# Friday Quick Graph: NHL 5v5 TOI Peak at 24, 25 Years Old

Some of you already know this, but I enjoy distributions, and I think they get sorely under-used in analysis (although, in the end, they are the basis of predictive work). This piece is a bit old (the data is across all skaters, 2007-08 through 2011-12, n = 3,334), but it shows the number of skaters with 200+ minutes of 5v5 time at each age grouping. The peak is clearly at 24 or 25 among this group, but we should be clear with what “peak” means. Although even-strength time can be a pretty good indicator of overall player talent, it’s still a shaky signal (c’mon, we know not all coaches put the “right” guys out there sometimes). Further, powerplay time can sometimes be a drag on better players’ energy for even-strength time, which can also compromise this signal. Nevertheless, if you were to sort all players into even-strength time groupings (say, forwards in 4 groups by ESTOI, and defensemen in 3 groups by ESTOI) you’d see that the top would generally perform better possession and offense-wise than the second, and so on down.

With that in mind, “peak” is also about health. Though we’ve not had much research into it (hint, hint), we have reason to suspect that injuries might drag on possession measures a bit. That said, 24-25 can also be a performance peak for the reason that players are less likely to have major injuries until that age or later.

I plan on digging into this data again (now that I have my ES data back to 1997-98) and splitting into forward and defense groups, but this is a good start.

# 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.

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

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.

# Friday Quick Graphs: Shooting and Playmaking Contributions, 1967-68 through 2012-13

Those of you who’ve been following me on Twitter know that I’ve put together a pretty substantial dataset, and I’ve been working through the data with a metric I’ve used for a while. %TSh is a player’s shots divided by his team’s estimated shot total in games they played (Team Shots / Team GP, multiplied by player GP). The measure gives us an idea of the player’s shooting contribution to the team’s offense. It moves outside the pesky variance of shooting percentage and gets closer to a stable indicator of offensive role. I’ve done the same with %TA, which is the same equation for assists. The reason for estimated team totals is we don’t yet have good macro-data on specific games that players played before 1987-88, but the metric runs essentially in lock-step with the real thing and I want to provide a useful, historical point of comparison. Doing this allows us to look 20 years further back.

The distribution above includes over 23,000 player seasons over 20 GP; the orange distribution is %TA, and black is %TSh. I used the marks to connect back to the previous week’s bizarre flame war over Ovechkin’s value and approach to the game; the top one shows Ovechkin’s peak year, 2008-09 (20%), which also happens to be the highest %TSh of all-time. The bottom mark is Ovechkin’s 2012-13 (16.3%), which I’m using because his current season is just slightly higher – it would be good for 16th best in NHL history.

I also did a second graph, wanting to look at the relationship of %TSh to %TA, to see just how much they ran together:

In the graph above, the x-axis is %TSh, and the y-axis %TA. Intuitively, these run together a fair amount, as shots create rebounds that can be counted as assists, and a player that shoots a lot is likely to be more heavily involved in the entire offense. That said, they don’t run nearly so close together as to render either measure moot. I think %TA can be a valuable counter-weight for assessing defensemen. Anyway, this is the tip of an enormous iceberg of data, so don’t be surprised to see me refer to and use %TSh and %TA again.

# Friday Quick Graph: Season Stories Using % of Team Shots, Gretzky, Lemieux, Sheppard, and Simpson in 1987-88

Part of the reason I like doing graph work is because a good graph (with a little bit of contextual knowledge) can tell a really interesting story. In the past, I’ve been a proponent of digging deeper into the historical data, and noted that even though we have less data of the pre-BTN era it doesn’t mean we can’t make some intriguing graphs. %TSh, or % of team shots (in the games a player participated), provides a great opportunity to do just that, not just in a player’s career (as I’ve done before) but also over the course of a season. In the graph above, I took two well-known players, Mario Lemieux and Wayne Gretzky, and matched them to two (to the younger readers) lesser-known players from 1987-88, Ray Sheppard and Craig Simpson; I expressed their %TSh cumulatively, game-by-game. Craig Simpson, at the tender age of 20, was having the best year of his career (56 goals on an incredible 31.6% shooting percentage), but a trade to the Oilers mid-season would alter his offensive role for that season and into the future. Ray Sheppard, like Simpson very young (21), over the course of the season earned Ted Sator’s trust and responded with a 38-goal rookie season. Sheppard would go on to be a very good offensive player for about a decade.

Yet their lines relative to Gretzky and Lemieux also remind us that, for as good as they were, neither were driving the boat to the level of those legends (and probably wouldn’t). So you do get some perspective on what some of the best-of-the-best were doing. Lemieux, who was entering his prime, was literally carrying a middling Penguins team on his shoulders, and his ability to do that would bring him, in 1988-89, to convince people that Dan Quinn and Rob Brown were really good.

