Score Effects

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.

The Defensive Shell is a good idea in theory. Unfortunately, it doesn’t work.

garik16 Theory Tags: , 5 Comments

The results of score effects are pretty basic hockey analytics knowledge at this point.  Teams down in goals tend to take more shots, while teams up tend to take less, with the effect becoming larger as the game goes on.

We often explain this effect by saying teams go into a “defensive shell”, playing extremely conservative on offense to avoid easy opponent scoring opportunities, at the cost of more time in the team’s defensive zone.  It is of course, not a one team effect either – we often emphasize that the other team is taking greater risks as well to try and score, which is why the shots taken by the team with the lead go in at a higher rate than normal.    That said, it’s pretty much accepted that going into a shell would be a losing strategy for a team to attempt over a whole game, which is why teams don’t attempt this strategy for a full game. Continue reading →

Friday Quick Graphs: When did “Score Effects” Emerge in NHL History?

Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: , , , , , , 6 Comments

Back in 2009, Tyler Dellow first elaborated on the idea of what we now call “score effects,” or how teams with a lead will go into a “defensive shell” and purposely withdraw from the possession battle to preserve their score. Score effects are the primary reason the go-to possession stat is “Fenwick Close” today – the “close” implies the importance of looking at possession measures when teams still have a reason to engage. The limits of historical shot recording, and the possibility of score effects, are precisely why I’ve advocated the use of 2pS% (shot-differential percentage from the first two periods) as an historical possession measure.

The one thing I never completely took for granted was that score effects had always existed in the NHL. To test this, I broke down each game into individual period shot battles, and looked separately at the correlation* of 1st, 2nd, or 3rd period shots-for percentages to final goals-for percentages. The result above clearly shows that the 3rd period SF% begins to drop away drastically after 1977 or so, after a quarter-century of running pretty close to the others. It does seem possible, then, that the re-introduction of overtime in 1983-84 (gone since 1943-44) had an impact on the growth of score effects (although I’m not sure how); on the other hand, the introduction of the “loser point” in 1999-2000 doesn’t seem to have had any effect. We can also do a similar graph of correlations to goals-for percentage to validate the use of 2pS%:

As you can see, score effects have essentially become the norm, much to the detriment of overall shot differential. At any rate, whomever put two-and-two together back in the 1970s probably had the right idea; I’d forward the hypothesis that the 1970s NHL was ripe for change and innovation (a lot of competition; growth of league = increase in decision-makers and opportunities to exploit market inefficiencies). In that kind of environment, protecting the lead quickly became a best practice, and it steadily grew to a league-wide practice by the mid-1990s or so.

* Or a -1.0 to +1.0 relationship of the variance in one variable to the variance in another; positive means as one goes up, the other tends to go up, suggesting a positive relationship or correlation. A negative correlation suggests that, as one goes up, the other tends to go down. The closer to 0.0, the less likely the variables have any relationship at all.