Apr 052013
 

I often get asked questions about hockey analytics, hockey fancy stats, how to use them, what they mean, etc. and there are plenty of good places to find definitions of various hockey stats but sometimes what is more important than a definition is some guidelines on how to use them. So, with that said, here are several tips that I have for people using advanced hockey stats.

Don’t over value Quality of Competition

I don’t know how often I’ll point out one players poor stats or another players good stats and immediately get the response “Yeah, but he always plays against the opponents best players” or “Yeah, but he doesn’t play against the oppositions best players” but most people that say that kind of thing have no real idea how much quality of opponent will affect the players statistics. The truth is it is not nearly as much as you might think.  Despite some coaches desperately trying to employ line matching techniques the variation in quality of competition metric is dwarfed by variation in quality of teammates, individual talent, and on-ice results. An analysis of Pavel Datsyuk and Valterri Filppula showed that if Filppula had Datsyuk’s quality of competition his CorsiFor% would drop from 51.05% to 50.90% and his GoalsFor% would drop from 55.65% to 55.02%. In the grand scheme of things, this are relatively minor factors.

Don’t over value Zone Stats either

Like quality of competition, many people will use zone starts to justify a players good/poor statistics. The truth is zone starts are not a significant factor either. I have found that the effect of zone starts is largely eliminated after about 10 seconds after a face off and this has been found true by others as well. I account for zone starts in statistics by eliminating the 10 seconds after an offensive or defensive zone face off and I have found doing this has relatively little effect on a players stats. Henrik Sedin is maybe the most extreme case of a player getting primarily offensive zone starts and all those zone starts took him from a 55.2 fenwick% player to a 53.8% fenwick% player when zone starts are factored out. In the most extreme case there is only a 1.5% impact on a players fenwick% and the majority of players are no where close to the zone start bias of Henrik Sedin. For the majority of players you are probably talking something under 0.5% impact on their fenwick%. As for individual stats over the last 3 seasons H. Sedin had 34 goals and 172 points in 5v5 situations and just 2 goals and 14 points came within 10 seconds of a zone face off, or about 5 points a year. If instead of 70% offensive zone face off deployment he had 50% offensive zone face off deployment instead of having 14 points during the 10 second zone face off time he may have had 10.  That’s a 4 point differential over 3 years for a guy who scored 172 points. In simple terms, about 2.3% of H. Sedin’s 5v5 points can be attributed to his offensive zone start bias.

A derivative of this is that if zone starts don’t matter much, a players face off winning percentage probably doesn’t matter much either which is consistent with other studies. It’s a nice skill to have, but not worth a lot either.

Do not ignore Quality of Teammates

I have just told you to pretty much ignore quality of competition and zone starts, what about quality of teammates? Well, to put it simply, do not ignore them. Quality of teammates matters and matters a lot. Sticking with the Vancouver Canucks, lets use Alex Burrows as an example. Burrows mostly plays with the Sedin twins but has played on Kesler’s line a bit too. Over the past 3 seasons he has played about 77.9% of his ice time with H. Sedin and about 12.3% of his ice time with Ryan Kesler and the reminder with Malhotra and others. Burrow’s offensive production is significantly better when playing with H. Sedin as 88.7% of his goals and 87.2% of his points came during the 77.9% ice time he played with H. Sedin. If Burrows played 100% of his ice time with H. Sedin and produced at the same rate he would have scored 6 (9.7%) more goals and 13 (11%) more 5v5 points over the past 3 seasons. This is far more significant than the 2.3% boost H. Sedin saw from all his offensive zone starts and I am not certain my Burrows example is the most extreme example in the NHL. How many more points would an average 3rd line get if they played mostly with H. Sedin instead of the average 3rd liner. Who you play with matters a lot. You can’t look at Tyler Bozak’s decent point totals and conclude he is a decent player without considering he plays a lot with Kessel and Lupul, two very good offensive players.

Opportunity is not talent

Kind of along the same lines as the Quality of Teammates discussion, we must be careful not to confuse opportunity and results. Over the past 2 seasons Corey Perry has the second most goals of any forward in the NHL trailing only Steven Stamkos. That might seem impressive but it is a little less so when you consider Perry also had the 4th most 5v5 minutes during that time and the 11th most 5v4 minutes.  Perry is a good goal scorer but a lot of his goals come from opportunity (ice time) as much as individual talent. Among forwards with at least 1500 minutes of 5v5 ice time the past 2 seasons, Perry ranks just 30th in goals per 60 minutes of ice time. That’s still good, but far less impressive than second only to Steven Stamkos and he is actually well behind teammate Bobby Ryan (6th) in this metric. Perry is a very good player but he benefits more than others by getting a lot of ice time  and PP ice time. Perry’s goal production is a large part talent, but also somewhat opportunity driven and we need to keep this in perspective.

Don’t ignore the percentages (shooting and save)

The percentages matter, particularly shooting percentages. I have shown that players can sustain elevated on-ice shooting percentages and I have shown that players can have an impact on their line mates shooting percentages and Tom Awad has shown that a significant portion of the difference between good players and bad players is finishing ability (shooting percentage).  There is even evidence that goal based metrics (which incorporate the percentages) are a better predictor of post season success than fenwick based metric. What corsi/fenwick metrics have going for them is more reliability over small sample sizes but once you approach a full seasons worth of data that benefit is largely gone and you get more benefit from having the percentages factored into the equation. If you want to get a better understanding of what considering the percentages can do for you, try to do a Malkin vs Gomez comparison or a Crosby vs Tyler Kennedy comparison over the past several years. Gomez and Kennedy actually look like relatively decent comparisons if you just consider shot based metrics, but both are terrible percentage players while Malkin and Crosby are excellent percentage players and it is the percentages that make Malkin and Crosby so special. This is an extreme example but the percentages should not be ignored if you want a true representation of a players abilities.