For frame of reference, in the BTN Era (2007-08 to present) only Ovechkin has been able to come close to the kind of shot volume Lemieux was demonstrating in 1987-88.

# Friday Quick Graphs: Toronto Maple Leafs, Chicago Blackhawks, Edmonton Oilers, and Boston Bruins Shot Distributions, 5 Years

What you see above are the even-strength shots-for locations for the near-indisputable top team of the last five seasons (Chicago Blackhawks) versus the near-indisputable worst team of the last five seasons. This is a sort of visual anti-shot quality argument, a demonstration of why, across these five seasons, the indisputable #1 team would shoot 9.9% while the indisputable #30 team would shoot 9.6%. Notice the horseshoe design, about where defensemen normally sit, then jump up into the play. Notice the dense cluster around the high slot. All teams make these plays, try to make them, the difference being some are better at possessing and moving the puck to make the shot. What’s the primary difference above? The amount of shots.

None of the above charting is possible without Greg Sinclair’s awesome site, Super Shot Search. Bookmark it, use it, love it.

Oh, hey, what if I was to look at the teams with the best and worst save percentage these last five years? Would they look different in even-strength shots-against? Well, let’s see, Toronto and Boston:

There is a difference here, I think. I mean, the initial difference are the numbers, Boston’s SV% (92.1%) versus Toronto’s (89.5%). Another difference is it seems the two charts maintain roughly the same shot distributions, but flip ends of the rink. Not much to dwell on there. One thing I will say, that could relate to the SV% discrepancy, is that it doesn’t appear that Toronto records many, if any, shots from right along the boards. Now, I don’t know if this is a recorder’s error or not; it seems to me it’s pretty hard to get a shot from right tight along the boards. Maybe one recorder does it based on where the body of the skater was located, I don’t know. Or…Toronto does allow shooters to come in a little tighter, and Boston owns the center ice a bit better. Could that explain a near-3% discrepancy? I don’t think so; we know Toronto’s had worse goaltending. But it might’ve “helped.”

# Friday Quick Graph: The Evolution of an NHL Forward’s Time On-Ice

Friday Quick Graphs are (initially) intended to revisit some of the better, potentially more-significant work I’ve posted over the past year on my Tumblr page (if you want to beat me to some of them, take a look at benwendorf.tumblr.com).

I did a similar GIF one week ago, using defensemen, in an effort to understand how a player’s playing time evolves over their career. Taking NHL player data from 2007-08 through 2011-12 and identifying year-t0-year change, I’m able to create a hypothetical forward that plays from age 18 to age 40, and how that player’s ice time would change.

For frame of reference, the hypothetical player is the dark blue triangle, the light, dotted triangle is the league average across the player population, and the light blue triangle is the league high in each situation.

There are some similarities to the defensemen GIF, primarily that player’s are given powerplay minutes early, but grow into penalty kill minutes. Unlike defensemen, though, forward TOI decreases uniformly at all strengths, whereas defensemen tend to retain some of their penalty kill time.

As with the previous post, it’s worth pointing out that a player playing from age 18 to age 40 would be a pretty unique, talented player, so this model is really just to demonstrate change.

# Friday Quick Graph: The Evolution of an NHL Defenseman’s Time On-Ice

Friday Quick Graphs are (initially) intended to revisit some of the better, potentially more-significant work I’ve posted over the past year on my Tumblr page (if you want to beat me to some of them, take a look at benwendorf.tumblr.com).

What you see above is a “Total Player Chart,” or TPC, a chart I developed about a year ago to visualize a player’s time on-ice (TOI) deployment. Using that chart, I took the NHL player population from 2007-08 through 2011-12 and recorded the year-to-year change in player’s TOI relative to their age and age +1 seasons. I took those trends and placed them upon an average 18-year old defenseman’s ice time, and tracked how that hypothetical player’s TOI would evolve if they played to the age of 40. The result is the GIF above.

For frame of reference, the hypothetical player is the dark blue triangle, the light, dotted triangle is the league average across the player population, and the light blue triangle is the league high in each situation.

As you can see, the trend is that young player’s tend to receive 5v4 minutes, and as they age they become more trusted with 4v5; as they get older, the 4v5 minutes stick around, but the 5v4 minutes fade.

It’s worth pointing out that this hypothetical defenseman, overall, is likely to be a decent player, by virtue of the fact that they would be getting NHL minutes at age 18 in the first place (and playing until 40).