Upload 34 files

app.py

@@ -9,40 +9,36 @@ import shinyswatch
 
 #Import pages
 from home import home
-# from about import about
-# from gsax_timeline import gsax_timeline
-# from on_ice_xg_rates import on_ice_xg
-# from gsax_leaderboard import gsax_leaderboard
-# from on_ice_xgfp import on_ice_xgfp
-# from team_xg_rates import team_xg_rates
-# from gsax_comparison import gsax_comparison
-# from game import game
+
 from spray 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 articles import articles
-# from xg_model import xg_model
+from rolling_batter import rolling_batter
+from statcast_compare import statcast_compare
+
+from rolling_pitcher import rolling_pitcher
+from pitching_summary_graphic_new_fg_api import pitching_summary_graphic_new
+from pitcher_scatter import pitcher_scatter
+
+
 
 # Create app
 routes = [
     Mount('/home', app=home),
-#     Mount('/about', app=about),
-#     Mount('/gsax-timeline', app=gsax_timeline),
-#     Mount('/skater-xg-rates', app=on_ice_xg),
-#     Mount('/gsax-leaderboard', app=gsax_leaderboard),
-#     Mount('/skater-xg-percentages', app=on_ice_xgfp),
-#     Mount('/team-xg-rates', app=team_xg_rates),
-#     Mount('/gsax-comparison',app=gsax_comparison),
+
     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('/game/{game_id}',app=game),
-#     Mount('/articles',app=articles),
-#     Mount('/xg-model',app=xg_model)
+    Mount('/rolling_batter',app=rolling_batter),
+    Mount('/statcast_compare',app=statcast_compare),
+
+    Mount('/rolling_pitcher',app=rolling_pitcher),
+    Mount('/pitching_summary_graphic_new',app=pitching_summary_graphic_new),
+    Mount('/pitcher_scatter',app=pitcher_scatter),
 ]
 
 #Run App

batter_scatter.py

@@ -19,6 +19,8 @@ from shinywidgets import output_widget, render_widget
 import pandas as pd
 from configure import base_url
 import shinyswatch
+import inflect
+from matplotlib.pyplot import text
 
 
 exit_velo_df_codes_summ_batter = pd.read_csv('summary_batter.csv',index_col=[0])
@@ -126,6 +128,7 @@ def server(input,output,session):
 
     @output
     @render.plot(alt="A histogram")
+    @reactive.event(input.go, ignore_none=False)
     def plot():
         sns.set_theme(style="whitegrid", palette="pastel")
         print(input.level_id())
@@ -387,6 +390,7 @@ batter_scatter = App(ui.page_fluid(
     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(
@@ -397,7 +401,11 @@ batter_scatter = App(ui.page_fluid(
         ui.nav_menu(
             "Batter Charts",
             ui.nav_control(
-             ui.a(
+            ui.a(
+                "Batting Rolling",
+                href="rolling_batter/"
+            ),
+            ui.a(
                 "Spray",
                 href="spray/"
             ),
@@ -416,48 +424,29 @@ batter_scatter = App(ui.page_fluid(
             ui.a(
                 "EV vs LA Plot",
                 href="ev_angle/"
+            ),
+            ui.a(
+                "Statcast Compare",
+                href="statcast_compare/"
             )
         ),
         ),
         ui.nav_menu(
-            "Goalie Charts",
+            "Pitcher Charts",
             ui.nav_control(
              ui.a(
-                "GSAx Timeline",
-                href="gsax-timeline/"
+                "Pitcher Rolling",
+                href="rolling_pitcher/"
             ),
              ui.a(
-                "GSAx Leaderboard",
-                href="gsax-leaderboard/"
+                "Pitcher Summary",
+                href="pitching_summary_graphic_new/"
             ),
              ui.a(
-                "GSAx Comparison",
-                href="gsax-comparison/"
+                "Pitcher Scatter",
+                href="pitcher_scatter/"
             )
         ),
-        ),ui.nav_menu(
-            "Team Charts",
-            ui.nav_control(
-             ui.a(
-                "Team xG Rates",
-                href="team-xg-rates/"
-            ),
-        ),
-        ),ui.nav_control(
-             ui.a(
-                "Games",
-                href="games/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "About",
-                href="about/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "Articles",
-                href="articles/"
-            ),
         )),ui.row(
     ui.layout_sidebar(
         
@@ -491,6 +480,7 @@ batter_scatter = App(ui.page_fluid(
         ui.row(
             ui.input_switch("names", "Toggle Names"),
             ui.input_switch("group_level", "Group Levels")),
+    ui.input_action_button("go", "Generate",class_="btn-primary"),
      ),
 
       ui.panel_main(

batter_scatter_2.py

@@ -0,0 +1,502 @@
+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 inflect
+from matplotlib.pyplot import text
+
+
+exit_velo_df_codes_summ_batter = pd.read_csv('summary_batter.csv',index_col=[0])
+#exit_velo_df_codes_summ = pd.read_csv('summary_pitcher.csv',index_col=[0])
+
+exit_velo_df_codes_summ_non_level = pd.read_csv('summary_batter_level.csv',index_col=[0]).reset_index(drop=True)
+
+exit_velo_df_codes_summ_non_level['levels'] = exit_velo_df_codes_summ_non_level.levels.str.split(', ')
+
+exit_velo_df_codes_summ_non_level = exit_velo_df_codes_summ_non_level.rename(columns={'levels':'level'})
+
+
+
+print(exit_velo_df_codes_summ_batter.bb_minus_k_percent)
+
+batter_dict_stat = { 'sweet_spot_percent':{'x_axis':'SweetSpot%','title':'SweetSpot%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'max_launch_speed':{'x_axis':'Max Exit Velocity','title':'Max Exit Velocity','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
+                'launch_speed_90':{'x_axis':'90th Percentile EV','title':'90th Percentile EV','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
+                'launch_speed':{'x_axis':'Exit Velocity','title':'Exit Velocity','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
+                'launch_angle':{'x_axis':'Launch Angle','title':'Launch Angle','flip_p':False,'decimal_format':'string_0','percent_adjust':100},
+                'avg':{'x_axis':'AVG','title':'AVG','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'obp':{'x_axis':'OBP','title':'OBP','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'slg':{'x_axis':'SLG','title':'SLG','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'ops':{'x_axis':'OPS','title':'OPS','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'k_percent':{'x_axis':'K%','title':'K%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'bb_percent':{'x_axis':'BB%','title':'BB%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'bb_over_k_percent':{'x_axis':'BB/K','title':'BB/K','flip_p':False,'decimal_format':'string_1','percent_adjust':100},
+                'bb_minus_k_percent':{'x_axis':'BB%-K%','title':'BB%-K%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'csw_percent':{'x_axis':'CSW%','title':'CSW%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'woba_percent':{'x_axis':'wOBA','title':'wOBA','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'hard_hit_percent':{'x_axis':'HardHit%','title':'HardHit%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'barrel_percent':{'x_axis':'Barrel%','title':'Barrel%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'zone_contact_percent':{'x_axis':'Z-Contact%','title':'Z-Contact%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'zone_swing_percent':{'x_axis':'Z-Swing%','title':'Z-Swing%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'zone_percent':{'x_axis':'Zone%','title':'Zone%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'chase_percent':{'x_axis':'O-Swing%','title':'O-Swing%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'chase_contact':{'x_axis':'O-Contact%','title':'O-Contact%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'swing_percent':{'x_axis':'Swing%','title':'Swing%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'whiff_rate':{'x_axis':'Whiff%','title':'Whiff%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'swstr_rate':{'x_axis':'SwStr%','title':'SwStr%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                }
+
+batter_dict_stat_small = { 'sweet_spot_percent':'SweetSpot%',
+                'max_launch_speed':'Max Exit Velocity',
+                'launch_speed_90':'90th Percentile EV',
+                'launch_speed':'Exit Velocity',
+                'launch_angle':'Launch Angle',
+                'avg':'AVG',
+                'obp':'OBP',
+                'slg':'SLG',
+                'ops':'OPS',
+                'k_percent':'K%',
+                'bb_percent':'BB%',
+                'bb_over_k_percent':'BB/K',
+                'bb_minus_k_percent':'BB%-K%',
+                'csw_percent':'CSW%',
+                'woba_percent':'wOBA',
+                'hard_hit_percent':'HardHit%',
+                'barrel_percent':'Barrel%',
+                'zone_contact_percent':'Z-Contact%',
+                'zone_swing_percent':'Z-Swing%',
+                'zone_percent':'Zone%',
+                'chase_percent':'O-Swing%',
+                'chase_contact':'O-Contact%',
+                'swing_percent':'Swing%',
+                'whiff_rate':'Whiff%',
+                'swstr_rate':'SwStr%',
+                }
+
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+
+level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A','ROK':'ROK'}
+
+batter_test_df = exit_velo_df_codes_summ_batter.sort_values(by='batter').drop_duplicates(subset='batter_id').reset_index(drop=True)[['batter_id','batter']]#['pitcher'].to_dict()
+batter_test_df = batter_test_df.set_index('batter_id')
+
+
+def decimal_format_assign(x):
+    if x['decimal_format'] == 'percent_1':
+        return mtick.PercentFormatter(1,decimals=1)
+    if x['decimal_format'] == 'string_3':
+        return mtick.FormatStrFormatter('%.3f')
+    if x['decimal_format'] == 'string_0':
+        return mtick.FormatStrFormatter('%.0f')
+    if x['decimal_format'] == 'string_1':
+        return mtick.FormatStrFormatter('%.1f')
+
+
+#test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
+
+batter_dict = batter_test_df['batter'].to_dict()
+
+exit_velo_df_codes_summ_batter.position = exit_velo_df_codes_summ_batter.position.replace(['LF','RF','CF','TWP'],['OF','OF','OF','DH'])
+exit_velo_df_codes_summ_non_level.position = exit_velo_df_codes_summ_non_level.position.replace(['LF','RF','CF','TWP'],['OF','OF','OF','DH'])
+
+position_list = ['All'] + list(exit_velo_df_codes_summ_batter.position.unique())
+team_list = ['All'] + sorted(list(exit_velo_df_codes_summ_batter.parent_org_abb.unique()))
+
+
+
+def server(input,output,session):
+
+
+    @output
+    @render.plot(alt="A histogram")
+    def plot():
+        sns.set_theme(style="whitegrid", palette="pastel")
+        print(input.level_id())
+        print(input.n())
+        print('we made it here',input.team_id(),input.position_id())
+        if input.group_level():
+            data_df = exit_velo_df_codes_summ_non_level.copy()
+
+            turth_list = []
+            #turth_list_2 = []
+            for x in range(0,len(data_df.level)):
+                turth_list_2 = []
+                for y in range(0,len(data_df.level[x])):
+                    #print(level_list[x][y])
+                    turth_list_2.append(data_df.level[x][y] in input.level_id())
+                turth_list.append(turth_list_2)
+
+            final_check_list = [True if True in x else False for x in turth_list]
+
+
+            data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())&(final_check_list)]
+        
+        
+        else:
+
+
+            data_df = exit_velo_df_codes_summ_batter.copy()
+            data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())&(data_df.level.isin(input.level_id()))]
+            print(data_df)
+            if 'All' in input.team_id():
+                print('nice')#data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())].reset_index(drop=True)
+            
+            else:
+                data_df = data_df[(data_df.parent_org_abb.isin(input.team_id()))].reset_index(drop=True)
+
+            if 'All' in input.position_id():
+                print('nice')#data_df = data_df[(data_df.level.isin(input.level_id()))&(data_df.pa >= input.n())&(data_df.age <= input.n_age())].reset_index(drop=True)
+            
+            else:
+                data_df = data_df[(data_df.position.isin(input.position_id()))].reset_index(drop=True)
+            
+            
+        #print('we made it here')
+        print(data_df)
+        data_df = data_df.sort_values(by='level').reset_index(drop=True)
+        print(batter_dict_stat[input.stat_x()]['flip_p'])
+
+
+
+        x_flip = batter_dict_stat[input.stat_x()]['flip_p']
+        y_flip = batter_dict_stat[input.stat_y()]['flip_p']
+        cbr_flip = batter_dict_stat[input.stat_z()]['flip_p']
+
+        
+
+        data_df[input.stat_x()+'_percent'] = data_df[input.stat_x()].rank(pct=True,ascending=abs(x_flip-1))
+
+        data_df[input.stat_y()+'_percent'] = data_df[input.stat_y()].rank(pct=True,ascending=abs(y_flip-1))
+
+        data_df[input.stat_z()+'_percent'] = data_df[input.stat_z()].rank(pct=True,ascending=abs(cbr_flip-1))
+
+
+
+        fig, ax = plt.subplots(1, 1, figsize=(9, 9))
+
+        #data_df['bb_over_obp'] = data_df['bb']/data_df['k']
+        
+        #data_df[input.stat_z()]= data_df[input.stat_z()].fillna(-100000)
+        
+        
+        if cbr_flip:
+            cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[0],colour_palette[3],colour_palette[1]])
+            norm = plt.Normalize(data_df[input.stat_z()].min(), data_df[input.stat_z()].max())
+
+        else:
+            cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[1],colour_palette[3],colour_palette[0]])
+            norm = plt.Normalize(data_df[input.stat_z()].min(), data_df[input.stat_z()].max())
+
+        sm = plt.cm.ScalarMappable(cmap=cmap_hue, norm=norm)
+        print('we made it here')
+
+        # sns.regplot(x = stat_x, y = stat_y, data=data_df, color = colour_palette[6],ax=ax,scatter=False,
+        #             line_kws=dict(alpha=0.3,linewidth=2,zorder=1))
+        # scatter_plot = sns.scatterplot(x = stat_x, y = stat_y, data=data_df, color = colour_palette[0],ax=ax,hue=stat_z,palette=cmap_hue)
+
+
+
+        # r, p = sp.stats.pearsonr(data_df[input.stat_x()], data_df[input.stat_y()])
+        # ax = plt.gca()
+        # # ax.text(.25, 0.3, 'r={:.2f}, p={:.2g}'.format(r, p),
+        # #         transform=ax.transAxes, fontsize=12)
+
+        # ax.annotate('R²={:.2f}'.format(r, p), ( math.ceil(data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust']/5)*5/batter_dict_stat[input.stat_x()]['percent_adjust']*(1-batter_dict_stat[input.stat_x()]['flip_p']), 
+        #                                       math.floor(data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust']/5)*5/batter_dict_stat[input.stat_y()]['percent_adjust']*(1-batter_dict_stat[input.stat_y()]['flip_p'])), 
+        #                                         fontsize=18,fontname='Century Gothic',ha='right')
+
+        if input.group_level():
+            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = '#b3b3b3')
+            #ax.get_legend().remove()
+            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = colour_palette[0],ax=ax,hue=input.stat_z(),palette=cmap_hue)
+        else:
+            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = '#b3b3b3',style='level')    
+            #ax.get_legend().remove()
+            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = colour_palette[0],ax=ax,hue=input.stat_z(),palette=cmap_hue,style='level')
+        sns.set_theme(style="whitegrid", palette="pastel")
+
+        fig.set_facecolor('#F0F0F0')
+        ax.set_facecolor('white')
+
+        print('we made it here')
+        # for i in range(0,len(pitch_group_unique)):
+        #     data_df = elly_zone_df[elly_zone_df.pitch_group==pitch_group_unique[i]]
+        #     len_df.append(len(data_df))
+        #     sns.lineplot(x=range(1,len(data_df)+1),y=data_df.swings.rolling(window=rolling_window_input).sum()/data_df.pitches.rolling(window=rolling_window_input).sum(),color=colour_palette[i],linewidth=3,ax=ax,
+        #                  label=f'{pitch_group_unique[i]} (Season Average {float(data_df.swings.sum()/data_df.pitches.sum()):.1%})',zorder=i+10)
+        #     ax.hlines(xmin=0,xmax=len(elly_zone_df),y=data_df.swings.sum()/data_df.pitches.sum(),color=colour_palette[i],linewidth=3,linestyle='-.',alpha=0.4,zorder=i)
+
+        ts=[]
+        print(input.player_id())
+
+        print(len(data_df))
+        if input.names():
+            for i in range(len(data_df)):
+                if (data_df[input.stat_x()+'_percent'].values[i] < input.n_percent_bot_x()  or data_df[input.stat_x()+'_percent'].values[i] > 1 - input.n_percent_top_x() ) \
+                or (data_df[input.stat_y()+'_percent'].values[i] < input.n_percent_bot_y()  or data_df[input.stat_y()+'_percent'].values[i] > 1 -input.n_percent_top_y()) \
+                or (data_df[input.stat_z()+'_percent'].values[i] < input.n_percent_bot_z()  or data_df[input.stat_z()+'_percent'].values[i] > 1 -input.n_percent_top_z() )\
+                or (str(data_df.batter_id[i]) in (input.player_id())):
+                    # print(data_df.batter[i])
+                    # ax.annotate(data_df.batter[i], xy=((data_df[input.stat_x()][i])+0.025/batter_dict_stat[input.stat_x()]['percent_adjust'], data_df[input.stat_y()][i]+0.01/batter_dict_stat[input.stat_x()]['percent_adjust']), xytext=(-20,20), 
+                    # textcoords='offset points', ha='center', va='bottom',fontsize=7,
+                    # bbox=dict(boxstyle='round,pad=0', fc=colour_palette[6], alpha=0.0),
+                    # arrowprops=dict(arrowstyle='->', connectionstyle="angle,angleA=-90,angleB=-10,rad=2", 
+                    #                 color=colour_palette[8]))
+                    
+                    #if data_df['batter'][i] != 'Jo Adell':
+                    # ax.annotate(data_df.batter[i], (data_df[input.stat_x()][i]-len(data_df.batter[i])*0.00025, data_df[input.stat_y()][i]+0.001),fontsize=8)
+                    ts.append(ax.text(data_df[input.stat_x()][i], data_df[input.stat_y()][i], data_df.batter[i],fontsize=8))
+
+
+
+        ax.hlines(xmin=(math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'],
+                    xmax= (math.ceil((data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust']+0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'],
+                    y=data_df[input.stat_y()].mean(),color='gray',linewidth=3,linestyle='dotted',alpha=0.4)
+
+        print('we made it here')
+
+        ax.vlines(ymin=(math.floor((data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'],
+                    ymax= (math.ceil((data_df[input.stat_y()].max()*batter_dict_stat[input.stat_y()]['percent_adjust']+0.01)/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'],
+                    x=data_df[input.stat_x()].mean(),color='gray',linewidth=3,linestyle='dotted',alpha=0.4)
+
+        print(data_df[input.stat_x()].min())
+        print(batter_dict_stat[input.stat_x()]['percent_adjust'])
+        print((math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'])
+
+
+        ax.set_xlim((math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'],
+                    (math.ceil((data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'])
+
+
+        ax.set_ylim((math.floor((data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'],
+                    (math.ceil((data_df[input.stat_y()].max()*batter_dict_stat[input.stat_y()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'])
+       
+
+
+        title_level = str([x .strip("\'")for x in input.level_id()]).strip('[').strip(']').replace("'",'')
+
+        if title_level == 'AAA, AA, A+, A':
+            title_level='MiLB'
+        #title_level = input.level_id()[0]
+        if input.n_age() >= 50:
+            title_spot = f'{title_level} Batter {batter_dict_stat[input.stat_y()]["title"]} vs {batter_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA)'
+        
+        else:
+            title_spot = f'{title_level} Batter {batter_dict_stat[input.stat_y()]["title"]} vs {batter_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA, Max Age {input.n_age()})'
+
+        ax.set_title(title_spot, fontsize=24/(len(title_spot)*0.03),fontname='Century Gothic')
+        # #vals = ax.get_yticks()
+        ax.set_xlabel(batter_dict_stat[input.stat_x()]['x_axis'], fontsize=16,fontname='Century Gothic')
+        ax.set_ylabel(batter_dict_stat[input.stat_y()]['x_axis'], fontsize=16,fontname='Century Gothic')
+        
+
+        if input.group_level():
+            ax.get_legend().remove()
+
+        if not input.group_level():
+            if len(input.level_id()) > 1:
+                h,l = scatter.get_legend_handles_labels()
+                l[-(len(input.level_id())+1)] = 'Level'
+                ax.legend(h[-(len(input.level_id())+1):],l[-(len(input.level_id())+1):], borderaxespad=0.1,loc=0)
+
+            else:
+                ax.get_legend().remove()
+
+        #plt.show(g)
+        # ax.figure.colorbar(sm, ax=ax)
+        
+        cbar  = ax.figure.colorbar(sm, ax=ax,format=decimal_format_assign(x=batter_dict_stat[input.stat_z()]),orientation='vertical',aspect=30)
+        cbar.set_label(batter_dict_stat[input.stat_z()]['x_axis'])
+        #fig.axes[0].invert_yaxis()
+        print('we made it here5')
+        fig.subplots_adjust(wspace=.02, hspace=.02)
+        # ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
+        #ax.set_yticks([0,0.1,0.2,0.3,0.4,0.5])
+        # fig.colorbar(plot_dist, ax=ax)
+        # fig.colorbar(plot_dist)
+
+        if batter_dict_stat[input.stat_x()]['flip_p']:
+            fig.axes[0].invert_xaxis()
+
+        if batter_dict_stat[input.stat_y()]['flip_p']:
+            fig.axes[0].invert_yaxis()
+
+
+        # ax.xaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=0))
+        # ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
+
+        
+        
+        
+
+        print('we made it here6')
+
+        ax.xaxis.set_major_formatter(decimal_format_assign(x=batter_dict_stat[input.stat_x()]))
+        ax.yaxis.set_major_formatter(decimal_format_assign(x=batter_dict_stat[input.stat_y()]))
+
+
+        print('we made it here7')
+        # ax.text(0.5, 0.5, '/u/tomstoms', transform=ax.transAxes,
+        #         fontsize=60, color='gray', alpha=0.075,
+        #         ha='center', va='center', rotation=45)
+
+        print(ts)
+        if len(ts) > 0:
+            adjust_text(ts,
+                        arrowprops=dict(arrowstyle="-", color=colour_palette[4], lw=1),ax=ax)
+
+        #ax.legend(fontsize='16')
+        fig.text(x=0.03,y=0.02,s='By: @TJStats',fontname='Century Gothic')
+        fig.text(x=1-0.03,y=0.02,s='Data: MLB',ha='right',fontname='Century Gothic')
+        fig.tight_layout()
+        #matplotlib.rcParams["figure.dpi"] = 600
+        #plt.show()
+
+
+rolling_pitcher = 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.navset_tab(
+        ui.nav_control(
+             ui.a(
+                "Home",
+                href="home/"
+            ),
+        ),
+        ui.nav_menu(
+            "Batter Charts",
+            ui.nav_control(
+             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.nav_menu(
+            "Goalie Charts",
+            ui.nav_control(
+             ui.a(
+                "GSAx Timeline",
+                href="gsax-timeline/"
+            ),
+             ui.a(
+                "GSAx Leaderboard",
+                href="gsax-leaderboard/"
+            ),
+             ui.a(
+                "GSAx Comparison",
+                href="gsax-comparison/"
+            )
+        ),
+        ),ui.nav_menu(
+            "Team Charts",
+            ui.nav_control(
+             ui.a(
+                "Team xG Rates",
+                href="team-xg-rates/"
+            ),
+        ),
+        ),ui.nav_control(
+             ui.a(
+                "Games",
+                href="games/"
+            ),
+        ),ui.nav_control(
+             ui.a(
+                "About",
+                href="about/"
+            ),
+        ),ui.nav_control(
+             ui.a(
+                "Articles",
+                href="articles/"
+            ),
+        )),ui.row(
+    ui.layout_sidebar(
+        
+   
+
+      ui.panel_sidebar(
+        #ui.input_select("id", "Select Batter",batter_dict,selected=675911,width=1,size=1),
+        ui.row(
+            ui.column(4,ui.input_select("level_id", "Select Level",level_dict,width=1,size=1,multiple=True,selected='MLB',selectize=True),),
+            ui.column(4,ui.input_select("team_id", "Select Team",team_list,width=1,size=1,multiple=True,selected='All',selectize=True),),
+            ui.column(4,ui.input_select("position_id", "Select Position",position_list,width=1,size=1,selected='All',multiple=True,selectize=True))),
+        ui.row(
+            ui.column(6,ui.input_numeric("n", "Minimum PA", value=100)),
+            ui.column(6,ui.input_numeric("n_age", "Maximum Age", value=50))),
+        ui.row(
+            ui.column(4,ui.input_select("stat_x", "X-Axis",batter_dict_stat_small,selected='k_percent',width=1,size=1)),
+            ui.column(4,ui.input_select("stat_y", "Y-Axis",batter_dict_stat_small,selected='bb_percent',width=1,size=1)),
+            ui.column(4,ui.input_select("stat_z", "Colour-Bar Axis",batter_dict_stat_small,selected='bb_over_k_percent',width=1,size=1))),
+       
+        ui.row(
+            ui.column(6,ui.input_numeric("n_percent_top_x", "Top 'n' Percentile X-Labels", value=0.01)),
+            ui.column(6,ui.input_numeric("n_percent_bot_x", "Bottom 'n' Percentile X-Labels", value=0.01))),
+        ui.row(
+            ui.column(6,ui.input_numeric("n_percent_top_y", "Top 'n' Percentile Y-Labels", value=0.01)),
+            ui.column(6,ui.input_numeric("n_percent_bot_y", "Bottom 'n' Percentile Y-Labels", value=0.01))),
+        ui.row(
+            ui.column(6,ui.input_numeric("n_percent_top_z", "Top 'n' Percentile Z-Labels", value=0.01)),
+            ui.column(6,ui.input_numeric("n_percent_bot_z", "Bottom 'n' Percentile Z-Labels", value=0.01))),
+
+        ui.input_select("player_id", "Label Player",batter_dict,width=1,size=1,multiple=True,selectize=True),
+        ui.row(
+            ui.input_switch("names", "Toggle Names"),
+            ui.input_switch("group_level", "Group Levels")),
+     ),
+
+      ui.panel_main(
+        ui.output_plot("plot",height = "1000px",width="1000px")
+      ,
+    ),
+    )),)),server)