More is definitely better

One of the reason many people have jumped on the shot attempt/corsi/fenwick band wagon is because they are more frequent events than goals and thus give you more reliable metrics. This is true over small sample sizes but as explained above, the percentages matter too and should not be ignored. Luckily, for most players we have ample data to get past the sample size issues. There is no reason to evaluate a player based on half a seasons data if that player has been in the league for several years. Look at 2, 3, 4 years of data.  Look for trends. Is the player consistently a higher corsi player? Is the player consistently a high shooting percentage player? Is the player improving? Declining? I have shown on numerous occassions that goals are a better predictor of future goal rates than corsi/fenwick starting at about one year of data but multiple years are definitely better. Any conclusion about a players talent level using a single season of data or less (regardless of whether it is corsi or goal based) is subject to a significant level of uncertainty. We have multiple years of data for the majority of players so use it. I even aggregate multiple years into one data set for you on stats.hockeyanalysis.com for you so it isn’t even time consuming. The data is there, use it. More is definitely better.

WOWY’s are where it is at

In my mind WOWY’s are the best tool for advanced player evaluation. WOWY stands for with or without you and looks at how a player performs while on the ice with a team mate and while on the ice without a team mate. What WOWY’s can tell you is whether a particular player is a core player driving team success or a player along for the ride. Players that consistently make their team mates statistics better when they are on the ice with them are the players you want on your team. Anze Kopitar is an example of a player who consistently makes his teammates better. Jack Johnson is an example of a player that does not, particularly when looking at goal based metrics.   Then there are a large number of players that are good players that neither drive your teams success nor hold it back, or as I like to say, complementary players. Ideally you build your team around a core of players like Kopitar that will drive success and fill it in with a group of complementary players and quickly rid yourself of players like Jack Johnson that act as drags on the team.

 

Jan 062011
 

The score of a game influences how a team plays.  When a team is trailing they play a more aggressive offensive game, when they are up a goal or more, they play a more defensive game.  The question I answer today is, how does score influence a teams save percentage.

To answer this question I looked at the past 3 seasons of 5v5 even strength save percentage data when the score is tied, when the team is up by a goal, when the team is up by 2 or more goals, when the team is down a goal and when the team is down by 2 or more goals.  For each team and score category I have a data point for 2007-08, 2008-09, 2009-10 as well as a three year average (2007-10).  For each score category I sorted from lowest to highest save percentage and then plotted them on one chart and got the following:

As you can see, when the game is tied generally produces higher save percentages than when a team is leading or trailing and when a team is trailing their save percentages are at their worst.  This is probably not surprising as a team will open up its game in hopes of creating offense but also puts them at risk defensively.  Now, what that table doesn’t tell us is if all teams experience the same score effects or, for whatever reason, do some teams actually have improved save percentages when trailing or leading.  The following chart shows each teams 3 year save percentage by score ordered from lowest 5v5 game tied save percentage.

The majority of teams have the majority of their leading or trailing save percentages below the game tied save percentages but there are a number of occassions where that doesn’t occur and they are mostly related to up2 or up2+ save percentages.  The only teams that had a down1 or down2+ save percentage above game tied save percentage were:

  1. Dallas – Down1: 92.51% vs Tied: 91.74%
  2. Detroit – Down1: 93.05% vs Tied: 92.16%
  3. Pittsburgh: Down2+: 92.87% vs Tied: 92.78%
  4. Minnesota:  Down2+: 93.21% vs Tied: 92.89%
  5. Florida: Down1: 93.92% vs Tied: 93.23%

On average, teams had their down 1 goal save percentage 1.3% lower than their game tied save percentage and their down 2+ goal save percentage 1.90% lower than their game tied save percentage.  The average team save percentage at 5v5 tied is 92.7% vs 91.4% down a goal, 90.8% down 2+ goals, 92.2% up a goal and 92.1% up 2 goals.  Tailing can have a sizable negative impact on save percentage where as leading can have a minor negative impact.

So what does this mean?  It means we need to be careful when evaluating goalies (and probably shooters to some extent) based on save percentage (special team effects) or even 5v5 even strength save percentage because the game situations a goalie has been exposed to will influence the goalies save percentage.  A goalie on a weak team will have his save percentage lowered simply because his team is going to be trailing more often and be forced to take chances to create offense and thus he will be exposed to tougher shots where as a goalie on a good team who leads the game more than they trail a lot will not face as many tough shots.

One interesting thing I noticed while doing all this was the Toronto Maple Leafs up by a single goal performance over the last 3 seasons.  While they were middle of the pack 5v5 game tied (16th in 3 year 5v5 game tied save percentage), they were downright horrific when they got up a goal.  They just couldn’t hold a lead.  The three worst single season save percentages when up a goal were the 2009-10 Leafs, 2008-09 Leafs, and the 2007-08 Leafs so they were three for three there.  Over the course of the past 3 seasons the Leafs posted an 88.4 save percentage when up a goal which was 3.44 standard deviations from the mean.  Next worse what the Ottawa Senators who were well ahead of them at 90.8, a mere 1.23 standard deviations from the mean.  The good news for Leaf fans is their 5v5 up a goal save percentage is much better this year: 95.6% (better than any team in any of the last 3 seasons), 97.2 for Gustavsson and 93.9% for Giguere so they are much better at maintaining the lead.  Unfortunately this season they can’t score well enough to get them a lead to protect.