decision_value.py

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_mlb = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)

### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['aaa_pitch_data_2023.csv' ])
dataset_train = dataset['train']
df_2023_aaa = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)

df_2023_mlb['level'] = 'MLB'
df_2023_aaa['level'] = 'AAA'

df_2023 = pd.concat([df_2023_mlb,df_2023_aaa])

#print(df_2023)
### Normalize Hit Locations
import joblib
swing_model =  joblib.load('swing.joblib')

no_swing_model =  joblib.load('no_swing.joblib')

# Now you can use the loaded model for prediction or any other task


batter_dict = df_2023.sort_values('batter_name').set_index('batter_id')['batter_name'].to_dict()

## Make Predictions
## Define Features and Target
features = ['px','pz','strikes','balls']
## Set up 2023 Data for Prediction of Run Expectancy
df_model_2023_no_swing = df_2023[df_2023.is_swing != 1].dropna(subset=features)
df_model_2023_swing = df_2023[df_2023.is_swing == 1].dropna(subset=features)


import xgboost as xgb
df_model_2023_no_swing['y_pred'] = no_swing_model.predict(xgb.DMatrix(df_model_2023_no_swing[features]))
df_model_2023_swing['y_pred'] = swing_model.predict(xgb.DMatrix(df_model_2023_swing[features]))

df_model_2023 = pd.concat([df_model_2023_no_swing,df_model_2023_swing])
import joblib
# # Dump the model to a file named 'model.joblib'
# model = joblib.load('xtb_model.joblib')

# ## Create a Dataset to calculate xRV/100 Pitches
# df_model_2023['pitcher_name'] = df_model_2023.pitcher.map(pitcher_dict)
# df_model_2023['player_team'] = df_model_2023.batter.map(team_player_dict)
df_model_2023_group = df_model_2023.groupby(['batter_id','batter_name','level']).agg(
    pitches = ('start_speed','count'),
    y_pred =  ('y_pred','mean'),
    )

## Minimum 500 pitches faced
#min_pitches = 300
#df_model_2023_group = df_model_2023_group[df_model_2023_group.pitches >= min_pitches]
## Calculate 20-80 Scale
df_model_2023_group['decision_value'] = zscore(df_model_2023_group['y_pred'])
df_model_2023_group['decision_value'] = (50+df_model_2023_group['decision_value']*10)

## Create a Dataset to calculate xRV/100 for Pitches Taken
df_model_2023_group_no_swing = df_model_2023[df_model_2023.is_swing!=1].groupby(['batter_id','batter_name','level']).agg(
    pitches = ('start_speed','count'),
    y_pred =  ('y_pred','mean')
    )

# Select Pitches with 500 total pitches
df_model_2023_group_no_swing = df_model_2023_group_no_swing[df_model_2023_group_no_swing.index.get_level_values(1).isin(df_model_2023_group.index.get_level_values(1))]
## Calculate 20-80 Scale
df_model_2023_group_no_swing['iz_awareness'] = zscore(df_model_2023_group_no_swing['y_pred'])
df_model_2023_group_no_swing['iz_awareness'] = (((50+df_model_2023_group_no_swing['iz_awareness']*10)))

## Create a Dataset for xRV/100 Pitches Swung At
df_model_2023_group_swing = df_model_2023[df_model_2023.is_swing==1].groupby(['batter_id','batter_name','level']).agg(
    pitches = ('start_speed','count'),
    y_pred =  ('y_pred','mean')
    )

# Select Pitches with 500 total pitches
df_model_2023_group_swing = df_model_2023_group_swing[df_model_2023_group_swing.index.get_level_values(1).isin(df_model_2023_group.index.get_level_values(1))]
## Calculate 20-80 Scale
df_model_2023_group_swing['oz_awareness'] = zscore(df_model_2023_group_swing['y_pred'])
df_model_2023_group_swing['oz_awareness'] = (((50+df_model_2023_group_swing['oz_awareness']*10)))

