Flames Forwards Shot Rates – Part 2, Expected Goals

In the first part one of the series, we looked Flames forwards shot rates, mostly with a view to putting the top guys rates in context, both from a team and league perspective. It took me three tries and some helpful comments to ultimately get the chart right, but I blame excel for that.

The follow-up builds on that base to illustrate the effect of possession on individual shot (and goal rates) at even strength as well as gross on-ice totals and ultimately goal differential. This exercise puts some flesh on the bones of corsi/possession theory for those who wonder about the practical applications of that sort of advanced analysis.

 The following table includes much of the data we had last week, but adds in some additional information to fill in some gaps.

NAME GP TOI/60 G SH TS ES ICE shots/game shots/60 SF/60 SA/60 total SF total SA shots % Individ SH%
DAVIDMOSS 32 11.5 2 74 76 368 2.38 12.39 30.5 26.1 187 160 53.9% 40.6%
LEESTEMPNIAK 61 12.57 12 95 107 767 1.75 8.37 27.1 27.6 346 353 49.5% 30.9%
JAROMEIGINLA 82 16.37 22 161 183 1342 2.23 8.18 23.9 29.2 535 653 45.0% 34.2%
MIKAELBACKLUND 41 13.02 2 70 72 534 1.76 8.09 28.2 25 251 222 53.0% 28.7%
OLLIJOKINEN 82 14.63 13 144 157 1200 1.91 7.85 25 31 500 620 44.6% 31.4%
MICHAELCAMMALLERI 66 13.76 17 98 115 908 1.74 7.60 26.4 28.9 400 437 47.7% 28.8%
BLAKECOMEAU 74 13.21 5 114 119 978 1.61 7.30 25.4 27.1 414 442 48.4% 28.8%
MATTSTAJAN 61 11.36 7 66 73 693 1.20 6.32 25 26.3 289 304 48.7% 25.3%
TOMKOSTOPOULOS 81 10.75 2 80 82 871 1.01 5.65 25.6 27.8 372 403 47.9% 22.1%
CURTISGLENCROSS 67 13.3 15 68 83 891 1.24 5.59 25.9 29.6 385 440 46.7% 21.6%
ALEXTANGUAY 64 13.82 10 46 56 884 0.88 3.80 23.6 29.2 348 430 44.7% 16.1%

After shots/60 (individual shots per 60/minutes of ice), SF and SA/60 shows the team’s rate of shots for and against with that player. Shots % is the players ratio of shots/for at even strength. Individual shot % is the percentage of total shots that player accounted for. For example, the Flames managed 187 shots at 5on5 with David Moss on the ice last year and he personally took 76 of them for an individual SH% of (78/187) 40.6%.

Finding Expected Goals

Shots% is a subset of corsi (which involves all shots at the net including block and misses) but for simplicity we’ll consider it a proxy for possession. As you can see, only Backlund and Moss we above water amongst those listed by this metric – meaning the Flames only outshot the bad guys when those two were on the ice last year in aggregate (although Comeau’s NYI results are mashed into his Flames outcomes, so take his number with a grain of salt. Ditto Cammalleri and Montreal).

The Flames big guns led the team in gross shots and goals for at ES and weren’t bad at personally generating shots/60. However, they were clearly on the wrong end of possession, suggesting a couple of things:

a.) with better possession, they could have spent more time in the offensive zone and therefore increased their shots on net/goal rates

b.) They gave up a lot of shots/goals against, limiting the value of their own totals

To model how an improved possession rate would affect those issues, I took the exisiting shot totals, assumed constant invidual shots % (ratio of personal shots to total shots for), a possession rate of 55% and applied league average shooting and save percentages to the results.

Here’s how things shake out:

Player Individ SH% Shots % total SF delta total SF expected TS delta TS expected goals expected GF delta total SA delta SA expected GA delta expected GD delta
DAVIDMOSS 40.6% 0.55 191 4 78 2 0.2 0.3 156 -4 -0.31 0.63
MIKAELBACKLUND 28.7% 0.55 260 9 75 3 0.3 0.8 213 -9 -0.75 1.53
LEESTEMPNIAK 30.9% 0.55 384 38 119 12 1.2 3.1 315 -38 -3.05 6.18
BLAKECOMEAU 28.8% 0.55 470 57 135 16 1.7 4.6 385 -57 -4.53 9.17
JAROMEIGINLA 34.2% 0.55 653 154 223 40 4.3 12.6 535 -119 -9.49 22.08
OLLIJOKINEN 31.4% 0.55 616 116 193 36 3.8 9.5 504 -116 -9.28 18.79
MICHAELCAMMALLERI 28.8% 0.55 460 61 133 18 1.9 5.0 377 -61 -4.86 9.84
MATTSTAJAN 25.4% 0.55 326 37 83 10 1.0 3.0 267 -37 -2.97 6.02
TOMKOSTOPOULOS 22.1% 0.55 426 55 94 12 1.3 4.5 349 -55 -4.38 8.86
CURTISGLENCROSS 21.6% 0.55 453 69 98 15 1.6 5.6 371 -69 -5.50 11.13
ALEXTANGUAY 16.1% 0.55 428 80 69 13 1.4 6.6 350 -80 -6.42 12.99

The invidiual SH% and total shots while the player was on the ice last year remain constant. The shots % of 55% is a hypothetical possession rate for each guy. The total shots for is how many shots that team would have had with each player on the ice given a 55% posssesion rate. The delta SF shows the difference between this total and his actual SF total last year.

