May 112014
 

I often feel that I am the sole defender of goal based hockey analyitics in a world dominated by shot attempt (corsi) based analytics. In recent weeks I have often heard the pro-corsi crowd cite example after example of where corsi-based analytics “got it right” or “predicted something fairly well”. While it is always good to be able to cite examples where you got things right a fair an honest evaluation looks at the complete picture, not just the good outcomes. Otherwise it is analytics by anecdotes which is an oxymoron if there every was one.

For example, Kent Wilson of FlamesNation.ca recent wrote about the “Dawning of the Age of Fancy Stats” in which he cited several instances of where hockey analytics got it right or did well in predicting outcomes.

The big test case which seems to have moved the needle in favour of the nerds is, of course, the Toronto Maple Leafs. Toronto came into the season with inflated expectations after an outburst of percentages during the lock-out shortened year saw them break into the post-season. Their awful underlying numbers caused the stats oriented amongst us to be far more circumspect about their chances, of course.

Toronto is the recent example that the hockey analytics crowd likes to bring up in support of their case but it is just one example. We don’t hear much about how many predicted the Ottawa Senators to be in the playoffs and some even had them challenging for the top spot in the eastern conference. We don’t hear much about how the New Jersey Devils missed the playoffs yet again despite having the 5th best 5v5close Fenwick% in the league, the year after missing the playoffs with the 3rd best 5v5close Fenwick% in the league. If we are truly interested in hockey analytics we need a complete and unbiased assessment of all outcomes, not just the ones that support our underlying belief.

In the same article Kent Wilson quoted a tweet from Dimitri Filipovic about the success of Corsi in predicting outcomes of playoff series.

Relevant #fact: since ’08 playoffs, teams that were 5+ % better than their opponent in 5v5 fenwick close during the regular season are 25-7.

While interesting, what it really doesn’t tell us a whole lot more than “when one team is significantly better at outshooting their opponents they more often than not win”. Well, that really isn’t saying a whole lot. It is more or less saying, when a dominant team plays a mediocre team, the dominant team usually wins. Not really that interesting when you think of it that way.

Here is another fact that puts that into perspective. Since the 2008 playoffs, the team with the better 5v5close Fenwick% has a 53-35-2 record (there were 2 cases where teams had identical fenwick% to 1 decimal place). That actually makes it sound like 5v5close Fenwick% is predictive overall, not just in cases where one team is significantly better than another. Of course, if we look at goals we find that the team with the better 5v5close goal% has a 54-34-1 record. In other words, 5v5close possession stats did no better at predicting playoff outcomes than 5v5close goal stats. It is easy to throw out stats that support a point of view, but it is far more important to look at the complete picture. That is what analytics is about.

A similar statistic was promoted by Michael Parkatti in a recent talk on hockey analytics at the University of Alberta. In that talk Parkatti stated that of the last 15 Stanley Cup winners all but 3 had a “ShotShare” (all situations) of at least 53%. The exceptions were Pittsburgh in 2009, Boston in 2011 and Carolina in 2006. I will note that it appears that all three of these teams are below 51% and 2009 Penguins were below 50%. That seems sort of impressive but I did some digging myself and found that every Stanley Cup winner since 1980 had a “GoalShare” (all situations) greater than 52%. Every single one. No exceptions. I didn’t look at any cup winners pre-1980 but the trend may very well go back a lot further. As impressive as 12 of 15 is, 34 of 34 is far more impressive.

Here is the thing. We know that goal percentage correlates with winning far better than corsi percentage. This is an indisputable fact. It is actually quite a bit better. The sole reason we use corsi is that goals are infrequent events and thus not necessarily indicative of true talent due to small sample size issues. This is a fair argument and one that I accept. In situations where you have small sample sizes definitely use corsi as your predictive metric (but understand its limitations). The question that needs to be answered is what constitutes a small sample size and more importantly what sample size do we need such that goals become as good or better of a predictor of future events than corsi. I have pegged this crossing point at about 1 seasons worth of data, maybe a bit more if looking at individual players who may not be getting 20 minutes of ice time a game (my guess is around >750 minutes of ice time is where I’d start to get more comfortable using goal data than corsi data). I am certain not everyone agrees but I haven’t see a lot of analyses attempting to find this “crossing point”.

Let’s take another look at how well 5v5close Fenwick% and Goal% predict playoff outcomes again but lets look by season rather than overall.

