## December 18, 2006

### Player Ranking attempt II

I’ve been working on one primary scheme to determine who responsible for the performance. I have chosen a regression on the performance of all team’s players cross products to determine who is responsible for the goals for and against. My last attempt was criticized and for good reason as I choose to use expected goals, which are a poor measure of actual goals to predict offensive ability. A player like Iginla who hasn’t taken many good shots, but has scored a bunch of goals (or had goals scored while on the ice) was completely underrated by my old system and this was noted to me. So I choose instead to use goals (I will continue to use expected goals against as their variability almost perfectly matches expected random variation, with a few exceptions). By using goals, I have chosen to increase my variability three times in order to have more accurate information, this means it’s a lot easier for a player to get randomly into the top groups, so these numbers measure “possible talent” as apposed to actual talent and similarly on the low end.

Last time I only used the team to determine skill level, this time I included strength of opposition. I calculated the same regression as the previous article for offense and defense. Then I multiplied the ice time of every opponent by the score of the opponent and divided by the total number of seconds of match ups to get an average score of the opposition for the given player. (So if you spend all of your 1000 seconds against only Iginla I’ll give you an opposition offensive score of 4.3 [Iginla’s score]), basically I consider the opposition score the expected number of goals against, now if a player is better than average they should be able to lower the number (have fewer goals against than average). So I calculate the players score for goals for and against and subtract opposition’s defensive score from their goals for per hour and opposition offensive score from defensive, then I just subtract their “plus” (offensive score) from their “minus” (defensive score):
(Even strength G/hr – Even strength opposition GA/hr)
- (Even strength GA/hr – Even strength opposition G/hr)
= VAL
This means if you score a lot of goals against an opposition that allows a lot of goals your score won’t improve, but if you have very few goals against a very tough opposition your score won’t be hurt either. Now differences in strength of opposition are not that different, but they are different, so the players performance scores are much more important than their opposition’s score, however opposition scores help remove the team effects so these scores should be comparable across teams and they are not effected by line mates significantly so a player being moved from one team to another should get the same score (plus/minus coaching effects) theoretically speaking of course. These scores are not a definitive measure of talent. Now using a scoring rate I multiply by the amount of time a player is being played to calculate their value to the team. A player who gets 20 minutes of even strength time is arguably twice as valuable as an identical player getting only 10 minutes. Due to the ice time multiplier defensemen appear on top more frequently than forwards.

What does VAL measure?
The units for VAL are goals per game, where goals is the expected goal differential for that individual, which doesn’t necessarily measure true value as a lower goal differential is acceptable for a player with a fewer events so defensive players will be undervalued. If all the players were identical and goal tending is average this should be the plus minus for that player every game, so 82*VAL = plus minus in an ideal system (all players on team identical with average goal tending). So a score of 0.33 would work out so +27, considering the best player last season as +35 this is probably a sort of limit value of this statistic (scores about 0.5 probably don’t mean much other than random error). Of course these scores are for individuals not lines so one player could easily do much better than his line (Selanne). For these metrics (even strength stats) I like to use Malik as he is a consistent plus player and former Canuck (+96 in over last 4 seasons). Malik scores an excellent 0.48 in my system for +40. I'm posting the results for the Northwest division on this site as it's what I'm familiar with, however the complete list can be found on my statistics website. I have also adapted my SH and PP scores on the site, I'm not 100% satisfied with the results yet for the however feel free to comment on them as well.
Vancouver
 I L VAL 1 D SEDIN 0.28 2 H SEDIN 0.26 3 A EDLER 0.25 4 M NASLUND 0.23 5 K BIEKSA 0.17 6 M OHLUND 0.14 7 J GREEN -0.01 8 B MORRISON -0.02 9 T LINDEN -0.05 10 A BURROWS -0.06 11 R FITZPATRICK -0.08 12 R KESLER -0.09 13 S SALO -0.09 14 J BULIS -0.11 15 M CHOUINARD -0.16 16 T PYATT -0.19 17 L KRAJICEK -0.20 18 M COOKE -0.31 19 W MITCHELL -0.42

