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# Tired Goalies and Rested Goalies

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

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.

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.