damage.py

@@ -165,6 +165,7 @@ 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 "":
@@ -391,6 +392,7 @@ def server(input,output,session):
 
     @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
@@ -505,6 +507,7 @@ damage = App(ui.page_fluid(
     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(
@@ -515,7 +518,11 @@ damage = App(ui.page_fluid(
         ui.nav_menu(
             "Batter Charts",
             ui.nav_control(
-             ui.a(
+            ui.a(
+                "Batting Rolling",
+                href="rolling_batter/"
+            ),
+            ui.a(
                 "Spray",
                 href="spray/"
             ),
@@ -534,48 +541,29 @@ damage = App(ui.page_fluid(
             ui.a(
                 "EV vs LA Plot",
                 href="ev_angle/"
+            ),
+            ui.a(
+                "Statcast Compare",
+                href="statcast_compare/"
             )
         ),
         ),
         ui.nav_menu(
-            "Goalie Charts",
+            "Pitcher Charts",
             ui.nav_control(
              ui.a(
-                "GSAx Timeline",
-                href="gsax-timeline/"
+                "Pitcher Rolling",
+                href="rolling_pitcher/"
             ),
              ui.a(
-                "GSAx Leaderboard",
-                href="gsax-leaderboard/"
+                "Pitcher Summary",
+                href="pitching_summary_graphic_new/"
             ),
              ui.a(
-                "GSAx Comparison",
-                href="gsax-comparison/"
+                "Pitcher Scatter",
+                href="pitcher_scatter/"
             )
         ),
-        ),ui.nav_menu(
-            "Team Charts",
-            ui.nav_control(
-             ui.a(
-                "Team xG Rates",
-                href="team-xg-rates/"
-            ),
-        ),
-        ),ui.nav_control(
-             ui.a(
-                "Games",
-                href="games/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "About",
-                href="about/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "Articles",
-                href="articles/"
-            ),
         )),ui.row(
     ui.layout_sidebar(
         
@@ -593,7 +581,8 @@ damage = App(ui.page_fluid(
                 ui.input_numeric("rolling_window",
                                  "Select Rolling Window",
                                  value=50,
-                                 min=1)),
+                                 min=1),
+                ui.input_action_button("go", "Generate",class_="btn-primary")),
 
    ui.panel_main(     
         ui.navset_tab(

decision_value.py

@@ -146,6 +146,7 @@ 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 "":
@@ -264,6 +265,7 @@ def server(input,output,session):
 
     @output
     @render.plot(alt="hex_plot")
+    @reactive.event(input.go, ignore_none=False)
     def dv_plot():
 
         if input.batter_id() is "":
@@ -357,6 +359,7 @@ def server(input,output,session):
 
     @output
     @render.plot(alt="hex_plot")
+    @reactive.event(input.go, ignore_none=False)
     def iz_plot():
           
         if input.batter_id() is "":
@@ -450,6 +453,7 @@ def server(input,output,session):
 
     @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))
@@ -557,6 +561,7 @@ decision_value = App(ui.page_fluid(
     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(
@@ -567,7 +572,11 @@ decision_value = App(ui.page_fluid(
         ui.nav_menu(
             "Batter Charts",
             ui.nav_control(
-             ui.a(
+            ui.a(
+                "Batting Rolling",
+                href="rolling_batter/"
+            ),
+            ui.a(
                 "Spray",
                 href="spray/"
             ),
@@ -586,48 +595,29 @@ decision_value = App(ui.page_fluid(
             ui.a(
                 "EV vs LA Plot",
                 href="ev_angle/"
+            ),
+            ui.a(
+                "Statcast Compare",
+                href="statcast_compare/"
             )
         ),
         ),
         ui.nav_menu(
-            "Goalie Charts",
+            "Pitcher Charts",
             ui.nav_control(
              ui.a(
-                "GSAx Timeline",
-                href="gsax-timeline/"
+                "Pitcher Rolling",
+                href="rolling_pitcher/"
             ),
              ui.a(
-                "GSAx Leaderboard",
-                href="gsax-leaderboard/"
+                "Pitcher Summary",
+                href="pitching_summary_graphic_new/"
             ),
              ui.a(
-                "GSAx Comparison",
-                href="gsax-comparison/"
+                "Pitcher Scatter",
+                href="pitcher_scatter/"
             )
         ),
-        ),ui.nav_menu(
-            "Team Charts",
-            ui.nav_control(
-             ui.a(
-                "Team xG Rates",
-                href="team-xg-rates/"
-            ),
-        ),
-        ),ui.nav_control(
-             ui.a(
-                "Games",
-                href="games/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "About",
-                href="about/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "Articles",
-                href="articles/"
-            ),
         )),ui.row(
     ui.layout_sidebar(
         
@@ -658,7 +648,9 @@ decision_value = App(ui.page_fluid(
                 ui.input_select("level_list",
                                  "Select Level",
                                  ['MLB','AAA'],
-                                 selected='MLB')),
+                                 selected='MLB'),
+                ui.input_action_button("go", "Generate",class_="btn-primary"),
+                                 ),
 
    ui.panel_main(     
         ui.navset_tab(

ev_angle.py

@@ -60,6 +60,7 @@ angle_ranges = list(range(8,51))
 def server(input,output,session):
     @output
     @render.plot(alt="A histogram")
+    @reactive.event(input.go, ignore_none=False)
     def plot():
         data_df = exit_velo_df[exit_velo_df.batter_id==int(input.id())]
         #pitch_list = exit_velo_df_small.pitch_type.unique()
@@ -182,6 +183,7 @@ ev_angle = App(ui.page_fluid(
     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(
@@ -192,7 +194,11 @@ ev_angle = App(ui.page_fluid(
         ui.nav_menu(
             "Batter Charts",
             ui.nav_control(
-             ui.a(
+            ui.a(
+                "Batting Rolling",
+                href="rolling_batter/"
+            ),
+            ui.a(
                 "Spray",
                 href="spray/"
             ),
@@ -211,48 +217,29 @@ ev_angle = App(ui.page_fluid(
             ui.a(
                 "EV vs LA Plot",
                 href="ev_angle/"
+            ),
+            ui.a(
+                "Statcast Compare",
+                href="statcast_compare/"
             )
         ),
         ),
         ui.nav_menu(
-            "Goalie Charts",
+            "Pitcher Charts",
             ui.nav_control(
              ui.a(
-                "GSAx Timeline",
-                href="gsax-timeline/"
+                "Pitcher Rolling",
+                href="rolling_pitcher/"
             ),
              ui.a(
-                "GSAx Leaderboard",
-                href="gsax-leaderboard/"
+                "Pitcher Summary",
+                href="pitching_summary_graphic_new/"
             ),
              ui.a(
-                "GSAx Comparison",
-                href="gsax-comparison/"
+                "Pitcher Scatter",
+                href="pitcher_scatter/"
             )
         ),
-        ),ui.nav_menu(
-            "Team Charts",
-            ui.nav_control(
-             ui.a(
-                "Team xG Rates",
-                href="team-xg-rates/"
-            ),
-        ),
-        ),ui.nav_control(
-             ui.a(
-                "Games",
-                href="games/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "About",
-                href="about/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "Articles",
-                href="articles/"
-            ),
         )),ui.row(
     ui.layout_sidebar(
         
@@ -261,8 +248,11 @@ ev_angle = App(ui.page_fluid(
 
       ui.panel_sidebar(
         ui.input_select("id", "Select Batter",batter_dict,width=1),
-        ui.input_select("plot_id", "Select Plot",{'scatter':'Scatter Plot','dist':'Distribution Plot'},width=1)
-      ),
+        ui.input_select("plot_id", "Select Plot",{'scatter':'Scatter Plot','dist':'Distribution Plot'},width=1),
+        
+                ui.input_action_button("go", "Generate",class_="btn-primary",
+                                       )),
+      
 
       ui.panel_main(
         ui.output_plot("plot",height = "1000px",width="1000px")

home.py

@@ -28,6 +28,7 @@ home = App(ui.page_fluid(
     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(
@@ -38,7 +39,11 @@ home = App(ui.page_fluid(
         ui.nav_menu(
             "Batter Charts",
             ui.nav_control(
-             ui.a(
+            ui.a(
+                "Batting Rolling",
+                href="rolling_batter/"
+            ),
+            ui.a(
                 "Spray",
                 href="spray/"
             ),
@@ -57,46 +62,77 @@ home = App(ui.page_fluid(
             ui.a(
                 "EV vs LA Plot",
                 href="ev_angle/"
+            ),
+            ui.a(
+                "Statcast Compare",
+                href="statcast_compare/"
             )
         ),
         ),
         ui.nav_menu(
-            "Goalie Charts",
+            "Pitcher Charts",
             ui.nav_control(
              ui.a(
-                "GSAx Timeline",
-                href="gsax-timeline/"
+                "Pitcher Rolling",
+                href="rolling_pitcher/"
             ),
              ui.a(
-                "GSAx Leaderboard",
-                href="gsax-leaderboard/"
+                "Pitcher Summary",
+                href="pitching_summary_graphic_new/"
             ),
              ui.a(
-                "GSAx Comparison",
-                href="gsax-comparison/"
+                "Pitcher Scatter",
+                href="pitcher_scatter/"
             )
         ),
-        ),ui.nav_menu(
-            "Team Charts",
-            ui.nav_control(
-             ui.a(
-                "Team xG Rates",
-                href="team-xg-rates/"
-            ),
-        ),
-        ),ui.nav_control(
-             ui.a(
-                "Games",
-                href="games/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "About",
-                href="about/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "Articles",
-                href="articles/"
-            ),
-        )),ui.tags.br(),ui.tags.h5("Welcome to TJStats!"),ui.tags.h6(""))), None)
+        )),ui.tags.h6(""),
+        ui.markdown(
+            """ 
+# Welcome to TJStats!
+---
+## Intro
+Hello, my name is Thomas Nestico, and welcome to my Baseball Analytics and Visualizations Website, TJStats!
+I am a Civil Engineering by trade, but I have a passion for data analytics and machine learning, specifically
+with respect to sports. This site and all of my visualizations were created using Python and its vast array of packages.
+The framework for this site and all of the apps is <a href='https://shiny.posit.co/py/'>Shiny Python</a><sup>1</sup>.
+All sources of data are referenced on each visualization.
+
+## Apps
+This site hosts my Baseball Data Viz Apps from the 2023 season. Here is a summary of what is accessible:
+
+* Batter Charts
+  - **Batter Rolling**: Rolling Stats Charts for MLB and MiLB batters
+  - **Spray**: Batted Ball Trajectory Distributions of MLB batters vs rest of league
+  - **Decision Value**: Scatter plot and Rolling Stats Chart for my <a href='https://medium.com/@thomasjamesnestico/modelling-batter-decision-value-dac74c55e20a'>Decision Value Model</a><sup>1</sup>
+  - **Damage Model**: Hexbin and Rolling Stat Chart for my Damage Model (Article TBD)
+  - **Batter Scatter**: Scatter Plots for MLB and MiLB batters
+  - **EV vs LA Plot**: Scatter plot visualizing a MLB Batter's Exit Velocities and Launch Angles
+  - **Statcast Compare**: Table which compares MLB Batter's Statcast metrics
+<br>
+* Pitcher Charts
+  - **Pitcher Rolling**: Rolling Stats Charts for MLB and MiLB pitchers
+  - **Pitcher Summary**: Pitching Summary for MLB pitchers for any date range during 2023
+  - **Pitcher Scatter**: Scatter Plots for MLB and MiLB pitchers
+
+
+## Articles
+I have published several articles on Medium covering different Baseball Analytics topics. In these articles I go through my methodology
+when applying machine learning algorithms to create machine learning models to help better understand baseball concepts. Here is a list of 
+articles which I have published:
+* <a href='https://medium.com/@thomasjamesnestico/modelling-tjstuff-d9a451765484'>Modelling tjStuff+</a><sup>1</sup>
+* <a href='https://medium.com/@thomasjamesnestico/modelling-batter-decision-value-dac74c55e20a'>Modelling Batter Decision Value</a><sup>1</sup>
+* <a href='https://medium.com/@thomasjamesnestico/classifying-mlb-pitch-zones-and-predicting-milb-zones-7e95cf308254'>Classifying MLB Pitch Zones and Predicting MiLB Zones</a><sup>1</sup>
+
+
+## 2024 Apps
+Thank you for checking out the site. If you would like to further support my endeavours in the Baseball Analytics sphere,
+here are few places where you can support me and get up to date baseball coverage and Apps for the 2024 season.
+
+* Patreon: https://www.patreon.com/tj_stats
+* Twitter: https://twitter.com/TJStats
+
+## About Me
+Here are a few other sites to learn more about me:
+* LinkedIn: https://www.linkedin.com/in/thomas-nestico-b66013173/
+* GitHub: https://github.com/tnestico
+  """))), None)