## Create df for plotting
# Merge Datasets
df_model_2023_group_swing_plus_no = df_model_2023_group_swing.merge(df_model_2023_group_no_swing,left_index=True,right_index=True,suffixes=['_swing','_no_swing'])
df_model_2023_group_swing_plus_no['pitches'] = df_model_2023_group_swing_plus_no.pitches_swing + df_model_2023_group_swing_plus_no.pitches_no_swing

# Calculate xRV/100 Pitches
df_model_2023_group_swing_plus_no['y_pred'] = (df_model_2023_group_swing_plus_no.y_pred_swing*df_model_2023_group_swing_plus_no.pitches_swing + \
                                              df_model_2023_group_swing_plus_no.y_pred_no_swing*df_model_2023_group_swing_plus_no.pitches_no_swing) / \
                                              df_model_2023_group_swing_plus_no.pitches

df_model_2023_group_swing_plus_no = df_model_2023_group_swing_plus_no.merge(right=df_model_2023_group,
                                                                            left_index=True,
                                                                            right_index=True,
                                                                            suffixes=['','_y'])

df_model_2023_group_swing_plus_no = df_model_2023_group_swing_plus_no.reset_index()
team_dict = df_2023.groupby(['batter_name'])[['batter_id','batter_team']].tail().set_index('batter_id')['batter_team'].to_dict()
df_model_2023_group_swing_plus_no['team'] = df_model_2023_group_swing_plus_no['batter_id'].map(team_dict)
df_model_2023_group_swing_plus_no = df_model_2023_group_swing_plus_no.set_index(['batter_id','batter_name','level','team'])

df_model_2023_group_swing_plus_no = df_model_2023_group_swing_plus_no[df_model_2023_group_swing_plus_no['pitches']>=250]
df_model_2023_group_swing_plus_no_copy = df_model_2023_group_swing_plus_no.copy()
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 scatter_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
        print(df_model_2023_group_swing_plus_no_copy)
        print(input.level_list())
        df_model_2023_group_swing_plus_no = df_model_2023_group_swing_plus_no_copy[df_model_2023_group_swing_plus_no_copy.index.get_level_values(2) == input.level_list()]
        print('this one')
        print(df_model_2023_group_swing_plus_no)
        batter_select_id = int(input.batter_id())
        # batter_select_name = 'Edouard Julien'
        #max(1,int(input.pitch_min()))
        plot_min =  max(250,int(input.pitch_min()))
        df_model_2023_group_swing_plus_no = df_model_2023_group_swing_plus_no[df_model_2023_group_swing_plus_no.pitches >= plot_min]
        ## Plot In-Zone vs Out-of-Zone Awareness
        sns.set_theme(style="whitegrid", palette="pastel")
        # fig, ax = plt.subplots(1,1,figsize=(12,12))
        fig = plt.figure(figsize=(12,12))
        gs = GridSpec(3, 3, height_ratios=[0.6,10,0.2], width_ratios=[0.25,0.50,0.25])

        axheader = fig.add_subplot(gs[0, :])
        #ax10 = fig.add_subplot(gs[1, 0])
        ax = fig.add_subplot(gs[1, :])  # Subplot at the top-right position
        #ax12 = fig.add_subplot(gs[1, 2])
        axfooter1 = fig.add_subplot(gs[-1, 0])
        axfooter2 = fig.add_subplot(gs[-1, 1])
        axfooter3 = fig.add_subplot(gs[-1, 2])

        cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[1],colour_palette[3],colour_palette[0]])
        norm = plt.Normalize(df_model_2023_group_swing_plus_no['y_pred'].min()*100, df_model_2023_group_swing_plus_no['y_pred'].max()*100)

        sns.scatterplot(
                        x=df_model_2023_group_swing_plus_no['y_pred_swing']*100,
                        y=df_model_2023_group_swing_plus_no['y_pred_no_swing']*100,
                        hue=df_model_2023_group_swing_plus_no['y_pred']*100,
                        size=df_model_2023_group_swing_plus_no['pitches_swing']/df_model_2023_group_swing_plus_no['pitches'],
                        palette=cmap_hue,ax=ax)

        sm = plt.cm.ScalarMappable(cmap=cmap_hue, norm=norm)
        cbar  = plt.colorbar(sm, cax=axfooter2, orientation='horizontal',shrink=1)
        cbar.set_label('Decision Value xRV/100 Pitches',fontsize=12)