Minnesota
 N I L VAL 1 K CARNEY 0.18 2 M GABORIK 0.14 3 S VEILLEUX 0.09 4 W WALZ 0.05 5 M PARRISH 0.04 6 B BURNS 0.00 7 B ROLSTON -0.01 8 M SKOULA -0.03 9 K JOHNSSON -0.09 10 T WHITE -0.12 11 B RADIVOJEVIC -0.12 12 P BOUCHARD -0.12 13 M KOIVU -0.24 14 W SMITH -0.29 15 P DUPUIS -0.31 16 N SCHULTZ -0.34 17 K FOSTER -0.42 18 P DEMITRA -0.45 19 P NUMMELIN -0.65

Edmonton
 N I L VAL 1 R TORRES 0.74 2 J STOLL 0.39 3 M BERGERON 0.37 4 F PISANI 0.34 5 D TJARNQVIST 0.23 6 P SYKORA 0.23 7 M GREENE 0.14 8 B WINCHESTER 0.11 9 A HEMSKY 0.11 10 P THORESEN 0.08 11 J LUPUL 0.06 12 J SMITH 0.04 13 S STAIOS 0.03 14 M REASONER 0.02 15 R SMYTH 0.01 16 L SMID -0.06 17 S HORCOFF -0.15 18 M POULIOT -0.18 19 T PETERSEN -0.28 20 J JACQUES -0.37 21 J HEJDA -0.61

 N I L VAL 1 K KLEE 0.62 2 J SAKIC 0.34 3 M SVATOS 0.32 4 T ARNASON 0.30 5 O VAANANEN 0.29 6 J LILES 0.28 7 W WOLSKI 0.19 8 A BRUNETTE 0.19 9 M HEJDUK 0.19 10 B CLARK 0.12 11 B MCLEAN 0.12 12 P BRISEBOIS 0.03 13 B RICHARDSON -0.03 14 P STASTNY -0.03 15 M RYCROFT -0.03 16 A LAAKSONEN -0.05 17 I LAPERRIERE -0.09 18 K SKRASTINS -0.21

Calgary
 N I L VAL 1 R WARRENER 0.64 2 M LOMBARDI 0.38 3 A TANGUAY 0.37 4 D PHANEUF 0.36 5 J IGINLA 0.36 6 D LANGKOW 0.28 7 R REGEHR 0.27 8 R HAMRLIK 0.16 9 M GIORDANO 0.14 10 K HUSELIUS 0.13 11 M NILSON 0.02 12 A FERENCE -0.07 13 C KOBASEW -0.12 14 J LUNDMARK -0.12 15 B RITCHIE -0.13 16 D MCCARTY -0.18 17 T AMONTE -0.25 18 J FRIESEN -0.26 19 A ZYUZIN -0.70

Remember these stats are for even strength and not power play and other places, some great players perform amazing on the power play, but are less than impressive at even strength. I would argue these stats emphasize offense over defense, so it likely undervalues defense.

David Johnson said...

Keith Carney the best Minnesota Wild player? Demitra the second worst?

Ken Klee the best Avalanche player?

Rhett Warrener the best Flame?

Hall Gill the top Leaf?

Niclas Wallin the top (regular) Hurricane?

Metropolit the best Thrasher?

Luke Richardson the 3rd best Lightning player?

All while you say it is biased towards offense? Something isn't quite right.

That said, there does appear to be some good stuff here and I do see some consistencies with my algorithm. I did notice that Martin Gelinas ranked relatively well (relative to his teammates anyway).

JavaGeek said...

It's funny those are the same players I noticed. And I don't think they'll stay in the top though...

First off the EV+/hr numbers are off by 1.8 EV/hr for the 10 minute players and 1.3 EV/hr for the 20 minute players. I think I'll regress the players input to the mean and see what that does.

You'll notice that many of the top scores have the best EV+/hr rates on the team (+4). Generally speaking a +4 rating is not sustainable... I'll see what I can do about that.

I appreciate the input.