Expected TS and delta TS are similar, except they are for each player’s personal shots on net totals and are arrived at by multiplying his indvidual SH% with the new total SF. "expected goals" shows the additional goals each player would have scored given a 55% possession rate and league average ES shooting percentage. "Expected GF delta" shows the additional goals the team would have scored with the player on the ice given the 55% possession rate.

The process was repeated for shots and goals against. The expected GD delta shows the difference in expected even strength goal differential between existing shots ratios and hypothetical shots ratios, assuming league average SH% and SV%.


Lots of numbers here, but the important columns are the delta’s (or "difference") which show how many more shots/goals each player could be expected given last year’s shot totals and if his possession rate had been 55% (and stable, league average percentages). In the case of Jarome Iginla and Olli Jokinen, for instance, they could have personally generated 40 and 36 more shots on net respectively had they managed possession rates of 55% (rather than 45%). In terms of total shots, the team could have accrued 154 more shots with Jarome on the ice and 116 more shots with Jokinen skating last year as opposed to what they actually managed.

As a result, Jokinen and Iginla would be expected to score 4.3 and 3.8 more even strength goals each with a 10% shift in possession. In terms of total goals for when they were on the ice, the expected goal difference is 12.6 and 9.5.

In contrast, the team would have given up 119 and 116 less shots against with each guy on the ice last year, saving the club 9.49 and 9.28 goals against at 5on5. All told, not only would both guys have scored about 8 more ES goals personally, but the team would be expected to improve their goal differential by more than 22 in just Iginla’s case (which is worth about 3.5 expected wins or 7 points in the standings). 

The effect is obviously less pronounced for guys with less ice time. The cnage for Backlund and Moss is especially muted because both players didn’t play much and were close to a 55% possession rate anyways.


– Keep in mind an indvidual 55% possession rate is excellent-to-elite in the NHL (but certainly not impossible).

– I’ve assumed personal percentage of total shots on net as a constant from the season sample, but it likely varies year-to-year somewhat. That said, Iginla is usually at 30% or above, while the stingy Tanguay is usally below 20%. Moss’ number is skewed by a small sample size.

– This inquiry used league average even strength personal and on-ice ES SH% and SV% to control for assumed randomness in the actual results. So that is 10.53% SH% at even strength for forwards last year, and 0.08 on-ice SH% and 0.92 on-ice SV%. 

This could be done for each player using his real 2011-12 on-ice SH% and SV% however. For example, Iginla personally shot at 12% at ES last year, while his on-ice ES SH% was 10.10 and on-ice SV% was .913. That expands his expected personal goal delta to 4.9, his expected goals for delta to 15.5 and expected goal differential delta to 25.83 (!!) – or 4.31 wins/about 8.5 points in the standings. .

If people have interest in this sort of comparision, I can re-run the results and post them later.

Previously By Kent Wilson

  • ChinookArchYYC

    Ya know Rex, I’ve suspected you were an academic for a while now. Math was never my strongest class either, but unlike you guys I avoided Stats in university all together. In the words of Chevy Chase “I was told there would be no math”.

    Does anyone want to speculate about whether a change in style of play may lead to more shots, or more importantly better CORSI ratings for most of the Flames players. By all accounts Bob Hartley’s an uptempo, high offense type coach.

    • RexLibris

      Stats are a requirement for the World Domination Undergraduate degree. How else do you expect us to throw around such classics as “60% of the time, it works EVERY time”?

      We are also required to take Maniacal Laughter 101, DeathRay Tech 211, and a 400-level course in either Scheme Elaboration or Villain Theatrics. I studied under the inimitable Dr. Horrible.

  • Now I feel bad for not publishing my very similar tables when I finished them rather than on a schedule.

    From what I can tell, goal scorers get around 30% of on ice shots, top ones can get up to around 35%.

    Expect your defensemen to be taking between 10 and 13% of shots themselves, 20-25% from the two of them.

      • I just took over a new site: A Winning Habit. They’ll be there.

        The other thing I figure is that you can approximately divide shots on ice into 4, possibly unequal parts, with defense getting about 25% of shots and the forwards averaging about 25% between them.

        But defense shooting% is about a 2 to 3 times less than forward so we’re approaching 85-90% of ES goals being from the the forwards.

        Which goes to show that defensemen goal scoring is pretty irrelevant on a per minute even strength except from the truly exceptional ones like a Karlsson.

        I’m going to have some interesting things to say about our mutual friend Rene Bourque when I publish. I think he deserves an entire supplementary article on his own.

  • RexLibris

    Thanks goodness that the Almighty saw fit to give you the mind for stats, Kent. Were I asked to do this it would come out looking like a dog’s breakfast.

    Some great work, and it looks pretty extensive as well.

    • Thanks Rex, although I don’t find stats intuitive at all. Math was usually my worst subject in school and I didn’t much enjoy my stats courses in University. Half the time I expect I’ve missed a step or done something wrong when I engage in these quantitative inquiries.

      Luckily Im so interested in the results it helps push me through the research/table making part of things.

      • RexLibris

        It doesn’t show.

        I subscribe to the Socratic idea that there are some innate mathematical traits in us as human beings, that nearly everybody can understand and comprehend mathematics to a degree. The trick is in the teaching and the method of comprehension. And enthusiasm for the end result makes anything easier to accomplish.

        Math was usually my worst subject, until I had a spate of good teachers who understood how the information needed to be presented so as to best permeate my thick skull, then I was off to the races. I still hated my stats class, but at least I understood what was going on. All of that being said, the advanced analysis aspect of hockey is still enough to make my eyes glaze over.