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) -4.362437   2.441508  -1.787  0.07397 . 
R            0.006073   0.036249   0.168  0.86695   
HR           0.191374   0.081437   2.350  0.01877 * 
RBI         -0.008626   0.033230  -0.260  0.79517   
SB          -0.022390   0.069608  -0.322  0.74771   
OBP         17.018019   5.965183   2.853  0.00433 **
K            0.008578   0.016497   0.520  0.60307   
QS           0.195369   0.132965   1.469  0.14174   
ERA         -0.372551   0.239074  -1.558  0.11916   
WHIP        -3.030862   1.582270  -1.916  0.05543 . 
SVHD         0.288158   0.120683   2.388  0.01695 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 295.26  on 215  degrees of freedom
Residual deviance: 210.71  on 205  degrees of freedom
AIC: 232.71

Number of Fisher Scoring iterations: 5

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

Coefficients:
            Estimate Std. Error z value  Pr(>|z|)    
(Intercept)  -23.253      5.612  -4.144 0.0000342 ***
R.R            5.127      1.496   3.428  0.000608 ***
R.HR           4.945      1.478   3.346  0.000820 ***
R.RBI          3.553      1.325   2.681  0.007338 ** 
R.SB           3.489      1.218   2.865  0.004170 ** 
R.OBP          5.363      1.477   3.632  0.000281 ***
R.K            5.120      1.460   3.507  0.000454 ***
R.QS           3.777      1.299   2.907  0.003645 ** 
R.ERA          5.574      1.571   3.549  0.000387 ***
R.WHIP         4.448      1.361   3.268  0.001085 ** 
R.SVHD         4.432      1.322   3.353  0.000800 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 294.130  on 214  degrees of freedom
Residual deviance:  51.745  on 204  degrees of freedom
  (1 observation deleted due to missingness)
AIC: 73.745

Number of Fisher Scoring iterations: 9

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

Ranking vs. Average Team

Given the average team each week, how many times does a team score enough to win. Average team is found taking each weeks average for each category.

How Does Your Team Win?

Spider charts with the ranking of each category vs. rest of the league.