## Beating save percentage to death: Teams

I used my Even Strength Save Data to look at the effect of Teams. There is not much there.

Using the dense data, I constructed the logits (ln(p/(1-p)) myself. I first corrected the observed save percentages for year.

> LinearModel.2 <- lm(CalcLogit ~ Team, data=Dataset, weights=ESA)

> anova(LinearModel.2)

Analysis of Variance Table

Response: CalcLogit

Df   Sum Sq Mean Sq  F value Pr(>F)

Team      29   1871.2 64.524   3.8031  1.240e-10 ***

Residuals 895 15184.5 16.966

At a first glance, that looks promising. However, the sum of squares is less than 1/3 the sum of squares we saw for goalies. And we know that teams and individual goalies are highly confounded.

Looking at teams and goalies (with no interaction term)

> LinearModel.4 <- lm(CalcLogit ~ last.first+Team, data=Dataset, weights=ESA)

> anova(LinearModel.4)

Analysis of Variance Table

Response: CalcLogit

Df  Sum Sq Mean Sq F value Pr(>F)

last.first 231 6041.3 26.153  1.6677  4.121e-07 ***

Team       29   601.9 20.755  1.3236  0.1209

Residuals 664 10412.6 15.682

Adding in goalies, the significance of the team effect goes away.

Looking at teams and goalies (with interaction)

> LinearModel.4 <- lm(CalcLogit ~ last.first*Team, data=Dataset, weights=ESA)

> anova(LinearModel.3)

Analysis of Variance Table

Response: CalcLogit

Df Sum Sq Mean Sq F value Pr(>F)

last.first      231 6041.3 26.153  1.6835  9.678e-07 ***

Team             29  601.9 20.755  1.3361  0.1156

last.first:Team 168 2707.5 16.116  1.0375  0.3772

Residuals       496 7705.0 15.534

So the effect of goaltenders remains highly significant. The team effect is not significant, and in magnitude is only about 1/10 that of the goaltenders. Finally, we can compare the models directly, to see if adding in team and the team*goalie interaction adds significance to the model.

> anova (LinearModel.1, LinearModel.3, test="F")

Analysis of Variance Table

Model 1: CalcLogit ~ last.first

Model 2: CalcLogit ~ last.first * Team

Res.Df RSS Df   Sum of Sq F      Pr(>F)

1 693    11014

2 496    7705 197 3309.5    1.0814 0.249

And it does not.

Conclusion

Using weighted logistic regression, there is not a statistically significant differences between NHL teams in even strength save percentages over the period 1997-2010 if you control for the effect of individual goaltenders.

## Trending Discussions

Log In Sign Up

forgot?
Log In Sign Up

### Forgot password?

We'll email you a reset link.

If you signed up using a 3rd party account like Facebook or Twitter, please login with it instead.

### Forgot password?

Try another email?

### Almost done,

By becoming a registered user, you are also agreeing to our Terms and confirming that you have read our Privacy Policy.

### Join Arctic Ice Hockey

You must be a member of Arctic Ice Hockey to participate.

We have our own Community Guidelines at Arctic Ice Hockey. You should read them.

### Join Arctic Ice Hockey

You must be a member of Arctic Ice Hockey to participate.

We have our own Community Guidelines at Arctic Ice Hockey. You should read them.

### Great!

Choose an available username to complete sign up.

In order to provide our users with a better overall experience, we ask for more information from Facebook when using it to login so that we can learn more about our audience and provide you with the best possible experience. We do not store specific user data and the sharing of it is not required to login with Facebook.