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app.py

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+from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
+import datasets
+from datasets import load_dataset
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+from scipy.stats import gaussian_kde
+import matplotlib
+from matplotlib.ticker import MaxNLocator
+from matplotlib.gridspec import GridSpec
+from scipy.stats import zscore
+import math
+import matplotlib
+from adjustText import adjust_text
+import matplotlib.ticker as mtick
+from shinywidgets import output_widget, render_widget
+import pandas as pd
+#from configure import base_url
+import shinyswatch
+import inflect
+from matplotlib.pyplot import text
+import rolling_batter_functions as rbf
+
+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_2024.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_2024.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_aaa])
+# 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],'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_mlb = exit_velo_df_mlb.set_index('batter_id')['batter_name'].to_dict()
+batter_dict_aaa = exit_velo_df_aaa.set_index('batter_id')['batter_name'].to_dict()
+
+level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A'}
+
+plot_dict_small = {
+'k':'K%',
+'bb':'BB%',
+'bb_minus_k':'BB-K%',
+'csw':'CSW%',
+'woba':'wOBA',
+'launch_speed':'Exit Velocity',
+'launch_speed_90':'90th Percentile Exit Velocity',
+'hard_hit':'HardHit%',
+'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):
+
+    @render.ui
+    def test():
+   
+            # @reactive.Effect
+            if input.my_tabs() == 'MLB':
+                
+                #test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
+                batter_dict = exit_velo_df_mlb.set_index('batter_id')['batter_name'].to_dict()
+                return ui.input_select("id", "Select Player",batter_dict,selectize=True)
+                
+
+            if input.my_tabs() == 'AAA':
+                #test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
+                batter_dict = exit_velo_df_aaa.set_index('batter_id')['batter_name'].to_dict()
+                return ui.input_select("id", "Select Player",batter_dict,selectize=True)
+                
+
+    @output
+    @render.plot(alt="A histogram")
+    @reactive.event(input.go, ignore_none=False)
+    def plot_mlb():
+         
+
+        rbf.rolling_plot(df = exit_velo_df_codes[exit_velo_df_codes['level']==input.my_tabs()],
+                         df_summ = exit_velo_df_codes_summ[exit_velo_df_codes_summ['level']==input.my_tabs()],
+                         player_id = input.id(),
+                         stat_id = input.stat_id(),
+                         batter_dict = batter_dict_mlb,
+                         window_select = input.n(),
+                         level_id = input.my_tabs())
+ 
+
+    @output
+    @render.plot(alt="A histogram")
+    @reactive.event(input.go, ignore_none=False)
+    def plot_aaa():
+         
+
+        rbf.rolling_plot(df = exit_velo_df_codes[exit_velo_df_codes['level']==input.my_tabs()],
+                         df_summ = exit_velo_df_codes_summ[exit_velo_df_codes_summ['level']==input.my_tabs()],
+                         player_id = input.id(),
+                         stat_id = input.stat_id(),
+                         batter_dict = batter_dict_aaa,
+                         window_select = input.n(),
+                         level_id = input.my_tabs())
+ 
+
+
+app = App(ui.page_fluid(
+#    ui.tags.base(href=base_url),
+    ui.tags.div(
+         {"style": "width:90%;margin: 0 auto;max-width: 1600px;"},
+        ui.tags.style(
+            """
+            h4 {
+                margin-top: 1em;font-size:35px;
+            }
+            h2{
+                font-size:25px;
+            }
+            """
+         ),
+    shinyswatch.theme.simplex(),
+    ui.tags.h4("TJStats"),
+    ui.tags.i("Baseball Analytics and Visualizations"),
+    # ui.markdown("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""),
+
+    # ui.navset_tab(
+    #     ui.nav_control(
+    #          ui.a(
+    #             "Home",
+    #             href="https://nesticot-tjstats-site.hf.space/home/"
+    #         ),
+    #     ),
+    #     ui.nav_menu(
+    #         "Batter Charts",
+    #         ui.nav_control(
+    #         ui.a(
+    #             "Batting Rolling",
+    #             href="https://nesticot-tjstats-site-rolling-batter.hf.space/"
+    #         ),
+    #         ui.a(
+    #             "Spray",
+    #             href="https://nesticot-tjstats-site-spray.hf.space/"
+    #         ),
+    #         ui.a(
+    #             "Decision Value",
+    #             href="https://nesticot-tjstats-site-decision-value.hf.space/"
+    #         ),
+    #         ui.a(
+    #             "Damage Model",
+    #             href="https://nesticot-tjstats-site-damage.hf.space/"
+    #         ),
+    #         ui.a(
+    #             "Batter Scatter",
+    #             href="https://nesticot-tjstats-site-batter-scatter.hf.space/"
+    #         ),
+    #         ui.a(
+    #             "EV vs LA Plot",
+    #             href="https://nesticot-tjstats-site-ev-angle.hf.space/"
+    #         ),
+    #         ui.a(
+    #             "Statcast Compare",
+    #             href="https://nesticot-tjstats-site-statcast-compare.hf.space/"
+    #         ),
+    #         ui.a(
+    #             "MLB/MiLB Cards",
+    #             href="https://nesticot-tjstats-site-mlb-cards.hf.space/"
+    #         )
+    #     ),
+    #     ),
+    #     ui.nav_menu(
+    #         "Pitcher Charts",
+    #         ui.nav_control(
+    #          ui.a(
+    #             "Pitcher Rolling",
+    #             href="https://nesticot-tjstats-site-rolling-pitcher.hf.space/"
+    #         ),
+    #          ui.a(
+    #             "Pitcher Summary",
+    #             href="https://nesticot-tjstats-site-pitching-summary-graphic-new.hf.space/"
+    #         ),
+    #          ui.a(
+    #             "Pitcher Scatter",
+    #             href="https://nesticot-tjstats-site-pitcher-scatter.hf.space"
+    #         )
+    #     ),
+    #     )),   
+        ui.row(
+    ui.layout_sidebar(
+
+      ui.panel_sidebar(
+        ui.output_ui('test','Select Player'),
+        #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.navset_tab(
+            # ui.nav("Raw Data",
+            #     ui.output_data_frame("raw_table")),                
+            # ui.nav("Season Summary",
+            #        ui.output_plot('plot',
+            #                       width='2000px',
+            #                       height='2000px')),
+            ui.nav("MLB",
+                ui.output_plot("plot_mlb",height = "1000px",width="1000px")),
+            ui.nav("AAA",
+                ui.output_plot("plot_aaa",height = "1000px",width="1000px"))
+
+
+
+                ,id="my_tabs")))))),server)

rolling_batter_functions.py

@@ -0,0 +1,338 @@
+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
+import pandas as pd
+from matplotlib.pyplot import text
+import inflect
+
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+
+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],'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},}
+
+
+level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A'}
+
+
+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_small = {
+'k':'K%',
+'bb':'BB%',
+'bb_minus_k':'BB-K%',
+'csw':'CSW%',
+'woba':'wOBA',
+'launch_speed':'Exit Velocity',
+'launch_speed_90':'90th Percentile Exit Velocity',
+'hard_hit':'HardHit%',
+'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 rolling_plot(df,df_summ,player_id,stat_id,batter_dict,window_select,level_id):
+        season_title = df['game_date'].str[0:4].values[0]
+        sns.set_theme(style="whitegrid", palette="pastel")
+        if player_id == "":
+            fig = plt.figure(figsize=(12, 12))
+            fig.text(s='Please Select a Pitcher',x=0.5,y=0.5)
+            return
+        
+        
+
+        swing_min = int(window_select)
+        fig, ax = plt.subplots(1, 1, figsize=(10, 10))
+
+        fig.set_facecolor('white')
+        #ax.set_facecolor('white')
+        #fig.patch.set_facecolor('lightblue')
+
+        print(stat_id)
+
+        if stat_id in pa_list:
+            print('we hAVE MADE IT TO THIS PART OF THE CODE')
+
+
+        if stat_id in pa_list:
+            elly_zone_df = df[(df.pa==1)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'pa'
+            print('this is short')
+            print(elly_zone_df)
+
+
+        if stat_id in balls_in_play_list:
+            elly_zone_df = df[(df.bip)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'bip'
+            #print('this is short')
+
+        if stat_id in balls_in_play_list:
+            elly_zone_df = df[(df.bip)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'bip'
+            print('this is short')
+
+        if stat_id in pitches_list:
+            elly_zone_df = df[(df.pitches == 1)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'pitches'
+
+        if stat_id in swings_list:
+            elly_zone_df = df[(df.swings == 1)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'swings'
+
+
+        if stat_id in in_zone_pitches_list:
+            elly_zone_df = df[(df.in_zone)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'in_zone'
+
+
+        if stat_id in in_zone_swings_list:
+            elly_zone_df = df[(df.zone_swing)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'zone_swing'
+
+
+        if stat_id in out_zone_pitches_list:
+            elly_zone_df = df[(df.in_zone == False)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'out_zone'
