Improving Adjusted Scoring and Comparing Scoring of Top Tier Players Across Eras
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04-29-2012, 09:20 PM
Join Date: Mar 2011
Originally Posted by
Czech Your Math
I was referring to your comment that you had done some work on scoring from one season to the next. Was this work also primarily focusing on the effects of schedule?
I didn't study it as thorough as you seem to have done. I didn't really feel/think it was as interesting as other things. But now that you posted this thread, and I got involved in a conversation with you, I have thought more about it. And I think what you're doing is interesting and a good approach. As you have pointed out in the thread, your method automatically accounts for several things, like partly strength of era. If I have the time, energy and focus, I intend to try to repeat your study and experiment with it a bit. I have things like age and nationality in my database. Nationality may be of intrerest if we for example want to study Canadian players in comparison to say European.
Originally Posted by
Czech Your Math
As I said before, I do remember at least some of your post(s) on the effect of schedule on team/individual scoring. I thought at the time that your work was worthwhile and your methodology seemed sound. Thank you for the additional explanation, this only further affirms my previous belief of your work.
Thank you. I think I have found the coding I made, but don't recall what is what so I thought I might redo it. If so, I will try to iterate it the way you suggest.
(With "what is what", I mean that I use many variables and table columns, and GF in one case should be compared to GA in another, etc., and sometimes one should multiply and sometimes divide. I need to sort of sort it out again.)
Being interested in stats, I wish the NHL could have a schedule where each team played each other the same number of times, as is standard in other leagues and in NHL too before it expanded.
Originally Posted by
Czech Your Math
This IMO is the type of effect that, once perfected, should be standardly incorporated into NHL adjusted statistics.
There is definitely a power play effect. This can be seen in many studies, including this one. Just as it seems proper that schedule is a standard adjustment, adjusting team/individual data from even strength vs. special teams scoring data seems like it should someday be standard as well. However, just as in the "assist per goal ratio", there is no standard for what the proper "even strength to special teams" ratio of scoring should be, since it varies over time.
Agree. But, maybe there are ways we can get around that, as at least partly suggested by your method.
As I tried to say yesterday... Traditional adjustment just makes each season equal, no matter what quality level. Each season gets the same number of goals per team to distribute throughout the league. First, we distribute the goals on a team level. Then we distribute them within each team.
The higher gap between the best and worst teams, the more "favoured" (rightfully or not) the players on those team's will be compared to seasons where the gap was smaller.
The higher gap between the best and "worst" players, scoring wise, within a team, the more favoured the best scorers will be.
"Traditional" adjustment of scoring is basically based on comparing teams and players to averages. But the average team of one era/season might be considerably better than the average team of another era. Same with players.
I have done similar things, for example looking at what the top 3 scorers on each team averaged and what different tiers of scorers averaged, both in comparison to league averages.
I found this interesting, but it seems to be much more dependent on other factors which are not easily removed, such as the quality and distribution of talent in the league.
Defensemen sounds like a difficult way to study the strength of season, which may make your results especially unique and interesting. I have thought looking at goalies would be another way to examine strength of season, but would also guess the small number of goalies (esp. in earlier eras) would yield a very small sample and less reliable data
Goalies seem hard. Defenceman may be harder than forwards, yes.
Goaltending obviously influences +/- in a dramatic way. Does adjusted plus-minus factor in goaltending at all? I'm guessing it doesn't, which (if I'm correct about this) would be an instance of making an assumption out of practicality (much more work in an attempt to remove an effect which may be more random than signficant to the results).
Regarding +/-, I'm currently leaning towards separating defencemen and forwards (which isn't black and white either). If we take forwards, they show a consistency in scoring, in that their scoring (except when playing with Mario, Gretzky, etc.) is fairly constistent from season to season. Their GA (goals against) may however change dramatically from season to season, and we also know that goaltending is hugely affecting +/-. So maybe the best thing for forwards would be to treat their "+" as they are, and then goalie adjust their GA and then perhaps even half them.
Because... It is often being said (at least it used to be said) that the "goalie is half the team". So why then "discredit" forwards so much for the goals againsts they are on the ice for? Maybe points scored is a better measure of forward performance than +/- is? I have experimented with formulas giving a point e.g. 1.25 and being on the ice on a goals for without getting a point say 0.7. Or maybe even 1.4 and 0.55. Then we get their offensive value. (Now I should have learnt more about Alan Ryder's methods.)
I haven't looked at even strength win% in some time either. I think I've posted the last thoughts and formulas I had on the matter. It definitely seemed to produce some good results, just not sure the limits of its accuracy. I think the eventual end results, when combined with special teams data, could produce something similar to HR's "point shares".
Yes. I took it further and integrated PP and SH. I was going to post it here, and even started writing about the methodology, etc., but didn't follow it through.
First, I was going to give each team a number of points. (To make it simple one could just look at the standings and take their factual points there. I, however, used another method.) Then one distribute those points among the players on the team.
I like the method. It rewards players for playing and for contributing. +/- doesn't work like that and may actually "punish" a player for playing, compared to a teammate who is benched. The win method also produce results that can be meaningfully divided by games played.
I found the weighted differential in team win% with or without a player in the lineup to be a great metric, because it combined simplicity with direct measurement of what we all agree is the most important hockey value (winning).
The limitation is that for players who don't miss many games, the amount of data without them is very small, so the results are very unreliable.
Yes. I have tried to handle that wisely.
I haven't yet digged deep into your analysis of the three "eras" you mention, but now is the time for others to give their thoughts on your findings. (If I remember right, what you write seem similar to the impressions others here have.)
Thanks again for positive words.
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