        ax.hlines(xmin=(math.floor((df_model_2023_group_swing_plus_no['y_pred_swing'].min()*100*100-0.01)/5))*5/100,
                xmax= (math.ceil((df_model_2023_group_swing_plus_no['y_pred_swing'].max()**100100+0.01)/5))*5/100,
                y=df_model_2023_group_swing_plus_no['y_pred_no_swing'].mean()*100,color='gray',linewidth=3,linestyle='dotted',alpha=0.4)

        ax.vlines(ymin=(math.floor((df_model_2023_group_swing_plus_no['y_pred_no_swing'].min()*100*100-0.01)/5))*5/100,
                ymax= (math.ceil((df_model_2023_group_swing_plus_no['y_pred_no_swing'].max()*100*100+0.01)/5))*5/100,
                x=df_model_2023_group_swing_plus_no['y_pred_swing'].mean()*100,color='gray',linewidth=3,linestyle='dotted',alpha=0.4)

        x_lim_min = (math.floor((df_model_2023_group_swing_plus_no['y_pred_swing'].min()*100*100)/5))*5/100
        x_lim_max = (math.ceil((df_model_2023_group_swing_plus_no['y_pred_swing'].max()*100*100)/5))*5/100

        y_lim_min = (math.floor((df_model_2023_group_swing_plus_no['y_pred_no_swing'].min()*100*100)/5))*5/100
        y_lim_max = (math.ceil((df_model_2023_group_swing_plus_no['y_pred_no_swing'].max()*100*100)/5))*5/100

        ax.set_xlim(x_lim_min,x_lim_max)
        ax.set_ylim(y_lim_min,y_lim_max)

        ax.tick_params(axis='both', which='major', labelsize=12)

        ax.set_xlabel('Out-of-Zone Awareness Value xRV/100 Swings',fontsize=16)
        ax.set_ylabel('In-Zone Awareness Value xRV/100 Takes',fontsize=16)
        ax.get_legend().remove()


        ts=[]


        # thresh = 0.5
        # thresh_2 = -0.9
        # for i in range(len(df_model_2023_group_swing_plus_no)):
        #         if (df_model_2023_group_swing_plus_no['y_pred'].values[i]*100) >= thresh or \
        #         (df_model_2023_group_swing_plus_no['y_pred'].values[i]*100) <= thresh_2 or \
        #                (str(df_model_2023_group_swing_plus_no.index.get_level_values(0).values[i]) in (input.name_list())) :
        #                 ts.append(ax.text(x=df_model_2023_group_swing_plus_no['y_pred_swing'].values[i]*100,
        #                                 y=df_model_2023_group_swing_plus_no['y_pred_no_swing'].values[i]*100,
        #                                 s=df_model_2023_group_swing_plus_no.index.get_level_values(1).values[i],
        #                                 fontsize=8))
        thresh = 0.5
        thresh_2 = -0.9
        for i in range(len(df_model_2023_group_swing_plus_no)):
                if (df_model_2023_group_swing_plus_no['y_pred_swing'].values[i]) >= df_model_2023_group_swing_plus_no['y_pred_swing'].quantile(0.98) or \
                (df_model_2023_group_swing_plus_no['y_pred_swing'].values[i])  <= df_model_2023_group_swing_plus_no['y_pred_swing'].quantile(0.02) or \
                (df_model_2023_group_swing_plus_no['y_pred_no_swing'].values[i]) >= df_model_2023_group_swing_plus_no['y_pred_no_swing'].quantile(0.98) or \
                (df_model_2023_group_swing_plus_no['y_pred_no_swing'].values[i])  <= df_model_2023_group_swing_plus_no['y_pred_no_swing'].quantile(0.02) or \
                (df_model_2023_group_swing_plus_no['y_pred'].values[i]) >= df_model_2023_group_swing_plus_no['y_pred'].quantile(0.98) or \
                (df_model_2023_group_swing_plus_no['y_pred'].values[i])  <= df_model_2023_group_swing_plus_no['y_pred'].quantile(0.02) or \
                       (str(df_model_2023_group_swing_plus_no.index.get_level_values(0).values[i]) in (input.name_list())) :
                        ts.append(ax.text(x=df_model_2023_group_swing_plus_no['y_pred_swing'].values[i]*100,
                                        y=df_model_2023_group_swing_plus_no['y_pred_no_swing'].values[i]*100,
                                        s=df_model_2023_group_swing_plus_no.index.get_level_values(1).values[i],
                                        fontsize=8))

