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In [1]:



    


from ggplot import *
import pandas as pd
import numpy as np



    











In [ ]:



    


%matplotlib inline



    











In [2]:



    


df = pd.read_csv("./baseball-pitches-clean.csv")
df = df[['pitch_time', 'inning', 'pitcher_name', 'hitter_name', 'pitch_type',
         'sz_top', 'sz_bottom',
         'px', 'pz', 'pitch_name', 'start_speed', 'end_speed', 'type_confidence']]
df.head()



    


















    
Out[2]:










  
    

      
      pitch_time
      inning
      pitcher_name
      hitter_name
      pitch_type
      sz_top
      sz_bottom
      px
      pz
      pitch_name
      start_speed
      end_speed
      type_confidence
    

  
  
    

      0
       2013-10-01 20:07:43 -0400
       1
       Francisco Liriano
       Shin-Soo Choo
       B
       3.10
       1.53
       0.628
       1.547
       Fastball
       93.2
       85.3
       0.894
    

    

      1
       2013-10-01 20:07:57 -0400
       1
       Francisco Liriano
       Shin-Soo Choo
       S
       3.06
       1.56
       0.545
       3.069
       Fastball
       93.4
       85.6
       0.895
    

    

      2
       2013-10-01 20:08:12 -0400
       1
       Francisco Liriano
       Shin-Soo Choo
       S
       3.25
       1.53
       0.120
       1.826
         Slider
       89.1
       82.8
       0.931
    

    

      3
       2013-10-01 20:08:31 -0400
       1
       Francisco Liriano
       Shin-Soo Choo
       S
       3.25
       1.53
      -0.229
       1.667
         Slider
       90.0
       83.3
       0.926
    

    

      4
       2013-10-01 20:09:09 -0400
       1
       Francisco Liriano
        Ryan Ludwick
       B
       3.62
       1.78
      -1.917
       0.438
         Slider
       87.7
       81.6
       0.915
    

  



5 rows × 13 columns

Let's take a look at where pitches wind up





In [3]:



    


ggplot(aes(x='px', y='pz', color='pitch_type'), data=df) +\
    geom_jitter()



    


















    

















    
Out[3]:








<ggplot: (277212125)>

















In [4]:



    


ggplot(aes(x='px', y='pz', color='pitch_type', shape='pitch_name'), data=df) +\
    geom_jitter()



    


















    

















    
Out[4]:








<ggplot: (294730357)>

Let's limit to 1 hitter





In [3]:



    


df.hitter_name.value_counts().head(10)



    


















    
Out[3]:








Mike Trout          610
Christian Yelich    587
Carlos Santana      581
Matt Carpenter      574
Joey Votto          572
Kyle Seager         560
Shin-Soo Choo       554
Brian Dozier        552
Jayson Werth        550
Evan Longoria       550
dtype: int64

















In [4]:



    


hitter = df[df.hitter_name=='Mike Trout']
hitter.head()



    


















    
Out[4]:










  
    

      
      pitch_time
      inning
      pitcher_name
      hitter_name
      pitch_type
      sz_top
      sz_bottom
      px
      pz
      pitch_name
      start_speed
      end_speed
      type_confidence
    

  
  
    

      3415
       2013-09-29 15:11:23 -0400
       1
       Yu Darvish
       Mike Trout
       B
       3.48
       1.57
       1.120
       1.995
                               Fastball
       92.1
       85.9
       0.911
    

    

      3416
       2013-09-29 15:11:39 -0400
       1
       Yu Darvish
       Mike Trout
       X
       3.48
       1.59
       0.156
       2.002
                               Fastball
       92.3
       87.4
       0.908
    

    

      3487
       2013-09-29 15:54:36 -0400
       4
       Yu Darvish
       Mike Trout
       S
       3.57
       1.61
       0.204
       1.687
                               Fastball
       91.4
       85.1
       0.909
    

    

      3488
       2013-09-29 15:54:56 -0400
       4
       Yu Darvish
       Mike Trout
       B
       3.57
       1.74
      -2.115
       2.987
                               Fastball
       89.7
       84.4
       0.872
    

    