pitcher_scatter.py

@@ -0,0 +1,508 @@
+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 inflect
+from matplotlib.pyplot import text
+
+
+exit_velo_df_codes_summ = pd.read_csv('summary_pitcher.csv',index_col=[0]).reset_index(drop=True)
+exit_velo_df_codes_summ_non_level = pd.read_csv('summary_pitcher_level.csv',index_col=[0]).reset_index(drop=True)
+
+exit_velo_df_codes_summ_non_level['levels'] = exit_velo_df_codes_summ_non_level.levels.str.split(', ')
+
+exit_velo_df_codes_summ_non_level = exit_velo_df_codes_summ_non_level.rename(columns={'levels':'level'})
+
+print(exit_velo_df_codes_summ.bb_minus_k_percent)
+exit_velo_df_codes_summ.bb_minus_k_percent = -1*exit_velo_df_codes_summ.bb_minus_k_percent
+exit_velo_df_codes_summ.bb_over_k_percent = exit_velo_df_codes_summ.k_percent/exit_velo_df_codes_summ.bb_percent
+
+exit_velo_df_codes_summ_non_level.bb_minus_k_percent = -1*exit_velo_df_codes_summ_non_level.bb_minus_k_percent
+exit_velo_df_codes_summ_non_level.bb_over_k_percent = exit_velo_df_codes_summ_non_level.k_percent/exit_velo_df_codes_summ_non_level.bb_percent
+
+pitcher_dict_stat = { 'sweet_spot_percent':{'x_axis':'SweetSpot%','title':'SweetSpot%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'max_launch_speed':{'x_axis':'Max Exit Velocity','title':'Max Exit Velocity','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
+                'launch_speed_90':{'x_axis':'90th Percentile EV','title':'90th Percentile EV','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
+                'launch_speed':{'x_axis':'Exit Velocity','title':'Exit Velocity','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
+                'launch_angle':{'x_axis':'Launch Angle','title':'Launch Angle','flip_p':False,'decimal_format':'string_0','percent_adjust':100},
+                'avg':{'x_axis':'AVG','title':'AVG','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'obp':{'x_axis':'OBP','title':'OBP','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'slg':{'x_axis':'SLG','title':'SLG','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'ops':{'x_axis':'OPS','title':'OPS','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'k_percent':{'x_axis':'K%','title':'K%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'bb_percent':{'x_axis':'BB%','title':'BB%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'bb_over_k_percent':{'x_axis':'K/BB','title':'K/BB','flip_p':True,'decimal_format':'string_1','percent_adjust':100},
+                'bb_minus_k_percent':{'x_axis':'K%-BB%','title':'K%-BB%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'csw_percent':{'x_axis':'CSW%','title':'CSW%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'woba_percent':{'x_axis':'wOBA','title':'wOBA','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
+                'hard_hit_percent':{'x_axis':'HardHit%','title':'HardHit%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'barrel_percent':{'x_axis':'Barrel%','title':'Barrel%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'zone_contact_percent':{'x_axis':'Z-Contact%','title':'Z-Contact%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'zone_swing_percent':{'x_axis':'Z-Swing%','title':'Z-Swing%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'zone_percent':{'x_axis':'Zone%','title':'Zone%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'chase_percent':{'x_axis':'O-Swing%','title':'O-Swing%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'chase_contact':{'x_axis':'O-Contact%','title':'O-Contact%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'swing_percent':{'x_axis':'Swing%','title':'Swing%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
+                'whiff_rate':{'x_axis':'Whiff%','title':'Whiff%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                'swstr_rate':{'x_axis':'SwStr%','title':'SwStr%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
+                }
+
+pitcher_dict_stat_small = { 'sweet_spot_percent':'SweetSpot%',
+                'max_launch_speed':'Max Exit Velocity',
+                'launch_speed_90':'90th Percentile EV',
+                'launch_speed':'Exit Velocity',
+                'launch_angle':'Launch Angle',
+                'avg':'AVG',
+                'obp':'OBP',
+                'slg':'SLG',
+                'ops':'OPS',
+                'k_percent':'K%',
+                'bb_percent':'BB%',
+                'bb_over_k_percent':'K/BB',
+                'bb_minus_k_percent':'K%-BB%',
+                'csw_percent':'CSW%',
+                'woba_percent':'wOBA',
+                'hard_hit_percent':'HardHit%',
+                'barrel_percent':'Barrel%',
+                'zone_contact_percent':'Z-Contact%',
+                'zone_swing_percent':'Z-Swing%',
+                'zone_percent':'Zone%',
+                'chase_percent':'O-Swing%',
+                'chase_contact':'O-Contact%',
+                'swing_percent':'Swing%',
+                'whiff_rate':'Whiff%',
+                'swstr_rate':'SwStr%',
+                }
+
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+
+level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A','ROK':'ROK'}
+
+batter_test_df = exit_velo_df_codes_summ.sort_values(by='pitcher').drop_duplicates(subset='pitcher_id').reset_index(drop=True)[['pitcher_id','pitcher']]#['pitcher'].to_dict()
+batter_test_df = batter_test_df.set_index('pitcher_id')
+
+
+def decimal_format_assign(x):
+    if x['decimal_format'] == 'percent_1':
+        return mtick.PercentFormatter(1,decimals=1)
+    if x['decimal_format'] == 'string_3':
+        return mtick.FormatStrFormatter('%.3f')
+    if x['decimal_format'] == 'string_0':
+        return mtick.FormatStrFormatter('%.0f')
+    if x['decimal_format'] == 'string_1':
+        return mtick.FormatStrFormatter('%.1f')
+
+
+#test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
+
+pitcher_dict = batter_test_df['pitcher'].to_dict()
+
+exit_velo_df_codes_summ.position = exit_velo_df_codes_summ.position.replace(['TWP'],['P'])
+
+exit_velo_df_codes_summ_non_level.position = exit_velo_df_codes_summ_non_level.position.replace(['TWP'],['P'])
+
+
+position_list = ['All'] + list(exit_velo_df_codes_summ.position.unique())
+team_list = ['All'] + sorted(list(exit_velo_df_codes_summ.parent_org_abb.unique()))
+
+
+
+def server(input,output,session):
+
+
+    @output
+    @render.plot(alt="A histogram")
+    @reactive.event(input.go, ignore_none=False)
+    def plot():
+        sns.set_theme(style="whitegrid", palette="pastel")
+
+
+        print(input.level_id())
+        print(input.n())
+        print('we made it here')
+
+        #data_df = exit_velo_df_codes_summ.copy()
+
+
+        if input.group_level():
+            data_df = exit_velo_df_codes_summ_non_level.copy()
+
+            turth_list = []
+            #turth_list_2 = []
+            for x in range(0,len(data_df.level)):
+                turth_list_2 = []
+                for y in range(0,len(data_df.level[x])):
+                    #print(level_list[x][y])
+                    turth_list_2.append(data_df.level[x][y] in input.level_id())
+                turth_list.append(turth_list_2)
+
+            final_check_list = [True if True in x else False for x in turth_list]
+
+
+            data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())&(final_check_list)]
+        
+        else:
+            data_df = exit_velo_df_codes_summ.copy()
+            
+            data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())&(data_df.level.isin(input.level_id()))]
+            print(data_df)
+            if 'All' in input.team_id():
+                print('nice')#data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())].reset_index(drop=True)
+            
+            else:
+                data_df = data_df[(data_df.parent_org_abb.isin(input.team_id()))].reset_index(drop=True)
+
+            if 'All' in input.position_id():
+                print('nice')#data_df = data_df[(data_df.level.isin(input.level_id()))&(data_df.pa >= input.n())&(data_df.age <= input.n_age())].reset_index(drop=True)
+            
+            else:
+                data_df = data_df[(data_df.position.isin(input.position_id()))].reset_index(drop=True)
+        
+        
+                
+
+
+
+        data_df = data_df.sort_values(by='level').reset_index(drop=True)
+        print(pitcher_dict_stat[input.stat_x()]['flip_p'])
+
+
+
+        x_flip = pitcher_dict_stat[input.stat_x()]['flip_p']
+        y_flip = pitcher_dict_stat[input.stat_y()]['flip_p']
+        cbr_flip = pitcher_dict_stat[input.stat_z()]['flip_p']
+
+        
+
+        data_df[input.stat_x()+'_percent'] = data_df[input.stat_x()].rank(pct=True,ascending=abs(x_flip-1))
+
+        data_df[input.stat_y()+'_percent'] = data_df[input.stat_y()].rank(pct=True,ascending=abs(y_flip-1))
+
+        data_df[input.stat_z()+'_percent'] = data_df[input.stat_z()].rank(pct=True,ascending=abs(cbr_flip-1))
+
+
+
+        fig, ax = plt.subplots(1, 1, figsize=(9, 9))
+
+        #data_df['bb_over_obp'] = data_df['bb']/data_df['k']
+        
+        #data_df[input.stat_z()]= data_df[input.stat_z()].fillna(-100000)
+        
+        
+        if not cbr_flip:
+            cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[0],colour_palette[3],colour_palette[1]])
+            norm = plt.Normalize(data_df[input.stat_z()].min(), data_df[input.stat_z()].max())
+
+        else:
+            cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[1],colour_palette[3],colour_palette[0]])
+            norm = plt.Normalize(data_df[input.stat_z()].min(), data_df[input.stat_z()].max())
+
+        sm = plt.cm.ScalarMappable(cmap=cmap_hue, norm=norm)
+        print('we made it here')
+
+        # sns.regplot(x = stat_x, y = stat_y, data=data_df, color = colour_palette[6],ax=ax,scatter=False,
+        #             line_kws=dict(alpha=0.3,linewidth=2,zorder=1))
+        # scatter_plot = sns.scatterplot(x = stat_x, y = stat_y, data=data_df, color = colour_palette[0],ax=ax,hue=stat_z,palette=cmap_hue)
+
+
+
+        # r, p = sp.stats.pearsonr(data_df[input.stat_x()], data_df[input.stat_y()])
+        # ax = plt.gca()
+        # # ax.text(.25, 0.3, 'r={:.2f}, p={:.2g}'.format(r, p),
+        # #         transform=ax.transAxes, fontsize=12)
+
+        # ax.annotate('R²={:.2f}'.format(r, p), ( math.ceil(data_df[input.stat_x()].max()*pitcher_dict_stat[input.stat_x()]['percent_adjust']/5)*5/pitcher_dict_stat[input.stat_x()]['percent_adjust']*(1-pitcher_dict_stat[input.stat_x()]['flip_p']), 
+        #                                       math.floor(data_df[input.stat_y()].min()*pitcher_dict_stat[input.stat_y()]['percent_adjust']/5)*5/pitcher_dict_stat[input.stat_y()]['percent_adjust']*(1-pitcher_dict_stat[input.stat_y()]['flip_p'])), 
+        #                                         fontsize=18,fontname='Century Gothic',ha='right')
+
+        if input.group_level():
+            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = '#b3b3b3')
+            #ax.get_legend().remove()
+            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = colour_palette[0],ax=ax,hue=input.stat_z(),palette=cmap_hue)
+        else:
+            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = '#b3b3b3',style='level')    
+            #ax.get_legend().remove()
+            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = colour_palette[0],ax=ax,hue=input.stat_z(),palette=cmap_hue,style='level')
+ 
+        
+        sns.set_theme(style="whitegrid", palette="pastel")
+
+        fig.set_facecolor('#F0F0F0')
+        ax.set_facecolor('white')
+
+        print('we made it here!')
+        # for i in range(0,len(pitch_group_unique)):
+        #     data_df = elly_zone_df[elly_zone_df.pitch_group==pitch_group_unique[i]]
+        #     len_df.append(len(data_df))
+        #     sns.lineplot(x=range(1,len(data_df)+1),y=data_df.swings.rolling(window=rolling_window_input).sum()/data_df.pitches.rolling(window=rolling_window_input).sum(),color=colour_palette[i],linewidth=3,ax=ax,
+        #                  label=f'{pitch_group_unique[i]} (Season Average {float(data_df.swings.sum()/data_df.pitches.sum()):.1%})',zorder=i+10)
+        #     ax.hlines(xmin=0,xmax=len(elly_zone_df),y=data_df.swings.sum()/data_df.pitches.sum(),color=colour_palette[i],linewidth=3,linestyle='-.',alpha=0.4,zorder=i)
+
+        ts=[]
+        xs=[]
+        ys=[]
+        labels=[]
+
+        print(input.player_id())
+        if input.names():
+            for i in range(len(data_df)):
+                if (data_df[input.stat_x()+'_percent'][i] < input.n_percent_bot_x()  or data_df[input.stat_x()+'_percent'][i] > 1 - input.n_percent_top_x() ) \
+                or (data_df[input.stat_y()+'_percent'][i] < input.n_percent_bot_y()  or data_df[input.stat_y()+'_percent'][i] > 1 -input.n_percent_top_y()) \
+                or (data_df[input.stat_z()+'_percent'][i] < input.n_percent_bot_z()  or data_df[input.stat_z()+'_percent'][i] > 1 -input.n_percent_top_z() )\
+                or (str(data_df.pitcher_id[i]) in (input.player_id())):
+                    ts.append(ax.text(data_df[input.stat_x()][i], data_df[input.stat_y()][i], data_df.pitcher[i],fontsize=8))
+
+
+                    # ax.annotate(data_df.pitcher[i], xy=((data_df[input.stat_x()][i])+0.025/pitcher_dict_stat[input.stat_x()]['percent_adjust'], data_df[input.stat_y()][i]+0.01/pitcher_dict_stat[input.stat_x()]['percent_adjust']), xytext=(-20,20), 
+                    # textcoords='offset points', ha='center', va='bottom',fontsize=7,
+                    # bbox=dict(boxstyle='round,pad=0', fc=colour_palette[6], alpha=0.0),
+                    # arrowprops=dict(arrowstyle='->', connectionstyle="angle,angleA=-90,angleB=-10,rad=5", 
+                    #                 color=colour_palette[4]))
+                    
+        #             print('CONDITION CHEK')
+        #             print(data_df[input.stat_x()][i])
+        #             #if data_df['pitcher'][i] != 'Jo Adell':
+        #             #ax.annotate(data_df.pitcher[i], ((data_df[input.stat_x()][i])+0.025/pitcher_dict_stat[input.stat_x()]['percent_adjust'], data_df[input.stat_y()][i]+0.01/pitcher_dict_stat[input.stat_x()]['percent_adjust']),fontsize=8)
+        #             #ts.append(data_df[input.stat_x()][i], data_df[input.stat_y()][i], data_df.pitcher[i],fontsize=8))
+        #             xs.append(data_df[input.stat_x()][i])
+        #             ys.append(data_df[input.stat_y()][i])
+        #             labels.append(data_df.pitcher[i])
+
+        # # adjust_text(ts, force_points=0.2, force_text=0.2,
+        # #             expand_points=(1, 1), expand_text=(1, 1),
+        # #             arrowprops=dict(arrowstyle="-", color='black', lw=0))
+        # #print(xs)
+        # if len(xs)>0: 
+        #     repel_labels(ax=ax, x = xs,y = ys, labels = labels, k=0.0025)
+
+        ax.hlines(xmin=math.floor((data_df[input.stat_x()].min()*pitcher_dict_stat[input.stat_x()]['percent_adjust']-0.01)/5)*5/pitcher_dict_stat[input.stat_x()]['percent_adjust'],
+                    xmax= math.ceil((data_df[input.stat_x()].max()*pitcher_dict_stat[input.stat_x()]['percent_adjust']+0.01)/5)*5/pitcher_dict_stat[input.stat_x()]['percent_adjust'],
+                    y=data_df[input.stat_y()].mean(),color='gray',linewidth=3,linestyle='dotted',alpha=0.4)
+
+        print('we made it here')
+        ax.vlines(ymin=(math.floor(data_df[input.stat_y()].min()*pitcher_dict_stat[input.stat_y()]['percent_adjust']-0.01)/5)*5/pitcher_dict_stat[input.stat_y()]['percent_adjust'],
+                    ymax= (math.ceil(data_df[input.stat_y()].max()*pitcher_dict_stat[input.stat_y()]['percent_adjust']+0.01)/5)*5/pitcher_dict_stat[input.stat_y()]['percent_adjust'],
+                    x=data_df[input.stat_x()].mean(),color='gray',linewidth=3,linestyle='dotted',alpha=0.4)
+
+        ax.set_xlim((math.floor(data_df[input.stat_x()].min()*pitcher_dict_stat[input.stat_x()]['percent_adjust']-0.01)/5)*5/pitcher_dict_stat[input.stat_x()]['percent_adjust'],
+                    (math.ceil(data_df[input.stat_x()].max()*pitcher_dict_stat[input.stat_x()]['percent_adjust']+0.01)/5)*5/pitcher_dict_stat[input.stat_x()]['percent_adjust'])
+
+        ax.set_ylim((math.floor(data_df[input.stat_y()].min()*pitcher_dict_stat[input.stat_y()]['percent_adjust']-0.01)/5)*5/pitcher_dict_stat[input.stat_y()]['percent_adjust'],
+                    (math.ceil(data_df[input.stat_y()].max()*pitcher_dict_stat[input.stat_y()]['percent_adjust']+0.01)/5)*5/pitcher_dict_stat[input.stat_y()]['percent_adjust'])
+        
+        title_level = str([x .strip("\'")for x in input.level_id()]).strip('[').strip(']').replace("'",'')
+
+        if title_level == 'AAA, AA, A+, A':
+            title_level='MiLB'
+
+
+        if input.n_age() >= 50:
+            title_spot = f'{title_level} Pitcher {pitcher_dict_stat[input.stat_y()]["title"]} vs {pitcher_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA)'
+        
+        else:
+            title_spot = f'{title_level} Pitcher {pitcher_dict_stat[input.stat_y()]["title"]} vs {pitcher_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA, Max Age {input.n_age()})'
+
+        #title_level = input.level_id()[0]
+        #title_spot = f'{title_level} pitcher {pitcher_dict_stat[input.stat_y()]["title"]} vs {pitcher_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA)'
+        
+        ax.set_title(title_spot, fontsize=24/(len(title_spot)*0.03),fontname='Century Gothic')
+        # #vals = ax.get_yticks()
+        ax.set_xlabel(pitcher_dict_stat[input.stat_x()]['x_axis'], fontsize=16,fontname='Century Gothic')
+        ax.set_ylabel(pitcher_dict_stat[input.stat_y()]['x_axis'], fontsize=16,fontname='Century Gothic')
+        
+
+        if input.group_level():
+            ax.get_legend().remove()
+
+        if not input.group_level():
+            if len(input.level_id()) > 1:
+                h,l = scatter.get_legend_handles_labels()
+                l[-(len(input.level_id())+1)] = 'Level'
+                ax.legend(h[-(len(input.level_id())+1):],l[-(len(input.level_id())+1):], borderaxespad=0.1,loc=0)
+
+            else:
+                ax.get_legend().remove()
+
+
+
+        #plt.show(g)
+        # ax.figure.colorbar(sm, ax=ax)
+        
+        cbar  = ax.figure.colorbar(sm, ax=ax,format=decimal_format_assign(x=pitcher_dict_stat[input.stat_z()]),orientation='vertical',aspect=30)
+        cbar.set_label(pitcher_dict_stat[input.stat_z()]['x_axis'])
+        #fig.axes[0].invert_yaxis()
+        print('we made it here5')
+        fig.subplots_adjust(wspace=.02, hspace=.02)
+        # ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
+        #ax.set_yticks([0,0.1,0.2,0.3,0.4,0.5])
+        # fig.colorbar(plot_dist, ax=ax)
+        # fig.colorbar(plot_dist)
+
+        if not pitcher_dict_stat[input.stat_x()]['flip_p']:
+            fig.axes[0].invert_xaxis()
+
+        if not pitcher_dict_stat[input.stat_y()]['flip_p']:
+            fig.axes[0].invert_yaxis()
+
+
+        # ax.xaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=0))
+        # ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
+
+        
+        
+        
+
+        print('we made it here6')
+
+        ax.xaxis.set_major_formatter(decimal_format_assign(x=pitcher_dict_stat[input.stat_x()]))
+        ax.yaxis.set_major_formatter(decimal_format_assign(x=pitcher_dict_stat[input.stat_y()]))
+
+
+        print('we made it here7')
+        # ax.text(0.5, 0.5, '/u/tomstoms', transform=ax.transAxes,
+        #         fontsize=60, color='gray', alpha=0.075,
+        #         ha='center', va='center', rotation=45)
+        adjust_text(ts,
+                    arrowprops=dict(arrowstyle="-", color=colour_palette[4], lw=1),ax=ax)
+
+        #ax.legend(fontsize='16')
+        fig.text(x=0.03,y=0.02,s='By: @TJStats',fontname='Century Gothic')
+        fig.text(x=1-0.03,y=0.02,s='Data: MLB',ha='right',fontname='Century Gothic')
+        fig.tight_layout()
+
+
+pitcher_scatter = 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_select("id", "Select Batter",batter_dict,selected=675911,width=1,size=1),
+        ui.row(
+            ui.column(4,ui.input_select("level_id", "Select Level",level_dict,width=1,size=1,multiple=True,selected='MLB',selectize=True),),
+            ui.column(4,ui.input_select("team_id", "Select Team",team_list,width=1,size=1,multiple=True,selected='All',selectize=True),),
+            ui.column(4,ui.input_select("position_id", "Select Position",position_list,width=1,size=1,selected='All',multiple=True,selectize=True))),
+        ui.row(
+            ui.column(6,ui.input_numeric("n", "Minimum PA", value=100)),
+            ui.column(6,ui.input_numeric("n_age", "Maximum Age", value=50))),
+        ui.row(
+            ui.column(4,ui.input_select("stat_x", "X-Axis",pitcher_dict_stat_small,selected='k_percent',width=1,size=1)),
+            ui.column(4,ui.input_select("stat_y", "Y-Axis",pitcher_dict_stat_small,selected='bb_percent',width=1,size=1)),
+            ui.column(4,ui.input_select("stat_z", "Colour-Bar Axis",pitcher_dict_stat_small,selected='bb_minus_k_percent',width=1,size=1))),
+       
+        ui.row(
+            ui.column(6,ui.input_numeric("n_percent_top_x", "Top 'n' Percentile X-Labels", value=0.01)),
+            ui.column(6,ui.input_numeric("n_percent_bot_x", "Bottom 'n' Percentile X-Labels", value=0.01))),
+        ui.row(
+            ui.column(6,ui.input_numeric("n_percent_top_y", "Top 'n' Percentile Y-Labels", value=0.01)),
+            ui.column(6,ui.input_numeric("n_percent_bot_y", "Bottom 'n' Percentile Y-Labels", value=0.01))),
+        ui.row(
+            ui.column(6,ui.input_numeric("n_percent_top_z", "Top 'n' Percentile Z-Labels", value=0.01)),
+            ui.column(6,ui.input_numeric("n_percent_bot_z", "Bottom 'n' Percentile Z-Labels", value=0.01))),
+
+        ui.input_select("player_id", "Label Player",pitcher_dict,width=1,size=1,multiple=True,selectize=True),
+        ui.row(
+            ui.input_switch("names", "Toggle Names"),
+            ui.input_switch("group_level", "Group Levels")),
+        ui.input_action_button("go", "Generate",class_="btn-primary"),
+     ),
+
+
+      ui.panel_main(
+        ui.output_plot("plot",height = "1000px",width="1000px")
+      ,
+    ),
+    )),)),server)

