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Relative Schedule Pace and the Benefits of Mediocrity

Today, I want to talk about a concept I call Schedule Pace or maybe more accurately, Relative Schedule Pace.

NHL teams each have a schedule that starts and finishes at approximately the same time. What I’m after is a definition of “pace” that addresses the fact that over the course of a season, some teams inevitably run well ahead or well behind other teams in terms of the number of games played when they face each other.

To what extent does it happen, and when it happens, does it matter?

So Ridiculous

I first became interested in this concept a couple of seasons ago, when the Oilers (for reasons I can no longer remember) started the season playing a ridiculous number of games in a very short period of time. As a result, the majority of their matchups that season (almost 60 games) were against opponents who had played fewer games than the Oilers had.

Those teams sometimes had played as many as four or five fewer games than the Mighty Oil!

Was that the reason the Oilers sometimes looked like the more tired team even in “guaranteed win*” situations? Was that the reason the Oilers suffered an unusually high (even for the Oilers) injury count that season?

Maybe.

*a ‘guaranteed win’ is when your team is at home and had at least one day off and is playing a road team that is on the second game of a back to back, and on its third game in four nights. The win%, in this case, falls precipitously for the road team

Does it Matter?

It turns out, this might have played a part, because schedule pace can make a difference.

I compared the records of home teams (data source: NHL.com) over the past four seasons between those that were disadvantaged (had played more games) vs those that were even up vs those that were significantly disadvantaged (had played >3 more games than their opponent).

  • Neutral record: 615-518 (54.3%) … a substantive home advantage
  • Disadvantaged: 1041-888 (54.0%)
  • Significantly disadvantaged: 69-65 (51.5%)

Notice that I ignore OTL/SOL in this analysis. Many argue ignoring the Bettman point is the ‘right’ way to assess a team’s record. I wish I had such noble intentions … the truth is that the nature of the NHL data I use makes it easy to figure out who won a game, but it’s a massive PITA to figure out if the game went to OT or SO. Feel free to pretend I’m doing it this way because it’s right and not because I’m lazy. 🙂

The Disadvantage is Significant But Only if it’s Significant

In any case, if you decompose those numbers, it’s clear that almost all of the observed difference is because of significant disadvantage situations. It really doesn’t make much difference to your win chances if you’ve played a game or two more than your opponents (unsurprisingly). But if you’ve played significantly more, it’s a different story.

While the traditional way of looking at easy/hard schedules is to look at either rested vs played-the-night-before, or perhaps the difficulty of teams faced, schedule pace is one of those things behind the scene that does seem to matter at the extremes.

Being four or more games more fatigued than your competition appears to negate most of a team’s home ice advantage.

(At the end of this article, for the power geeks among you, I describe the quickie Bayesian analysis that I used to confirm that the ‘significantly disadvantaged’ numbers are likely meaningful, despite the limited sample size)

This Year’s Pace

Here’s a chart that shows the relative pace numbers for all the teams for 2018-19. The brown bars are games where the team has played fewer games (has an advantage). The green ones show games where the team has played more games (is at a disadvantage). Games, where the game count are equal, are not shown:

The chart is sorted by the average disadvantage – in other words, not just the raw number of disadvantaged games, but the extent of that disadvantage. That’s a bit of a relief for Oilers fans – like last year, the Oilers have a pretty balanced pace, neither advantaged nor disadvantaged.

This year, the Panthers have the advantage of a slow-paced schedule for most of the season, while Pittsburgh faces a disadvantage.

Digging into the Details

As I noted earlier, it’s not just how many, it’s how deep.

The histograms for the three teams that give specific counts for game disadvantages are shown below. A shift to the right shows a disadvantage, while a shift to the left indicates an advantage. It’s the extremes that matter.

Notice that the Oilers’ chart is nicely balanced. Yay us!

Florida, on the other hand, has a strong bias to the left, showing that they play fewer games than most of their opponents. Almost a fifth of their season finds them with a ‘4 or more’ game advantage. In two cases, they will go into games having played six and seven fewer games than their opponent!

