Visuals for NFL Receiver Clusters

2018 regular season statistics for all players targeted at least once.
Data sourced from Pro-Football-Reference



In [1]:

    
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
import yellowbrick.datasets as ybdata

%matplotlib inline



In [2]:

    
nfl_df = (ybdata.load_nfl(return_dataset=True)
                .to_dataframe()
                .query('Tgt >= 20'))



In [3]:

    
nfl_df.head()









    Out[3]:







  
    
      
      Rk
      Player
      Id
      Tm
      Age
      G
      GS
      Tgt
      Rec
      Ctch_Rate
      ...
      FirstTeamAllPro
      C_pos
      CB_pos
      DT_pos
      FB_pos
      QB_pos
      RB_pos
      T_pos
      TE_pos
      WR_pos
    
  
  
    
      0
      1
      Michael Thomas
      ThomMi05
      NOR
      25
      16
      16
      147
      125
      0.850
      ...
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      1
      2
      Zach Ertz
      ErtzZa00
      PHI
      28
      16
      16
      156
      116
      0.744
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
    
    
      2
      3
      DeAndre Hopkins
      HopkDe00
      HOU
      26
      16
      16
      163
      115
      0.706
      ...
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      3
      4
      Julio Jones
      JoneJu02
      ATL
      29
      16
      16
      170
      113
      0.665
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      4
      5
      Adam Thielen
      ThieAd00
      MIN
      28
      16
      16
      153
      113
      0.739
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
  

5 rows × 29 columns



In [4]:

    
features = ['Rec', 'Yds', 'TD', 'Fmb', 'Ctch_Rate']
X = nfl_df[features].values



In [5]:

    
elbow_visualizer = KElbowVisualizer(KMeans(random_state=42), k=(2,10), timings=False)
elbow_visualizer.fit(X)
elbow_visualizer.size = (600, 400)
elbow_visualizer.show()



In [6]:

    
model = KMeans(4, random_state=42)
silhouette_visualizer = SilhouetteVisualizer(model, colors='yellowbrick')

silhouette_visualizer.fit(X)
elbow_visualizer.size = (600, 400)
silhouette_visualizer.show()



In [ ]:

	Rk	Player	Id	Tm	Age	G	GS	Tgt	Rec	Ctch_Rate	...	FirstTeamAllPro	TE_pos	WR_pos
0	1	Michael Thomas	ThomMi05	NOR	25	16	16	147	125	0.850	...	1	0	1
1	2	Zach Ertz	ErtzZa00	PHI	28	16	16	156	116	0.744	...	0	1	0
2	3	DeAndre Hopkins	HopkDe00	HOU	26	16	16	163	115	0.706	...	1	0	1
3	4	Julio Jones	JoneJu02	ATL	29	16	16	170	113	0.665	...	0	0	1
4	5	Adam Thielen	ThieAd00	MIN	28	16	16	153	113	0.739	...	0	0	1