In [1]:

    

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import pandas as pd
import sqlite3
import statsmodels.api as sm
%matplotlib inline


    

In [2]:

    

con = sqlite3.connect("nflPPdb.sqlite")
players = pd.read_sql_query('SELECT * FROM players', con)
combine = pd.read_sql_query('SELECT * FROM combine', con)
rr = pd.read_sql_query('SELECT * FROM rr', con)
passing = pd.read_sql_query('SELECT * FROM passing', con)


    

In [3]:

    

rr['total_yards'] = rr['receiving_yards'] + rr['rushing_yards']
ydsData = rr[rr['position'].isin(['QB' , 'RB' , 'qb' , 'rb'])]
ydsData = ydsData.set_index(ydsData['name'])
ydsData = ydsData.groupby(['name'])['total_yards', 'receiving_yards','rushing_yards'].sum()
ydsData = ydsData.dropna()
ydsData = ydsData.drop_duplicates()


    

In [4]:

    

combineData = combine[combine['position'].isin(['QB' , 'RB', 'qb' , 'rb'])]
combineData = combineData.set_index(combineData['name'])
combineData = combineData[['speedscore','BMI']]
combineData = combineData.drop_duplicates()


    

In [5]:

    

regdata = pd.concat([combineData[0:120],ydsData[0:120]],axis=1)
regdata = regdata.dropna()
regdata


    

    
Out[5]:






  

    

      

      
speedscore
      
BMI
      
total_yards
      
receiving_yards
      
rushing_yards
    
  
  

    

      
Aaron Brooks
      
90.676491
      
25.304709
      
174
      
1
      
1534
    
    

      
Aaron Stecker
      
76.536367
      
30.857143
      
659
      
211
      
448
    
    

      
Amos Zereoue
      
96.412474
      
31.608997
      
2555
      
935
      
1620
    
    

      
Anthony Thomas
      
104.088696
      
30.109569
      
3305
      
377
      
2928
    
    

      
Chad Pennington
      
85.562836
      
28.545706
      
116
      
7
      
420
    
    

      
Correll Buckhalter
      
107.336054
      
30.534810
      
2273
      
503
      
1770
    
    

      
Daunte Culpepper
      
122.184590
      
31.786704
      
416
      
0
      
2626
    
    

      
Derrick Blaylock
      
107.370696
      
28.506122
      
785
      
246
      
539
    
    

      
Deuce McAllister
      
117.389327
      
29.994370
      
7452
      
1539
      
5913
    
    

      
Donovan McNabb
      
96.219465
      
29.320672
      
1123
      
-1
      
3146
    
    

      
Drew Brees
      
78.274639
      
28.778382
      
392
      
73
      
686
    
    

      
Edgerrin James
      
117.378162
      
29.183712
      
13327
      
2759
      
10568
    
  

In [6]:

    

mod = sm.OLS(regdata['speedscore'],regdata['total_yards'])


    

In [7]:

    

res = mod.fit()


    

In [8]:

    

print res.summary()


    

    




                            OLS Regression Results                            
==============================================================================
Dep. Variable:             speedscore   R-squared:                       0.415
Model:                            OLS   Adj. R-squared:                  0.362
Method:                 Least Squares   F-statistic:                     7.799
Date:                Wed, 09 Mar 2016   Prob (F-statistic):             0.0175
Time:                        10:41:04   Log-Likelihood:                -69.195
No. Observations:                  12   AIC:                             140.4
Df Residuals:                      11   BIC:                             140.9
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===============================================================================
                  coef    std err          t      P>|t|      [95.0% Conf. Int.]
-------------------------------------------------------------------------------
total_yards     0.0140      0.005      2.793      0.018         0.003     0.025
==============================================================================
Omnibus:                       15.674   Durbin-Watson:                   0.490
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               10.206
Skew:                          -1.803   Prob(JB):                      0.00608
Kurtosis:                       5.722   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.






    




/Users/long/miniconda2/lib/python2.7/site-packages/scipy/stats/stats.py:1557: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=12
  "anyway, n=%i" % int(n))

In [9]:

    

sm.graphics.plot_partregress('speedscore', 'total_yards', [], data = regdata, obs_labels=False)


    

    
Out[9]:












    

In [ ]:

    

X = regdata[['speedscore','BMI']]
Y = regdata['total_yards']
X = sm.add_constant(X)
mod2 = sm.OLS(Y,X)


    

In [ ]:

    

res2 = mod2.fit()


    

In [ ]:

    

xx1, xx2 = np.meshgrid(np.linspace(regdata['speedscore'].min(), regdata['speedscore'].max(), 100), 
                       np.linspace(regdata['BMI'].min(), regdata['BMI'].max(), 100))


    

In [ ]:

    

res2.params


    

In [ ]:

    

Z = res2.params[0] + res2.params[1] * xx1 + res2.params[2] * xx2


    

In [ ]:

    

fig = plt.figure(figsize=(12, 8))
ax = Axes3D(fig, azim=-115, elev=15)
surf = ax.plot_surface(xx1, xx2, Z, cmap=plt.cm.RdBu_r, alpha=0.6, linewidth=0)
resid = Y - res2.predict(X)
ax.scatter(X[resid >= 0]['speedscore'], X[resid >= 0]['BMI'], Y[resid >= 0], c='r',
            marker = 'o')
ax.scatter(X[resid < 0]['speedscore'], X[resid < 0]['BMI'], Y[resid < 0], color='black', alpha=1.0)
ax.set_xlabel('speedscore')
ax.set_ylabel('BMI')
ax.set_zlabel('Totalyards')


    

In [ ]:

    

resid


    

In [ ]:

    

combine_sort = combine.groupby(['college'])['index'].count()
combine_sort = combine_sort.sort_values(ascending =False)
combine_sort[0:10].plot(kind = 'Bar')


    

In [ ]: