Category Win Model

Creating a logistical regression model to find out the likelihood of winning a category based upon how your team does.

Total Win Model

Given what we know, can we predict that any categories are more or less important to win?


Call:
glm(formula = Win ~ R + HR + RBI + SB + OBP + K + QS + ERA + 
    WHIP + SVHD, family = binomial, data = df.n1)

Coefficients:
             Estimate Std. Error z value Pr(>|z|)   
(Intercept) -10.01144    3.54634  -2.823  0.00476 **
R             0.08264    0.05334   1.549  0.12131   
HR            0.18975    0.10480   1.811  0.07019 . 
RBI          -0.01158    0.04150  -0.279  0.78019   
SB           -0.01802    0.08653  -0.208  0.83505   
OBP          15.52123    7.38919   2.101  0.03568 * 
K             0.02986    0.02197   1.359  0.17409   
QS            0.54061    0.18321   2.951  0.00317 **
ERA          -0.57358    0.32174  -1.783  0.07463 . 
WHIP         -1.02193    2.17116  -0.471  0.63787   
SVHD          0.29186    0.16172   1.805  0.07111 . 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 213.69  on 155  degrees of freedom
Residual deviance: 132.86  on 145  degrees of freedom
AIC: 154.86

Number of Fisher Scoring iterations: 5

How Lucky Have People Been?

Average of Opponents Weekly Results

What is the average score of all of a teams opponents by category.

Ranking (1 high, 12 low) of Average Opponent Results