+
+
+        if stat_id in out_zone_swings_list:
+            elly_zone_df = df[(df.ozone_swing)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'ozone_swing'
+
+        if stat_id in woba_list:
+            elly_zone_df = df[(df.woba_codes==1)&(df.batter_id == int(player_id))&(df.level==level_id)]
+            divisor_x = 'woba_codes'            
+            
+        # penguins = sns.load_dataset("penguins")
+        # sns.histplot(data=penguins, x="flipper_length_mm")
+        # print('we made it here:')
+        # print(int(player_id))
+        # print(stat_id)
+        # print(level_id)
+        # print(df[(df.batter_id == int(player_id))&(df.level==level_id)])
+        # print(df.columns)
+        # print(elly_zone_df[plot_dict[stat_id]["x_value"]].sum())
+
+        df_summ_new = df_summ.copy()
+        df_summ_new = df_summ_new.set_index('batter_id','batter_name','level')
+        df_summ_new = df_summ_new[df_summ_new[divisor_x] >= int(window_select)]
+        df_summ_new = df_summ_new[df_summ_new.level==level_id]
+
+        df_summ_rank = df_summ_new.rank(method='max',ascending=False)
+        df_summ_rank.columns = df_summ_rank.columns+['_rank']
+
+        df_summ_rank_percent =  df_summ_new.rank(pct=True)
+        df_summ_rank_percent.columns = df_summ_rank_percent.columns+['_percent']
+        
+
+        df_summ_new = df_summ_new.reset_index()
+        df_summ_rank = df_summ_rank.reset_index()
+        df_summ_rank_percent = df_summ_rank_percent.reset_index()
+        print('Table columns:')
+        
+        df_summ_new.batter_id = df_summ_new.batter_id.astype(int)
+        df_summ_rank.batter_id = df_summ_rank.batter_id.astype(int)
+        df_summ_rank_percent.batter_id = df_summ_rank_percent.batter_id.astype(int)
+
+        print('Table columns2:')
+        df_summ_new = df_summ_new.merge(df_summ_rank,left_on=['batter_id'],right_on=['batter_id'],how='left',suffixes=['','_rank'])
+
+        df_summ_new = df_summ_new.merge(df_summ_rank_percent,left_on=['batter_id'],right_on=['batter_id'],how='left',suffixes=['','_percent'])
+
+
+        print(df_summ_new)
+        print(df_summ_rank)
+        print(df_summ_rank_percent)
+        
+
+        
+
+        #sns.scatterplot(x=data_df.launch_speed_90,y=data_df.zone_contact,color=colour_palette[0],s=75,label=int(player_id))
+
+        df_summ_new_select = df_summ_new[df_summ_new.batter_id == int(player_id)].reset_index(drop=True)
+        print('whiffing')
+        print(df)
+        print('Player _df:')
+        print(df_summ_new_select)
+
+        if len(df_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()
+
+        df_summ_new_select = df_summ_new_select.loc[:,~df_summ_new_select.columns.duplicated(keep='last')].copy()
+        print('Table for the player:')
+        print(list(df_summ_new_select.columns))
+        print(plot_dict[stat_id]["percentile_label"])
+        print(plot_dict[stat_id]["percentile_label"]+'_percent')
+        print(df_summ_new_select)
+        print(1*plot_dict[stat_id]["flip_p"])
+        print(round(df_summ_new_select[plot_dict[stat_id]["percentile_label"]+"_percent"][0],2))
+        print((1*plot_dict[stat_id]["flip_p"]-round(df_summ_new_select[plot_dict[stat_id]["percentile_label"]+"_percent"][0],2))*100)
+
+        # print(df_summ_new_select[plot_dict[stat_id]["percentile_label"]+'_percent'])
+
+        if plot_dict[stat_id]['percent']:
+            label_1=f'{level_id} Average {df[df.level == level_id][plot_dict[stat_id]["x_value"]].sum()/df[df.level == level_id][divisor_x].sum():.1%}'
+            label_2=f'{batter_dict[int(player_id)]} Average {elly_zone_df[plot_dict[stat_id]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%} ({p.ordinal(abs(int((1*plot_dict[stat_id]["flip_p"]-round(df_summ_new_select[plot_dict[stat_id]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(player_id)]} Average {elly_zone_df[plot_dict[stat_id]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%}'
+            ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
+
+        else:
+            label_1=f'{level_id} Average {df[df.level == level_id][plot_dict[stat_id]["x_value"]].sum()/df[df.level == level_id][divisor_x].sum():.1f}'
+            label_2=f'{batter_dict[int(player_id)]} Average {elly_zone_df[plot_dict[stat_id]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1f} ({p.ordinal(abs(int((1*plot_dict[stat_id]["flip_p"]-round(df_summ_new_select[plot_dict[stat_id]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(player_id)]} Average {elly_zone_df[plot_dict[stat_id]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1f}'
+            #ax.yaxis.set_major_formatter(mtick.int)
+        
+
+        if plot_dict[stat_id]['percentile']:
+            label_1=f'{level_id} Average {df[df.level == level_id][plot_dict[stat_id]["x_value"]].quantile(0.9):.1f}'