rolling_batter.py

@@ -0,0 +1,732 @@
+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 inflect
+from matplotlib.pyplot import text
+
+def percentile(n):
+    def percentile_(x):
+        return np.nanpercentile(x, n)
+    percentile_.__name__ = 'percentile_%s' % n
+    return percentile_
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+
+
+
+print('Starting Everything:')
+# exit_velo_df = milb_a_ev_df.append([triple_a_ev_df,double_a_ev_df,a_high_a_ev_df,single_a_ev_df]).reset_index(drop=True)
+# player_df_all = mlb_a_player_df.append([triple_a_player_df,double_a_player_df,a_high_a_player_df,single_a_player_df]).reset_index(drop=True)
+# exit_velo_df = pd.read_csv('exit_velo_df_all.csv',index_col=[0])
+# player_df_all = pd.read_csv('player_df_all.csv',index_col=[0])
+
+# pa_df = pd.read_csv('pa_df_all.csv',index_col=[0])
+# pa_df_full_na = pa_df.dropna()
+
+### Import Datasets
+dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2023.csv',
+                                                         ])
+dataset_train = dataset['train']
+exit_velo_df_mlb = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+#print(df_2023)
+exit_velo_df_mlb['level'] = 'MLB'
+
+# ### Import Datasets
+# dataset = load_dataset('nesticot/mlb_data', data_files=['aaa_pitch_data_2023.csv',
+#                                                          ])
+# dataset_train = dataset['train']
+# exit_velo_df_aaa = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+# #print(df_2023)
+# exit_velo_df_aaa['level'] = 'AAA'
+
+# ### Import Datasets
+# dataset = load_dataset('nesticot/mlb_data', data_files=['aa_pitch_data_2023.csv',
+#                                                          ])
+# dataset_train = dataset['train']
+# exit_velo_df_aa = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+# #print(df_2023)
+# exit_velo_df_aa['level'] = 'AA'
+
+# ### Import Datasets
+# dataset = load_dataset('nesticot/mlb_data', data_files=['high_a_pitch_data_2023.csv',
+#                                                          ])
+# dataset_train = dataset['train']
+# exit_velo_df_ha = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+# #print(df_2023)
+# exit_velo_df_ha['level'] = 'A+'
+
+# ### Import Datasets
+# dataset = load_dataset('nesticot/mlb_data', data_files=['a_pitch_data_2023.csv',
+#                                                          ])
+# dataset_train = dataset['train']
+# exit_velo_df_a = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+# #print(df_2023)
+# exit_velo_df_a['level'] = 'A'
+
+# exit_velo_df = pd.concat([exit_velo_df_mlb,exit_velo_df_aaa,exit_velo_df_aa,exit_velo_df_ha,exit_velo_df_a])
+exit_velo_df = pd.concat([exit_velo_df_mlb])
+# exit_velo_df_copy = exit_velo_df.copy()
+
+# exit_velo_df = exit_velo_df_copy.copy()
+
+end_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
+       'double', 'sac_fly', 'force_out', 'home_run',
+       'grounded_into_double_play', 'fielders_choice', 'field_error',
+       'triple', 'sac_bunt', 'double_play', 'intent_walk',
+       'fielders_choice_out', 'strikeout_double_play',
+       'sac_fly_double_play', 'catcher_interf', 'other_out']
+
+
+
+exit_velo_df['pa'] = exit_velo_df.event_type.isin(end_codes)
+#exit_velo_df['pa'] = 1
+exit_velo_df['k'] = exit_velo_df.event_type.isin(list(filter(None, [x if 'strikeout' in x else '' for x in exit_velo_df.event_type.fillna('None').unique()])))
+exit_velo_df['bb'] = exit_velo_df.event_type.isin(list(filter(None, [x if 'walk' in x else '' for x in exit_velo_df.event_type.fillna('None').unique()])))
+
+#exit_velo_df['k_minus_bb'] = exit_velo_df['k'].astype(np.float32)-exit_velo_df['bb'].astype(np.float32)
+exit_velo_df['bb_minus_k'] = exit_velo_df['bb'].astype(np.float32)-exit_velo_df['k'].astype(np.float32)
+
+
+
+exit_velo_df = exit_velo_df.drop_duplicates(subset=['play_id'])
+
+
+
+swing_codes = ['Swinging Strike', 'In play, no out', 
+        'Foul', 'In play, out(s)', 
+       'In play, run(s)', 'Swinging Strike (Blocked)',
+       'Foul Bunt','Foul Tip', 'Missed Bunt','Foul Pitchout','Swinging Pitchout']
+
+swings_in = ['Swinging Strike', 'In play, no out', 
+        'Foul', 'In play, out(s)', 
+       'In play, run(s)', 'Swinging Strike (Blocked)',
+       'Foul Bunt','Foul Tip', 'Missed Bunt','Foul Pitchout','Swinging Pitchout']       
+
+swing_strike_codes = ['Swinging Strike',
+ 'Swinging Strike (Blocked)','Missed Bunt','Foul Tip','Swinging Pitchout']
+
+
+contact_codes = ['In play, no out', 
+        'Foul', 'In play, out(s)', 
+       'In play, run(s)', 
+       'Foul Bunt']
+
+codes_in = ['In play, out(s)',
+'Swinging Strike',
+'Ball',
+'Foul',
+'In play, no out',
+'Called Strike',
+'Foul Tip',
+'In play, run(s)',
+'Hit By Pitch',
+'Ball In Dirt',
+'Pitchout',
+'Swinging Strike (Blocked)',
+'Foul Bunt',
+'Missed Bunt',
+'Foul Pitchout',
+'Intent Ball',
+'Swinging Pitchout']
+
+exit_velo_df['in_zone'] = exit_velo_df['zone'] < 10
+
+
+exit_velo_df = exit_velo_df.drop_duplicates(subset=['play_id'])
+
+exit_velo_df_codes = exit_velo_df[exit_velo_df.play_description.isin(codes_in)].dropna(subset=['in_zone'])
+
+exit_velo_df_codes['bip'] = ~exit_velo_df_codes.launch_speed.isna()
+conditions = [
+    (exit_velo_df_codes['launch_speed'].isna()),
+    (exit_velo_df_codes['launch_speed']*1.5 - exit_velo_df_codes['launch_angle'] >= 117 ) & (exit_velo_df_codes['launch_speed'] + exit_velo_df_codes['launch_angle'] >= 124) & (exit_velo_df_codes['launch_speed'] > 98) & (exit_velo_df_codes['launch_angle'] >= 8) & (exit_velo_df_codes['launch_angle'] <= 50)
+]
+
+choices = [False,True]
+exit_velo_df_codes['barrel'] = np.select(conditions, choices, default=np.nan)
+
+conditions_ss = [
+    (exit_velo_df_codes['launch_angle'].isna()),
+    (exit_velo_df_codes['launch_angle'] >= 8 ) * (exit_velo_df_codes['launch_angle'] <= 32 ) 
+]
+
+choices_ss = [False,True]
+exit_velo_df_codes['sweet_spot'] = np.select(conditions_ss, choices_ss, default=np.nan)
+
+
+conditions_hh = [
+    (exit_velo_df_codes['launch_speed'].isna()),
+    (exit_velo_df_codes['launch_speed'] >= 94.5 ) 
+]
+
+choices_hh = [False,True]
+exit_velo_df_codes['hard_hit'] = np.select(conditions_hh, choices_hh, default=np.nan)
+
+
+conditions_tb = [
+    (exit_velo_df_codes['event_type']=='single'),
+    (exit_velo_df_codes['event_type']=='double'),
+    (exit_velo_df_codes['event_type']=='triple'),
+    (exit_velo_df_codes['event_type']=='home_run'),
+]
+
+choices_tb = [1,2,3,4]
+
+exit_velo_df_codes['tb'] = np.select(conditions_tb, choices_tb, default=np.nan)
+
+conditions_woba = [
+    (exit_velo_df_codes['event_type']=='walk'),
+    (exit_velo_df_codes['event_type']=='hit_by_pitch'),    
+    (exit_velo_df_codes['event_type']=='single'),
+    (exit_velo_df_codes['event_type']=='double'),
+    (exit_velo_df_codes['event_type']=='triple'),
+    (exit_velo_df_codes['event_type']=='home_run'),
+]
+
+choices_woba = [0.705,
+                0.688,
+                0.897,
+                1.233,
+                1.612,
+                2.013]
+
+exit_velo_df_codes['woba'] = np.select(conditions_woba, choices_woba, default=np.nan)
+
+
+woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
+       'double', 'sac_fly', 'force_out', 'home_run',
+       'grounded_into_double_play', 'fielders_choice', 'field_error',
+       'triple', 'sac_bunt', 'double_play',
+       'fielders_choice_out', 'strikeout_double_play',
+       'sac_fly_double_play', 'other_out']
+
+
+
+
+
+conditions_woba_code = [
+    (exit_velo_df_codes['event_type'].isin(woba_codes))
+]
+
+choices_woba_code = [1]
+
+exit_velo_df_codes['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan)
+
+
+#exit_velo_df_codes['barrel']  = (exit_velo_df_codes.launch_speed >= 98) & (exit_velo_df_codes.launch_angle >= (26 - (-98 + exit_velo_df_codes.launch_speed))) & (exit_velo_df_codes.launch_angle <= 30 + (-98 + exit_velo_df_codes.launch_speed)) & (exit_velo_df_codes.launch_angle >= 8) & (exit_velo_df_codes.launch_angle <= 50)
+
+
+
+
+
+#exit_velo_df_codes['barrel']  = (exit_velo_df_codes.launch_speed >= 98) & (exit_velo_df_codes.launch_angle >= (26 - (-98 + exit_velo_df_codes.launch_speed))) & (exit_velo_df_codes.launch_angle <= 30 + (-98 + exit_velo_df_codes.launch_speed)) & (exit_velo_df_codes.launch_angle >= 8) & (exit_velo_df_codes.launch_angle <= 50)
+exit_velo_df_codes['pitches'] = 1
+exit_velo_df_codes['whiffs'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')) else 0 for x in exit_velo_df_codes.play_code]
+exit_velo_df_codes['csw'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')|(x == 'C')) else 0 for x in exit_velo_df_codes.play_code]
+exit_velo_df_codes['swings'] = [1 if x in swings_in else 0 for x in exit_velo_df_codes.play_description]
+
+exit_velo_df_codes['out_zone'] = exit_velo_df_codes.in_zone == False
+exit_velo_df_codes['zone_swing'] = (exit_velo_df_codes.in_zone == True)&(exit_velo_df_codes.swings == 1)
+exit_velo_df_codes['zone_contact'] = (exit_velo_df_codes.in_zone == True)&(exit_velo_df_codes.swings == 1)&(exit_velo_df_codes.whiffs == 0)
+exit_velo_df_codes['ozone_swing'] = (exit_velo_df_codes.in_zone==False)&(exit_velo_df_codes.swings == 1)
+exit_velo_df_codes['ozone_contact'] = (exit_velo_df_codes.in_zone==False)&(exit_velo_df_codes.swings == 1)&(exit_velo_df_codes.whiffs == 0)
+
+
+
+exit_velo_df_codes_summ = exit_velo_df_codes.groupby(['batter_id','batter_name','level']).agg(
+    pa = ('pa','sum'), 
+    k = ('k','sum'), 
+    bb = ('bb','sum'), 
+    bb_minus_k = ('bb_minus_k','sum'), 
+    csw = ('csw','sum'), 
+    bip = ('bip','sum'),
+    tb = ('tb','sum'),
+    woba = ('woba','sum'),
+    woba_codes = ('woba_codes','sum'),
+    hard_hit = ('hard_hit','sum'), 
+    barrel = ('barrel','sum'), 
+    sweet_spot = ('sweet_spot','sum'), 
+    max_launch_speed = ('launch_speed','max'),
+    launch_speed_90 =  ('launch_speed',percentile(90)),
+    launch_speed = ('launch_speed','mean'),
+    launch_angle = ('launch_angle','mean'),
+    pitches = ('pitches','sum'),
+    swings =  ('swings','sum'),
+    in_zone = ('in_zone','sum'),
+    out_zone = ('out_zone','sum'),
+    whiffs = ('whiffs','sum'),
+    zone_swing = ('zone_swing','sum'),
+    zone_contact = ('zone_contact','sum'),
+    ozone_swing = ('ozone_swing','sum'),
+    ozone_contact = ('ozone_contact','sum'),
+    ).reset_index()
+
+#exit_velo_df_codes_summ['out_zone'] = ~exit_velo_df_codes_summ.in_zone
+#bip_min_input = int(input())
+#bip_min = min(bip_min_input,50)
+#exit_velo_df_codes_summ =  exit_velo_df_codes_summ[exit_velo_df_codes_summ.balls_in_play>=bip_min]
+
+
+exit_velo_df_codes_summ['k_percent'] = [exit_velo_df_codes_summ.k[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+exit_velo_df_codes_summ['bb_percent'] =[exit_velo_df_codes_summ.bb[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+exit_velo_df_codes_summ['bb_minus_k_percent'] =[exit_velo_df_codes_summ.bb_minus_k[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['csw_percent'] =[exit_velo_df_codes_summ.csw[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+
+exit_velo_df_codes_summ['sweet_spot_percent'] = [exit_velo_df_codes_summ.sweet_spot[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['woba_percent'] = [exit_velo_df_codes_summ.woba[x]/exit_velo_df_codes_summ.woba_codes[x] if exit_velo_df_codes_summ.woba_codes[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+#exit_velo_df_codes_summ['hard_hit_percent'] = [exit_velo_df_codes_summ.sweet_spot[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+exit_velo_df_codes_summ['hard_hit_percent'] = [exit_velo_df_codes_summ.hard_hit[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+
+exit_velo_df_codes_summ['barrel_percent'] = [exit_velo_df_codes_summ.barrel[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['zone_contact_percent'] = [exit_velo_df_codes_summ.zone_contact[x]/exit_velo_df_codes_summ.zone_swing[x] if exit_velo_df_codes_summ.zone_swing[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['zone_swing_percent'] = [exit_velo_df_codes_summ.zone_swing[x]/exit_velo_df_codes_summ.in_zone[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['zone_percent'] = [exit_velo_df_codes_summ.in_zone[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['chase_percent'] = [exit_velo_df_codes_summ.ozone_swing[x]/(exit_velo_df_codes_summ.pitches[x] - exit_velo_df_codes_summ.in_zone[x]) if (exit_velo_df_codes_summ.pitches[x]- exit_velo_df_codes_summ.in_zone[x]) != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['chase_contact'] = [exit_velo_df_codes_summ.ozone_contact[x]/exit_velo_df_codes_summ.ozone_swing[x] if exit_velo_df_codes_summ.ozone_swing[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['swing_percent'] = [exit_velo_df_codes_summ.swings[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['whiff_rate'] = [exit_velo_df_codes_summ.whiffs[x]/exit_velo_df_codes_summ.swings[x] if exit_velo_df_codes_summ.swings[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['swstr_rate'] = [exit_velo_df_codes_summ.whiffs[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ = exit_velo_df_codes_summ.dropna(subset=['bip'])
+
+woba_list = ['woba']
+pa_list = ['k','bb','bb_minus_k']
+balls_in_play_list = ['hard_hit','launch_speed','launch_speed_90','launch_angle','barrel','sweet_spot']
+pitches_list = ['zone_percent','swing_percent','sw_str','csw']
+swings_list = ['whiff_percent']
+in_zone_pitches_list = ['zone_swing']
+in_zone_swings_list = ['zone_contact']
+out_zone_pitches_list = ['chase_percent']
+out_zone_swings_list = ['chase_contact']
+
+plot_dict = {
+'k':{'x_axis':'Plate Appearances','y_axis':'K%','title':'K%','x_value':'k','x_range':[0.0,0.1,0.2,0.3,0.4],'percent':True,'percentile_label':'k_percent','flip_p':True,'percentile':False,'avg_adjust':False},
+'bb':{'x_axis':'Plate Appearances','y_axis':'BB%','title':'BB%','x_value':'bb','x_range':[0.0,0.1,0.2,0.3],'percent':True,'percentile_label':'bb_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'bb_minus_k':{'x_axis':'Plate Appearances','y_axis':'BB-K%','title':'BB-K%','x_value':'bb_minus_k','x_range':[-0.3,-0.2,-0.1,0,0.1,0.2],'percent':True,'percentile_label':'bb_minus_k_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'csw':{'x_axis':'Pitches','y_axis':'CSW%','title':'CSW%','x_value':'csw','x_range':[.2,.25,.3,.35,.4],'percent':True,'percentile_label':'csw_percent','flip_p':True,'percentile':False,'avg_adjust':False},
+'woba':{'x_axis':'wOBA PA','y_axis':'wOBA','title':'wOBA','x_value':'woba','x_range':[.20,.30,.40,.50],'percent':False,'percentile_label':'woba_percent','flip_p':False,'percentile':False,'avg_adjust':True},
+'launch_speed':{'x_axis':'Balls In Play','y_axis':'Exit Velocity','title':'Exit Velocity','x_value':'launch_speed','x_range':[85,90,95,100],'percent':False,'percentile_label':'launch_speed','flip_p':False,'percentile':False,'avg_adjust':False},
+'launch_speed_90':{'x_axis':'Balls In Play','y_axis':'90th Percentile Exit Velocity','title':'90th Percentile Exit Velocity','x_value':'launch_speed','x_range':[95,100,105,110,115],'percent':False,'percentile_label':'launch_speed_90','flip_p':False,'percentile':True,'avg_adjust':False},              
+'hard_hit':{'x_axis':'Balls In Play','y_axis':'HardHit%','title':'HardHit%','x_value':'hard_hit','x_range':[0.2,0.3,0.4,0.5,0.6,0.7],'percent':True,'percentile_label':'hard_hit_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'sweet_spot':{'x_axis':'Balls In Play','y_axis':'SweetSpot%','title':'SweetSpot%','x_value':'sweet_spot','x_range':[0.2,0.3,0.4,0.5],'percent':True,'percentile_label':'sweet_spot_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'launch_angle':{'x_axis':'Balls In Play','y_axis':'Launch Angle','title':'Launch Angle','x_value':'launch_angle','x_range':[-20,-10,0,10,20],'percent':False,'percentile_label':'launch_angle','flip_p':False,'percentile':False,'avg_adjust':False},
+'barrel':{'x_axis':'Balls In Play','y_axis':'Barrel%','title':'Barrel%','x_value':'barrel','x_range':[0,0.05,0.10,.15,.20,.25,.30],'percent':True,'percentile_label':'barrel_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'zone_percent':{'x_axis':'Pitches','y_axis':'Zone%','title':'Zone%','x_value':'in_zone','x_range':[0.3,0.4,0.5,0.6,0.7],'percent':True,'percentile_label':'zone_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'swing_percent':{'x_axis':'Pitches','y_axis':'Swing%','title':'Swing%','x_value':'swings','x_range':[0.2,0.3,0.4,0.5,0.6,0.7,0.8],'percent':True,'percentile_label':'swing_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'whiff_percent':{'x_axis':'Swings','y_axis':'Whiff%','title':'Whiff%','x_value':'whiffs','x_range':[0.0,0.1,0.2,0.3,0.4,0.5],'percent':True,'percentile_label':'whiff_rate','flip_p':True,'percentile':False,'avg_adjust':False},
+'sw_str':{'x_axis':'Pitches','y_axis':'SwStr%','title':'SwStr%','x_value':'whiffs','x_range':[0.0,0.05,0.1,0.15,0.2,0.25],'percent':True,'percentile_label':'swstr_rate','flip_p':True,'percentile':False,'avg_adjust':False},
+'zone_swing':{'x_axis':'In-Zone Pitches','y_axis':'Z-Swing%','title':'Z-Swing%','x_value':'zone_swing','x_range':[0.3,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],'percent':True,'percentile_label':'zone_swing_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'zone_contact':{'x_axis':'In-Zone Swings','y_axis':'Z-Contact%','title':'Z-Contact%','x_value':'zone_contact','x_range':[0.5,0.6,0.7,0.8,0.9,1],'percent':True,'percentile_label':'zone_contact_percent','flip_p':False,'percentile':False,'avg_adjust':False},
+'chase_percent':{'x_axis':'Out-of-Zone Pitches','y_axis':'O-Swing%','title':'O-Swing%','x_value':'ozone_swing','x_range':[0.0,0.1,0.2,0.3,0.4,0.5],'percent':True,'percentile_label':'chase_percent','flip_p':True,'percentile':False,'avg_adjust':False},
+'chase_contact':{'x_axis':'Out-of-Zone Swings','y_axis':'O-Contact%','title':'O-Contact%','x_value':'ozone_contact','x_range':[0.2,0.3,0.4,0.5,0.6,0.7,0.8],'percent':True,'percentile_label':'chase_contact','flip_p':False,'percentile':False,'avg_adjust':False},}
+
+
+
+
+test_df = exit_velo_df.sort_values(by='batter_name').drop_duplicates(subset='batter_id').reset_index(drop=True)[['batter_id','batter_name']]#['pitcher'].to_dict()
+test_df = test_df.dropna()
+test_df['batter_id'] = test_df['batter_id'].astype(int)
+test_df = test_df.set_index('batter_id')
+#test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
+
+batter_dict = test_df['batter_name'].to_dict()
+
+level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A'}
+
+plot_dict_small = {
+'k':'K%',
+'bb':'BB%',
+'csw':'CSW%',
+'launch_speed':'Exit Velocity',
+'launch_speed_90':'90th Percentile Exit Velocity',
+'sweet_spot':'SweetSpot%',
+'launch_angle':'Launch Angle',
+'zone_percent':'Zone%',
+'barrel':'Barrel%',
+'swing_percent':'Swing%',
+'whiff_percent':'Whiff%',
+'sw_str':'SwStr%',
+'zone_swing':'Z-Swing%',
+'zone_contact':'Z-Contact%',
+'chase_percent':'O-Swing%',
+'chase_contact':'O-Contact%',}
+
+
+def server(input,output,session):
+
+    @output
+    @render.plot(alt="A histogram")
+    @reactive.event(input.go, ignore_none=False)
+    def plot():
+        # np.random.seed(19680801)
+        # x = 100 + 15 * np.random.randn(437)
+        # fig, ax = plt.subplots()
+        # ax.hist(x, input.n(), density=True)
+        # return fig
+        sns.set_theme(style="whitegrid", palette="pastel")
+        if input.id() is "":
+            fig = plt.figure(figsize=(12, 12))
+            fig.text(s='Please Select a Pitcher',x=0.5,y=0.5)
+            return
+        
+        swing_min = int(input.n())
+        fig, ax = plt.subplots(1, 1, figsize=(10, 10))
+
+        fig.set_facecolor('white')
+        #ax.set_facecolor('white')
+        #fig.patch.set_facecolor('lightblue')
+
+        print(input.stat_id())
+
+        if input.stat_id() in pa_list:
+            print('we hAVE MADE IT TO THIS PART OF THE CODE')
+
+
+        if input.stat_id() in pa_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.pa==1)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'pa'
+            print('this is short')
+            print(elly_zone_df)
+
+
+        if input.stat_id() in balls_in_play_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.bip)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'bip'
+            #print('this is short')
+
+        if input.stat_id() in balls_in_play_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.bip)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'bip'
+            print('this is short')
+
+        if input.stat_id() in pitches_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.pitches == 1)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'pitches'
+
+        if input.stat_id() in swings_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.swings == 1)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'swings'
+
+
+        if input.stat_id() in in_zone_pitches_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.in_zone)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'in_zone'
+
+
+        if input.stat_id() in in_zone_swings_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.zone_swing)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'zone_swing'
+
+
+        if input.stat_id() in out_zone_pitches_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.in_zone == False)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'out_zone'
+
+
+        if input.stat_id() in out_zone_swings_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.ozone_swing)&(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'ozone_swing'
+
+        # penguins = sns.load_dataset("penguins")
+        # sns.histplot(data=penguins, x="flipper_length_mm")
+        # print('we made it here:')
+        # print(int(input.id()))
+        # print(input.stat_id())
+        # print(input.level_id())
+        # print(exit_velo_df_codes[(exit_velo_df_codes.batter_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())])
+        # print(exit_velo_df_codes.columns)
+        # print(elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum())
+
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ.copy()
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new.set_index('batter_id','batter_name','level')
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new[divisor_x] >= int(input.n())]
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new.level==input.level_id()]
+
+        exit_velo_df_codes_summ_rank = exit_velo_df_codes_summ_new.rank(method='max',ascending=False)
+        exit_velo_df_codes_summ_rank.columns = exit_velo_df_codes_summ_rank.columns+['_rank']
+
+        exit_velo_df_codes_summ_rank_percent =  exit_velo_df_codes_summ_new.rank(pct=True)
+        exit_velo_df_codes_summ_rank_percent.columns = exit_velo_df_codes_summ_rank_percent.columns+['_percent']
+        
+
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new.reset_index()
+        exit_velo_df_codes_summ_rank = exit_velo_df_codes_summ_rank.reset_index()
+        exit_velo_df_codes_summ_rank_percent = exit_velo_df_codes_summ_rank_percent.reset_index()
+        print('Table columns:')
+        
+        exit_velo_df_codes_summ_new.batter_id = exit_velo_df_codes_summ_new.batter_id.astype(int)
+        exit_velo_df_codes_summ_rank.batter_id = exit_velo_df_codes_summ_rank.batter_id.astype(int)
+        exit_velo_df_codes_summ_rank_percent.batter_id = exit_velo_df_codes_summ_rank_percent.batter_id.astype(int)
+
+        print('Table columns2:')
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new.merge(exit_velo_df_codes_summ_rank,left_on=['batter_id'],right_on=['batter_id'],how='left',suffixes=['','_rank'])
+
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new.merge(exit_velo_df_codes_summ_rank_percent,left_on=['batter_id'],right_on=['batter_id'],how='left',suffixes=['','_percent'])
+
+
+        print(exit_velo_df_codes_summ_new)
+        print(exit_velo_df_codes_summ_rank)
+        print(exit_velo_df_codes_summ_rank_percent)
+        
+
+        
+
+        #sns.scatterplot(x=data_df.launch_speed_90,y=data_df.zone_contact,color=colour_palette[0],s=75,label=int(input.id()))
+
+        exit_velo_df_codes_summ_new_select = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new.batter_id == int(input.id())].reset_index(drop=True)
+        print('whiffing')
+        print(exit_velo_df_codes)
+        print('Player _df:')
+        print(exit_velo_df_codes_summ_new_select)
+
+        if len(exit_velo_df_codes_summ_new_select) < 1:
+            ax.text(x=0.5,y=0.5,s='Please Select Different Parameters to Produce a plot',fontsize=18,ha='center')
+            return 
+
+        p = inflect.engine()
+
+        exit_velo_df_codes_summ_new_select = exit_velo_df_codes_summ_new_select.loc[:,~exit_velo_df_codes_summ_new_select.columns.duplicated(keep='last')].copy()
+        print('Table for the player:')
+        print(list(exit_velo_df_codes_summ_new_select.columns))
+        print(plot_dict[input.stat_id()]["percentile_label"])
+        print(plot_dict[input.stat_id()]["percentile_label"]+'_percent')
+        print(exit_velo_df_codes_summ_new_select)
+        print(1*plot_dict[input.stat_id()]["flip_p"])
+        print(round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))
+        print((1*plot_dict[input.stat_id()]["flip_p"]-round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))*100)
+
+        # print(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+'_percent'])
+
+        if plot_dict[input.stat_id()]['percent']:
+            label_1=f'{input.level_id()} Average {exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].sum()/exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][divisor_x].sum():.1%}'
+            label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%} ({p.ordinal(abs(int((1*plot_dict[input.stat_id()]["flip_p"]-round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%}'
+            ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
+
+        else:
+            label_1=f'{input.level_id()} Average {exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].sum()/exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][divisor_x].sum():.1f}'
+            label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1f} ({p.ordinal(abs(int((1*plot_dict[input.stat_id()]["flip_p"]-round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1f}'
+            #ax.yaxis.set_major_formatter(mtick.int)
+        
+
+        if plot_dict[input.stat_id()]['percentile']:
+            label_1=f'{input.level_id()} Average {exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].quantile(0.9):.1f}'
+            label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].quantile(0.9):.1f} ({p.ordinal(abs(int((1*plot_dict[input.stat_id()]["flip_p"]-round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%}'
+            #ax.yaxis.set_major_formatter(mtick.int)
+
+
+        print(plot_dict[input.stat_id()]["x_value"])
+        print(divisor_x)
+
+        # exit_velo_df_codes_summ_new = exit_velo_df_codes_summ.copy()
+        # exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new.balls_in_play >= int(input.n())]
+        # exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new.level==input.level_id()]
+
+        
+        print('this is here:')
+        print(exit_velo_df_codes_summ_new.head())
+        print(exit_velo_df_codes_summ_new.columns)
+
+
+        if plot_dict[input.stat_id()]["flip_p"] == False:
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.9),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[2],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.75),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[3],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.25),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[4],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.1),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[5],linestyle='dotted',alpha=0.5)
+
+
+            hard_hit_dates = [(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.9),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.75),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.25),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.1)]
+            hard_hit_text = ['90th %','75th %','25th %','10th %']
+            for i, x in enumerate(hard_hit_dates):
+                text(min(input.n()+input.n()/100,+input.n()+1), x ,hard_hit_text[i], rotation=0, ha='left',
+                    bbox=dict(facecolor='white',alpha=0.5, edgecolor=colour_palette[2+i], pad=2))
+
+
+
+        if plot_dict[input.stat_id()]["flip_p"] == True:
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.1),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[2],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.25),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[3],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.75),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[4],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.9),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[5],linestyle='dotted',alpha=0.5)
+
+            hard_hit_dates = [(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.9),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.75),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.25),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.1)]
+            hard_hit_text = ['10th %','25th %','75th %','90th %']
+            for i, x in enumerate(hard_hit_dates):
+                text(min(input.n()+input.n()/100,input.n()+input.n()+3), x ,hard_hit_text[i], rotation=0, ha='left',
+                    bbox=dict(facecolor='white',alpha=0.5, edgecolor=colour_palette[2+i], pad=2))
+
+
+        
+
+
+
+        if plot_dict[input.stat_id()]["percentile"] == False:
+            ax.hlines(y=exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].sum()/exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][divisor_x].sum(),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[1],linestyle='-.',label=label_1)
+
+            ax.hlines(y=elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum(),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[0],linestyle='--',label=label_2)
+
+            sns.lineplot(x=range(1,len(elly_zone_df)+1),y=elly_zone_df[plot_dict[input.stat_id()]["x_value"]].fillna(0).rolling(window=swing_min).sum()/swing_min,color=colour_palette[0],linewidth=3,ax=ax)
+
+
+
+        if plot_dict[input.stat_id()]["percentile"] == True:
+
+            ax.hlines(y=exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].quantile(0.9),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[1],linestyle='-.',label=label_1)
+
+            ax.hlines(y=elly_zone_df[plot_dict[input.stat_id()]["x_value"]].fillna(0).quantile(0.9),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[0],linestyle='--',label=label_2)
+
+            sns.lineplot(x=range(1,len(elly_zone_df)+1),y=elly_zone_df[plot_dict[input.stat_id()]["x_value"]].fillna(0).rolling(window=swing_min).quantile(0.9),color=colour_palette[0],linewidth=3,ax=ax)
+
+
+        #ax.set_xlim(input.n(),exit_velo_df_small.pitch.max())
+        #plt.yticks([0,0.2,0.4,0.6,0.8,1])
+        #ax.set_ylim(math.floor((min(exit_velo_df_codes_summ.zone_contact)/5)*100)*5/100,1)
+        ax.set_xlim(math.floor(swing_min),len(elly_zone_df))
+        ax.set_title(f'{batter_dict[int(input.id())]} - {input.level_id()} - {swing_min} {plot_dict[input.stat_id()]["x_axis"]} Rolling {plot_dict[input.stat_id()]["title"]}', fontsize=16,fontname='Century Gothic',)
+        #vals = ax.get_yticks()
+        ax.set_xlabel(plot_dict[input.stat_id()]['x_axis'], fontsize=16,fontname='Century Gothic')
+        ax.set_ylabel(plot_dict[input.stat_id()]['y_axis'], fontsize=16,fontname='Century Gothic')
+
+        #fig.axes[0].invert_yaxis()
+        
+        #fig.subplots_adjust(wspace=.02, hspace=.02)
+        #ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
+        ax.set_yticks(plot_dict[input.stat_id()]["x_range"])
+        #fig.colorbar(plot_dist, ax=ax)
+        #fig.colorbar(plot_dist)
+        #fig.axes[0].invert_yaxis()
+        ax.legend(fontsize='16')
+        fig.text(x=0.03,y=0.02,s='By: @TJStats',fontname='Century Gothic')
+        fig.text(x=1-0.03,y=0.02,s='Data: MLB',ha='right',fontname='Century Gothic')
+        fig.tight_layout()
+
+
+rolling_batter = 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_select("id", "Select Pitcher",batter_dict,selected=675911,width=1,size=1,selectize=True),
+        ui.input_select("level_id", "Select Level",level_dict,width=1,size=1),
+        ui.input_select("stat_id", "Select Stat",plot_dict_small,width=1,size=1),
+        ui.input_numeric("n", "Rolling Window Size", value=50),
+        ui.input_action_button("go", "Generate",class_="btn-primary"),
+        ui.output_table("result")
+      ),
+
+      ui.panel_main(
+        ui.output_plot("plot",height = "1000px",width="1000px")
+      ),
+    )),)),server)