And then at the other end of the spectrum, our poor dethroned Cup champions play a significant proportion of games at a schedule disadvantage – five games with the dreaded ‘4 or more’. Hey, at least they have some Cups to show for it. As we’ll see in the next section, the Oilers had to deal with this scenario despite being a lottery team. Again.

The Bad Old Days

And here’s what the same chart looks like back in 2015, plus the detailed histogram for the Oilers. I assume you can see what I mean about the Oilers’ brutal pace that season.

Conclusion

So there you have it … a look at what I call “schedule pace”.

While the traditional way of looking at easy/hard schedules is to look at either previous game rest levels or difficulty of teams faced, pace is an idea few spend time on.

Understandably because for the most part, it is not a big deal. But it seems to matter at the extremes. Being four or more games more fatigued than your competition appears to negate most of a team’s home ice advantage. Likely it works mostly the same way – probably more so – on the road.

And, thankfully, the Oilers are not at the extreme downside this year. Although one of these years, it would be cool to see us advantaged.

Geek Appendix

A bit of background on whether the observed differences we see are meaningful.

The raw data over four seasons indicates a relationship between being pace disadvantaged and having a weaker record, dropping from a win% of  54.3% when pace is equal, to 54.0% for any (1 or more games) pace disadvantage, all the way to 51.5% when looking only at significant (4 or more games) disadvantage.

To suss out whether the observed differences were meaningful, I did a quickie Bayesian analysis, where I modelled the win-loss records as a simple proportion (i.e. used a Beta distribution), and assigned a modest prior distribution of a completely breakeven season i.e. Beta(41,41). For no other reason than this prior gives a reasonably broad anchor to neutrality – I’m a believer that such priors are better than both uninformed and unjustly informed priors.

The result of running the different records through the Bayesian update process produces posterior distributions that look like this:

The home-ice advantage is clearly visible, as all of the distributions are generally shifted well north of 50%.

It’s also pretty clear that, in aggregate, there is really no meaningful difference between being disadvantaged and being neutral. There might be if we had lots more data, but not with just four years worth.

However, some of that is because the effect of the extremes is drowned. When we compare the situation between significant disadvantage and neutral, there is a noticeable difference, despite the large uncertainty caused by the low sample size.

By using the standard Bayesian technique of drawing and comparing a large number of samples (1,000,000) from each distribution, these results indicate that there is an ~80% chance that the observed difference is meaningful.

At some point, it might be worth calculating a regression to estimate the relationship and the error bars directly. For now, though … good enough!



    • Oilerslosers

      Seriously baggedmilk what’s wrong with you. When the Oilers win you still complain(face palmers).
      Is the entire Oilersnation staff a bunch of complaining b i t c h e s that don’t cheer for the Oilers. Absolutely pathetic.

    • Oilerslosers

      Seriously baggedmilk what’s wrong with you. When the Oilers win you still complain(face palmers).
      Is the entire Oilersnation staff a bunch of complaining b i t c h e s that don’t cheer for the Oilers. Absolutely pathetic. !

  • Oil_in_the_Desert

    Excellent work. Interested to know, (but not smart enough to figure out) if distance travelled could be also be factored in to ‘schedule pace’ somehow.

    • Thanks!

      I’d say a more complete fatigue model would include rest days, schedule pace, travel distance and timing, practices, and probably try to factor in cumulative individual ice time.

      That’s the kind of thing a really analytics savvy team might consider modeling, and using that to maybe adjust things like flight timings, individual ice times, and practice length and intensity.

  • RexHolez

    So if you were 4-6 games ahead of pace during the season and that gave you a disadvantage, would you then gain an advantage at the end of the season going into the playoffs as you would be more rested while the pace catches up?

    • It’s possible, but you’d have to test it to see if it holds true.

      The couple of disadvantaged teams that I did look at, the catchup on the schedule late in the season was fairly quick, so I don’t think the extra rest helped much. And of course by end of season, everyone has played the same number of games and gets a week rest between season and playoffs.

      If the catchup came with quite a ways to go and allowed to team to play quite slowly late in the season, conceivably it might have some effect.

  • OriginalPouzar

    This is very interesting – thank you.

