Possession

NHL Scoring Trends, 2007-08 to 2017-18: Is the League Getting More Competitive?

Benjamin Wendorf Data Analysis, League Wide Report, NHL General Managers, NHL League-Wide Analysis Tags: , , , , , Leave a comment

Photo by Bobby Schultz, via Wikimedia Commons

Though it was completely tangential to @SteveBurtch’s line of thinking, his brief comments pondering the competitiveness between the middle of NHL lineups yesterday (which I can’t locate now, natch) got me thinking about whether the NHL and team management has gotten any more efficient or competitive overall the last decade. With 10 years in the books for complex Corsi data, and hockey’s seeming “Moneyball moment” fully here regardless of the quibbling on social and mainstream media, is the league getting any tighter?

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Rebounds, Extended Zone Time, and the Quest For More Offense

stringhama Hockey Talk, Neutral Zone Analysis, NHL Topical Analysis Tags: , , 4 Comments

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Long has it been argued that sustained zone time is a reliable way to not only prevent your opponents from scoring but as a way to produce offense of your own. The argument that is often made, or at least the one that’s often heard, is that the longer you are in the offensive zone the more likely it is that the defense will become fatigued and make a mistake that leaves someone open for a prime scoring opportunity. 

So let’s test that theory by asking a more data driven question; does sustained zone time lead to an increase in shooting percentage?

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Why Possession and Zone Entries Matter: Two Quick Charts

Benjamin Wendorf NHL League-Wide Analysis, Theory Tags: , , , , , , , , , , Leave a comment

As some of you know, the NHL tracked offensive zone time for two seasons, 2000-01 and 2001-02, then inexplicably stopped. As some of you also know, I have a lot of historical game data, and that includes all the zone time from these seasons. Taking those performances, and focusing on the first two periods to avoid any major score effects (or “protecting the lead“), I charted every single game alongside 2pS%, the historical possession metric.

It’s pretty clear that the spread in shots-for in these games was quite a bit greater than the spread in zone times. Curious, I decided to do a distribution plot, the one that you see leading this piece (2pS% and offensive zone time % in the x-axis, percentage of total performances in the y-axis). Zone time, or generally speaking the flow of the game, has a tighter, much more normal distribution that the distribution of shots. What does this mean? This means that things like how you enter the zone (zone entries), and how you control the puck in the zone (possession, or passing) can make a pretty big difference in how you generate scoring opportunities.

Note: The data I used for these quick graphs were from home team’s perspective, hence why our distribution was a bit north of 50. Keeping that in mind, the 60-40 Rule we established here a year ago looks pretty good for assessing game flow, but there are ways within that flow that can tip the scale.

Increasingly in the NHL, the Best Defense is a Good Offense

Benjamin Wendorf NHL League-Wide Analysis, Theory Tags: , , , , , , , , , 10 Comments
Photo by Lisa Gansky, via Wikimedia Commons; altered by author

Photo by Lisa Gansky, via Wikimedia Commons; altered by author

While preparing statistics for a few upcoming posts on on-ice contributions, I decided to do a quick study on the share of on-ice shot attempts taken by defensemen versus forwards. The metric I’m using, which is a spin-off of an old one whose name doesn’t quite capture it right, is what I’m calling on-ice shooting proportion, or OSP. The results were quite interesting, and I decided that I should test the data a little further and see what we could find.

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Will the 2015-16 Calgary Flames follow the 2014-2015 Colorado Avalanche?

Garret Hohl NHL Topical Analysis Tags: , , , , , , , , , 3 Comments

Odds are, a team that performs like the 2014-2015 Calgary Flames in shots, possession, and chances will miss the playoffs. The odds also indicate if they do make it they are more likely going to be eliminated in the first round. Calgary beat the odds, though, and pushed into the second round until their eventual elimination at the hands of the Anaheim Ducks.

Odds are not destiny; out-shot teams make the playoffs all the time.

Just last season the 2013-2014 Colorado Avalanche finished the season with 112 points and were favorites to falter in the 2014-2015 season by the analytical community. This has led to comparisons between the 2014-15 Flames and the 2013-14 Avalanche.

How similar are the two teams? Let’s take a look.

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The Pressures of Parity

Benjamin Wendorf Exploring Context, NHL League-Wide Analysis, Theory Tags: , , , , , , , , 2 Comments

File:Balanced scale of Justice (blue).svg

Two nights ago, when no one was looking, I tweeted out a telling statistic to understand how teams have reacted to the salary cap post-lockout.

Boulerice wasn’t the only one scraping the bottom of the barrel in 2005-06; Colton Orr was nearby with his 2:49 per game, and you didn’t have to look much further to see Andrew Peters (3:15) and Eric Godard (3:27). In fact, 19 skaters played over 20 games that season and recorded even-strength TOI/G lower than Peluso’s from this year. Teams have realized that, in a salary-capped league, even league-minimum dollars can’t justify players who cannot be trusted with regular minutes.

