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Mark Scheifele’s points-per-game progression from draft year (1) to his last CHL season (3), as compared to other 1st round forwards who similarly spent the next two seasons in the CHL. To avoid era score effects (the player population pulls from back to 1999), I used average percentage change from year 1 to year 2, and year 2 to year 3, to build the (admittedly ramshackle) predictive model.
Here’s a time-lapse of Olli Jokinen’s 2012-13 TPC, where I take his ice-time at evens, the powerplay, and the penalty kill and express it as a percentage of the team-high TOI for that strength. Because they are so different in terms of TOI, I separate forwards and defensemen to do this. I used a rolling 10-game average to smooth out the wrinkles and demonstrate trends, and GIF’d because who doesn’t like a good GIF?
I call this piece “The Joker Fade.”
Further to the “Possession is Everything” Argument…as you can see here, when I use 2-period zone time figures from 2001-02 (the NHL’s previous attempt at measuring possession, which they inexplicably stopped doing), zone time stabilizes more quickly over progressive seasonal measurement. I’d put that at roughly 20 games…2-period shot percentage (not to be confused with shooting percentage) catches up about 10-15 games later, hence why we’re able to predict with alarming accuracy regressions in the second half by the midway point in 82-game seasons. For the record, I’m using 2-period measures to avoid most score effects.
Looking over the same spread of 3,000 shots as this post, except now we’re looking at 10-game rolling average SV%. As you can see, Pavelec’s performance only briefly moved above league average, on a memorable spell in the early- to mid-season of 2011-12. Otherwise, he’s been pretty consistently below average. As with the previous post on Pavelec, this is using basic SV%.
Taking the last 3,000 shots (using this post as my reference; SV% at all strengths) as an indicator of Pavelec’s most recent demonstrated talent, I’ve shown how those 3,000 shots have progressively stabilized to his observed SV% up to the most recent game. The red line denotes stabilization of league-average save percentage over this same period. I might be questioned on using all-strengths save percentage, but when you are using roughly 100-150 games to assess talent and future performance, overall SV% performs virtually identical to even-strength save percentage.
Much like the previous post on juniors forwards, found here, this comes from a large data set. Once again, these are 20+ GP players (n = 12,868). The distribution is essentially identical to the forwards’, which is understandable as age restrictions/allowances definitely favor this kind of distribution. You can once again observe it’s not particularly unique to play 20+ games as a rookie 17-year old or sophomore 18-year old.
This is taken from a massive data set I’ve put together of juniors players of the OHL, WHL, QMJHL, and USHL from the 1969-70 season to the present. This shows the age and experience breakdown of the forward population (20+ GP, n = 24,132) across these years. Quite clearly, it isn’t unique to have your first year as a 17-year old forward, nor your second year as an 18-year old.
This demonstrates the effect of taking a lot of penalties on your shot differential. At your worst or best you’re looking at adding or giving up about 12 shots on goal, or adding or giving up 1 goal, for a swing of 1/6 of a win/loss. For shot differential percentage purposes, it can drop or raise your percentage a half point at the most…data is from 2007-08 through 2011-12.
Data’s taken from the same 450+ games from 1952-53 to 1954-55 that I used for this graph. This is good news for my approach, using Corsi% from the first two periods rather than the whole game to avoid the score effects in the graph I just mentioned. You’ll notice the easier slope of the 2-Periods Corsi%…two groups are being pulled out of the center, the legitimate strong-Corsi teams giving up Corsi ground in the third and the lower-level Corsi teams gaining ground in the third. So it appears, at least in the assessment of 1950s teams, that the 2-Period approach is doing what I want it to do.
Here is an interesting graph thanks to the awesome Hockey Summary Project and its box scores. I took 450+ games from 1952-53 to 1954-55 and recorded the shot totals, scores, as well as the shot totals and scores through two periods. There definitely appears to be score effects at play here, and it seems pretty uniform at those tails. The number of samples for each goal state were for Up 3+ (or 3 on the chart)…44, Up 2…61, Up 1…92, Even…114, Down 1…66, Down 2…51, Down 3+ (-3 on the chart)…26. The distribution wasn’t even because I recorded each of the games once, from the home team’s perspective.
A bit of evidence that simply calling more penalties won’t be the silver bullet for low scoring rates. For a while, an uptick in penalty-calling correlated to an uptick in goals/game, but after about 1984 that wasn’t the case anymore. A better-trained player population to choose from and a league that has been the same size for over a decade now are certainly factors, as are a solidification of best practices for goaltenders and significant improvements in the weight and form retention of goaltender equipment.
This, I think, is where you probably see Orr’s greatest effect on defenseman evolution…you can imagine there’s probably a similar curve on powerplay assists. To some degree, though, it appears that this revolution of the position was concurrent with his career, suggesting there were a number of other coaches and defensemen starting to make this a feasible shift. Regardless, no defensemen has ever played quite like Orr did since, and so that evolution never seemed to change the majority of play (aka even-strength play). Be sure to view the others in this thread, presented in order here, here, here, and here. This is the last one in the thread.
Our shooting percentages have undergone quite the transformation over the years, and a little later we’ll look to see how that relates to fluctuation of powerplay opportunities. It’s safe to say that that has had an impact, but it should also be considered that the WHA and the influx of European talent, along with the expansion of the league, certainly aided this fluctuation as well. Be sure to view the others in this thread, presented in order here, here, here, and here. This is the second one in the thread.
