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Rested/Tired B2B: The Differentials

I’ve been looking at the rested/tired scenario for years, but I like this approach Tyler Dellow introduced using Corsi ratings after various periods of rest. The analysis is housed here. I like how it provides intricate insight as to what really transpired during those games instead of a straight end result as a win/loss as a factor.

Dellow broke games down to the amount of days between games (rested). I wanted to get into the specific of how tired teams and rested teams actually performed in back-to-back sets.

To do this I needed to pull some data with a large enough sample to gauge true conclusions, and eliminating short term random fluctuations associated with games in a short periods of time.

I used to retrieve individual game data, running a script to obtain Corsi/Fenwick ratings from games ranges from 2007-08 to 2011-12.

I kept this current season out of the analysis due to the partial results and excluded the lockout shortened 2012-13 season due to restrictions of playing only within the Conference, which I feel takes away from some of the scheduling effects in some of the more high volume areas, like Anaheim-Los Angeles, teams in Alberta and Florida, stretching through to the New York area where any combination of a NY Rangers – NY Islanders – New Jersey Devils travel schedule can invoke a rested/tired scenario.

All in all, approximately 1620 games of data available which is a significant sample to normalize as will be shown below.

Using I pulled the data for 5v5 game situations, returning Corsi/Fenwick events and percentages at 5v5 excluding any empty net situations. I then ran the numbers for games ‘Close’ defined as games up or down by a goal in the first two periods, or tied in the third period to determine a true review of game situations reflected when games are close and overall.

A great resource for how to use is provided by Red Line Station.

My definition of what constitutes a rested or tired team, I’ll refer you to one of the many posts I’ve written about this subject, including Alberta’s little secret, housing a variety of links for a proper historical look using the available data of the time.

The definitions are below.

  • Rested – team that has not played the previous night facing a team in the second night of a back-to-back set on consecutive nights.
  • Tired – a team playing its second game on consecutive nights versus a team that is rested and not played the previous night.

SchedsI liked Dellow’s approach for the meaningful method in which it portrays the minutiae of the game situations and confirm/denies common knowledge of the expectations for each team in each situation. I lacked the data to provide as insightful analysis until running the timeonice scripts.

Generally, teams play to a .596 winning percentage as a rested team, and to .495 as a tired team 

Game Type Games Wins Win%
Total GP 17220 8607 0.558
Non B2B GP 14168 7222 0.569
B2B both teams 794 397 0.552
Rested B2B 2258 1270 0.596
Tired B2B 2258 988 0.495

Let’s look at each situation for tired and rested teams. The tables below have the following legend:


AvFF% - 5v5 Average Fenwick For %

AVFFclose – 5v5 Average Fenwick For % close

AvCF% - 5v5 Corsi For %

AvCFclose – 5v5 Average Corsi For % close

Av OI EV SV% - Average on-ice even strength save %

Av OI EVSH% - Average on-ice even strength shooting %

Av OIcl EVSV% - Average on-ice (close) even strength save %

Av OIcl EVSH% - Average on-ice (close) even strength shooting %


The first look is at how teams fared as a tired team. Expectations would naturally be a tired team to be competitive from the start, but likely in association with an average .495 winning percentage, a Corsi rating under 50%. Here’s the table containing the results.

AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
0.481 0.481 0.482 0.482 0.908 0.917 8.756 8.128

Results fall into expected ranges, with sub 50% all round, with very little differentiation between game results overall or close. This changes when filtering out game decisions.

Below are results of tired teams but only in Wins:

AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
0.512 0.478 0.507 0.473 0.953 0.946 13.645 10.969

Judging by the data, it seems like teams come out with strong starts posting 51.2% FF% in close situations, and then likely having score effects determine the drop to overall FF%. The dramatic drop could be the combination of score effects and perhaps fatigue setting in, especially true for teams that had to travel immediately after the game the previous night and on to the next city, which is a more likely scenario than teams playing back-to-backs at home for any prolonged period of games.

Goaltending played an important part of generating wins in these situations, as did some timely shooting with a shooting percentage north of 10% and 13.6% in close situations.