    The Oilers seem to have an “advantaged” schedule for this coming year but I wonder if it really is the case. Without digging, if I remember correctly, through the Europe trip and the eastern road trip that follows it, the Oilers actually have quite a few days off between games. I am thinking much of these games will rank as them “having played less games” but, at the same time, its a function of starting the season over-seas and recovery from jet lag, major travel, etc.

      • OriginalPouzar

        I was originally quite concerned about, firstly, Europe and then, secondly, the tough October schedule but I’ve flipped a bit towards optimism.

        From some high level research, it seems teams that have started overseas have generally had nice stats to the season so maybe Europe isn’t a big negative.

        The toughness of the schedule is what it is but, at the same time, I’m thinking it might be good to play those tough teams early in the season – they are mainly playoff teams and sometimes, anecdotally, those teams take a bit of time to really get going. It might be a good time to catch them – I mean, Ovechkin and Oshie might still be in full party mode.

        If they can tread water through October, be fake .500 – that could set them up nice for the rest of the season.

        Fantasy?

        • I guess it just speaks to how tough it is to predict that stuff. The good start for the overseas teams is actually a bit of a surprise. I’m stuck in the past – I remember when they first started doing these, the teams that went overseas had horrible starts to the season. So now I’ve just been assuming it causes problems, but it sounds like that isn’t true anymore. Maybe it was just a coincidence, or alternatively, if it wasn’t a coincidence, it makes complete sense that teams would do a better job of preparation and recovery to mitigate the effects.

    • Oilerslosers

      Who it’s another non-original comment by OP. I like how you take something you read on the Athletic and make it your own. You try way too hard to be an Oilers fan. Give it up man. Get a life please. Get some friends.

  • Gravis82

    Percentage of the odds of winning that is able to be predicted by the following factors: 1) Pace ratio of team vs. opponent in any one game, 2) metric of rest differential between teams, 3) metric of travel differential.

    Result and significance? Estimate of what percentage of a teams performance is solely a function of their schedule.

    Also run for each team specifically and see which team has the highest percentage of performance dictated by the schedule and which has the lowest and if this is correlated with the odds of a playoff berth.

    Determine if there is any way to to use this information to improve NHL scheduling system to make it more fair.

    Sell company, make millions.

    You are welcome

  • dabears318

    I work in visual analytics and wanted to extend some praise. Clearly demonstrated the ability to:

    – filter out the noise in the data
    – tell the story based on what the data is saying (vs. bias going in)
    -simplify advanced concepts into easy to digest visualizations

    Would love to see more articles like this. Two thumbs way up!

  • OILFANMEXICO

    The world is getting too complicated for me. We won five cups without analytics. They were getting drunk,stoned and partied there arses off half the season and still found a way to hoist 5 cups. You build a well balanced team, that are like BROTHERS(our last cup run proves my point). Without that you have a room full of guys waiting for their paycheck.

    • camdog

      I’m still waiting on the analytics on how Russell had more even strength points than McDavid, RnH, Eberle and Pouliot combined during the playoff run. Oilersnerdalert is good with numbers but most guys are just making things up based on the numbers they choose are important.

      • Most of the effort in analytics goes into testing the numbers, to see which ones repeat, and which ones predict – and therefore which ones matter.

        Conversely, it’s the people who observe some random detail and create an elaborate but meaningless narrative (“culture issues”, “bad in the room”, “good in the corners”) around that who decide without any basis at all what is and isn’t important.

        Your comment is a perfect example of that – I write an article on schedule pace, including formally testing whether it matters or not, and for some reason that says something about “Russell” to you, and your “proof” is the demonstrably nearly-meaningless sample of games that two playoff series represent.

        Unless … you know … you’re *actually* arguing that Russell is better than McDavid?

        Analytics people – in any venture, not just hockey – by definition must apply rigour and test the numbers to find which are important. We don’t choose the numbers that matter, they choose us. That’s a fundamental precept.