rolling_pitcher.py

@@ -0,0 +1,719 @@
+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 inflect
+from matplotlib.pyplot import text
+
+def percentile(n):
+    def percentile_(x):
+        return np.nanpercentile(x, n)
+    percentile_.__name__ = 'percentile_%s' % n
+    return percentile_
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+
+
+
+print('Starting Everything:')
+# exit_velo_df = milb_a_ev_df.append([triple_a_ev_df,double_a_ev_df,a_high_a_ev_df,single_a_ev_df]).reset_index(drop=True)
+# player_df_all = mlb_a_player_df.append([triple_a_player_df,double_a_player_df,a_high_a_player_df,single_a_player_df]).reset_index(drop=True)
+# exit_velo_df = pd.read_csv('exit_velo_df_all.csv',index_col=[0])
+# player_df_all = pd.read_csv('player_df_all.csv',index_col=[0])
+
+# pa_df = pd.read_csv('pa_df_all.csv',index_col=[0])
+# pa_df_full_na = pa_df.dropna()
+
+### Import Datasets
+dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2023.csv',
+                                                         ])
+dataset_train = dataset['train']
+exit_velo_df_mlb = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+#print(df_2023)
+exit_velo_df_mlb['level'] = 'MLB'
+
+### Import Datasets
+dataset = load_dataset('nesticot/mlb_data', data_files=['aaa_pitch_data_2023.csv',
+                                                         ])
+dataset_train = dataset['train']
+exit_velo_df_aaa = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+#print(df_2023)
+exit_velo_df_aaa['level'] = 'AAA'
+
+### Import Datasets
+dataset = load_dataset('nesticot/mlb_data', data_files=['aa_pitch_data_2023.csv',
+                                                         ])
+dataset_train = dataset['train']
+exit_velo_df_aa = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+#print(df_2023)
+exit_velo_df_aa['level'] = 'AA'
+
+### Import Datasets
+dataset = load_dataset('nesticot/mlb_data', data_files=['high_a_pitch_data_2023.csv',
+                                                         ])
+dataset_train = dataset['train']
+exit_velo_df_ha = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+#print(df_2023)
+exit_velo_df_ha['level'] = 'A+'
+
+### Import Datasets
+dataset = load_dataset('nesticot/mlb_data', data_files=['a_pitch_data_2023.csv',
+                                                         ])
+dataset_train = dataset['train']
+exit_velo_df_a = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
+#print(df_2023)
+exit_velo_df_a['level'] = 'A'
+
+exit_velo_df = pd.concat([exit_velo_df_mlb,exit_velo_df_aaa,exit_velo_df_aa,exit_velo_df_ha,exit_velo_df_a])
+# exit_velo_df_copy = exit_velo_df.copy()
+
+# exit_velo_df = exit_velo_df_copy.copy()
+
+end_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
+       'double', 'sac_fly', 'force_out', 'home_run',
+       'grounded_into_double_play', 'fielders_choice', 'field_error',
+       'triple', 'sac_bunt', 'double_play', 'intent_walk',
+       'fielders_choice_out', 'strikeout_double_play',
+       'sac_fly_double_play', 'catcher_interf', 'other_out']
+
+
+
+exit_velo_df['pa'] = exit_velo_df.event_type.isin(end_codes)
+#exit_velo_df['pa'] = 1
+exit_velo_df['k'] = exit_velo_df.event_type.isin(list(filter(None, [x if 'strikeout' in x else '' for x in exit_velo_df.event_type.fillna('None').unique()])))
+exit_velo_df['bb'] = exit_velo_df.event_type.isin(list(filter(None, [x if 'walk' in x else '' for x in exit_velo_df.event_type.fillna('None').unique()])))
+exit_velo_df['k_minus_bb'] = exit_velo_df['k'].astype(np.float32)-exit_velo_df['bb'].astype(np.float32)
+
+exit_velo_df = exit_velo_df.drop_duplicates(subset=['play_id'])
+
+
+
+swing_codes = ['Swinging Strike', 'In play, no out', 
+        'Foul', 'In play, out(s)', 
+       'In play, run(s)', 'Swinging Strike (Blocked)',
+       'Foul Bunt','Foul Tip', 'Missed Bunt','Foul Pitchout','Swinging Pitchout']
+
+swings_in = ['Swinging Strike', 'In play, no out', 
+        'Foul', 'In play, out(s)', 
+       'In play, run(s)', 'Swinging Strike (Blocked)',
+       'Foul Bunt','Foul Tip', 'Missed Bunt','Foul Pitchout','Swinging Pitchout']       
+
+swing_strike_codes = ['Swinging Strike',
+ 'Swinging Strike (Blocked)','Missed Bunt','Foul Tip','Swinging Pitchout']
+
+
+contact_codes = ['In play, no out', 
+        'Foul', 'In play, out(s)', 
+       'In play, run(s)', 
+       'Foul Bunt']
+
+codes_in = ['In play, out(s)',
+'Swinging Strike',
+'Ball',
+'Foul',
+'In play, no out',
+'Called Strike',
+'Foul Tip',
+'In play, run(s)',
+'Hit By Pitch',
+'Ball In Dirt',
+'Pitchout',
+'Swinging Strike (Blocked)',
+'Foul Bunt',
+'Missed Bunt',
+'Foul Pitchout',
+'Intent Ball',
+'Swinging Pitchout']
+
+exit_velo_df['in_zone'] = exit_velo_df['zone'] < 10
+
+
+exit_velo_df = exit_velo_df.drop_duplicates(subset=['play_id'])
+
+exit_velo_df_codes = exit_velo_df[exit_velo_df.play_description.isin(codes_in)].dropna(subset=['in_zone'])
+
+exit_velo_df_codes['bip'] = ~exit_velo_df_codes.launch_speed.isna()
+conditions = [
+    (exit_velo_df_codes['launch_speed'].isna()),
+    (exit_velo_df_codes['launch_speed']*1.5 - exit_velo_df_codes['launch_angle'] >= 117 ) & (exit_velo_df_codes['launch_speed'] + exit_velo_df_codes['launch_angle'] >= 124) & (exit_velo_df_codes['launch_speed'] > 98) & (exit_velo_df_codes['launch_angle'] >= 8) & (exit_velo_df_codes['launch_angle'] <= 50)
+]
+
+choices = [False,True]
+exit_velo_df_codes['barrel'] = np.select(conditions, choices, default=np.nan)
+
+conditions_ss = [
+    (exit_velo_df_codes['launch_angle'].isna()),
+    (exit_velo_df_codes['launch_angle'] >= 8 ) * (exit_velo_df_codes['launch_angle'] <= 32 ) 
+]
+
+choices_ss = [False,True]
+exit_velo_df_codes['sweet_spot'] = np.select(conditions_ss, choices_ss, default=np.nan)
+conditions_hh = [
+    (exit_velo_df_codes['launch_speed'].isna()),
+    (exit_velo_df_codes['launch_speed'] >= 94.5 ) 
+]
+
+choices_hh = [False,True]
+exit_velo_df_codes['hard_hit'] = np.select(conditions_hh, choices_hh, default=np.nan)
+
+
+conditions_tb = [
+    (exit_velo_df_codes['event_type']=='single'),
+    (exit_velo_df_codes['event_type']=='double'),
+    (exit_velo_df_codes['event_type']=='triple'),
+    (exit_velo_df_codes['event_type']=='home_run'),
+]
+
+choices_tb = [1,2,3,4]
+
+exit_velo_df_codes['tb'] = np.select(conditions_tb, choices_tb, default=np.nan)
+
+conditions_woba = [
+    (exit_velo_df_codes['event_type']=='walk'),
+    (exit_velo_df_codes['event_type']=='hit_by_pitch'),    
+    (exit_velo_df_codes['event_type']=='single'),
+    (exit_velo_df_codes['event_type']=='double'),
+    (exit_velo_df_codes['event_type']=='triple'),
+    (exit_velo_df_codes['event_type']=='home_run'),
+]
+
+choices_woba = [0.705,
+                0.688,
+                0.897,
+                1.233,
+                1.612,
+                2.013]
+
+exit_velo_df_codes['woba'] = np.select(conditions_woba, choices_woba, default=np.nan)
+
+
+woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
+       'double', 'sac_fly', 'force_out', 'home_run',
+       'grounded_into_double_play', 'fielders_choice', 'field_error',
+       'triple', 'sac_bunt', 'double_play',
+       'fielders_choice_out', 'strikeout_double_play',
+       'sac_fly_double_play', 'other_out']
+
+
+
+
+
+conditions_woba_code = [
+    (exit_velo_df_codes['event_type'].isin(woba_codes))
+]
+
+choices_woba_code = [1]
+
+exit_velo_df_codes['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan)
+
+
+#exit_velo_df_codes['barrel']  = (exit_velo_df_codes.launch_speed >= 98) & (exit_velo_df_codes.launch_angle >= (26 - (-98 + exit_velo_df_codes.launch_speed))) & (exit_velo_df_codes.launch_angle <= 30 + (-98 + exit_velo_df_codes.launch_speed)) & (exit_velo_df_codes.launch_angle >= 8) & (exit_velo_df_codes.launch_angle <= 50)
+
+
+
+#exit_velo_df_codes['barrel']  = (exit_velo_df_codes.launch_speed >= 98) & (exit_velo_df_codes.launch_angle >= (26 - (-98 + exit_velo_df_codes.launch_speed))) & (exit_velo_df_codes.launch_angle <= 30 + (-98 + exit_velo_df_codes.launch_speed)) & (exit_velo_df_codes.launch_angle >= 8) & (exit_velo_df_codes.launch_angle <= 50)
+exit_velo_df_codes['pitches'] = 1
+exit_velo_df_codes['whiffs'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')) else 0 for x in exit_velo_df_codes.play_code]
+exit_velo_df_codes['csw'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')|(x == 'C')) else 0 for x in exit_velo_df_codes.play_code]
+exit_velo_df_codes['swings'] = [1 if x in swings_in else 0 for x in exit_velo_df_codes.play_description]
+
+exit_velo_df_codes['out_zone'] = exit_velo_df_codes.in_zone == False
+exit_velo_df_codes['zone_swing'] = (exit_velo_df_codes.in_zone == True)&(exit_velo_df_codes.swings == 1)
+exit_velo_df_codes['zone_contact'] = (exit_velo_df_codes.in_zone == True)&(exit_velo_df_codes.swings == 1)&(exit_velo_df_codes.whiffs == 0)
+exit_velo_df_codes['ozone_swing'] = (exit_velo_df_codes.in_zone==False)&(exit_velo_df_codes.swings == 1)
+exit_velo_df_codes['ozone_contact'] = (exit_velo_df_codes.in_zone==False)&(exit_velo_df_codes.swings == 1)&(exit_velo_df_codes.whiffs == 0)
+
+exit_velo_df_codes_summ = exit_velo_df_codes.groupby(['pitcher_id','pitcher_name','level']).agg(
+    pa = ('pa','sum'), 
+    k = ('k','sum'), 
+    bb = ('bb','sum'), 
+    k_minus_bb = ('k_minus_bb','sum'), 
+    csw = ('csw','sum'), 
+    bip = ('bip','sum'), 
+    tb = ('tb','sum'),
+    woba = ('woba','sum'),
+    woba_codes = ('woba_codes','sum'),
+    hard_hit = ('hard_hit','sum'), 
+    barrel = ('barrel','sum'), 
+    sweet_spot = ('sweet_spot','sum'), 
+    max_launch_speed = ('launch_speed','max'),
+    launch_speed_90 =  ('launch_speed',percentile(90)),
+    launch_speed = ('launch_speed','mean'),
+    launch_angle = ('launch_angle','mean'),
+    pitches = ('pitches','sum'),
+    swings =  ('swings','sum'),
+    in_zone = ('in_zone','sum'),
+    out_zone = ('out_zone','sum'),
+    whiffs = ('whiffs','sum'),
+    zone_swing = ('zone_swing','sum'),
+    zone_contact = ('zone_contact','sum'),
+    ozone_swing = ('ozone_swing','sum'),
+    ozone_contact = ('ozone_contact','sum'),
+    ).reset_index()
+
+
+
+exit_velo_df_codes_summ['k_percent'] = [exit_velo_df_codes_summ.k[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+exit_velo_df_codes_summ['bb_percent'] =[exit_velo_df_codes_summ.bb[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+exit_velo_df_codes_summ['k_minus_bb_percent'] =[exit_velo_df_codes_summ.k_minus_bb[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+
+exit_velo_df_codes_summ['sweet_spot_percent'] = [exit_velo_df_codes_summ.sweet_spot[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['woba_percent'] = [exit_velo_df_codes_summ.woba[x]/exit_velo_df_codes_summ.woba_codes[x] if exit_velo_df_codes_summ.woba_codes[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+
+exit_velo_df_codes_summ['hard_hit_percent'] = [exit_velo_df_codes_summ.hard_hit[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+
+
+exit_velo_df_codes_summ['csw_percent'] = [exit_velo_df_codes_summ.csw[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+
+exit_velo_df_codes_summ['barrel_percent'] = exit_velo_df_codes_summ.barrel = [exit_velo_df_codes_summ.barrel[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['zone_contact_percent'] = [exit_velo_df_codes_summ.zone_contact[x]/exit_velo_df_codes_summ.zone_swing[x] if exit_velo_df_codes_summ.zone_swing[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['zone_swing_percent'] = [exit_velo_df_codes_summ.zone_swing[x]/exit_velo_df_codes_summ.in_zone[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['zone_percent'] = [exit_velo_df_codes_summ.in_zone[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['chase_percent'] = [exit_velo_df_codes_summ.ozone_swing[x]/(exit_velo_df_codes_summ.pitches[x] - exit_velo_df_codes_summ.in_zone[x]) if (exit_velo_df_codes_summ.pitches[x]- exit_velo_df_codes_summ.in_zone[x]) != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['chase_contact'] = [exit_velo_df_codes_summ.ozone_contact[x]/exit_velo_df_codes_summ.ozone_swing[x] if exit_velo_df_codes_summ.ozone_swing[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['swing_percent'] = [exit_velo_df_codes_summ.swings[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['whiff_rate'] = [exit_velo_df_codes_summ.whiffs[x]/exit_velo_df_codes_summ.swings[x] if exit_velo_df_codes_summ.swings[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ['swstr_rate'] = [exit_velo_df_codes_summ.whiffs[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
+
+exit_velo_df_codes_summ = exit_velo_df_codes_summ.dropna(subset=['bip'])
+
+print('whiffing')
+print(exit_velo_df_codes_summ['whiff_rate'])
+
+exit_velo_df_codes_summ.head()
+
+woba_list = ['woba']
+pa_list = ['k','bb','k_minus_bb','bb_minus_k']
+balls_in_play_list = ['hard_hit','launch_speed','launch_speed_90','launch_angle','barrel','sweet_spot']
+pitches_list = ['zone_percent','swing_percent','sw_str','csw']
+swings_list = ['whiff_percent']
+in_zone_pitches_list = ['zone_swing']
+in_zone_swings_list = ['zone_contact']
+out_zone_pitches_list = ['chase_percent']
+out_zone_swings_list = ['chase_contact']
+
+plot_dict = {
+'k':{'x_axis':'Plate Appearances','y_axis':'K%','title':'K%','x_value':'k','x_range':[0.0,0.1,0.2,0.3,0.4],'percent':True,'percentile_label':'k_percent','flip_p':False,'percentile':False},
+'bb':{'x_axis':'Plate Appearances','y_axis':'BB%','title':'BB%','x_value':'bb','x_range':[0.0,0.1,0.2,0.3],'percent':True,'percentile_label':'bb_percent','flip_p':True,'percentile':False},
+'csw':{'x_axis':'Pitches','y_axis':'CSW%','title':'CSW%','x_value':'csw','x_range':[.2,.25,.3,.35,.4],'percent':True,'percentile_label':'csw_percent','flip_p':False,'percentile':False},
+'launch_speed':{'x_axis':'Balls In Play','y_axis':'Exit Velocity','title':'Exit Velocity','x_value':'launch_speed','x_range':[85,90,95,100,105],'percent':True,'percentile_label':'launch_speed','flip_p':True,'percentile':False},
+'launch_speed_90':{'x_axis':'Balls In Play','y_axis':'90th Percentile Exit Velocity','title':'90th Percentile Exit Velocity','x_value':'launch_speed','x_range':[90,95,100,105,110,115,120],'percent':False,'percentile_label':'launch_speed_90','flip_p':True,'percentile':True},              
+'sweet_spot':{'x_axis':'Balls In Play','y_axis':'SweetSpot%','title':'SweetSpot%','x_value':'sweet_spot','x_range':[0.2,0.3,0.4,0.5,0.6],'percent':True,'percentile_label':'sweet_spot_percent','flip_p':True,'percentile':False},
+'launch_angle':{'x_axis':'Balls In Play','y_axis':'Launch Angle','title':'Launch Angle','x_value':'launch_angle','x_range':[-20,-10,0,10,20],'percent':False,'percentile_label':'launch_angle','flip_p':True,'percentile':False},
+'barrel':{'x_axis':'Balls In Play','y_axis':'Barrel%','title':'Barrel%','x_value':'barrel','x_range':[0,0.05,0.10,.15,.20,.25,.30],'percent':True,'percentile_label':'barrel_percent','flip_p':False,'percentile':False},
+'zone_percent':{'x_axis':'Pitches','y_axis':'Zone%','title':'Zone%','x_value':'in_zone','x_range':[0.3,0.4,0.5,0.6,0.7],'percent':True,'percentile_label':'zone_percent','flip_p':True,'percentile':False},
+'swing_percent':{'x_axis':'Pitches','y_axis':'Swing%','title':'Swing%','x_value':'swings','x_range':[0.2,0.3,0.4,0.5,0.6,0.7,0.8],'percent':True,'percentile_label':'swing_percent','flip_p':False,'percentile':False},
+'whiff_percent':{'x_axis':'Swings','y_axis':'Whiff%','title':'Whiff%','x_value':'whiffs','x_range':[0.0,0.1,0.2,0.3,0.4,0.5],'percent':True,'percentile_label':'whiff_rate','flip_p':False,'percentile':False},
+'sw_str':{'x_axis':'Pitches','y_axis':'SwStr%','title':'SwStr%','x_value':'whiffs','x_range':[0.0,0.05,0.1,0.15,0.2,0.25],'percent':True,'percentile_label':'swstr_rate','flip_p':False,'percentile':False},
+'zone_swing':{'x_axis':'In-Zone Pitches','y_axis':'Z-Swing%','title':'Z-Swing%','x_value':'zone_swing','x_range':[0.3,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],'percent':True,'percentile_label':'zone_swing_percent','flip_p':True,'percentile':False},
+'zone_contact':{'x_axis':'In-Zone Swings','y_axis':'Z-Contact%','title':'Z-Contact%','x_value':'zone_contact','x_range':[0.5,0.6,0.7,0.8,0.9,1],'percent':True,'percentile_label':'zone_contact_percent','flip_p':True,'percentile':False},
+'chase_percent':{'x_axis':'Out-of-Zone Pitches','y_axis':'O-Swing%','title':'O-Swing%','x_value':'ozone_swing','x_range':[0.0,0.1,0.2,0.3,0.4,0.5],'percent':True,'percentile_label':'chase_percent','flip_p':False,'percentile':False},
+'chase_contact':{'x_axis':'Out-of-Zone Swings','y_axis':'O-Contact%','title':'O-Contact%','x_value':'ozone_contact','x_range':[0.2,0.3,0.4,0.5,0.6,0.7,0.8],'percent':True,'percentile_label':'chase_contact','flip_p':True,'percentile':False},}
+
+test_df = exit_velo_df.sort_values(by='pitcher_name').drop_duplicates(subset='pitcher_id').reset_index(drop=True)[['pitcher_id','pitcher_name']]#['pitcher'].to_dict()
+test_df = test_df.set_index('pitcher_id')
+
+#test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
+
+batter_dict = test_df['pitcher_name'].to_dict()
+
+level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A'}
+
+plot_dict_small = {
+'k':'K%',
+'bb':'BB%',
+'csw':'CSW%',
+'launch_speed':'Exit Velocity',
+'launch_speed_90':'90th Percentile Exit Velocity',
