Photo by Michael Miller, via Wikimedia Commons
I couldn’t find this data (if it’s out there, please point me to it), so I went back to 1987 and pulled goaltender performance vs games rest. We knew goalies did poorly in the second game of a back-to-back pair, but I’m surprised to see such a large gap for two and three games. (The overall dataset is roughly 40000 games.)
| Days between Games | % of Games | Mins (G1) | Mins (G2) | Shots Vs (G1) | Shots Vs (G2) | Sv% (G1) | Sv% (G2) | W% (G1) | W% (G2) |
| 1 | 9.5 | 54.7 | 55.0 | 28.9 | 29.7 | 0.905 | 0.897 | 0.498 | 0.421 |
| 2 | 35.6 | 57.0 | 56.8 | 28.7 | 28.7 | 0.908 | 0.901 | 0.522 | 0.486 |
| 3 | 19.2 | 57.1 | 56.7 | 29.0 | 29.0 | 0.905 | 0.900 | 0.514 | 0.481 |
| 4 | 12.1 | 56.7 | 56.3 | 29.2 | 28.7 | 0.899 | 0.898 | 0.477 | 0.487 |
| 5 | 7.2 | 55.4 | 55.2 | 29.0 | 28.8 | 0.892 | 0.899 | 0.440 | 0.448 |
There are lots of systematic issues here (e.g. most back-to-back games are on the road) but simplistically, this would mean goalie rest obscures the bulk of a goaltender’s value. That seems implausible and worth looking at in more detail…
Hockey Graphs 
Maybe I am missing something obvious, but is this at all accounting for the fact that back-up goaltenders (i.e. usually worse goalies) typically have much longer rest between starts? Is that something we are able or would even want to control for?
1987? I suspect that unless you are expressing the save %’s in relation to the league average for the season whatever insights are available are obscured by confounding factors.
The other issue that would be worth considering is the frequency of specific rest periods each season, did they play more or fewer b2b games back in 1991?
Agree with J-CA regarding sv% in relation to league average.
Also, QoC is a factor – if you play a terrible team game one but a better team game two, you’re always more likely to lose game two. Not sure this would have a terribly large impact, given the large sample size and the fact that there is overall parity between teams