Let’s look at the situation in losses. At this point, select patterns emerge with expectations being a mirror opposite of the situation in Wins.

AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
0.457 0.484 0.463 0.489 0.872 0.894 4.896 5.890

Typically what would be expected in a loss is indicative in the data. Not only is goaltending affected with a sub .900 save percentage, along with accompanying deflated shooting percentage, but the close results indicate that goals weighed heavily on the rest of the contest as the rested team scored enough to engage score effects.

Possession stats follow suit. The indications are that with the game close, teams may be falling behind as indicated by the 45.7% FF% and 46.3% CF% after score effects kick in, boosting overall FF% and CF%.

As expected the data certainly supports the typical expectations of teams playing tired against a rested opponent on the back end of a B2B set.


Once again the mirror effects are expected here as the tired team, and once again the data confirms the impact a rested team has playing a tired club, right down to the score effects. The first table encompasses a rested team overall regardless of game situation/result.

AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
0.521 0.519 0.520 0.518 0.914 0.920 8.774 8.281

Data supports the probability. A rested team that seems to go up in score early and actively presses the play. Once attaining a lead, score effects take hold with an end result to decrease the overall FF% and CF%.

On-ice stats support these conclusions. The overall shooting percentage drops as getting pucks to the net and killing time take precedent over generating scoring chances, with the corresponding even-strength save percentage increasing due to the amount of shots generated by the trailing team looking to get back in the game, tie the score or putting the effort expected from coaching staff in situations that weight teams against winning the game outright.

Let’s break it down by game result/situation starting with wins.

AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
0.549 0.516 0.542 0.510 0.959 0.946 12.878 10.915

Rested teams could take advantage early, scoring at an above average pace indicative of an inflated on-ice shooting percentage in games close. Couple that outcome with a puffed up save percentage and the quick start seems to signify the doom of a tired team, ending in an eventual loss.

The numbers fall back as the game overall, with score effects, lowers the overall average FF% and average CF% from the exorbitant high of close situations.

Losses also follow the typically anticipated results.

AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
0.487 0.523 0.492 0.529 0.858 0.887 3.857 5.055

This is actually very interesting and led me to go back to recheck the data. Once a quality control was placed to ensure the data was indeed correct (it was), the next step was to break down the actual losses by extra time games played. In all, 742 games were decided in extra time, 288 in overtime and 454 in a shootout.

Score effects are clearly at play here, but the gap from Av FF% close to the overall 5v5 result is substantial at slightly less than 4%.

Let’s see the effect by isolating extra time games.


The tables above incorporate extra time games without differentiation to shootout or overtime in overall losses.

Removing extra time games alters the end result of isolated losses with higher values as a result; in particular CF% jumps to 53.6% while the FF% stays fairly close to the original 52.9% as rested losses.

This leads me to believe a factor influencing the increase lies in the increased blocked shots boosting the rate stat slightly higher with FF% essentially similar.

AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
0.481 0.529 0.488 0.536 0.839 0.877 3.560 4.760

Extra time games results without the isolation between overtime and shootouts reveal consistency in results for both rested/tired teams.

  AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
R 0.504 0.507 0.504 0.509 0.917 0.917 4.774 5.943
T 0.476 0.473 0.478 0.475 0.926 0.925 0.063 0.072

Uniformity changes when introducing the separation of results from overtime or shootouts.

Rested extra time

  AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
SO 0.519 0.519 0.520 0.520 0.940 0.932 5.166 6.143
OT 0.511 0.517 0.508 0.515 0.917 0.915 7.220 7.429

Tired extra time

  AvFFclose AvFF% AvCFclose AvCF% Av OIcl EVSV% Av OI EVSV% Av OIcl EVSH% Av OI EVSH%
SO 0.484 0.485 0.484 0.485 0.944 0.936 6.767 7.280
OT 0.488 0.484 0.492 0.487 0.921 0.921 8.357 8.436

With the above information providing the norms NHL-wide in various situations, the next step would be to gauge team’s performances relative to the norm.

That’s the next post.


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