      3489
       2013-09-29 15:55:21 -0400
       4
       Yu Darvish
       Mike Trout
       S
       3.64
       1.81
       0.033
       2.472
       Fastball (sinker|split-fingered)
       84.5
       80.6
       0.936
    

  



5 rows × 13 columns



















In [5]:



    


ggplot(aes(x='px', y='pz', color='pitch_type'), data=hitter) +\
    geom_point()



    


















    

















    
Out[5]:








<ggplot: (291762161)>

















In [6]:



    


ggplot(aes(x='px', y='pz', color='pitch_type'), data=hitter) +\
    geom_point() +\
    scale_color_brewer()



    


















    

















    
Out[6]:








<ggplot: (291746041)>

















In [8]:



    


ggplot(aes(x='px', y='pz', color='pitch_type'), data=hitter) +\
    geom_point() +\
    scale_color_brewer(type='qual', palette=4)



    


















    

















    
Out[8]:








<ggplot: (290664317)>

















In [6]:



    


ggplot(aes(x='px', y='pz', color='pitch_type'), data=hitter) +\
    geom_point() +\
    geom_hline(yintercept=[hitter.sz_bottom.mean(), hitter.sz_top.mean()], color='red')



    


















    

















    
Out[6]:








<ggplot: (281108505)>

















In [7]:



    


ggplot(aes(x='px', y='pz', color='pitch_type'), data=hitter) +\
    geom_point() +\
    geom_hline(yintercept=[hitter.sz_bottom.mean(), hitter.sz_top.mean()], color='red') +\
    geom_vline(xintercept=[-1, 1], color='red')



    


















    

















    
Out[7]:








<ggplot: (284822549)>

















In [8]:



    


ggplot(aes(x='px', y='pz', color='pitch_type'), data=hitter) +\
    geom_point() +\
    geom_hline(yintercept=[hitter.sz_bottom.mean(), hitter.sz_top.mean()], color='red') +\
    geom_vline(xintercept=[-1, 1], color='red') +\
    coord_equal()



    


















    

















    
Out[8]:








<ggplot: (283370949)>

How do pitches change speed?





In [9]:



    


fastballs = df[df.pitch_name=='Fastball']
fastballs.head()



    


















    
Out[9]:










  
    

      
      pitch_time
      inning
      pitcher_name
      hitter_name
      pitch_type
      sz_top
      sz_bottom
      px
      pz
      pitch_name
      start_speed
      end_speed
      type_confidence
    

  
  
    

      0 
       2013-10-01 20:07:43 -0400
       1
       Francisco Liriano
        Shin-Soo Choo
       B
       3.10
       1.53
       0.628
       1.547
       Fastball
       93.2
       85.3
       0.894
    

    

      1 
       2013-10-01 20:07:57 -0400
       1
       Francisco Liriano
        Shin-Soo Choo
       S
       3.06
       1.56
       0.545
       3.069
       Fastball
       93.4
       85.6
       0.895
    

    

      8 
       2013-10-01 20:10:57 -0400
       1
       Francisco Liriano
           Joey Votto
       X
       3.58
       1.67
       0.614
       3.155
       Fastball
       95.1
       87.3
       0.920
    

    

      10
       2013-10-01 20:14:30 -0400
       1
            Johnny Cueto
       Starling Marte
       S
       3.51
       1.53
       0.366
       3.226
       Fastball
       93.0
       85.5
       0.948
    

    

      11
       2013-10-01 20:14:45 -0400
       1
            Johnny Cueto
       Starling Marte
       X
       3.51
       1.53
       0.371
       3.595
       Fastball
       93.7
       86.2
       0.947
    

  



5 rows × 13 columns



















In [10]:



    


ggplot(aes(x='start_speed', y='end_speed', color='start_speed - end_speed'), data=fastballs.head(10000)) +\
    geom_point() +\
    scale_color_gradient()



    


















    

















    
Out[10]:








<ggplot: (282992077)>

















In [12]:



    


ggplot(aes(x='start_speed', y='end_speed', color='start_speed - end_speed'), data=fastballs.head(10000)) +\
    geom_point() +\
    scale_color_gradient(low="blue", high="red") +\
    geom_abline(intercept=10) +\
    coord_equal()



    


















    

















    
Out[12]:








<ggplot: (291747837)>

Pitch speed as a function of location

How does C.J. Wilson use his pitches?





