[EDIT: The table relating to the penalty kill was labeled incorrectly and suggested that I was looking at shorthanded scoring ( I accidentally put 'PKSF/60' and PK 'GF/60' instead of PKSA/60' and PKGA/60, respectively). The table has now been fixed.]
This post is a tad overdue.
It's the second of two follow up posts relating to powerplay performance. While the first post dealt with the relationship between shooting percentage at even strength and shooting percentage on the powerplay, this post relates to predicting future powerplay performance.
The variation in powerplay shooting percentage at the team level, over the course of a single regular season, is approximately 90% luck, 10% skill. Not surprisingly, powerplay shot rate is a stronger predictor of future powerplay performance than raw powerplay performance (provided that the sample size with which one is dealing isn't overly large). This is precisely what Gabriel Desjardins demonstrated in a post published in early April.
What this post is concerned with, however, is whether the inclusion of missed and blocked shots in the sample has residual value with respect to predicting to predicting powerplay efficiency in the future. While such is the case at even strength, special teams may be a different ballgame. What does the data say?
One preliminary issue that must be dealt with is shot recording bias. Recording bias doesn't really present a problem with respect to even strength shot metrics due to the fact that:
A. What were ultimately interested in is shot ratio/percentage or shot differential, and
B. None of the scorers appear to favor one team over the other (i.e. recording bias is largely, if not entirely, symmetrical).
Not so with special teams. With special teams, we're generally interested with rate stats, in which case recording bias becomes relevant. This is especially true when it comes to the recording of missed and blocked shots. Below is a table showing each team's home/road ratio in recorded shots (saved shots + goals), misses and blocks over the last three regular seasons (from 2008-09 to 2010-11). All game situations were included, although empty net goals or shots that resulted in same were not.
As one might notice, the recording of shots that actually make it to the goal isn't that bad. New Jersey and Minnesota appear to undercount, and Colorado appears to overcount. But every other location is reasonably good.
The recording of misses and blocks, by contrast, is generally fucked up. The N.J, CHI, ATL and BOS scorers seem very reluctant to record misses. Conversely, the guys in L.A, CAR, DAL and TOR seem overly eager.
The data for blocks reveals a similar story. The scorers in ANA, BOS, FLA and N.J count too few, whereas the scorers in NYI, MTL, EDM, S.J, TOR and WSH count too many.
It's a god damn nightmare.
Fortunately, there is a solution. Recording bias can be more or less controlled for by dividing the observed number of home missed or blocked shots by the appropriate co-efficient (that being the applicable H/R ratio, as displayed in the above table).
Once this correction is made, one can determine whether including missed and blocked shots adds value with respect to predicting future powerplay performance.
The following experiment was performed:
- I randomly selected 40 games from the 2010-11 season
- I calculated each team's PP GF/60, PP SF/60, PP Fenwick/60, PP Corsi/60 over that selected sample
- PP Fenwick/60 = [(powerplay shots + powerplay missed shots)/PP TOI]*60
- PP Corsi/60 = [(powerplay shots + powerplay missed shots + powerplay blocked shots)/PP TOI]*60
- I then selected an independent 40 game sample, and calculated each team's PP GF/60 in respect thereof
- I then looked at how each of the four above variables ( PP GF/60 , PP SF/60, PP Fenwick/60, PP Corsi/60), as calculated over the 1st sample of games, predicted PP GF/60 over the 2nd sample of games
- I repeated this exercise 1000 times
- I then repeated the entire exercise for the 2008-09 and 2009-10 regular seasons
The results:
Just like Gabe Desjardins found, shot production is a better predictor of future powerplay success relative to raw performance (with respect to 40 game sample sizes). And while missed shots have some informational value, blocked shots do not.
Does the same apply to the penalty skill? Interestingly, no.
Unlike with the powerplay, raw performance on the penalty kill (over a 40 game sample) is a superior predictor of future PK performance than is shot prevention. Part of that can be attributed to the fact that penalty skill save percentage is considerably more reliable than powerplay shooting percentage.
Furthermore, including misses and blocks is of no assistance. It seems as though better penalty kills force their opponents to take a greater proportion of missed and blocked shots.
What this post is concerned with, however, is whether the inclusion of missed and blocked shots in the sample has residual value with respect to predicting to predicting powerplay efficiency in the future. While such is the case at even strength, special teams may be a different ballgame. What does the data say?
One preliminary issue that must be dealt with is shot recording bias. Recording bias doesn't really present a problem with respect to even strength shot metrics due to the fact that:
A. What were ultimately interested in is shot ratio/percentage or shot differential, and
B. None of the scorers appear to favor one team over the other (i.e. recording bias is largely, if not entirely, symmetrical).
