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Tired Goalies and Rested Goalies

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The definitive look. Rested goalie? Tired goalie? It really doesn't matter.

Not sure if he is rested or tired.  It doesn't matter.
Not sure if he is rested or tired. It doesn't matter.
Jasen Vinlove-USA TODAY Sports

When I wrote "On: Why an NHL team should never play a tired goalie", my first draft ended with the phrase:

There are problems with the data that suggest it is unreasonable to pool tired goalies from 2013 with tired goalies from 2011-12.

I should have gone with my first instinct and just ended there. But I wanted to make a point. So I went ahead and pooled the goalies. Heads exploded. Twitter erupted. So I felt like I needed to take a broader look at the data and clarify what is going on.

I took all the data from 2007-08 through 2013-14. I pulled all the back-to-back games. I looked at the second game of the pair. I noted whether the goalie was at home or away, and whether he was rested or tired. I used the RTSS files and pulled the 5v5 shots. This eliminates the ES/PK/PP shot mix issue.

Again, these are 5v5. The weird data for "2013" (here 2012-13) we saw in the previous article is gone.

Year

Status

Saves

Goals

Save Percentage

2007

rested

4336

346

0.926

2007

tired

3730

310

0.923

2008

rested

4279

364

0.922

2008

tired

3873

323

0.923

2009

rested

5311

474

0.918

2009

tired

3469

286

0.924

2010

rested

5101

441

0.920

2010

tired

3829

333

0.920

2011

rested

4863

418

0.921

2011

tired

3326

269

0.925

2012

rested

2928

254

0.920

2012

tired

2225

190

0.921

2013

rested

6079

556

0.916

2013

tired

2723

226

0.923

Total

rested

33028

2868

0.920

Total

tired

23458

1957

0.923

There is no meaningful difference between rested goalies and tired goalies. Tired goalies certainly are not worse than rested goalies.

Statistical Significance?

Nope.

> prop.test(goalies)

2-sample test for equality of proportions with continuity correction

data: goalies

X-squared = 1.6812, df = 1, p-value = 0.1948

alternative hypothesis: two.sided

95 percent confidence interval:

-0.007243215 0.001451793

sample estimates:

prop 1 prop 2

0.9201025 0.9229982

Practical Significance?

Nope. Play either goalie, whenever, wherever. Doesn't matter.

Let me reinforce this: if there is not statistical significance, you should not say there is a difference. Saying there is a difference is a "Type 1 Error".

Type 2 Error?

Sure, maybe. But who cares?

Maybe there really is a small effect but 61,000 or so shots just doesn't give you enough power to see it. If so, so what? If you can't see an effect in pooled data of 30 teams over 7 seasons, you aren't going to see it in 20 games, and you sure as hell aren't going to see it in a single game.

Home and Road

Nothing there either.

Where

Saves

Goals

Save Percentage

Home

18760

1660

0.919

Away

37726

3165

0.923

> prop.test(goalies)

2-sample test for equality of proportions with continuity correction

data: goalies

X-squared = 2.7918, df = 1, p-value = 0.09475

alternative hypothesis: two.sided

95 percent confidence interval:

-0.0084847988 0.0007008886

sample estimates:

prop 1 prop 2

0.9187071 0.9225991


What went wrong in 2012-13?

No idea. I'm not seeing it at 5v5. In the previous article we saw tired goalies gave up 284 goals on 2624 shots, a 0.892 save percentage. They should have given up about 231. That's 53 extra goals.  Either that's a one-in-a-zillion outlier or something went wrong in the collection of the data.

Conclusions

There is no statistical or practical difference between the play of tired goaltender and the play of rested goaltenders.

The data at 5v5 in 2012-13 is similar to the other 6 years in this data. The data in the original article has to reflect a mammoth statistical outlier in situations other than 5v5 or there was a problem in the collection of the data.