Update app.py

app.py

@@ -1,45 +1,736 @@
-### Thomas Nestico
-### @TJStats
-#Import modules
-from starlette.applications import Starlette
-from starlette.routing import Mount
-from starlette.staticfiles import StaticFiles
-from shiny import App, ui
+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 pages
-from home import home
+### 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)
 
-from spray_new import spray
-from decision_value import decision_value
-from damage import damage
-from batter_scatter import batter_scatter
-#from ev_angle import ev_angle
-from rolling_batter import rolling_batter
-from statcast_compare import statcast_compare
+### 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)
 
-from rolling_pitcher import rolling_pitcher
-from pitching_summary_graphic_new_fg_api import pitching_summary_graphic_new
-from pitcher_scatter import pitcher_scatter
+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')
 
-# Create app
-routes = [
-    Mount('/home', app=home),
+no_swing_model =  joblib.load('no_swing.joblib')
 
-    Mount('/spray',app=spray),
-    Mount('/decision_value',app=decision_value),
-    Mount('/damage_model',app=damage),
-    Mount('/batter_scatter',app=batter_scatter),
-    #Mount('/ev_angle',app=ev_angle),
-    Mount('/rolling_batter',app=rolling_batter),
-    Mount('/statcast_compare',app=statcast_compare),
+# Now you can use the loaded model for prediction or any other task
 
-    Mount('/rolling_pitcher',app=rolling_pitcher),
-    Mount('/pitching_summary_graphic_new',app=pitching_summary_graphic_new),
-    Mount('/pitcher_scatter',app=pitcher_scatter),
-]
 
-#Run App
-app = Starlette(routes=routes)
+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)   
+
+app = 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 & Damage",
+    #             href="https://nesticot-tjstats-site-spray.hf.space/"
+    #         ),
+    #         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.navset_tab(
+        ui.nav_control(
+             ui.a(
+                "Home",
+                href="home/"
+            ),
+        ),
+        ui.nav_menu(
+            "Batter Charts",
+            ui.nav_control(
+            ui.a(
+                "Batting Rolling",
+                href="https://nesticot-tjstats-site-rolling-batter.hf.space/"
+            ),
+            ui.a(
+                "Spray",
+                href="https://nesticot-tjstats-site-spray.hf.space/"
+            ),
+            ui.a(
+                "Decision Value",
+                href="https://nesticot-tjstats-site-decision-value.hf.space/"
+            ),
+            ui.a(
+                "Damage Model",
+                href="https://nesticot-tjstats-site-damage.hf.space/"
+            ),
+            ui.a(
+                "Batter Scatter",
+                href="https://nesticot-tjstats-site-batter-scatter.hf.space/"
+            ),
+            ui.a(
+                "EV vs LA Plot",
+                href="https://nesticot-tjstats-site-ev-angle.hf.space/"
+            ),
+            ui.a(
+                "Statcast Compare",
+                href="https://nesticot-tjstats-site-statcast-compare.hf.space/"
+            ),
+            ui.a(
+                "MLB/MiLB Cards",
+                href="https://nesticot-tjstats-site-mlb-cards.hf.space/"
+            )
+        ),
+        ),
+        ui.nav_menu(
+            "Pitcher Charts",
+            ui.nav_control(
+             ui.a(
+                "Pitcher Rolling",
+                href="https://nesticot-tjstats-site-rolling-pitcher.hf.space/"
+            ),
+             ui.a(
+                "Pitcher Summary",
+                href="https://nesticot-tjstats-site-pitching-summary-graphic-new.hf.space/"
+            ),
+             ui.a(
+                "Pitcher Scatter",
+                href="https://nesticot-tjstats-site-pitcher-scatter.hf.space"
+            )
+        ),
+        )),    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)