Not so with special teams. With special teams, we're generally interested with rate stats, in which case recording bias becomes relevant. This is especially true when it comes to the recording of missed and blocked shots. Below is a table showing each team's home/road ratio in recorded shots (saved shots + goals), misses and blocks over the last three regular seasons (from 2008-09 to 2010-11). All game situations were included, although empty net goals or shots that resulted in same were not.
As one might notice, the recording of shots that actually make it to the goal isn't that bad. New Jersey and Minnesota appear to undercount, and Colorado appears to overcount. But every other location is reasonably good.
The recording of misses and blocks, by contrast, is generally fucked up. The N.J, CHI, ATL and BOS scorers seem very reluctant to record misses. Conversely, the guys in L.A, CAR, DAL and TOR seem overly eager.
The data for blocks reveals a similar story. The scorers in ANA, BOS, FLA and N.J count too few, whereas the scorers in NYI, MTL, EDM, S.J, TOR and WSH count too many.
It's a god damn nightmare.
Fortunately, there is a solution. Recording bias can be more or less controlled for by dividing the observed number of home missed or blocked shots by the appropriate co-efficient (that being the applicable H/R ratio, as displayed in the above table).
Once this correction is made, one can determine whether including missed and blocked shots adds value with respect to predicting future powerplay performance.
The following experiment was performed:
- I randomly selected 40 games from the 2010-11 season
- I calculated each team's PP GF/60, PP SF/60, PP Fenwick/60, PP Corsi/60 over that selected sample
- PP Fenwick/60 = [(powerplay shots + powerplay missed shots)/PP TOI]*60
- PP Corsi/60 = [(powerplay shots + powerplay missed shots + powerplay blocked shots)/PP TOI]*60
- I then selected an independent 40 game sample, and calculated each team's PP GF/60 in respect thereof
- I then looked at how each of the four above variables ( PP GF/60 , PP SF/60, PP Fenwick/60, PP Corsi/60), as calculated over the 1st sample of games, predicted PP GF/60 over the 2nd sample of games
- I repeated this exercise 1000 times
- I then repeated the entire exercise for the 2008-09 and 2009-10 regular seasons
The results:
Just like Gabe Desjardins found, shot production is a better predictor of future powerplay success relative to raw performance (with respect to 40 game sample sizes). And while missed shots have some informational value, blocked shots do not.
Does the same apply to the penalty skill? Interestingly, no.
Unlike with the powerplay, raw performance on the penalty kill (over a 40 game sample) is a superior predictor of future PK performance than is shot prevention. Part of that can be attributed to the fact that penalty skill save percentage is considerably more reliable than powerplay shooting percentage.
Furthermore, including misses and blocks is of no assistance. It seems as though better penalty kills force their opponents to take a greater proportion of missed and blocked shots.
Thanks JLikens.
ReplyDeleteOn the PK: I find it very interesting that GA is a better predictor of future performance than SA. You mention save percentage being more persistent than shooting percentage as an explanation. Do you think this has more to do with goaltender quality or with shot quality?
On the PP: I'm a bit suprised that Fenwick outpaces shots to be honest, and not at all surprised to see that Corsi is a pretty big step back.
On both: In that the game is about goal differential (and in fact, some teams play more aggressively on the PK in order to generate scoring chancnes), was there any reason you decided to go with goals for (PP) and goals against (PK) rather than goal differential?
Good questions, Scott.
ReplyDeleteSkill differences in team save percentage are greater as compared to team shooting percentage, and that's true on both special teams and at even strength.
To illustrate this, here are the talent standard deviations for EV SH%, EV SV%, PP SH% and PK SV%, all at the team level.
EV SH% 0.0028
EV SV% 0.0037
PP SH% 0.0020
PK SV% 0.0076
But as you can see, the talent standard deviation for PK SV% is much larger than for EV SV% - over twice as large, in fact.
The possible implication of this is that team factors (i.e shot quality factors) are more important in respect of PK SV%.
I plan to put up a post on this very subject in the next few weeks.
As for your other question, it was merely an issue of keeping things simple. But you're right in that any comprehensive approach ought not to discount shorthanded scoring, even if it's ultimately not terribly important.
Not that anyone cares, but it has occurred to me that the talent standard deviations that I listed above are wrong. The correct values are:
ReplyDeleteEV SH% 0.0048
EV SV% 0.0055
PP SH% 0.0047
PK SV% 0.0115
It is impressive that you can predict powerplay with strength only but it makes things little more interesting.
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