+'sweet_spot':'SweetSpot%',
+'launch_angle':'Launch Angle',
+'zone_percent':'Zone%',
+'barrel':'Barrel%',
+'swing_percent':'Swing%',
+'whiff_percent':'Whiff%',
+'sw_str':'SwStr%',
+'zone_swing':'Z-Swing%',
+'zone_contact':'Z-Contact%',
+'chase_percent':'O-Swing%',
+'chase_contact':'O-Contact%',}
+
+
+def server(input,output,session):
+
+    @output
+    @render.plot(alt="A histogram")
+    @reactive.event(input.go, ignore_none=False)    
+    def plot():
+        # np.random.seed(19680801)
+        # x = 100 + 15 * np.random.randn(437)
+        # fig, ax = plt.subplots()
+        # ax.hist(x, input.n(), density=True)
+        # return fig
+        sns.set_theme(style="whitegrid", palette="pastel")
+        if input.id() is "":
+            fig = plt.figure(figsize=(12, 12))
+            fig.text(s='Please Select a Pitcher',x=0.5,y=0.5)
+            return
+        
+        swing_min = int(input.n())
+        fig, ax = plt.subplots(1, 1, figsize=(10, 10))
+
+        fig.set_facecolor('white')
+        #ax.set_facecolor('white')
+        #fig.patch.set_facecolor('lightblue')
+
+        print(input.stat_id())
+
+        if input.stat_id() in pa_list:
+            print('we hAVE MADE IT TO THIS PART OF THE CODE')
+
+
+        if input.stat_id() in pa_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.pa==1)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'pa'
+            print('this is short')
+            print(elly_zone_df)
+
+
+        if input.stat_id() in balls_in_play_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.bip)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'bip'
+            #print('this is short')
+
+        if input.stat_id() in balls_in_play_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.bip)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'bip'
+            print('this is short')
+
+        if input.stat_id() in pitches_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.pitches == 1)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'pitches'
+
+        if input.stat_id() in swings_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.swings == 1)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'swings'
+
+
+        if input.stat_id() in in_zone_pitches_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.in_zone)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'in_zone'
+
+
+        if input.stat_id() in in_zone_swings_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.zone_swing)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'zone_swing'
+
+
+        if input.stat_id() in out_zone_pitches_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.in_zone == False)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'out_zone'
+
+
+        if input.stat_id() in out_zone_swings_list:
+            elly_zone_df = exit_velo_df_codes[(exit_velo_df_codes.ozone_swing)&(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())]
+            divisor_x = 'ozone_swing'
+
+        # penguins = sns.load_dataset("penguins")
+        # sns.histplot(data=penguins, x="flipper_length_mm")
+        # print('we made it here:')
+        # print(int(input.id()))
+        # print(input.stat_id())
+        # print(input.level_id())
+        # print(exit_velo_df_codes[(exit_velo_df_codes.pitcher_id == int(input.id()))&(exit_velo_df_codes.level==input.level_id())])
+        # print(exit_velo_df_codes.columns)
+        # print(elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum())
+
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ.copy()
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new.set_index('pitcher_id','pitcher','level')
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new[divisor_x] >= int(input.n())]
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new.level==input.level_id()]
+
+        exit_velo_df_codes_summ_rank = exit_velo_df_codes_summ_new.rank(method='max',ascending=False)
+        exit_velo_df_codes_summ_rank.columns = exit_velo_df_codes_summ_rank.columns+['_rank']
+
+        exit_velo_df_codes_summ_rank_percent =  exit_velo_df_codes_summ_new.rank(pct=True)
+        exit_velo_df_codes_summ_rank_percent.columns = exit_velo_df_codes_summ_rank_percent.columns+['_percent']
+        
+
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new.reset_index()
+        exit_velo_df_codes_summ_rank = exit_velo_df_codes_summ_rank.reset_index()
+        exit_velo_df_codes_summ_rank_percent = exit_velo_df_codes_summ_rank_percent.reset_index()
+        print('Table columns:')
+        
+        exit_velo_df_codes_summ_new.pitcher_id = exit_velo_df_codes_summ_new.pitcher_id.astype(int)
+        exit_velo_df_codes_summ_rank.pitcher_id = exit_velo_df_codes_summ_rank.pitcher_id.astype(int)
+        exit_velo_df_codes_summ_rank_percent.pitcher_id = exit_velo_df_codes_summ_rank_percent.pitcher_id.astype(int)
+
+        print('Table columns2:')
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new.merge(exit_velo_df_codes_summ_rank,left_on=['pitcher_id'],right_on=['pitcher_id'],how='left',suffixes=['','_rank'])
+
+        exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new.merge(exit_velo_df_codes_summ_rank_percent,left_on=['pitcher_id'],right_on=['pitcher_id'],how='left',suffixes=['','_percent'])
+
+
+        print(exit_velo_df_codes_summ_new)
+        print(exit_velo_df_codes_summ_rank)
+        print(exit_velo_df_codes_summ_rank_percent)
+        
+
+        
+
+        #sns.scatterplot(x=data_df.launch_speed_90,y=data_df.zone_contact,color=colour_palette[0],s=75,label=int(input.id()))
+
+        exit_velo_df_codes_summ_new_select = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new.pitcher_id == int(input.id())].reset_index(drop=True)
+        print('whiffing')
+        print(exit_velo_df_codes)
+        print('Player _df:')
+        print(exit_velo_df_codes_summ_new_select)
+
+        if len(exit_velo_df_codes_summ_new_select) < 1:
+            ax.text(x=0.5,y=0.5,s='Please Select Different Parameters to Produce a plot',fontsize=18,ha='center')
+            return 
+
+        p = inflect.engine()
+
+        exit_velo_df_codes_summ_new_select = exit_velo_df_codes_summ_new_select.loc[:,~exit_velo_df_codes_summ_new_select.columns.duplicated(keep='last')].copy()
+        print('Table for the player:')
+        print(list(exit_velo_df_codes_summ_new_select.columns))
+        print(plot_dict[input.stat_id()]["percentile_label"])
+        print(plot_dict[input.stat_id()]["percentile_label"]+'_percent')
+        print(exit_velo_df_codes_summ_new_select)
+        print(1*plot_dict[input.stat_id()]["flip_p"])
+        print(round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))
+        print((1*plot_dict[input.stat_id()]["flip_p"]-round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))*100)
+
+        # print(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+'_percent'])
+
+        if plot_dict[input.stat_id()]['percent']:
+            label_1=f'{input.level_id()} Average {exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].sum()/exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][divisor_x].sum():.1%}'
+            label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%} ({p.ordinal(abs(int((1*plot_dict[input.stat_id()]["flip_p"]-round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%}'
+            ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
+
+        else:
+            label_1=f'{input.level_id()} Average {exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].sum()/exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][divisor_x].sum():.1f}'
+            label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1f} ({p.ordinal(abs(int((1*plot_dict[input.stat_id()]["flip_p"]-round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1f}'
+            #ax.yaxis.set_major_formatter(mtick.int)
+        
+
+        if plot_dict[input.stat_id()]['percentile']:
+            label_1=f'{input.level_id()} Average {exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].quantile(0.9):.1f}'
+            label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].quantile(0.9):.1f} ({p.ordinal(abs(int((1*plot_dict[input.stat_id()]["flip_p"]-round(exit_velo_df_codes_summ_new_select[plot_dict[input.stat_id()]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(input.id())]} Average {elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%}'
+            #ax.yaxis.set_major_formatter(mtick.int)
+
+
+        print(plot_dict[input.stat_id()]["x_value"])
+        print(divisor_x)
+
+        # exit_velo_df_codes_summ_new = exit_velo_df_codes_summ.copy()
+        # exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new.balls_in_play >= int(input.n())]
+        # exit_velo_df_codes_summ_new = exit_velo_df_codes_summ_new[exit_velo_df_codes_summ_new.level==input.level_id()]
+
+        
+        print('this is here:')
+        print(exit_velo_df_codes_summ_new.head())
+        print(exit_velo_df_codes_summ_new.columns)
+
+
+        if plot_dict[input.stat_id()]["flip_p"] == False:
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.9),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[2],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.75),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[3],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.25),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[4],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.1),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[5],linestyle='dotted',alpha=0.5)
+
+
+            hard_hit_dates = [(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.9),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.75),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.25),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.1)]
+            hard_hit_text = ['90th %','75th %','25th %','10th %']
+            for i, x in enumerate(hard_hit_dates):
+                text(min(input.n()+input.n()/100,+input.n()+1), x ,hard_hit_text[i], rotation=0, ha='left',
+                    bbox=dict(facecolor='white',alpha=0.5, edgecolor=colour_palette[2+i], pad=2))
+
+
+
+        if plot_dict[input.stat_id()]["flip_p"] == True:
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.1),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[2],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.25),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[3],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.75),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[4],linestyle='dotted',alpha=0.5)
+            ax.hlines(y=(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.9),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[5],linestyle='dotted',alpha=0.5)
+
+            hard_hit_dates = [(exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.9),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.75),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.25),
+                              (exit_velo_df_codes_summ_new[plot_dict[input.stat_id()]["percentile_label"]]).quantile(0.1)]
+            hard_hit_text = ['10th %','25th %','75th %','90th %']
+            for i, x in enumerate(hard_hit_dates):
+                text(min(input.n()+input.n()/100,input.n()+input.n()+3), x ,hard_hit_text[i], rotation=0, ha='left',
+                    bbox=dict(facecolor='white',alpha=0.5, edgecolor=colour_palette[2+i], pad=2))
+
+
+        
+
+
+
+        if plot_dict[input.stat_id()]["percentile"] == False:
+            ax.hlines(y=exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].sum()/exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][divisor_x].sum(),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[1],linestyle='-.',label=label_1)
+
+            ax.hlines(y=elly_zone_df[plot_dict[input.stat_id()]["x_value"]].sum()/elly_zone_df[divisor_x].sum(),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[0],linestyle='--',label=label_2)
+
+            sns.lineplot(x=range(1,len(elly_zone_df)+1),y=elly_zone_df[plot_dict[input.stat_id()]["x_value"]].fillna(0).rolling(window=swing_min).sum()/swing_min,color=colour_palette[0],linewidth=3,ax=ax)
+
+
+
+        if plot_dict[input.stat_id()]["percentile"] == True:
+
+            ax.hlines(y=exit_velo_df_codes[exit_velo_df_codes.level == input.level_id()][plot_dict[input.stat_id()]["x_value"]].quantile(0.9),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[1],linestyle='-.',label=label_1)
+
+            ax.hlines(y=elly_zone_df[plot_dict[input.stat_id()]["x_value"]].fillna(0).quantile(0.9),xmin=swing_min,xmax=len(elly_zone_df),color=colour_palette[0],linestyle='--',label=label_2)
+
+            sns.lineplot(x=range(1,len(elly_zone_df)+1),y=elly_zone_df[plot_dict[input.stat_id()]["x_value"]].fillna(0).rolling(window=swing_min).quantile(0.9),color=colour_palette[0],linewidth=3,ax=ax)
+
+
+        #ax.set_xlim(input.n(),exit_velo_df_small.pitch.max())
+        #plt.yticks([0,0.2,0.4,0.6,0.8,1])
+        #ax.set_ylim(math.floor((min(exit_velo_df_codes_summ.zone_contact)/5)*100)*5/100,1)
+        ax.set_xlim(math.floor(swing_min),len(elly_zone_df))
+        ax.set_title(f'{batter_dict[int(input.id())]} - {input.level_id()} - {swing_min} {plot_dict[input.stat_id()]["x_axis"]} Rolling {plot_dict[input.stat_id()]["title"]}', fontsize=16,fontname='Century Gothic',)
+        #vals = ax.get_yticks()
+        ax.set_xlabel(plot_dict[input.stat_id()]['x_axis'], fontsize=16,fontname='Century Gothic')
+        ax.set_ylabel(plot_dict[input.stat_id()]['y_axis'], fontsize=16,fontname='Century Gothic')
+
+        #fig.axes[0].invert_yaxis()
+        
+        #fig.subplots_adjust(wspace=.02, hspace=.02)
+        #ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
+        ax.set_yticks(plot_dict[input.stat_id()]["x_range"])
+        #fig.colorbar(plot_dist, ax=ax)
+        #fig.colorbar(plot_dist)
+        #fig.axes[0].invert_yaxis()
+        ax.legend(fontsize='16')
+        fig.text(x=0.03,y=0.02,s='By: @TJStats',fontname='Century Gothic')
+        fig.text(x=1-0.03,y=0.02,s='Data: MLB',ha='right',fontname='Century Gothic')
+        fig.tight_layout()
+
+
+rolling_pitcher = 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_select("id", "Select Pitcher",batter_dict,selected=675911,width=1,size=1,selectize=True),
+        ui.input_select("level_id", "Select Level",level_dict,width=1,size=1),
+        ui.input_select("stat_id", "Select Stat",plot_dict_small,width=1,size=1),
+        ui.input_numeric("n", "Rolling Window Size", value=50),
+        ui.input_action_button("go", "Generate",class_="btn-primary"),
+
+        ui.output_table("result")
+      ),
+
+      ui.panel_main(
+        ui.output_plot("plot",height = "1000px",width="1000px")
+      ),
+    )),)),server)

spray.py

@@ -81,6 +81,7 @@ batter_dict = df_2023_bip.sort_values('batter_name').set_index('batter_id')['bat
 def server(input,output,session):
     @output
     @render.plot(alt="plot")
+    @reactive.event(input.go, ignore_none=False)
     def plot():
 
         batter_id_select = int(input.batter_id())
@@ -342,8 +343,9 @@ spray = App(ui.page_fluid(
             """
          ),
     shinyswatch.theme.simplex(),
-    ui.tags.h4("Stats By Zach"),
-    ui.tags.i("A website for hockey analytics"),
+    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(
@@ -354,7 +356,11 @@ spray = App(ui.page_fluid(
         ui.nav_menu(
             "Batter Charts",
             ui.nav_control(
-             ui.a(
+            ui.a(
+                "Batting Rolling",
+                href="rolling_batter/"
+            ),
+            ui.a(
                 "Spray",
                 href="spray/"
             ),
@@ -373,48 +379,29 @@ spray = App(ui.page_fluid(
             ui.a(
                 "EV vs LA Plot",
                 href="ev_angle/"
+            ),
+            ui.a(
+                "Statcast Compare",
+                href="statcast_compare/"
             )
         ),
         ),
         ui.nav_menu(
-            "Goalie Charts",
+            "Pitcher Charts",
             ui.nav_control(
              ui.a(
-                "GSAx Timeline",
-                href="gsax-timeline/"
+                "Pitcher Rolling",
+                href="rolling_pitcher/"
             ),
              ui.a(
-                "GSAx Leaderboard",
-                href="gsax-leaderboard/"
+                "Pitcher Summary",
+                href="pitching_summary_graphic_new/"
             ),
              ui.a(
-                "GSAx Comparison",
-                href="gsax-comparison/"
+                "Pitcher Scatter",
+                href="pitcher_scatter/"
             )
         ),
-        ),ui.nav_menu(
-            "Team Charts",
-            ui.nav_control(
-             ui.a(
-                "Team xG Rates",
-                href="team-xg-rates/"
-            ),
-        ),
-        ),ui.nav_control(
-             ui.a(
-                "Games",
-                href="games/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "About",
-                href="about/"
-            ),
-        ),ui.nav_control(
-             ui.a(
-                "Articles",
-                href="articles/"
-            ),
         )),ui.row(
     ui.layout_sidebar(
         