        • camdog

          Joke., I was giving you a thumbs up. However said dozens of stories were written on this site about how bad Russell was bad last season/off season. None were written on how McDavid, RnH, Pouliot, and Eberle scored 3 even strength points the entire playoffs. From a pure statistics perspective the Oilersnation bloggers sure do like to write on only a few storylines, leads one to conclude that bias on what gets written on this site is significant.

          • Ha, OK, I totally missed the sarcasm. Need a damn sarcasm font, eh!

            For sure, though, as I noted in a reply to “Future”, I do try and write about topics that others don’t, with a slightly heavier emphasis on rigour than most.

            It’s a tough line to walk at times, though, as popular albeit at times repetitive topics drive readership, while more esoteric subjects and higher rigour will mean fewer readers.

            I do credit baggedmilk and the guys at OilersNation for wanting to find that balance rather than taking the easy route.

  • Serious Gord

    It’s a good start. But using season-Long numbers is weak. Fl starting late means they play a very busy schedule later in the year and costs them then. Using something like a rolling two week pace calculation might give more meaningful answers.

    MY biggest beef has two parts: the schedules for teams are so varying with some teams last year having played sometimes seven or eight more games than others. This makes the standings based on points highly inaccurate. Baseball long ago recognized this and publishes standings on a win percentage basis. Unfortunately the loser point in hockey makes this impossible as some games are worth three points and others worth just two. The league needs to fix this by making regulation wins worth three points.

    • I’m not sure how to connect your comment with what I wrote.

      If your argument is that varying schedule pace makes the points standings error prone by nature, hey, I agree. Not only do I typically use some sort of games-adjusted number to normalize the comparison, but (for example) last year I regularly tweeted charts showing the Pacific Division points pace, with a “likely playoff” midline showing the pace to 95.

  • VK63

    I find it a curiosity that NHL teams are so late to the mitigating effect of Nootropics and basic light therapy techniques in regards to jet lag and circadian rhythms. Given that players and teams get so much time to rest between games and lactic acid building exertions I’m surprised at how poorly they optimize to those known challenges. Look at a bastion of knowledge like Altis in Arizona and their customized techniques make an average NHL team look completely clueless. I find that very odd given the value of winning to an NHL teams revenue and from what we know about the OEG and mr Katz….. it’s all about the Benjamins $$$$

    • People castigate me for being a “bad fan” because I’m often scathing critical of Oilers management … which I think is silly, because frankly it’s easy to tell the difference between cheering on the team on the ice and the hired gun buffoons that have been ruining it for years now.

      But more generally speaking (and I should add, having spent 15 years in executive management), I am of the opinion that overall, the management talent level in the NHL is lower than that of virtually any other business venture on Earth.

      Think about it. In most other business sector, it’s not unusual to hire an executive from a different sector. I once went from technology to energy services. Yikes!

      Now … can you imagine *any* non-hockey company on Earth hiring an NHL GM because of their wide-ranging management skills?

      No, you can’t. It would be a laugh. Most of them aren’t qualified to do the job they’re doing, let alone a similar role in a vertical in which they’re entirely unexposed.

      So for me it’s not at all surprising to find that NHL teams lag well behind most industries when it comes to anything that isn’t hockey-related. The management horsepower just isn’t there.

  • The Future Never Comes

    That took a good amount of effort its good to see. Some of these other bloggers can do a little research other than writing opinions on bounce back seasons for the seventh time. For example Talbot’s last season was outlier from his career percentages that has been regurgitated umpteen times.

    • Thanks.

      In general, you’ll find I don’t do opinion pieces (except in the comment section, :-)) – most of my work entails a hefty amount of analysis effort up front before I start writing. And I try and do work that is different from what others are publishing.

      I’m helped by the fact that I have a strong technical & analytics background (other bloggers are certainly knowledgeable in that regard as well, but not typically to the level of working in analytics professionally), and I have built my own NHL play by play database that lets me pull data on almost anything I want.

      Sometimes the results of the analysis are uninteresting, and I end up not being able to write anything.

      Par for the course …

  • Kneedroptalbot

    2 Stats I’d like to figure out?
    1. Why the Oilers historically play poorly on 1st games back from a road trip?
    2. Why the Oilers play so badly on HNIC?