Re-examining Fenwick and Playoff Success

Pavel Datsyuk

Pavel Datsyuk and the best Fenwick team in recent history lifted the Cup in 2008

Image from Dan4th Nicholas via Wikimedia Commons

Back in April of 2013, Chris Boyle presented his study of the relationship between a team’s Fenwick percentage in close-score situations and their eventual success in the Stanley Cup playoffs. Since then, there’s been two Stanley Cup playoffs played. Also the previous 2007-08 start point for shot attempt data was extended two years backwards thanks to War on Ice. All told, it’s an another four seasons of data added to the five Boyle examined.

Worth another look, in my opinion.

Continue reading

Bayes-Adjusted Fenwick Close Numbers: Week 2

Adding John Scott may have added a needed goal scorer to the Sharks, but their possession numbers are falling fast.

Another week, another week of possession data in the NHL.  Thankfully, while last week several teams had only played 3 games, this week the minimum # of games played is 6, so our minimum sample size for our Bayes-Adjusted Fenwick Close #s (BAFC) is now 6 games of data.   And with more data, we’re starting to see the numbers for this year have a greater impact on the possession rankings so far.

In case you missed our introduction to BAFC last week, BAFC is simply taking last year’s possession numbers and, weighting them by # of games played this year (more games, less importance), combining them with this year’s possession numbers to try and come up with a more predictive estimate of what each team’s true talent fenwick close really is.  It’s far from perfect, and indeed, the weighting formula is definitely arbitrary, but it does paint a nice picture that is not prone to overreacting to small samples.

There were 2 interesting objections to the numbers after I presented them last week.  The first was one I mentioned in the piece – technically it would’ve been better to weight by shots, not games.  I actually took a look at how that would go this week and the changes are so minor as to be irrelevant.  As such, I’m sticking with weighting by games to have a more consistent week to week approach , since that’s what I started with.

The second, by Steve Burtch, is that it’s technically not Bayesian to weight the older season the way I am, with the older season’s weight fading to 0 at a certain point – a better bayesian approach would simply have the new signal (the 14-15 data) get stronger and stronger, so that while the prior (the 13-14 data) never disappears entirely, it is eventually overwhelmed and of minor impact.  This is indeed true, but I’m sticking with the current system as I suspect after 25 games most of us are going to completely ignore the past season’s results anyway plus it’s the same method practically as the other ranking systems for college sports that I used as inspiration for this project.

None of this is to say these objections don’t have merit, and if others are interested, seriously, go right ahead and create your own numbers by your own methods: this is clearly arbitrary to an extent, so there isn’t a wrong answer as long as your process is sound.

With that ado, below are the Bayes-Adjusted Fenwick Close #s through 10/25/14.
BAFC W2

I’ve added another column this week, which shows the change each team’s #s show from last year. With still a small amount of data from this year, the differences are mainly small, but not always. For example, the Sharks continue to collapse and are now over 3 percentage points worse in possession than last year – that’s still a good team, but that’s not really a contender anymore. By contrast both the Islanders and Ducks are clearly rising.

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