@@ -424,7 +411,9 @@ spray = App(ui.page_fluid(
                                  batter_dict,
                                  width=1,
                                  size=1,
-                                 selectize=True)),
+                                 selectize=True),
+                ui.input_action_button("go", "Generate",class_="btn-primary",
+                                       )),
 
    ui.panel_main(     
         ui.navset_tab(

statcast_compare.py

@@ -0,0 +1,701 @@
+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 inflect
+from matplotlib.pyplot import text
+
+def percentile(n):
+    def percentile_(x):
+        return np.nanpercentile(x, n)
+    percentile_.__name__ = 'percentile_%s' % n
+    return percentile_
+
+from matplotlib.colors import Normalize
+
+print('Running')
+
+cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])
+df = pd.read_csv('statcast_20152023.csv')
+df['last_name'] = df['last_name, first_name'].str.split(',').str[0]
+df['first_name'] = df['last_name, first_name'].str.split(',').str[1].str.strip(' ')
+df['name'] = df['first_name'] +' ' +df['last_name']
+
+df[[x for x in df if x[-7:] == 'percent']] = df[[x for x in df if x[-7:] == 'percent']]/100
+df['barrel_batted_rate'] = df['barrel_batted_rate']/100
+
+player_dict = df[['player_id','name']].drop_duplicates().sort_values('name').set_index('player_id').to_dict()['name']
+
+df_median = df[df.pa>=400]
+
+format_dict = {
+ 'k_percent':{'format':':.1%','average':df.strikeout.sum()/df.pa.sum(),'a_d_good':False,'tab_name':'K%'},
+ 'bb_percent':{'format':':.1%','average':df.walk.sum()/df.pa.sum(),'a_d_good':True,'tab_name':'BB%'},
+ 'batting_avg':{'format':':.3f','average':df.hit.sum()/df.ab.sum(),'a_d_good':True,'tab_name':'AVG'},
+ 'on_base_plus_slg':{'format':':.3f','average':df.on_base_plus_slg.mean()/df.pa.mean(),'a_d_good':True,'tab_name':'OPS'},
+ 'isolated_power':{'format':':.3f','average':(df.single.sum() + df.double.sum()*2 + df.triple.sum()*3 + df.home_run.sum()*4)/df.ab.sum() - df.hit.sum()/df.ab.sum(),'a_d_good':True,'tab_name':'ISO'},
+ 'xba':{'format':':.3f','average':df_median.xba.median(),'a_d_good':True,'tab_name':'xBA'},
+ 'xslg':{'format':':.3f','average':df_median.xslg.median(),'a_d_good':True,'tab_name':'xSLG'},
+ 'woba':{'format':':.3f','average':df_median.woba.median(),'a_d_good':True,'tab_name':'wOBA'},
+ 'xwoba':{'format':':.3f','average':df_median.xwoba.median(),'a_d_good':True,'tab_name':'xwOBA'},
+ 'xobp':{'format':':.3f','average':df_median.xobp.median(),'a_d_good':True,'tab_name':'xOBP'},
+ 'xiso':{'format':':.3f','average':df_median.xiso.median(),'a_d_good':True,'tab_name':'xISO'},
+ 'wobacon':{'format':':.3f','average':df_median.wobacon.median(),'a_d_good':True,'tab_name':'wOBACON'},
+ 'xwobacon':{'format':':.3f','average':df_median.xwobacon.median(),'a_d_good':True,'tab_name':'xwOBACON'},
+ 'bacon':{'format':':.1f','average':df_median.bacon.median(),'a_d_good':True,'tab_name':'BACON'},
+ 'xbacon':{'format':':.1f','average':df_median.xbacon.median(),'a_d_good':True,'tab_name':'xBACON'},
+ 'xbadiff':{'format':':.3f','average':df_median.xbadiff.median(),'a_d_good':True,'tab_name':'BA-xBA'},
+ 'xslgdiff':{'format':':.3f','average':df_median.xslgdiff.median(),'a_d_good':True,'tab_name':'SLG-xSLG'},
+ 'wobadiff':{'format':':.3f','average':df_median.wobadiff.median(),'a_d_good':True,'tab_name':'wOBA-xwOBA'},
+ 'exit_velocity_avg':{'format':':.1f','average':(df.exit_velocity_avg * df.batted_ball).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'EV'},
+ 'launch_angle_avg':{'format':':.1f','average':(df.launch_angle_avg * df.batted_ball).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'LA'},
+ 'barrel':{'format':':.0f','average':df_median.barrel.median(),'a_d_good':True,'tab_name':'Barrel'},
+ 'barrel_batted_rate':{'format':':.1%','average':(df.barrel).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'Barrel%'},
+ 'avg_best_speed':{'format':':.1f','average':(df.avg_best_speed * df.batted_ball).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'Best Speed'},
+ 'avg_hyper_speed':{'format':':.1f','average':(df.avg_hyper_speed * df.batted_ball).sum() / (df.batted_ball).sum(),'a_d_good':True,'tab_name':'Hyper Speed'},
+ 'out_zone_swing_miss':{'format':':.0f','average':df_median.out_zone_swing_miss.mean(),'a_d_good':True,'tab_name':'O-Whiff%'},
+ 'out_zone_swing':{'format':':.0f','average':df_median.out_zone_swing.mean(),'a_d_good':True,'tab_name':'O-Swing'},
+ 'out_zone':{'format':':.0f','average':df_median.out_zone.mean(),'a_d_good':True,'tab_name':'O-Zone'},
+ 'pitch_count_offspeed':{'format':':.0f','average':df_median.pitch_count_offspeed.mean(),'a_d_good':True,'tab_name':'Pitch Off-Speed'},
+ 'pitch_count_fastball':{'format':':.0f','average':df_median.pitch_count_fastball.mean(),'a_d_good':True,'tab_name':'Pitch Fastball'},
+ 'pitch_count_breaking':{'format':':.0f','average':df_median.pitch_count_breaking.mean(),'a_d_good':True,'tab_name':'Pitch Breaking'},
+ 'pitch_count':{'format':':.0f','average':df_median.pitch_count.mean(),'a_d_good':True,'tab_name':'Pitches'},
+ 'in_zone_swing_miss':{'format':':.0f','average':df_median.in_zone_swing_miss.mean(),'a_d_good':False,'tab_name':'Z-Whiff'},
+ 'in_zone_swing':{'format':':.0f','average':df_median.in_zone_swing.mean(),'a_d_good':True,'tab_name':'Z-Swing'},
+ 'in_zone':{'format':':.0f','average':df_median.in_zone.mean(),'a_d_good':True,'tab_name':'Zone'},
+ 'edge':{'format':':.0f','average':df_median.edge.mean(),'a_d_good':True,'tab_name':'Edge'},
+ 'batted_ball':{'format':':.0f','average':df_median.batted_ball.mean(),'a_d_good':True,'tab_name':'Batted Balls'},
+ 'groundballs':{'format':':.0f','average':df_median.groundballs.mean(),'a_d_good':True,'tab_name':'Groundballs'},
+ 'flyballs':{'format':':.0f','average':df_median.flyballs.mean(),'a_d_good':True,'tab_name':'Flyballs'},
+ 'linedrives':{'format':':.0f','average':df_median.linedrives.mean(),'a_d_good':True,'tab_name':'Linedrives'},
+ 'popups':{'format':':.0f','average':df_median.popups.mean(),'a_d_good':True,'tab_name':'Popups'},
+ 'n_bolts':{'format':':.0f','average':df_median.n_bolts.mean(),'a_d_good':True,'tab_name':'Bolts'},
+ 'hp_to_1b':{'format':':.2f','average':df_median.hp_to_1b.mean(),'a_d_good':True,'tab_name':'Home Plate to 1st'},
+ 'sprint_speed':{'format':':.1f','average':df_median.sprint_speed.mean(),'a_d_good':True,'tab_name':'Sprint Speed'},
+
+ 'slg_percent':{'format':':.1%','average':(df.single.sum() + df.double.sum()*2 + df.triple.sum()*3 + df.home_run.sum()*4)/df.ab.sum(),'a_d_good':True,'tab_name':'SLG'},
+ 'on_base_percent':{'format':':.1%','average':df_median.on_base_percent.median(),'a_d_good':True,'tab_name':'OBP'},
+ 'sweet_spot_percent':{'format':':.1%','average':df_median.sweet_spot_percent.median(),'a_d_good':True,'tab_name':'SweetSpot%'},
+ 'solidcontact_percent':{'format':':.1%','average':df_median.solidcontact_percent.median(),'a_d_good':True,'tab_name':'Solid%'},
+ 'flareburner_percent':{'format':':.1%','average':df_median.flareburner_percent.median(),'a_d_good':False,'tab_name':'Flare/Burner%'},
+ 'poorlyunder_percent':{'format':':.1%','average':df_median.poorlyunder_percent.median(),'a_d_good':False,'tab_name':'Under%'},
+ 'poorlytopped_percent':{'format':':.1%','average':df_median.poorlytopped_percent.median(),'a_d_good':False,'tab_name':'Topped%'},
+ 'poorlyweak_percent':{'format':':.1%','average':df_median.poorlyweak_percent.median(),'a_d_good':False,'tab_name':'Weak%'},
+ 'hard_hit_percent':{'format':':.1%','average':df_median.hard_hit_percent.median(),'a_d_good':True,'tab_name':'HardHit%'},
+ 'z_swing_percent':{'format':':.1%','average':df.in_zone_swing.sum()/df.in_zone.sum(),'a_d_good':True,'tab_name':'Z-Swing%'},
+ 'z_swing_miss_percent':{'format':':.1%','average':df.in_zone_swing_miss.sum()/df.in_zone_swing.sum(),'a_d_good':False,'tab_name':'Z-Whiff%'},
+
+ 'out_zone_percent':{'format':':.1%','average':df.out_zone.sum()/df.pitch_count.sum(),'a_d_good':True,'tab_name':'O-Zone%'},
+ 'meatball_swing_percent':{'format':':.1%','average':df_median.meatball_swing_percent.median(),'a_d_good':True,'tab_name':'Meatball Swing%'},
+ 'meatball_percent':{'format':':.1%','average':df_median.meatball_percent.median(),'a_d_good':True,'tab_name':'Meatball%'},
+ 'iz_contact_percent':{'format':':.1%','average':1 - df.in_zone_swing_miss.sum()/df.in_zone_swing.sum(),'a_d_good':True,'tab_name':'Z-Contact%'},
+ 'in_zone_percent':{'format':':.1%','average':df.in_zone.mean()/df.pitch_count.sum(),'a_d_good':True,'tab_name':'Zone%'},
+ 'oz_swing_percent':{'format':':.1%','average':df.out_zone_swing.sum()/df.out_zone.sum(),'a_d_good':False,'tab_name':'O-Swing%'},
+ 'oz_swing_miss_percent':{'format':':.1%','average':df.out_zone_swing_miss.sum()/df.out_zone_swing.sum(),'a_d_good':False,'tab_name':'O-Whiff%'},
+ 'oz_contact_percent':{'format':':.1%','average':1 - df.out_zone_swing_miss.sum()/df.out_zone_swing.sum(),'a_d_good':True,'tab_name':'O-Contact%'},
+ 'edge_percent':{'format':':.1%','average':df_median.edge_percent.median(),'a_d_good':True,'tab_name':'Edge%'},
+ 'whiff_percent':{'format':':.1%','average':(df.in_zone_swing_miss.sum() + df.out_zone_swing_miss.sum()) / (df.in_zone_swing.sum() + df.out_zone_swing.sum()),'a_d_good':False,'tab_name':'Whiff%'},
+ 'swstr_percent':{'format':':.1%','average':(df.in_zone_swing_miss.sum() + df.out_zone_swing_miss.sum()) / (df.pitch_count.sum()),'a_d_good':False,'tab_name':'SwStr%'},
+ 'swing_percent':{'format':':.1%','average':(df.in_zone_swing.sum() + df.out_zone_swing.sum()) / (df.pitch_count.sum()),'a_d_good':True,'tab_name':'Swing%'},
+ 'pull_percent':{'format':':.1%','average':df_median.hit.median(),'a_d_good':True,'tab_name':'Pull%'},
+ 'straightaway_percent':{'format':':.1%','average':df_median.hit.median(),'a_d_good':True,'tab_name':'Straightaway%'},
+ 'opposite_percent':{'format':':.1%','average':df_median.hit.median(),'a_d_good':True,'tab_name':'Opposite%'},
+ 'f_strike_percent':{'format':':.1%','average':df_median.hit.median(),'a_d_good':False,'tab_name':'1st Strike%'},
+ 'groundballs_percent':{'format':':.1%','average':df.groundballs.sum()/df.batted_ball.sum(),'a_d_good':False,'tab_name':'GB%'},
+ 'flyballs_percent':{'format':':.1%','average':df.flyballs.sum()/df.batted_ball.sum(),'a_d_good':True,'tab_name':'FB%'},
+ 'linedrives_percent':{'format':':.1%','average':df.linedrives.sum()/df.batted_ball.sum(),'a_d_good':True,'tab_name':'LD%'},
+ 'popups_percent':{'format':':.1%','average':df.popups.sum()/df.batted_ball.sum(),'a_d_good':False,'tab_name':'PU%'},}
+
+column_dict = pd.DataFrame(format_dict.keys(),[format_dict[x]['tab_name'] for x in format_dict.keys()]).reset_index().set_index(0).to_dict()['index']
+
+
+def server(input,output,session):
+
+
+    @output
+    @render.text
+    @reactive.event(input.go, ignore_none=False)    
+    def txt_title():
+        if input.player_id() == '':
+            return 'Select a Player'
+
+        player_input = int(input.player_id())
+        season_1 = max(2015,int(input.season_1()))
+        season_2 = min(2023,int(input.season_2()))
+
+        if season_1 < season_2:
+            season_pick = [season_1,season_2]
+
+        elif season_1 > season_2:
+            season_pick = [season_2,season_1]
+
+        if len(str(input.player_id())) == 0:
+            return 'Select a Batter'
+        if str(input.season_1()) == str(input.season_2()):
+            return 'Select Different Seasons'
+        if len(df[(df.player_id == player_input)&(df.year == season_pick[0])] )== 0 or len(df[(df.player_id == player_input)&(df.year == season_pick[1])] )== 0:
+            return 'Select Different Seasons'
+        return   f'{player_dict[int(input.player_id())]} Statcast Season Comparison'
+
+    @output
+    @render.text
+    @reactive.event(input.go, ignore_none=False)    
+    def txt_title_compare():
+        if type(input.player_id()) is int or input.player_id()=='':
+            return
+
+        if type(input.player_id_2()) is int or input.player_id_2()=='':
+            return    
+
+        player_input_1 = int(input.player_id())
+        player_input_2 = int(input.player_id_2())
+
+
+        # season_pick = [input.season_1(),input.season_2()]
+        columns_i_want = list(input.row_select())
+        print(columns_i_want)
+        season_1 = max(2015,int(input.season_1()))
+        season_2 = min(2023,int(input.season_2()))
+
+        player_list = [player_input_1,player_input_2]
+        name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]
+        season_pick_list = [season_1,season_2]
+
+        if len(str(input.player_id())) == 0:
+            return 'Select a Batter'
+        if len(str(input.player_id_2())) == 0:
+            return 'Select a Batter'
+
+        if str(input.player_id()) == str(input.player_id_2()) and str(input.season_1()) == str(input.season_2()):
+            return 'Select Different Seasons'
+        if len(df[(df.player_id == player_list[0])&(df.year == season_pick_list[0])] )== 0 or len(df[(df.player_id == player_list[1])&(df.year == season_pick_list[1])])== 0:
+            return 'No Data for Specified Batter in Given Season'
+        
+        return   f'Statcast Season Comparison'
+
+
+
+
+    @output
+    @render.text
+    @reactive.event(input.go, ignore_none=False)
+    def text_2022():
+        if input.player_id() == '':
+            return 'Select a Player'
+        return   f'{int(input.season_1())} Season Results Compares to MLB Average'
+
+
+    @output
+    @render.text
+    @reactive.event(input.go, ignore_none=False)
+    def text_2022_1():
+        if type(input.player_id()) is int or input.player_id()=='':
+            return
+
+        if type(input.player_id_2()) is int or input.player_id_2()=='':
+            return     
+        season_1 = max(2015,int(input.season_1()))
+        season_2 = min(2023,int(input.season_2()))
+        season_pick_list = [season_1,season_2]
+        player_input_1 = int(input.player_id())
+        player_input_2 = int(input.player_id_2())
+        name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]
+        return   f"{name_list[0]} '{str(season_pick_list[0])[2:]} Season Results Compares to MLB Average"
+
+    @output
+    @render.text
+    @reactive.event(input.go, ignore_none=False)
+    def text_2023():
+        if input.player_id() == '':
+            return 'Select a Player'
+        return   f'{int(input.season_2())} Season Results Compares to MLB Average'
+
+    @output
+    @render.text
+    @reactive.event(input.go, ignore_none=False)
+    def text_2023_1():
+        if type(input.player_id()) is int or input.player_id()=='':
+            return
+
+        if type(input.player_id_2()) is int or input.player_id_2()=='':
+            return    
+        season_1 = max(2015,int(input.season_1()))
+        season_2 = min(2023,int(input.season_2()))
+        season_pick_list = [season_1,season_2]
+        player_input_1 = int(input.player_id())
+        player_input_2 = int(input.player_id_2())
+        name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]
+        return   f"{name_list[1]} '{str(season_pick_list[1])[2:]} Season Results Compares to MLB Average"
+
+    @output
+    @render.text
+    @reactive.event(input.go, ignore_none=False)
+    def text_diff():
+        if input.player_id() == '':
+            return 'Select a Player'
+        return   f'Difference Compares {int(input.season_2())} Results to {int(input.season_1())} Results'
+
+    @output
+    @render.text
+    @reactive.event(input.go, ignore_none=False)
+    def text_diff_compare():
+        if type(input.player_id()) is int or input.player_id()=='':
+            return
+
+        if type(input.player_id_2()) is int or input.player_id_2()=='':
+            return    
+        player_input_1 = int(input.player_id())
+        player_input_2 = int(input.player_id_2())
+        name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]
+        return   f'Difference Compares {name_list[0]} Results to {name_list[1]} Results'
+
+
+
+    @output
+    @render.table
+    @reactive.event(input.go, ignore_none=False)
+    def statcast_compare():
+        if input.player_id() == '':
+            return
+
+        if len(str(input.player_id())) == 0:
+            return
+        if len(str(input.player_id_2())) == 0:
+            return
+
+
+        player_input = int(input.player_id())
+
+
+        # season_pick = [input.season_1(),input.season_2()]
+        columns_i_want = list(input.row_select())
+        print(columns_i_want)
+        season_1 = max(2015,int(input.season_1()))
+        season_2 = min(2023,int(input.season_2()))
+
+
+        if season_1 < season_2:
+            season_pick = [season_1,season_2]
+
+        elif season_1 > season_2:
+            season_pick = [season_2,season_1]
+
+        else:
+            return
+
+        print(df[(df.player_id == player_input)&(df.year == season_pick[0])])
+    
+
+        if len(df[(df.player_id == player_input)&(df.year == season_pick[0])] )== 0 or len(df[(df.player_id == player_input)&(df.year == season_pick[1])] )== 0:
+            return
+
+        df_compare = pd.concat([df[(df.player_id == player_input)&(df.year == season_pick[0])][[ 'player_age', 'pa']+columns_i_want],
+                                df[(df.player_id == player_input)&(df.year == season_pick[1])][[ 'player_age', 'pa']+columns_i_want]]).reset_index(drop=True).T
+
+
+        print('test')
+        print(sum(df.player_id == input.player_id()))
+        df_compare.columns = season_pick
+
+        df_compare['Difference'] = df_compare.loc[columns_i_want][season_pick[1]] - df_compare.loc[columns_i_want][season_pick[0]]
+
+        df_compare_style = df_compare.style.format(
+                    "{:.0f}")
+        df_compare_style = df_compare_style.set_properties(**{'background-color': 'white',
+                                'color': 'white'},subset=(['player_age', 'pa'],df_compare_style.columns[2])).set_properties(
+                    **{'min-width':'100px'},overwrite=False).set_table_styles(
+            [{'selector': 'th:first-child', 'text-align': 'center','props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr:first-child','text-align': 'center', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'index','text-align': 'center', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'th', 'text-align': 'center','props': [('line-height', '40px'),('min-width', '30px')]}],overwrite=False).set_properties(
+                
+            **{'Height': '20px'},**{'text-align': 'center'},overwrite=False).set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('font-size', '20px'),('Height','20px'),('min-width','200px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '20px'),('min-width','100px')]}],overwrite=False)
+
+
+        for r in columns_i_want:
+            if format_dict[r]['a_d_good']:
+                cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])
+            else:
+                cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
+
+
+            colormap = plt.get_cmap(cmap)
+            norm = Normalize(vmin=0.7, vmax=1.3)
+
+            normalized_value = norm(df_compare[df_compare.columns[0]][r]/format_dict[r]['average'])
+            df_compare_style.format(
+                    f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[0])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),
+                                'color': 'black'},subset=(r,df_compare_style.columns[0]))
+
+            norm = Normalize(vmin=0.7, vmax=1.3)
+            normalized_value = norm(df_compare[df_compare.columns[1]][r]/format_dict[r]['average'])
+            df_compare_style.format(
+                    f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[1])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),
+                                'color': 'black'},subset=(r,df_compare_style.columns[1]))
+
+            norm = Normalize(vmin=0.7, vmax=1.3)
+            normalized_value = norm(df_compare[df_compare.columns[1]][r]/df_compare[df_compare.columns[0]][r])
+            df_compare_style.format(
+                    f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[2])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),
+                                'color': 'black'},subset=(r,df_compare_style.columns[2]))
+
+
+
+
+        df_compare_style.relabel_index(['Age', 'PA']+[format_dict[x]['tab_name'] for x in columns_i_want]).set_properties(
+                    **{'border': '1px black solid !important'},overwrite=False).set_table_styles(
+            [{"selector": "", "props": [("border", "1px solid")]},
+            {"selector": "tbody td", "props": [("border", "1px solid")]},
+            {"selector": "th", "props": [("border", "1px solid")]}],overwrite=False)
+
+        #df_compare = df_compare.fillna(np.nan)     
+
+        return df_compare_style
+
+
+
+    @output
+    @render.table
+    @reactive.event(input.go, ignore_none=False)
+    def statcast_compare_2():
+
+        # if input.player_id() == 0:
+        #     return
+        if type(input.player_id()) is int or input.player_id()=='':
+            return
+
+        if type(input.player_id_2()) is int or input.player_id_2()=='':
+            return      
+        # if len(str(input.player_id())) == 0:
+        #     return
+
+        player_input_1 = int(input.player_id())
+        player_input_2 = int(input.player_id_2())
+
+
+        # season_pick = [input.season_1(),input.season_2()]
+        columns_i_want = list(input.row_select())
+        print(columns_i_want)
+        season_1 = max(2015,int(input.season_1()))
+        season_2 = min(2023,int(input.season_2()))
+
+
+        # if season_1 < season_2:
+        #     season_pick = [season_1,season_2]
+
+        # elif season_1 > season_2:
+        #     season_pick = [season_2,season_1]
+
+        # else:
+        #     return
+
+        #print(df[(df.player_id == player_input)&(df.year == season_pick[0])])
+        #player_list = ['Elly De La Cruz','Aaron Judge']
+        player_list = [player_input_1,player_input_2]
+        name_list = [player_dict[int(player_input_1)],player_dict[int(player_input_2)]]
+        season_pick_list = [season_1,season_2]
+
+        if len(df[(df.player_id == player_list[0])&(df.year == season_pick_list[0])] )== 0 or len(df[(df.player_id == player_list[1])&(df.year == season_pick_list[1])] )== 0:
+            return
+        if str(input.player_id()) == str(input.player_id_2()) and str(input.season_1()) == str(input.season_2()):
+            return
+
+
+        df_compare = pd.concat([df[(df.player_id == player_list[0])&(df.year == season_pick_list[0])][[ 'year','player_age', 'pa']+columns_i_want],
+        df[(df.player_id == player_list[1])&(df.year == season_pick_list[1])][[ 'year','player_age', 'pa']+columns_i_want]]).reset_index(drop=True).T
+
+        df_compare.columns = [f"{name_list[0]} '{str(season_pick_list[0])[2:]}",f"{name_list[1]} '{str(season_pick_list[1])[2:]}"]
+        df_compare['Difference'] = df_compare.loc[columns_i_want][df_compare.columns [0]] - df_compare.loc[columns_i_want][df_compare.columns[1]]
+        #df_compare = df_compare.fillna(np.nan)
+
+        df_compare_style = df_compare.style.format(
+                    "{:.0f}")
+        df_compare_style = df_compare_style.set_properties(**{'background-color': 'white',
+                                'color': 'white'},subset=(['year','player_age', 'pa'],df_compare_style.columns[2])).set_properties(
+                    **{'min-width':'125px'},overwrite=False).set_table_styles(
+            [{'selector': 'th:first-child', 'text-align': 'center','props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr:first-child','text-align': 'center', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'index','text-align': 'center', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'th', 'text-align': 'center','props': [('line-height', '40px'),('min-width', '30px')]}],overwrite=False).set_properties(
+                
+            **{'Height': '20px'},**{'text-align': 'center'},overwrite=False).set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('font-size', '20px'),('Height','20px'),('min-width','200px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '20px'),('min-width','100px')]}],overwrite=False)
+
+
+        for r in columns_i_want:
+            if format_dict[r]['a_d_good']:
+                cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])
+            else:
+                cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
+
+
+            colormap = plt.get_cmap(cmap)
+            norm = Normalize(vmin=0.5, vmax=1.5)
+            normalized_value = norm(df_compare[df_compare.columns[0]][r]/format_dict[r]['average'])
+            df_compare_style.format(
+                    f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[0])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),
+                                'color': 'black'},subset=(r,df_compare_style.columns[0]))
+
+            norm = Normalize(vmin=0.5, vmax=1.5)
+            normalized_value = norm(df_compare[df_compare.columns[1]][r]/format_dict[r]['average'])
+            df_compare_style.format(
+                    f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[1])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),
+                                'color': 'black'},subset=(r,df_compare_style.columns[1]))
+
+            norm = Normalize(vmin=0.8, vmax=1.2)
+            normalized_value = norm(df_compare[df_compare.columns[0]][r]/df_compare[df_compare.columns[1]][r])
+            df_compare_style.format(
+                    f"{{{format_dict[r]['format']}}}",subset=(r,df_compare_style.columns[2])).set_properties(**{'background-color': '#%02x%02x%02x' % (int(colormap(normalized_value)[0] *255), int(colormap(normalized_value)[1] *255), int(colormap(normalized_value)[2] *255)),
+                                'color': 'black'},subset=(r,df_compare_style.columns[2]))
+
+        df_compare_style = df_compare_style
+
+
+        df_compare_style.relabel_index(['Year','Age', 'PA']+[format_dict[x]['tab_name'] for x in columns_i_want]).set_properties(
+                    **{'border': '1px black solid !important'},overwrite=False).set_table_styles(
+            [{"selector": "", "props": [("border", "1px solid")]},
+            {"selector": "tbody td", "props": [("border", "1px solid")]},
+            {"selector": "th", "props": [("border", "1px solid")]}],overwrite=False)
+
+        #df_compare = df_compare.fillna(np.nan)     
+
+        return df_compare_style
+
+    @output
+    @render.table
+    @reactive.event(input.go, ignore_none=False)
+    def colour_scale():
+        off_b2b_df = pd.DataFrame(data={'one':-0.30,'two':0,'three':0.30},index=[0])
+        off_b2b_df_style = off_b2b_df.style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),(
+                    'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
+                    **{'background-color':'White','index':'White','min-width':'150px'},overwrite=False).set_table_styles(
+            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(
+            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).set_properties(
+                **{'background-color':'#4285F4'},subset=off_b2b_df.columns[0]).set_properties(
+                **{'background-color':'white'},subset=off_b2b_df.columns[1]).set_properties(
+                **{'background-color':'#FBBC04'},subset=off_b2b_df.columns[2]).set_properties(
+                **{'color':'black'},subset=off_b2b_df.columns[:]).hide_index().set_table_styles([
+            {'selector': 'thead', 'props': [('display', 'none')]}
+            ]).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))).set_properties(
+                    **{'min-width':'130'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])),overwrite=False).set_properties(**{
+                                'color': 'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))) .format(
+                    "{:+.0%}")
+        return off_b2b_df_style
+
+    @output
+    @render.table
+    @reactive.event(input.go, ignore_none=False)
+    def colour_scale_2():
+        off_b2b_df = pd.DataFrame(data={'one':-0.30,'two':0,'three':0.30},index=[0])
+        off_b2b_df_style = off_b2b_df.style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),(
+                    'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
+                    **{'background-color':'White','index':'White','min-width':'150px'},overwrite=False).set_table_styles(
+            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(
+            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).set_properties(
+                **{'background-color':'#4285F4'},subset=off_b2b_df.columns[0]).set_properties(
+                **{'background-color':'white'},subset=off_b2b_df.columns[1]).set_properties(
+                **{'background-color':'#FBBC04'},subset=off_b2b_df.columns[2]).set_properties(
+                **{'color':'black'},subset=off_b2b_df.columns[:]).hide_index().set_table_styles([
+            {'selector': 'thead', 'props': [('display', 'none')]}
+            ]).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))).set_properties(
+                    **{'min-width':'130'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])),overwrite=False).set_properties(**{
+                                'color': 'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))) .format(
+                    "{:+.0%}")
+
+
+        return off_b2b_df_style
+# test = test.fillna(0)
+#test['PP TOI'] = ["%d:%02d" % (int(x),(x*60)%60) if x>0 else '0:00' for x in test['PP TOI']]
+
+
+statcast_compare = 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_date_range("date_range_id", "Date range input",start = statcast_df.game_date.min(), end = statcast_df.game_date.max()),
+        ui.input_select("player_id", "Select Player 1",player_dict,width=1,size=1,selectize=True,multiple=False,selected=592450),
+        ui.input_select("player_id_2", "Select Player 2 (For Player Compare Tab)",player_dict,width=1,size=1,selectize=True,multiple=False,selected=592450),
+        ui.input_numeric("season_1", "Season 1", value=2022,min=2015,max=2023),
+        ui.input_numeric("season_2", "Season 2", value=2023,min=2015,max=2023),
+        ui.input_select("row_select", "Select Stats",
+                        column_dict,width=1,size=1,selectize=True,
+                        multiple=True,
+                        selected=['k_percent','bb_percent','woba','xwoba','iz_contact_percent','oz_swing_percent','whiff_percent']),
+                ui.input_action_button("go", "Generate",class_="btn-primary",
+                                       )),
+
+        ui.panel_main(ui.tags.h3(""),
+            ui.navset_tab(
+                      ui.nav("Single Player",
+                        ui.div({"style": "font-size:2.1em;"},ui.output_text("txt_title")),
+                        #ui.tags.h2("Fantasy Hockey Schedule Summary"),
+                        ui.tags.h5("Created By: @TJStats,     Data: MLB"),
+                        #ui.div({"style": "font-size:1.6em;"},ui.output_text("txt")),
+                        ui.output_table("statcast_compare"),
+                        #ui.tags.h5('Legend'),
+                        ui.tags.h3(""),
+                        ui.tags.h5('Colour Scale:'),
+                        ui.output_table("colour_scale"),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("text_2022")),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("text_2023")),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("text_diff"))),
+
+                    ui.nav("Player Compare",
+                        ui.div({"style": "font-size:2.1em;"},ui.output_text("txt_title_compare")),
+                        #ui.tags.h2("Fantasy Hockey Schedule Summary"),
+                        ui.tags.h5("Created By: @TJStats,     Data: MLB"),
+                        #ui.div({"style": "font-size:1.6em;"},ui.output_text("txt")),
+                        ui.output_table("statcast_compare_2"),
+                        ui.tags.h3(""),
+                        ui.tags.h5('Colour Scale:'),
+                        ui.output_table("colour_scale_2"),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("text_2022_1")),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("text_2023_1")),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("text_diff_compare")))
+        )
+    ),
+    )),)),server)

