Bayes-Adjusted Fenwick Close Numbers – An Introduction

With the season upon us, and multiple stat sites now hosting team and player fancystats, it is pretty tempting for a hockey fan (well, one who’s into fancystats) to try and check how his team is doing in possession in close situations – in other words, in Fenwick Close (alternatively, score adjusted fenwick). The problem with this, of course, is that the sample sizes are currently so small as to make the #s pretty meaningless – some teams have played as few as 3 games, so you can’t make any judgments based upon these numbers on their own.

But, as I mentioned on twitter, we can still try and take these numbers and make something out of them, using our prior knowledge of the NHL to make judgments. For example, I can look at current fenwick close #s and pretty confidently state “Buffalo is going to be a terrible terrible team” at this point, despite the sample size, given our prior knowledge of what the Sabres are. In other words, we can incorporate current fenwick close #s into a Bayesian Analysis.

Continue reading

How much does matching competition matter on a team level?

This is certainly a terrible matchup – Matt Martin vs Alex Ovechkin – but it’s not an example really of terrible line management.

Quite frequently in talk about lines of a hockey team, you’ll find talk about how a certain team should be matching up certain lines against certain opponents.  For example, a recent comment to me on twitter stated roughly that: “As long as the Isles match-up the Frans Nielsen line with the Canes’ Eric Staal line, they’ll be in great shape” – as the Canes basically only had one quality line (the Staal line) at the time of that comment.  But as I replied on twitter, that isn’t quite right:

Competition, on a possession level, is pretty much a zero sum game in hockey.

Continue reading

2014-2015 Season Preview: The Metro Division

Image from Michael Miller via Wikimedia Commons

Last year, in preseason, the Metro Division, was considered by far the strongest division in the East and the likely bet to take both Wild Cards.  The whole division, minus the Pens, promptly started the season by getting hammered, only recovering later in the season to grab one of the two wild cards.

This year again, the top 5 of the division looks strong enough to take two wild cards.  The bottom 3, particularly the bottom 2, are very weak, but the top 5 is strong and near evenly matched such that they could wind up in any order.  But, given the requirement to project the division, these are how I believe the division should finish up, from worst to first:

Continue reading

2014-15 Preview: The Central Divison

Image from Matt Boulton via Wikimedia Commons

If you’re a fan of a Central Division team that doesn’t employ Ondrej Pavelec, you’re probably feeling optimistic as we approach the upcoming season. And you should: this is clearly the best division in the NHL, and all six of its non-Manitoban clubs have legitimate playoff hopes.

Of course, not all six will reach that milestone; at least one will join Winnipeg on the outside looking in. At this time, however, few can agree on how the standings will shake out. The Stars have been projected anywhere from second to fifth; the Avalanche have been slotted everywhere but last. Some are high on the Blues, others are sick of them constantly disappointing.

This uncertainty should make for an exciting year in “Conference III.” Below is a team-by-team breakdown of the league’s toughest division:

Continue reading

2014-15 Season Preview: The Atlantic Division

Image from Sarah Connors via Wikimedia Commons

Finishing last season with an average of 87.6 points per team, the Atlantic/Flortheast Division was the worst in the NHL. I see that gap widening, not narrowing, in 2014-15.

The battle at the top of the division will, in my eyes, come down to two teams: the Boston Bruins and the Tampa Bay Lightning. The Bruins have placed either first or second in their division (the Atlantic or the former Northeast) in each of the past four seasons. The 2nd place Lightning finished a full 16 points behind the Bruins in 2013-14, but a strong off-season combined with a full season of Steven Stamkos and rookie Jonathan Drouin potentially making an impact has them near even money with the Bruins.

