This came up in a twitter conversation today and since I will be referencing both of these as part of my RIT Hockey Analytics Conference talk it might be a good idea to re-introduce them to anyone who are not familiar with them.
At their core, Rel and RelTM stats both attempt to account for the quality of teammates a player plays with. The problem with Corsi%, as Paul Bissonette points out, is that it is heavily driven by the players one plays with.
Ultimately, teams don’t draft based on Corsi or possession numbers. You draft a player because you’ve watched how they perform on the ice and then consider their potential to improve.
But somehow they’re starting to use this bullshit in contract negotiations. You have teams saying, “Oh, wow, look at this player in Chicago who had a 60 percent possession number.” Well, yeah, because he’s an average player playing with Toews and Kane. So all of the sudden a team signs him for $3 million a year even though he’s a $1.5 million a year player, and they’re shocked when his possession isn’t as good. Are you kidding me?
This is a flaw I think everyone agrees with which is why Rel and RelTM were developed. A player playing with all-star players will naturally put up (or at least have an opportunity to put up) far better numbers than players playing with mediocre 3rd and 4th line players. A player on the Blackhawks will have an opportunity to put up far better numbers than a player on the Sabres. The Blackhawks had a team Corsi% of 53.6% last year compared to the Sabres 37.5%. Rel and RelTM stats attempt to factor out the team aspect and isolate the individual contribution.
What is Rel?
Rel (or Relative) stats are calculated by taking the players “on-ice” stats (or what the team does when they player is on the ice) and comparing them to the players “off-ice” stats (or what the team does when they are not on the ice). So, CF% Rel is nothing more than on-ice CF% minus off-ice CF%. The resulting metric is an attempt to look at how the player is performing relative to the rest of the team.
The flaw with Rel stats is that it still doesn’t account for the player who plays with Jonathan Toews and Marian Hossa. The third guy on that line might not be a very good player but will look good because Toews and Hossa drive the play and boost his stats. Rel stats also fail when it comes to teams that are well balanced with no superstars but 3 very good lines where as a player on the first line of a very top heavy team will probably look better than they really should.
I’d say that Rel stats do a decent job of factoring out team-level system driven results but do not do a great job of isolating the players performance from his line mates.
What is RelTM?
RelTM (Relative to Team Mates) attempts to account for the 3rd guy on the Toews/Hossa line getting unfairly credited for playing with a pair of superstars. It does this by looking at how each of the players teammates perform with and apart from him and to what extent. So while Rel is a team level on-ice minus off-ice stat, RelTM is a player level with minus apart stat. Some people have referred to this as a “Combined WOWY” (combined with or without you) stat. It can be a little difficult to wrap your head around but it looks at Toews’ (and Hossa’s and every other teammate) performance apart from the 3rd guy on the Toews/Hossa line and Toews’ performance with him and attempts to determine if Toews performed better or worse with him than apart from him. This gets done for every player and in essence RelTM tries to figure out how much on average he boosts his team mates statistics. The more team mates that have better statistics with him than apart from him the better.
While on some level this sounds like an improved statistic than Rel since it is directly looking at how much better the guys he actually plays with are with him than apart it isn’t perfect either. The most significant reason is probably that if you are the 3rd guy on a Toews/Hossa line it will be very difficult to boost the stats Toews/Hossa because they are so good. Another flaw is that often the result is that you end up just being compared to the next most important player at your position. When the regular first line RW isn’t playing with the regular first line C and LW it most likely will be the second line RW that is playing with them. So the RelTM stats to some extent tell you how much better/worse are you than the next best/worst player at your position on your team.
Which one of Rel and RelTM is better? I am a bit biased but I personally like how RelTM stats are calculated as I think looking at direct impact on the players teammates is a better route to go. With that said, there is some evidence that Rel is a little more persistent from year to year which may make it more useful. I’ll have more to say about this at the RIT Hockey Analytics conference but mostly I’ll show that both Rel and RelTM stats are almost certainly vastly superior to the raw stat counter part (i.e. CF% Rel and CF% RelTM are vastly superior to CF%). In different ways both attempt to and are relatively successful at factoring out quality of team and quality of teammates. Rel and RelTM stats are generally fairly highly correlated as well so there likely isn’t a huge difference between them. Furthermore, the more a team juggles its line up the better both of these stats, particularly the RelTM stat, will correctly isolate individual contribution and talent.
(if there is any confusion on these stats, feel free to ask questions in the comments. I’ll be happy to clarify and/or provide more details.)
Finally, if you have a chance to make it to Rochester, NY on October 10th consider signing up for and attending the RIT Hockey Analytics Conference. It looks like one of the best line ups of speakers these conferences have ever had with a wide variety of topics.