+            label_2=f'{batter_dict[int(player_id)]} Average {elly_zone_df[plot_dict[stat_id]["x_value"]].quantile(0.9):.1f} ({p.ordinal(abs(int((1*plot_dict[stat_id]["flip_p"]-round(df_summ_new_select[plot_dict[stat_id]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(player_id)]} Average {elly_zone_df[plot_dict[stat_id]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%}'
+            #ax.yaxis.set_major_formatter(mtick.int)
+
+        if plot_dict[stat_id]['avg_adjust']:
+            label_1=f'{level_id} Average {df[df.level == level_id][plot_dict[stat_id]["x_value"]].sum()/df[df.level == level_id][divisor_x].sum():.3f}'
+            label_2=f'{batter_dict[int(player_id)]} Average {elly_zone_df[plot_dict[stat_id]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.3f} ({p.ordinal(abs(int((1*plot_dict[stat_id]["flip_p"]-round(df_summ_new_select[plot_dict[stat_id]["percentile_label"]+"_percent"][0],2))*100)))} Percentile)'
+            #label_2=f'{batter_dict[int(player_id)]} Average {elly_zone_df[plot_dict[stat_id]["x_value"]].sum()/elly_zone_df[divisor_x].sum():.1%}'
+            #ax.yaxis.set_major_formatter(mtick.int)
+
+        print(plot_dict[stat_id]["x_value"])
+        print(divisor_x)
+
+        # df_summ_new = df_summ.copy()
+        # df_summ_new = df_summ_new[df_summ_new.balls_in_play >= int(window_select)]
+        # df_summ_new = df_summ_new[df_summ_new.level==level_id]
+
+        
+        print('this is here:')
+        print(df_summ_new.head())
+        print(df_summ_new.columns)
+
+
+        if plot_dict[stat_id]["flip_p"] == False:
+            ax.hlines(y=(df_summ_new[plot_dict[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=(df_summ_new[plot_dict[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=(df_summ_new[plot_dict[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=(df_summ_new[plot_dict[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 = [(df_summ_new[plot_dict[stat_id]["percentile_label"]]).quantile(0.9),
+                              (df_summ_new[plot_dict[stat_id]["percentile_label"]]).quantile(0.75),
+                              (df_summ_new[plot_dict[stat_id]["percentile_label"]]).quantile(0.25),
+                              (df_summ_new[plot_dict[stat_id]["percentile_label"]]).quantile(0.1)]
+            hard_hit_text = ['90th %','75th %','25th %','10th %']
+            for i, x in enumerate(hard_hit_dates):
+                text(min(window_select+window_select/100,+window_select+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[stat_id]["flip_p"] == True:
+            ax.hlines(y=(df_summ_new[plot_dict[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=(df_summ_new[plot_dict[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=(df_summ_new[plot_dict[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=(df_summ_new[plot_dict[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 = [(df_summ_new[plot_dict[stat_id]["percentile_label"]]).quantile(0.9),
+                              (df_summ_new[plot_dict[stat_id]["percentile_label"]]).quantile(0.75),
+                              (df_summ_new[plot_dict[stat_id]["percentile_label"]]).quantile(0.25),
+                              (df_summ_new[plot_dict[stat_id]["percentile_label"]]).quantile(0.1)]
+            hard_hit_text = ['10th %','25th %','75th %','90th %']
+            for i, x in enumerate(hard_hit_dates):
+                text(min(window_select+window_select/100,window_select+window_select+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[stat_id]["percentile"] == False:
+            ax.hlines(y=df[df.level == level_id][plot_dict[stat_id]["x_value"]].sum()/df[df.level == 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[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[stat_id]["x_value"]].fillna(0).rolling(window=swing_min).sum()/swing_min,color=colour_palette[0],linewidth=3,ax=ax)
+
+
+
+        if plot_dict[stat_id]["percentile"] == True:
+
+            ax.hlines(y=df[df.level == level_id][plot_dict[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[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[stat_id]["x_value"]].fillna(0).rolling(window=swing_min).quantile(0.9),color=colour_palette[0],linewidth=3,ax=ax)
+
+
+        #ax.set_xlim(window_select,exit_velo_df_small.pitch.max())
+        #plt.yticks([0,0.2,0.4,0.6,0.8,1])
+        #ax.set_ylim(math.floor((min(df_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(player_id)]} - {season_title} - {level_id} - {swing_min} {plot_dict[stat_id]["x_axis"]} Rolling {plot_dict[stat_id]["title"]}', fontsize=16,fontname='Century Gothic',)
+        #vals = ax.get_yticks()
+        ax.set_xlabel(plot_dict[stat_id]['x_axis'], fontsize=16,fontname='Century Gothic')
+        ax.set_ylabel(plot_dict[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[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()
+        return