        ax.text(x=x_lim_min+abs(x_lim_min)*0.02,y=y_lim_max-abs(y_lim_max-y_lim_min)*0.02,s=f'Min. {plot_min} Pitches',fontsize='10',fontstyle='oblique',va='top',
                bbox=dict(facecolor='white', edgecolor='black'))
        # ax.text(x=x_lim_min+abs(x_lim_min)*0.02,y=y_lim_max-abs(y_lim_max-y_lim_min)*0.06,s=f'Labels for Batters with\nDescion Value xRV/100 > {thresh:.2f}\nDescion Value xRV/100 < {thresh_2:.2f}',fontsize='10',fontstyle='oblique',va='top',
        #         bbox=dict(facecolor='white', edgecolor='black'))
        ax.text(x=x_lim_min+abs(x_lim_min)*0.02,y=y_lim_max-abs(y_lim_max-y_lim_min)*0.06,s=f'Point Size Represents Swing%',fontsize='10',fontstyle='oblique',va='top',
                bbox=dict(facecolor='white', edgecolor='black'))

        adjust_text(ts,
                arrowprops=dict(arrowstyle="-", color=colour_palette[4], lw=1),ax=ax)
     
        axfooter1.axis('off')
        axfooter3.axis('off')
        axheader.axis('off')

        axheader.text(s=f'{input.level_list()} In-Zone vs Out-of-Zone Awareness Value',fontsize=24,x=0.5,y=0,va='top',ha='center')

        axfooter1.text(0.05, -0.5,"By: Thomas Nestico\n      @TJStats",ha='left', va='bottom',fontsize=12)
        axfooter3.text(0.95, -0.5, "Data: MLB",ha='right', va='bottom',fontsize=12)   
        fig.subplots_adjust(left=0.01, right=0.99, top=0.975, bottom=0.025)

    @output
    @render.plot(alt="hex_plot")
    @reactive.event(input.go, ignore_none=False)
    def dv_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
        
        player_select = int(input.batter_id())
        player_select_full = batter_dict[player_select]


        df_will = df_model_2023[df_model_2023.batter_id == player_select].sort_values(by=['game_date','start_time'])
        df_will = df_will[df_will['level']==input.level_list()]
        # df_will['y_pred'] = df_will['y_pred'] - df_will['y_pred'].mean()

        win = max(1,int(input.rolling_window()))
        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_will.y_pred.rolling(window=win).mean())+1),
                y= df_will.y_pred.rolling(window=win).mean().dropna()*100,
                color=colour_palette[0],linewidth=2,ax=ax,zorder=100)

        ax.hlines(y=df_will.y_pred.mean()*100,xmin=win,xmax=len(df_will),color=colour_palette[0],linestyle='--',
                label=f'{player_select_full} Average: {df_will.y_pred.mean()*100:.2} xRV/100 ({p.ordinal(int(np.around(percentileofscore(df_model_2023_group_swing_plus_no.y_pred,df_will.y_pred.mean(), kind="strict"))))} Percentile)')

        # ax.hlines(y=df_model_2023.y_pred.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_model_2023_group_swing_plus_no.y_pred.mean()*100,xmin=win,xmax=len(df_will),color=colour_palette[1],linestyle='-.',alpha=1,
                label = f'{input.level_list()} Average: {df_model_2023_group_swing_plus_no.y_pred.mean()*100:.2f} xRV/100')

        ax.legend()

        hard_hit_dates = [df_model_2023_group_swing_plus_no.y_pred.quantile(0.9)*100,
                        df_model_2023_group_swing_plus_no.y_pred.quantile(0.75)*100,
                        df_model_2023_group_swing_plus_no.y_pred.quantile(0.25)*100,
                        df_model_2023_group_swing_plus_no.y_pred.quantile(0.1)*100]