In [13]:



    


pitcher = df[df.pitcher_name=="C.J. Wilson"]



    











In [70]:



    


ggplot(aes(x='px', y='pz', color='start_speed'), data=pitcher) +\
    geom_point() +\
    scale_color_gradient(low='yellow', high='red') +\
    coord_equal() + \
    xlab("Horizontal Position at Home Plate") +\
    ylab("Vertical Position at Home Plate") +\
    ggtitle("C.J. Wilson Pitch Map")



    


















    

















    
Out[70]:








<ggplot: (292500581)>

















In [14]:



    


ggplot(aes(x='px', y='pz', color='start_speed', shape='pitch_name'), data=pitcher) +\
    geom_point() +\
    scale_color_gradient(low='white', high='black') +\
    coord_equal()



    


















    

















    
Out[14]:








<ggplot: (287898201)>

















In [16]:



    


def normalize_pitch(speed):
    return (speed - speed.min()) / (speed.max() - speed.min())
    
pitcher['pitch_speed_norm'] = pitcher.groupby(["pitcher_name", "pitch_name"]).start_speed.apply(normalize_pitch)



    


















    






-c:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead

















In [71]:



    


ggplot(aes(x='px', y='pz', color='pitch_speed_norm'), data=pitcher) +\
    geom_point() +\
    geom_hline(yintercept=[pitcher.sz_bottom.mean(), pitcher.sz_top.mean()], color='blue') +\
    geom_vline(xintercept=[-1, 1], color='blue') +\
    scale_color_gradient(low='yellow', high='red') +\
    facet_wrap("pitch_name", scales="fixed") +\
    ggtitle("C.J. Wilson Normalized Pitch Speeds")



    


















    

















    
Out[71]:








<ggplot: (289434297)>

















In [68]:



    


ggplot(aes(x='px', y='pz', color='pitch_speed_norm'), data=pitcher[pitcher.pitch_name=="Slider"]) +\
    geom_point() +\
    geom_hline(yintercept=[pitcher.sz_bottom.mean(), pitcher.sz_top.mean()], color='blue') +\
    geom_vline(xintercept=[-1, 1], color='blue') +\
    scale_color_gradient(low='yellow', high='red') +\
    facet_wrap("pitch_name", scales="fixed")



    


















    

















    
Out[68]:








<ggplot: (292455361)>

Themes

ggplot ships with a few themes that are easy to customize. My favorite is seaborn.





In [72]:



    


ggplot(aes(x='px', y='pz', color='pitch_speed_norm'), data=pitcher[pitcher.pitch_name=="Slider"]) +\
    geom_point() +\
    geom_hline(yintercept=[pitcher.sz_bottom.mean(), pitcher.sz_top.mean()], color='blue') +\
    geom_vline(xintercept=[-1, 1], color='blue') +\
    scale_color_gradient(low='yellow', high='red') +\
    facet_wrap("pitch_name", scales="fixed") +\
    theme_seaborn()



    


















    

















    
Out[72]:








<ggplot: (292445261)>

















In [73]:



    


ggplot(aes(x='px', y='pz', color='pitch_speed_norm'), data=pitcher[pitcher.pitch_name=="Slider"]) +\
    geom_point() +\
    geom_hline(yintercept=[pitcher.sz_bottom.mean(), pitcher.sz_top.mean()], color='blue') +\
    geom_vline(xintercept=[-1, 1], color='blue') +\
    scale_color_gradient(low='yellow', high='red') +\
    facet_wrap("pitch_name", scales="fixed") +\
    theme_xkcd()



    


















    

















    
Out[73]:








<ggplot: (292401777)>

















In [78]:



    


ggplot(aes(x='inning', y='start_speed'), data=df) + stat_smooth(color='steelblue') + theme_xkcd()



    


















    

















    
Out[78]:








<ggplot: (294730413)>

















In [ ]:

Content source: wllmtrng/ggplot-tutorial

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