statcast_pitch_summary.csv

@@ -0,0 +1,19 @@
+,pitch_name,pitches,swings,whiff,csw,out_zone,chase,pitch_velocity,spin_rate,exit_velocity,pitch_velocity_std,spin_rate_std,exit_velocity_std,pfx_x,pfx_z,extension,release_x,release_z,zone_percent,xwOBA,pitch,whiff_rate,csw_rate,chase_percent,pitch_percent
+0,4-Seam Fastball,230929.0,111299.0,24750.0,64996.0,105087,25567.0,94.19093625756828,2282.8438012609377,83.74365680792623,2.4786532395740055,166.7404996241093,13.467960960835905,-0.2653508555392431,1.3087501028552622,6.512675018103453,-0.8060702360690668,5.833463472307033,0.5448427871770111,0.40198316316909566,,0.2223739656241296,0.28145447301984594,0.243293651926499,0.16085685746486877
+1,Slider,126071.0,60687.0,20453.0,39120.0,69402,21991.0,85.11885798600645,2408.7220697139937,81.64673187847214,3.3273820646525754,254.3806272913541,15.94645745407159,0.25503050110660713,0.1340754872641023,6.415392251066094,-0.9940703310302156,5.742570977542618,0.4494213578063155,0.36039362273548514,,0.3370244039085801,0.3103013381348605,0.31686406731794475,0.08781653615376793
+2,Sinker,110881.0,50193.0,7235.0,31514.0,49458,12493.0,93.31904199285688,2150.2386523758873,83.87984289276808,2.892717921060944,168.33013944189094,15.447823735442787,-0.5781807359307359,0.6536634199134199,6.419496957697072,-0.8578973665223666,5.636833874458874,0.5539452205517627,0.3630746667835847,,0.14414360568206722,0.28421460845410845,0.25259816409883135,0.07723572705273965
+3,Changeup,78512.0,39518.0,12295.0,19910.0,47870,16185.0,85.45176986078029,1789.468468007219,80.71900963512873,3.4111014745537154,305.36591130209456,16.47019099576866,-0.396463507833397,0.474831486434849,6.458975699314073,-0.568952999617883,5.749977200356643,0.3902588139392704,0.34566906762705085,,0.3111240447391062,0.2535918076217648,0.3381031961562565,0.054688642800522144
+4,Cutter,55805.0,27665.0,6714.0,15683.0,27167,7768.0,89.22766029952471,2387.958273978037,82.31847715218278,3.2905648267822385,206.66655976707708,15.415902082385763,0.12278660592581965,0.639279718774661,6.36341704390314,-0.9624070952005166,5.842086232871798,0.5123017650748141,0.36965886615098403,,0.24268931863365262,0.28103216557656124,0.2859351419000994,0.03887176115094684
+5,Curveball,51679.0,21553.0,6701.0,16148.0,29175,8309.0,79.2795228051164,2529.729456384324,81.09545750923714,3.845559953202193,303.1060225022905,16.897464856909227,0.3128750556318814,-0.7793070686352289,6.357315891472868,-0.6464800015480177,5.931275179473287,0.4354573424408367,0.36297365675534693,,0.3109079942467406,0.31246734650438285,0.2847986289631534,0.03599773755971296
+6,Sweeper,30983.0,14387.0,4714.0,9472.0,17497,5501.0,81.87104541200013,2573.167409915042,79.5424205782126,2.9169456469962296,242.3612934233338,16.05341300254306,0.626698834844915,0.10297743924087403,6.440382997994436,-1.0498637962753767,5.449356421263274,0.4352709550398606,0.34346382040489076,,0.32765691249044276,0.3057160378271956,0.3143967537292107,0.021581646371109865
+7,Split-Finger,15570.0,8130.0,2828.0,3995.0,9884,3589.0,86.61461143224149,1356.4218921536972,81.58194705023973,3.210384887902199,330.0858364738183,17.118239986777326,-0.8700796403339756,0.2713461785484907,6.4252829218107,-1.7122678227360306,5.850094412331407,0.36518946692357096,0.3443354243542435,,0.34784747847478475,0.25658317276814385,0.36311210036422503,0.01084550346958592
+8,Knuckle Curve,12154.0,5439.0,1805.0,3752.0,7059,2292.0,81.43667105479678,2469.610794751176,81.99410356180783,3.3353055020455646,284.5354624498529,16.265551322730666,0.4980812901102518,-0.7952756294224125,6.3597479614529275,-1.0805693598815205,5.938246667763699,0.41920355438538753,0.37180873734683006,,0.33186247471961755,0.30870495310185947,0.3246918827029324,0.00846604040907818
+9,Slurve,2330.0,984.0,273.0,767.0,1262,365.0,82.12420600858368,2524.4065886432595,80.52035398230089,2.558741806283268,306.1556785170377,16.356798085102586,0.3707424892703863,-0.25004291845493565,6.026536796536797,-0.7101459227467811,5.605351931330472,0.45836909871244635,0.3978255208333333,,0.2774390243902439,0.32918454935622316,0.28922345483359746,0.00162299441773508
+10,Other,1099.0,553.0,39.0,192.0,576,161.0,66.9722474977252,1636.2980856882407,84.21832669322708,9.112324944344907,265.8499548102452,16.429985208785887,-0.4521565059144677,1.3244676979071883,4.820714940421631,-1.647452229299363,6.3448225659690625,0.47588717015468607,0.44125391849529777,,0.0705244122965642,0.17470427661510465,0.2795138888888889,0.0007655239764338425
+11,Forkball,778.0,358.0,199.0,247.0,590,214.0,82.96773778920308,1079.639686684073,77.80469798657718,1.5813830914720153,180.5812639240975,16.635375590080205,-0.6015809768637532,0.10609254498714654,6.465681233933163,-1.8326992287917736,5.97146529562982,0.2416452442159383,0.31482432432432433,,0.5558659217877095,0.31748071979434445,0.36271186440677966,0.0005419268914154044
+12,Eephus,522.0,226.0,7.0,79.0,339,92.0,47.99444444444444,1168.9176245210729,85.22054794520548,6.869976110853827,235.4947805488423,16.73137435394275,0.008371647509578542,1.393352490421456,4.532567049808429,-1.4747318007662835,6.651915708812261,0.3505747126436782,0.3907325581395349,,0.030973451327433628,0.15134099616858238,0.2713864306784661,0.00036360647470288056
+13,Knuckleball,190.0,81.0,20.0,44.0,97,18.0,75.68631578947368,337.4578947368421,79.05087719298245,4.960630411514611,393.10082691661756,16.785560576270584,-0.2866315789473684,-0.16442105263157897,6.280526315789474,-1.598,5.561368421052632,0.48947368421052634,0.3248333333333333,,0.24691358024691357,0.23157894736842105,0.18556701030927836,0.00013234718427882627
+14,Screwball,74.0,34.0,5.0,10.0,41,10.0,80.18648648648649,2094.9594594594596,83.2423076923077,1.450679984566931,133.61049905317344,13.026010074521608,-0.7537837837837837,-0.37148648648648647,6.20945945945946,-1.2633783783783783,6.0683783783783785,0.44594594594594594,0.40330000000000005,,0.14705882352941177,0.13513513513513514,0.24390243902439024,5.1545745455963914e-05
+15,Slow Curve,51.0,21.0,1.0,2.0,36,8.0,59.17450980392157,2058.7551020408164,77.07000000000001,11.825867293983224,429.4794101880903,17.29673227428872,0.06490196078431372,-0.7588235294117648,5.652941176470589,-0.72,6.276274509803921,0.29411764705882354,0.21123076923076922,,0.047619047619047616,0.0392156862745098,0.2222222222222222,3.5524770516948104e-05
+16,Pitch Out,46.0,0.0,0.0,0.0,46,0.0,90.01086956521739,2212.1304347826085,,2.9954950072006916,143.40565751983308,,-0.6036956521739131,1.2219565217391304,6.254347826086956,-1.803478260869565,5.74195652173913,0.0,,,,0.0,0.0,3.204194987803162e-05
+17,All,717944.0,341263.0,88076.0,206012.0,365586,104563.0,88.99871914935127,2249.1345844165853,82.5959800585916,6.109454214467076,339.2167730889254,15.224075051082256,-0.12493170194679414,0.5903365570869159,6.43911718239724,-0.8595453392605805,5.7720100196491035,0.4902945076496217,0.37195099345233773,1.0,0.25808833656153757,0.2869471713671261,0.28601478174765993,0.5000940361572508

test_data_mlb.ipynb

@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import requests\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import figure\n",
+    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
+    "from scipy import stats\n",
+    "import matplotlib.lines as mlines\n",
+    "import matplotlib.transforms as mtransforms\n",
+    "import numpy as np\n",
+    "import time\n",
+    "#import plotly.express as px\n",
+    "#!pip install chart_studio\n",
+    "#import chart_studio.tools as tls\n",
+    "from bs4 import BeautifulSoup\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import matplotlib.font_manager as font_manager\n",
+    "from datetime import datetime\n",
+    "import pytz\n",
+    "from matplotlib.ticker import MaxNLocator\n",
+    "from matplotlib.patches import Ellipse\n",
+    "import matplotlib.transforms as transforms\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "from datasets import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting Everything:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset csv (C:/Users/thoma/.cache/huggingface/datasets/nesticot___csv/nesticot--mlb_data-a391519415fcbccf/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n",
+      "100%|██████████| 1/1 [00:00<00:00,  2.02it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "colour_palette = ['#FFB000','#648FFF','#785EF0',\n",
+    "                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']\n",
+    "\n",
+    "print('Starting Everything:')\n",
+    "# exit_velo_df = milb_a_ev_df.append([triple_a_ev_df,double_a_ev_df,a_high_a_ev_df,single_a_ev_df]).reset_index(drop=True)\n",
+    "# player_df_all = mlb_a_player_df.append([triple_a_player_df,double_a_player_df,a_high_a_player_df,single_a_player_df]).reset_index(drop=True)\n",
+    "# exit_velo_df = pd.read_csv('exit_velo_df_all.csv',index_col=[0])\n",
+    "# player_df_all = pd.read_csv('player_df_all.csv',index_col=[0])\n",
+    "\n",
+    "# pa_df = pd.read_csv('pa_df_all.csv',index_col=[0])\n",
+    "# pa_df_full_na = pa_df.dropna()\n",
+    "\n",
+    "### Import Datasets\n",
+    "dataset = load_dataset('nesticot/mlb_data', data_files=['a_pitch_data_2023.csv',\n",
+    "                                                         ])\n",
+    "dataset_train = dataset['train']\n",
+    "exit_velo_df = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)\n",
+    "colour_palette = ['#FFB000','#648FFF','#785EF0',\n",
+    "                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         True\n",
+       "1         True\n",
+       "2         True\n",
+       "3         True\n",
+       "4         True\n",
+       "          ... \n",
+       "575260    True\n",
+       "575261    True\n",
+       "575262    True\n",
+       "575263    True\n",
+       "575264    True\n",
+       "Name: is_pitch, Length: 575265, dtype: bool"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exit_velo_df['is_pitch']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tl_df = exit_velo_df[exit_velo_df['batter_id'] == 699073].groupby(['batter_id','batter_name','batter_hand']).agg(\n",
+    "    pitches = ('is_pitch','sum'),\n",
+    "    swings = ('is_swing','sum'),\n",
+    "    whiffs = ('is_whiff','sum')\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tl_df['whiff_rate'] = tl_df['whiffs'] / tl_df['swings']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>pitches</th>\n",
+       "      <th>swings</th>\n",
+       "      <th>whiffs</th>\n",
+       "      <th>whiff_rate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>batter_id</th>\n",
+       "      <th>batter_name</th>\n",
+       "      <th>batter_hand</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">699073</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Thayron Liranzo</th>\n",
+       "      <th>L</th>\n",
+       "      <td>1344</td>\n",
+       "      <td>554</td>\n",
+       "      <td>189</td>\n",
+       "      <td>0.341155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R</th>\n",
+       "      <td>343</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0.375</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       pitches swings whiffs whiff_rate\n",
+       "batter_id batter_name     batter_hand                                  \n",
+       "699073    Thayron Liranzo L               1344    554    189   0.341155\n",
+       "                          R                343    160     60      0.375"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tl_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}