        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred.quantile(0.9)*100,xmin=win,xmax=len(df_will),color=colour_palette[2],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred.quantile(0.75)*100,xmin=win,xmax=len(df_will),color=colour_palette[3],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred.quantile(0.25)*100,xmin=win,xmax=len(df_will),color=colour_palette[4],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred.quantile(0.1)*100,xmin=win,xmax=len(df_will),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/1000,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_will))
        #ax.set_ylim(-1.5,1.5)
        ax.set_yticks([-1.5,-1,-0.5,0,0.5,1,1.5])
        ax.set_xlabel('Pitch')
        ax.set_ylabel('Expected Run Value Added per 100 Pitches (xRV/100)')

        axheader.text(s=f'{player_select_full} - {input.level_list()} - {win} Pitch Rolling Swing Decision Expected Run Value Added',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)
        #fig.set_facecolor(colour_palette[5])

    @output
    @render.plot(alt="hex_plot")
    @reactive.event(input.go, ignore_none=False)
    def iz_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
        
        player_select = int(input.batter_id())
        player_select_full = batter_dict[player_select]


        df_will = df_model_2023[df_model_2023.batter_id == player_select].sort_values(by=['game_date','start_time'])
        df_will = df_will[df_will['level']==input.level_list()]
        df_will = df_will[df_will['is_swing'] != 1]
        
        win = max(1,int(input.rolling_window()))
        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_will.y_pred.rolling(window=win).mean())+1),
                y= df_will.y_pred.rolling(window=win).mean().dropna()*100,
                color=colour_palette[0],linewidth=2,ax=ax,zorder=100)

        ax.hlines(y=df_will.y_pred.mean()*100,xmin=win,xmax=len(df_will),color=colour_palette[0],linestyle='--',
                label=f'{player_select_full} Average: {df_will.y_pred.mean()*100:.2} xRV/100 ({p.ordinal(int(np.around(percentileofscore(df_model_2023_group_swing_plus_no.y_pred_no_swing,df_will.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_model_2023_group_swing_plus_no.y_pred_no_swing.mean()*100,xmin=win,xmax=len(df_will),color=colour_palette[1],linestyle='-.',alpha=1,
                label = f'{input.level_list()} Average: {df_model_2023_group_swing_plus_no.y_pred_no_swing.mean()*100:.2} xRV/100')

        ax.legend()

        hard_hit_dates = [df_model_2023_group_swing_plus_no.y_pred_no_swing.quantile(0.9)*100,
                        df_model_2023_group_swing_plus_no.y_pred_no_swing.quantile(0.75)*100,
                        df_model_2023_group_swing_plus_no.y_pred_no_swing.quantile(0.25)*100,
                        df_model_2023_group_swing_plus_no.y_pred_no_swing.quantile(0.1)*100]



        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred_no_swing.quantile(0.9)*100,xmin=win,xmax=len(df_will),color=colour_palette[2],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred_no_swing.quantile(0.75)*100,xmin=win,xmax=len(df_will),color=colour_palette[3],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred_no_swing.quantile(0.25)*100,xmin=win,xmax=len(df_will),color=colour_palette[4],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred_no_swing.quantile(0.1)*100,xmin=win,xmax=len(df_will),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/1000,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_will))
        ax.set_yticks([1.0,1.5,2.0,2.5,3.0])
        # ax.set_ylim(1,3)

        ax.set_xlabel('Takes')
        ax.set_ylabel('Expected Run Value Added per 100 Pitches (xRV/100)')

        axheader.text(s=f'{player_select_full} - {input.level_list()} - {win} Pitch Rolling In-Zone Awareness Expected Run Value Added',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)

