In the 2018 season, each team played 40 games, two games per week for 20 weeks. That was deemed to long and grueling of a schedule, so subsequent seasons have featured just 28-game seasons, often with longer, sometimes weeks-long, breaks between seasons.

If all the pros had to do was play on stage, then the equivalent of eight to 10 competitive games per week — which is what season one was — shouldn’t be too taxing. But in between games, they have to practice, the same as athletes in other sports do. The intensity of practice schedules and the need to do so much in between games was cited as a reason for the shorter schedules in 2019.

That led me to wonder how teams perform with certain amounts of rest. How much better are they when they get extra days between games, as intended by a more spread-out schedule? More time to prepare should lead to better results, shouldn’t it?

To figure this out, I took the 2019 season and counted the days between each game, which ranged from one (many times) to 37 (for one team) and counted teams’ records for that number of rest days. Both regular-season and playoff games were included. Here are the raw results, broken down by individual number of days:

Days Rest Games W L Win% 1 76 40 36 0.526 A 2 126 61 65 0.484 A 3 39 22 17 0.564 A 4 41 19 22 0.463 A 5 71 40 31 0.563 A 6 74 30 44 0.405 A 7 59 33 26 0.559 A 8 25 9 16 0.360 A 9 5 3 2 0.600 A 10 2 1 1 0.500 A 11 7 4 3 0.571 A 12 18 13 5 0.722 A 13 14 3 11 0.214 A 14 13 9 4 0.692 A 15 3 0 3 0.000 A 16 1 0 1 0.000 A 17 0 0 0 A 18 4 2 2 0.500 A 19 4 1 3 0.250 A 20 2 2 0 1.000 A 21 2 1 1 0.500 A 22 0 0 0 A 23 0 0 0 A 24 0 0 0 A 25 3 2 1 0.667 A 26 3 3 0 1.000 A 27 5 2 3 0.400 A 28 7 5 2 0.714 A 29 2 1 1 0.500 A 30 0 0 0 A 31 0 0 0 A 32 1 1 0 1.000 A 33 6 1 5 0.167 A 34 1 0 1 0.000 A 35 3 1 2 0.333 A 36 0 0 0 A 37 1 0 1 0.000 A

It’s hard for me to make too much out of that, so I decided to group things up in the following ways:

1-3 days. This is for teams that played twice in one week, with the maximum possible time representing playing on Thursday and then Sunday.

This is for teams that played twice in one week, with the maximum possible time representing playing on Thursday and then Sunday. 4-6 days. This is for teams that played in consecutive weeks, but less than a full week apart.

This is for teams that played in consecutive weeks, but less than a full week apart. 7-10 days. This is for teams that played in consecutive weeks, with the maximum possible time representing playing on Thursday of one week and Sunday of the next.

This is for teams that played in consecutive weeks, with the maximum possible time representing playing on Thursday of one week and Sunday of the next. 11+ days. This is for teams that skipped at least an entire week.

I polled my Twitter followers to see if they could guess which grouping would have the best winning percentage. They thought the 4-6 group would come out on top, but the actual results were:

Days Rest Games W L Win% 1-3 241 123 118 0.5104 B 4-6 186 89 97 0.4785 B 7-10 91 46 45 0.5055 B 11+ 100 51 49 0.5100 B

Well, that’s something. This seems to indicate that teams with the least rest performed the best, albeit only very slightly. That seems counter-intuitive and maybe goes against the notion that teams needed those additional days off.

To get an even more accurate picture of how things went, I should probably have compared teams’ rest when they faced each other. That would mostly eliminate games in the 11+ category — probably to the point of small-sample-size irrelevance — since most of those were between teams with long between-stage breaks, but the 1-to-3 category would improve its winning percentage a little bit, since there would be plenty of other games to make up and each one I remove would shift the winning percentage further away from .500.

(Also, I was able to put these numbers together fairly quickly. Having to cross-reference teams’ games and compare their rest would have been much more complex and time-consuming. If I can figure out a relatively simple way to do it in the future, I’ll try to do it.)

So why did teams with relatively little rest fare so well? When the 2020 season was about to begin and we learned about hero pools, one of the concerns that was brought up was that it would require pros to adjust more rapidly and cause chaos with practice, and the games themselves. If your primary strategy revolves around a certain set of heroes, and they’re now unavailable, it could throw your entire team into chaos. Even the way Overwatch normally changes the meta, with new heroes and maps, as well as game-changing patches, has been cited as a reason for a number of players experiencing burnout and leaving the league.

My advice, for what it’s worth, was for teams and players to embrace that chaos. Accept that you won’t be able to plan for every eventuality and just go out there and play to the best of your ability — which, by the way, is pretty darn good. Maybe that’s why teams with less rest fared well in 2019. Maybe having a week or more to plan for your game against another team is too much time, causing you to over-analyze and rely too much on fixed strategies rather than being flexible and playing more instinctively, as you’d have to do if you had less time to prepare.

I might run the same analysis for the 2018 season, though it’s likely to be a little less interesting since, stage breaks aside, no team would have a longer than eight-day layoff. And since games are only played on Saturday or Sunday in 2020, there will be a lot more with just one day of rest and none with two through six (except for the games that have been rescheduled due to the coronavirus outbreak), probably making the comparisons a little less interesting.

For now, I think this is a surprising result. I’m not sure if it speaks to what teams should do, but it will at least allow me to raise an eyebrow the next time a caster or analyst infers that Team A might have an advantage over Team B because B played yesterday but A is well-rested.