Practical Concerns: “The Blind Side”, Intangibles and My Off-Season Plan At McGill

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(Photo credit: Derek Drummond)

At VANHAC, I was asked by a few people about how we use analytics in our program. Every season is different, and to gain a full appreciation of my intentions this summer, it’s worth digging into the central thesis of a football book.

What Really Drives Results?

“[Quarterback Joe Montana, wide receiver Jerry Rice and running back Roger Craig] are stars. They accumulated the important statistics: yards, touchdowns, receptions, completions. [Left tackle Steve] Wallace is not considered a producer. He has no statistics.” – The Blind Side: Evolution of a Game (Michael Lewis, 2006)

While Michael Lewis’ Moneyball did much to improve the popular understanding of analytics in sports, I happen to think that The Blind Side can help bridge the gap between traditionalist and numbers-driven analysts just as much as Moneyball did.

If you peel away the diverse storylines in The Blind Side, this is the central question behind Lewis’ book: What does a good left tackle do for his quarter-back (and by extension, their team)? And how much is that worth?

Very valuable, as it turned out. Unless an NFL team wanted your multi-million dollar quarterback seriously maimed by an opposing pass-rusher, it had better hire a left tackle with the size, speed and sense to keep up. The problem is, if this player does his job well, nothing happens that can directly be attributed to him – he has no statistics.

But conceptually, his impact on the game is not all that hard to identify. A good left tackle provides a safe, productive (and dare I say, fun) work environment for his teammates. By paying attention to the process of football, you can probably come up with a few good ways to account for that.

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(credit: Derek Drummond)

Building A Bridge

When you arrive to this conclusion about football players, it becomes a lot easier to see why the idea of “being a good teammate” and “having intangibles” matters to people working in hockey. I’ve alluded to this elsewhere, but there are really two aspects to creating that good working environment for other people – one can’t be expressed in numbers conveniently, but I reckon the other already can be. Both matter a great deal to the end result, and to how people feel in the process to getting there.

I didn’t have time to really dig into this during my talk at VANHAC, but this is probably the most important realization I’ve had in two years working for the McGill Martlets hockey program.

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NHL Player Physical Peak Estimation

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Probably want to keep that middle guy out for the next shift. Photo by Conrad Poirier, via Wikimedia Commons

Determining NHL player peaks has frequently focused on production and, occasionally, wrinkles are added to account for the steeper fall-off for goal-scoring as opposed to playmaking. Generally, the peak appears to be around the ages 23-25, with some skills like shooting exhibiting fairly early peaks and others a bit later.

Poking around some spreadsheets, I came across data that I’ve always meant to get to: time per shift. The NHL has been keeping a measure of average time per shift for players going back to 1997-98, so I licked my chops over the robust data set. The “Why?” for looking at it, I think, takes us to an interesting place. To some degree, time per shift can allude to a player’s stamina and overall physical fitness; it can also allude to the coaching staff’s assessment of their performance — though there are plenty of shifts ended on the fly in a hockey game. What’s more, we simply haven’t had a lot of player peak estimations using time on-ice, and when done carefully, I think we can capture something like a total physical peak for players.

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Friday Quick Graph: Player Career Charting by Percentage of Team Shots, 1967-68 to 2012-13

Embedding interactive graphs into blog posts, especially blogs with a narrow runner like ours, is frequently an awkward process. Just about the time things look good, you tinker with it and it looks bad. Nevertheless, I had a bunch of old data I put together, once upon a time, and I wanted to get it out there in a form that you could tinker with. Basically, in the past I have used the percentage of team shots in the games a player participated (%TSh; explanation here) as a way to capture a player’s contribution to the shot load; I also think it strongly implies a player’s involvement and contribution to team offense overall.

In the case of today’s graph, I took %TSh and looked at aging curves with a multitude of players from 1967-68 through 2012-13 (like I said, the data is a little old). I prepared this with a selected group of players available for the filter, the majority of whom are stronger, more familiar players of the years covered. I also included some players that struggled by the metric, for the sake of comparison. To filter, click on the “Name” bar, click on “Filter,” and let your imaginations run wild. Feel free to download if you wish.

Note: I believe I set the cut-off at 20 GP before I would record the point of data. It’s old. I’m old. We’re all getting older.

NHL Forwards vs. Defensemen Height & Weight, 1917-18 to 2014-15

Photo by Eric Kilby, via Wikimedia Commons

Photo by Eric Kilby, via Wikimedia Commons

Building on my post from last week on overall skater height going back to 1917-18, I wanted to dig a little further into the the complexity of the data to see if there were any interesting takeaways. This included breaking the data into forward and defense data, to see if there was every any substantial increase in defenseman size or any other allusions to an attitude change in terms of size trends and preferences. While there are some slight differences, most interesting to me was, for as many changes as the NHL has undergone, there seems to be a uniform attitude about size when looking at forwards and defensemen.
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Friday Quick Graph: NHL 5v5 TOI Peak at 24, 25 Years Old

This is the distribution of the skater performances w/200+ 5v5 TOI from the seasons 2007-08 through 2011-12 (n = 3,334). Use as reference for the below two charts. Notice that our line gets a little wacky as our n drops near the tails.

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: 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

Age progression TPCs for a hypothetical defenseman who has played from age 18 through 40. The progression is built on year-to-year age trends across the entire NHL defenseman population from 2007-08 through 2011-12.

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).