This was a fairly stark evolution of player usage, but it led me to wonder if there were any other things we could see by looking at finer-grained data from 2005-06 to the present. The salary cap was a game-changer because it pushed teams at the top and bottom closer together, and that compelled teams to stop employing players they couldn’t trust at evens; what are some other areas we see the pressure of parity?

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A Tank Battle in Pictures: Toronto Maple Leafs, Edmonton Oilers, Arizona Coyotes, & Buffalo Sabres in 2014-15

Benjamin Wendorf NHL League-Wide Analysis Tags: , , , , , , , , , , , Leave a comment

Having just added the 2014-15 season to our historical comparison charts, now was a good time to revisit (as I promised in my posts here and here on Pittsburgh’s 1983-84 tank battle) this season’s battle between Arizona, Toronto, Edmonton, and Buffalo. To do this, I tracked the progression of each teams shots-for percentage across two periods (or 2pS%), a possession proxy I developed for historical data that can help us compare teams back to 1952. As you can see above, the perception of the tank battle among these four teams wasn’t quite accurate to their results; Edmonton and Buffalo did not seem to have a marked drop-off in the final quarter-season.

Arizona and Toronto, on the other hand, did noticeably drop, and in Arizona’s case to a level below the hapless Sabres. Ultimately, the fight was more to maintain their improved odds, because Buffalo managed to hold at rock bottom. As I asked when I wrote about the topic with Pittsburgh in mind, it still gives rise to an interesting question: is it more wrong to tank than to maintain a low level all year? In some cases, a team that’s already laid low doesn’t need to tank deliberately…but on the flip side, I suppose that team also assumes risk in losing support and fans by not appearing competitive all season.

How did the Coyotes and Maple Leafs compare to what I’ve christened the “gold standard” for tanks, the 1983-84 Pittsburgh Penguins’ tank for Mario Lemieux? Well, the nice thing is that the plus- and minus-one standard deviations in 2pS% were virtually identical in 1983-84 and 2014-15, so I didn’t have to tinker with them:

While Pittsburgh had probably the starkest, earliest drop-off, both Arizona and Toronto were able to reach the same kinds of lows by the end of the season. To their credit, while the Coyotes and Leafs were, at best, in the lower half of the league in possession, they certainly did their best in the race to the bottom. You could question the wisdom of this kind of thing, since Pittsburgh was guaranteed the top pick if they reached the cellar, while this year’s tanks were struggling for a higher probability.

HG’s Interactive NHL Graphs: A Tutorial

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I’ve had a couple of people ask about how to use the new interactive visualizations we offer at Hockey Graphs, so I thought I’d take the time to provide a tutorial with some visual demonstrations.

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Friday Quick Graphs: Are the 2014-15 Buffalo Sabres the Worst Team of All-Time?

Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: , , , , , , Leave a comment

This is part-opportunity to finally explore this question, and part-opportunity to tout some existing and upcoming data visualizations for HG. Travis Yost has been following the absolutely terrible Sabres season all year, and has raised some questions about whether it’s an all-time worst team. He’s only been able to reach back to the admittedly bad early 2000s Atlanta Thrashers, but the historically bad team by which all others need to be measured is the 1974-75 Washington Capitals squad. Using an historical metric like 2pS%, or a team’s share of all on-ice shots-for in the first 2 periods (expressed as a percentage), we can bring the 2014-15 Sabres together with the 74-75 Caps to see where both teams stand. Note: I used the cumulative version of the measure below, and added lines for one standard deviation below league-average in both seasons.

For as bad as Buffalo has been, they haven’t quite matched the futility of the 74-75 Capitals…nor should they. The Capitals were an expansion team that year, and unlike in other years the NHL did not really reach out to ensure the expansion teams in 1974-75 were given a good base to build from. These were also the peak years of the World Hockey Association, which made professional level talent even more diffuse than normal. The other expansion team in 74-75, the Kansas City Scouts, lasted two years before moving to Colorado to become the Rockies (the team subsequently moved to New Jersey in 1982-83 and changed their name to the Devils).

I included the standard deviations for the leagues in 1974-75 and 2013-14 (I haven’t compiled the data for 2014-15 yet, but this should be close enough), and even by those markers the Capitals compared markedly worse to their league than did the Sabres. But once again, the Capitals had a reasonable excuse, while the Sabres have walked into this situation with eyes wide open.

For those interested, I also put together 2-period shots-for and shot-against rates (and stretched them out to per 60 minutes) to get a rough sense of offense-versus-defense for both teams.

I added a couple extra filters to the charts, league-averages and standard deviations as well as 20-game moving averages in all the measures I used, which you can select by clicking on the grey “Team” bars and clicking on “Filter.”