_will.y_pred.rolling(window=win).mean().min()*100,
# color=colour_palette[0],linewidth=2,ax=ax,zorder=100,s=100,edgecolor=colour_palette[7])
ax.hlines(y=df_2023_bip_train['y_pred'].mean(),xmin=win,xmax=len(df_batter_og),color=colour_palette[1],linestyle='-.',alpha=1,
label = f'MLB Average: {df_2023_bip_train["y_pred"].mean():.3f} xSLGCON')
ax.legend()
hard_hit_dates = [df_2023_output['xslgcon'].quantile(0.9),
df_2023_output['xslgcon'].quantile(0.75),
df_2023_output['xslgcon'].quantile(0.25),
df_2023_output['xslgcon'].quantile(0.1)]
ax.hlines(y=df_2023_output['xslgcon'].quantile(0.9),xmin=win,xmax=len(df_batter_og),color=colour_palette[2],linestyle='dotted',alpha=0.5,zorder=1)
ax.hlines(y=df_2023_output['xslgcon'].quantile(0.75),xmin=win,xmax=len(df_batter_og),color=colour_palette[3],linestyle='dotted',alpha=0.5,zorder=1)
ax.hlines(y=df_2023_output['xslgcon'].quantile(0.25),xmin=win,xmax=len(df_batter_og),color=colour_palette[4],linestyle='dotted',alpha=0.5,zorder=1)
ax.hlines(y=df_2023_output['xslgcon'].quantile(0.1),xmin=win,xmax=len(df_batter_og),color=colour_palette[5],linestyle='dotted',alpha=0.5,zorder=1)
hard_hit_text = ['90th %','75th %','25th %','10th %']
for i, x in enumerate(hard_hit_dates):
ax.text(min(win+win/50,win+win+5), x ,hard_hit_text[i], rotation=0,va='center', ha='left',
bbox=dict(facecolor='white',alpha=0.7, edgecolor=colour_palette[2+i], pad=2),zorder=11)
# # Annotate with an arrow
# ax.annotate('June 6, 2023\nSeason Worst Decision Value', xy=(976, df_will.y_pred.rolling(window=win).mean().min()*100-0.03),
# xytext=(976 - 150, df_will.y_pred.rolling(window=win).mean().min()*100 - 0.2),
# arrowprops=dict(facecolor=colour_palette[7], shrink=0.01),zorder=150,fontsize=10,
# bbox=dict(facecolor='white', edgecolor='black'),va='top')
ax.set_xlim(win,len(df_batter_og))
# ax.set_ylim(0.2,max(1,))
ax.set_yticks([0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])
ax.set_xlabel('Balls In Play')
ax.set_ylabel('Expected Total Bases per Ball In Play (xSLGCON)')
from matplotlib.ticker import FormatStrFormatter
ax.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
axheader.text(s=f'{batter_select_name} - MLB - {win} Rolling BIP Expected Slugging on Contact (xSLGCON)',x=0.5,y=-0.5,ha='center',va='bottom',fontsize=14)
axfooter1.text(.05, 0.2, "By: Thomas Nestico",ha='left', va='bottom',fontsize=12)
axfooter1.text(0.95, 0.2, "Data: MLB",ha='right', va='bottom',fontsize=12)
fig.subplots_adjust(left=0.01, right=0.99, top=0.98, bottom=0.02)
damage = App(ui.page_fluid(
ui.tags.base(href=base_url),
ui.tags.div(
{"style": "width:95%;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.markdown("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""),
ui.navset_tab(
ui.nav_control(
ui.a(
"Home",
href="home/"
),
),
ui.nav_menu(
"Batter Charts",
ui.nav_control(
ui.a(
"Batting Rolling",
href="rolling_batter/"
),
ui.a(
"Spray",
href="spray/"
),
ui.a(
"Decision Value",
href="decision_value/"
),
ui.a(
"Damage Model",
href="damage_model/"
),
ui.a(
"Batter Scatter",
href="batter_scatter/"
),
# ui.a(
# "EV vs LA Plot",
# href="ev_angle/"
# ),
ui.a(
"Statcast Compare",
href="statcast_compare/"
)
),
),
ui.nav_menu(
"Pitcher Charts",
ui.nav_control(
ui.a(
"Pitcher Rolling",
href="rolling_pitcher/"
),
ui.a(
"Pitcher Summary",
href="pitching_summary_graphic_new/"
),
ui.a(
"Pitcher Scatter",
href="pitcher_scatter/"
)
),
)),ui.row(
ui.layout_sidebar(
ui.panel_sidebar(
ui.input_select("batter_id",
"Select Batter",
batter_dict,
width=1,
size=1,
selectize=True),
ui.input_numeric("quant",
"Select Percentile",
value=50,
min=0,max=100),
ui.input_numeric("rolling_window",
"Select Rolling Window",
value=50,
min=1),
ui.input_action_button("go", "Generate",class_="btn-primary")),
ui.panel_main(
ui.navset_tab(
ui.nav("Damage Hex",
ui.output_plot('hex_plot',
width='1200px',
height='1200px')),
ui.nav("Damage Roll",
ui.output_plot('roll_plot',
width='1200px',
height='1200px'))
))
)),)),server)