    @output
    @render.plot(alt="hex_plot")
    @reactive.event(input.go, ignore_none=False)
    def oz_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
        
        player_select = int(input.batter_id())
        player_select_full = batter_dict[player_select]



        df_will = df_model_2023[df_model_2023.batter_id == player_select].sort_values(by=['game_date','start_time'])
        df_will = df_will[df_will['level']==input.level_list()]
        df_will = df_will[df_will['is_swing'] == 1]

        win = max(1,int(input.rolling_window()))
        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_will.y_pred.rolling(window=win).mean())+1),
                y= df_will.y_pred.rolling(window=win).mean().dropna()*100,
                color=colour_palette[0],linewidth=2,ax=ax,zorder=100)

        ax.hlines(y=df_will.y_pred.mean()*100,xmin=win,xmax=len(df_will),color=colour_palette[0],linestyle='--',
                label=f'{player_select_full} Average: {df_will.y_pred.mean()*100:.2} xRV/100 ({p.ordinal(int(np.around(percentileofscore(df_model_2023_group_swing_plus_no.y_pred_swing,df_will.y_pred.mean(), kind="strict"))))} Percentile)')

        # ax.hlines(y=df_model_2023.y_pred_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_model_2023_group_swing_plus_no.y_pred_swing.mean()*100,xmin=win,xmax=len(df_will),color=colour_palette[1],linestyle='-.',alpha=1,
                label = f'{input.level_list()} Average: {df_model_2023_group_swing_plus_no.y_pred_swing.mean()*100:.2} xRV/100')

        ax.legend()

        hard_hit_dates = [df_model_2023_group_swing_plus_no.y_pred_swing.quantile(0.9)*100,
                        df_model_2023_group_swing_plus_no.y_pred_swing.quantile(0.75)*100,
                        df_model_2023_group_swing_plus_no.y_pred_swing.quantile(0.25)*100,
                        df_model_2023_group_swing_plus_no.y_pred_swing.quantile(0.1)*100]



        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred_swing.quantile(0.9)*100,xmin=win,xmax=len(df_will),color=colour_palette[2],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred_swing.quantile(0.75)*100,xmin=win,xmax=len(df_will),color=colour_palette[3],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred_swing.quantile(0.25)*100,xmin=win,xmax=len(df_will),color=colour_palette[4],linestyle='dotted',alpha=0.5,zorder=1)
        ax.hlines(y=df_model_2023_group_swing_plus_no.y_pred_swing.quantile(0.1)*100,xmin=win,xmax=len(df_will),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/1000,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_will))
        #ax.set_ylim(-3.25,-1.25)
        ax.set_yticks([-3.25,-2.75,-2.25,-1.75,-1.25])
        ax.set_xlabel('Swing')
        ax.set_ylabel('Expected Run Value Added per 100 Pitches (xRV/100)')

        axheader.text(s=f'{player_select_full} - {input.level_list()} - {win} Pitch Rolling Out of Zone Awareness Expected Run Value Added',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)   

decision_value = App(ui.page_fluid(
    ui.tags.base(href=base_url), 
    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.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_numeric("pitch_min",
                                 "Select Pitch Minimum [min. 250] (Scatter)",
                                 value=500,
                                 min=250),                 

                ui.input_select("name_list",
                                 "Select Players to List (Scatter)",
                                 batter_dict,
                                 selectize=True,
                                 multiple=True),
                ui.input_select("batter_id",
                                "Select Batter (Rolling)",
                                 batter_dict,
                                 width=1,
                                 size=1,
                                 selectize=True),
                ui.input_numeric("rolling_window",
                                 "Select Rolling Window (Rolling)",
                                 value=100,
                                 min=1),                

                ui.input_select("level_list",
                                 "Select Level",
                                 ['MLB','AAA'],
                                 selected='MLB'),
                ui.input_action_button("go", "Generate",class_="btn-primary"),
                                 ),

   ui.panel_main(     
        ui.navset_tab(

            ui.nav("Scatter Plot",
                   ui.output_plot('scatter_plot',
                                  width='1000px',
                                  height='1000px')),
            ui.nav("Rolling DV",
                   ui.output_plot('dv_plot',
                                  width='1000px',
                                  height='1000px')),
            ui.nav("Rolling In-Zone",
                   ui.output_plot('iz_plot',
                                  width='1000px',
                                  height='1000px')),
            ui.nav("Rolling Out-of-Zone",
                   ui.output_plot('oz_plot',
                                  width='1000px',
                                  height='1000px'))
        ))
    )),)),server)