Why Possession and Zone Entries Matter: Two Quick Charts

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.

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

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.

The Art of Tanking: The Pittsburgh Penguins in 1983-84

While tanking is a hot topic in this year’s NHL, the act of tanking is as old as the idea of granting the worst teams a shot at the #1 pick in the draft. Case in-point: the 1983-84 Pittsburgh Penguins, routinely considered the most overt of tankers in NHL history. The graph above is just one example of their tank, and man is that bad. The yellow and grey lines indicate one standard deviation above and below league-average historical possession (using 2-Period Shot Percentage, or 2pS%, explained here). The blue line is a 20-game moving average (the orange is cumulative), and you’re seeing that right; a team close to the middle of the pack dropped nearly two standard deviations, or from near the top to near the bottom of the league. That graph, and all the ones below, are just some examples of the kind of tinkering you can do with our new interactive graphs, which I highly recommend you check out.

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

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.”

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.
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Using NHL Coaching Changes to Identify Historically Good and Bad Coaches

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

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:
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What to Expect When You’re Expecting: Does Switching NHL Head Coaches Make a Difference?

Bruce Boudreau

Photo by Matthew Miller, via Wikimedia Commons; altered by author

How good do you feel because your team has a new coach? I mean, really…it’s almost like a new-car smell. So many possibilities – This time, things will be different. With the exception of coaching changes due to disastrous, unexpected things, the typical hockey fan was ready for that moment, and were happy to see the coach go. But is that eagerness for change based on real results?

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Friday Quick Graph: How the Possession Battle Stabilizes

Surely you’ve been exhausted with graphs from this December 30th, 1981 Oilers-Flyers game, but allow me one more. I wanted to demonstrate both how many possessions it took for the possession battle to grant us a clear picture, and also further speak to the value of 2pS%. The chart above demonstrate what happens when I establish a rolling possession-for % (as indicated by the y-axis, possession-for % is done from the perspective of Edmonton) using the last 10 possessions, then the last 20 possessions, and so on to 60 possessions. I stop there because we then arrive at a point where we are primarily measuring (in 60-120 on the x-axis) the 1st and 2nd period in-tandem. What we see is that, by that point, our possession battle has calmed down much closer to something that resembles the final battle (a 52% to 48% victory for Philadelphia). The y-axis shows how far above or below .500 (or 50% possession) the battle went; once again, this was measured from Edmonton’s perspective, so below the line is Philadelphia winning the battle, above is Edmonton (hence the color-coding). We also see, then, that the battle doesn’t calm down to a spread below the 60-40 possession benchmark until 40 possessions…which means it doesn’t really reach the likelihood of truly reflecting demonstrated possession talent until that point. For this reason, I think we can derive confidence in the signal that two-periods provide us with regards to possession battles. Additionally, it speaks to the potential problem with focusing on single periods of data.

NHL Defensemen and Shooting Contributions back to 1967-68

File:Defenseman Ray Bourque 1979.jpg

Photo by Dave Stanley via Wikimedia Commons

I have kicked around this data in the past, most prominently in my theoretical post on offensive systems, but I really wanted to get further into the intricacies of defensemen and their historical place in team shooting (among other offensive contributions). By looking at how much a defenseman contributes to a team’s shot generation (expressed as a percentage of team shots in the games a player played, or %TSh), we can draw some interesting comparisons across NHL eras, but I haven’t yet explored how the role of the defenseman has (or hasn’t) evolved from the Expansion Era to the present, nor have I taken a look at some of the more exceptional defense shooting teams. Let me correct that now.

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