Continue reading

2014-15 NHL Season Preview: The Pacific Division

Photo by "Kaz Andrew", via Wikimedia Commons

Photo by “Kaz Andrew”, via Wikimedia Commons

Whenever I put together something as broad as a division preview, especially since the divisions have expanded, I usually try to slap something together that helps me get a quick impression of the teams as compared to one another. This time around, I put a little work into generating a 5v5 simulation of this coming season, specifically among the projected top 6 forwards, top 4 defensemen, and goaltenders. As 5v5 play comprises a little over 80% of all NHL gameplay, and these players tend to more consistently drive results (as players of around 3/5 to 2/3 of gameplay), focusing on their 5v5 performances from last year bring us to use a bit more stable indicators of future team performance. The quick-and-dirty approach here benefits from the fact that most of the Pacific lineups are quite similar from last year, and the top 6 and top 4 players tend to be deployed in the same roles from year to year. So, I took the average 5v5 Corsi-For% of the entire of the top 6 and top 4 for each team, the average 5v5 shooting percentage of the same group (for Johnny Gaudreau, I assumed a forward league-average 9%), and the career 5v5 save percentage of the projected goaltenders (for Fredrik Andersen I assumed a goaltender league-average 92.1%), and ended up with a projected 5v5 season that looked like this:
Continue reading

Five Players to Avoid in Your Fantasy Draft

Image from Ivan Makarov via Wikimedia Commons

Image from Ivan Makarov via Wikimedia Commons

Fantasy hockey season is just around the corner, and many drafts will take place in the upcoming two weeks. I’ve identified five players whose underlying (and in some cases overlying) numbers suggest their 2013-14 statlines may contain some mirage-like components, and who are getting picked higher than they likely should be.

1. Joe Pavelski

Pavelski finished third in league goal scoring last season with 41 goals, shattering his previous career high of 31 goals set in 2011-12. What should be concerning to potential fantasy owners is that the spike in goal scoring was driven by a jump in shooting percentage, not an increase in shots on goal. In fact, Pavelski’s 2.74 shots on goal per game was his second lowest mark since his sophomore 2007-08 season. His drop in shots was more than made up for by his shooting percentage jumping up to 18.2%, well up from his previous career mark of 10.0%.

Continue reading

Gordie Howe vs. Bobby Orr vs. Wayne Gretzky vs. Sidney Crosby: Not Your Typical WOWY

Photo by "Djcz", via Wikimedia Commons

Photo by “Djcz”, via Wikimedia Commons

With or Without You analysis, often referred to as WOWY, frequently involves either comparing the performance of a team or particular players when a single player is and isn’t playing. While the approach is a risky one (sample size is a pretty big issue), it can actually be quite telling when you collect enough data.

The value of modern WOWY is that you can definitely get data from precisely the seconds a player played apart from the seconds they weren’t on the ice. Historical WOWY, on the other hand, cannot do much better than taking data from games a player played versus games they didn’t. To this end, then, I wanted to see if historical WOWY can tell us much of anything, and the best way to do that is to focus on players that are undisputed in their value. In this case, I went for WOWYs of the big guns, four of the best players across the eras of NHL history: Gordie Howe, Bobby Orr, Wayne Gretzky, Sidney Crosby.
Continue reading

Using NHL Coaching Changes to Identify Historically Good and Bad Coaches

Iron Mike no like. - Photo by "Resolute", via Wikimedia Commons; altered by author

Iron Mike no like. – Photo by “Resolute”, via Wikimedia Commons; altered by author

Having now looked at the overall effect a coaching change might have on a team, and identified some outstanding examples where a coaching change had a drastic impact on a team, it’s now time to shift over to some juicier matters. For the most part, I don’t think one coaching change is necessarily sufficient to say a coach is good or bad; there is a possibility the previous coach was just that bad. But if the coach returns the same signal a couple of times or more, you are probably getting closer to a true reading on what they might bring to the table.

Across the 140 or so coaching changes these last 60 years where both coaches led the team 20+ games, there were 69 coaches who were a part of that change twice or more (which, to me, is quite a remarkable number). The full list, followed by an explanation of the measures:
Continue reading

Remembering Dellow: A few graphs to convince you on Corsi

From Wikipedia Commons

Over the past year, I based a lot of research off of  former work by Tyler Dellow. It is a bit funny because I actually never read any of Dellow’s work until well after I started writing about underlying metrics in hockey. I knew of him, but mostly was brought up on Gabriel Desjardins, Eric Tulsky, Ben Wendorf (yes, Hockey-Graphs’ own Wendorff), and a few others. It is also a bit difficult now because Dellow’s website has gone dark with his hiring, which removed the work I quoted or built upon.

One Dellow article that will be severely missed is Two Graphs and 480 words will convince you on Corsi.

Dellow presented analytical data in simple and effective ways. It made understanding of complex concepts -such as regression in goal differentials- easy.

Continue reading