FF% GF%
2008 7-7-1 6-9
2009 9-6 11-4
2010 9-6 11-4
2011 10-5 11-4
2012 7-7-1 7-7-1
2013 11-4 8-7
Total 53-35-2 54-35-1

In full seasons not affected by lockouts we find that GF% was generally the better predictor (only 2008 did GF% under perform FF%) but in last years lockout shortened season FF% significantly outperformed GF%. Was this a coincidence or is it evidence that 48 games is not a large enough sample size to rely on GF% more than CF% but 82 games probably is?

I have seen numerous other examples in recent weeks where “analytics” supporters have used what amounts to not much more than anecdotal evidence to support their claims. This is not analytics. Analytics is a fair, unbiased and complete fact based assessment of reality. Showing why a technique is a good predictor some of the time is not enough. You need to show why it is overall a better predictor all of the time or at least define when it is and when it isn’t.

I recently wrote an article on whether last years statistics predicted this years playoff teams and found that GF% seemed to do at least as well as CF% despite last season being a lock-out shortened year.

With all that said, you will frequently find me using “possession” statistics so I certainly don’t think they are useless. It is just my opinion that puck possession is just one aspect of the game and puck possession analytics has largely been oversold when it comes to how useful it is as a predictor. Conversely goal based analytics has been largely given a bad rap which I find a little unfortunate.

(Another article worth reading is Matt Rudnitsky’s MONEYPUCK: Why Most People Need To Shut Up About ‘Advanced Stats’ In The NHL.)

 

Apr 292014
 

It seems every time a new hockey person gets hired these days they will get asked “do you believe in hockey analytics?” It started with Trevor Linden in Vancouver. Then Brendan Shanahan in Toronto. And today Brad Treliving in Calgary. Nichols on hockey has a good rundown on both Treliving’s and Burke’s response to the question today so go give it a read.

As we all know, Brian Burke is an analytics skeptic to say the least. A popular Brian Burke quote is the following:

“Let’s get the record straight on that too. The first analytics systems I see that’ll help us win, I’ll buy it. I’ll pay cash so that no one else can use it. I’m not a dinosaur on that.”

What Burke gets wrong here is that analytics is not a “system” you can buy but rather it is a thought process and a way of doing business. Walmart is famous for using analytics to maximize the profits of their retail operation by knowing their customers buying habits, knowing what their customers will buy, how much they will buy and when they will buy it based on everything from the weather to the economy. Analytics is a huge part of their success. That said, there is no analytics “system” that another retailer can purchase off the shelf that will allow them to do the same. It isn’t a system that makes Walmart so successful it is the way they use analytics to operate their business that permeates the entire operation that makes them successful. Every retail operation has a different customer base. Every retail situation has a different set of products they sell. Every retail situation has a different cost structure. There is no single “system” that can be applied that will guarantee retail success. That doesn’t mean that every retail operation can’t benefit from analytics because analytics is a way of doing business. It is the mindset of wanting to know as much as you can and applying unbiased analyticical techniques to that knowledge to drive decision making. It is the mindset of wanting to know as much about your customers buying habits as you possibly can. It is the mindset of wanting to know what your customers will want to buy and when and why. It is a mindset of knowing how many employees you need on staff at a given time to maximize sales and profits. It is about wanting to know how long a line up customers will tolerate before the leave and make a purchase elsewhere. Analytics is a way of thinking that permeates throughout your organization, it is not a “system” that you can buy and apply.

I don’t know the extent that NHL teams are using hockey analytics but I get the feeling that there are very few that are doing so in a real serious way. Being a highly analytical person I may be biased but to me an NHL team that truly adopts hockey analytics would see the idea of analytics permeate throughout the organization. Analytics should be an important driver of coaching tactics and decisions. It should be an important driver of scouting and player evaluation. It should be an important driver of team building. It should be an important driver of maximizing salary cap commitments. It also should not be one-directional as I firmly believe hockey analytics can benefit significantly from the hockey knowledge of players, coaches, general managers and scouts to improve and test analytical techniques. I have my doubts that there are many NHL organizations that have truly adopted hockey analytics when defined in that way. Some may be dabbling, few are truly adopting.

Interestingly though, I suspect there isn’t one NHL organization that doesn’t use analytics in a significant way on the business side of the organization to do everything from setting ticket, beer and hot dog prices, to setting advertising rates to evaluating their sales staff effectiveness. I am certain analytics permeates through the business side of an NHL organization in a significant way so it is kind of surprising there is any resistance to it on the hockey side.

 

Apr 122014
 

As of last night games all 16 playoff teams have been determined. Before I get into any playoff predictions, lets take a look at how last seasons 5v5 close statistics do at predicting who would make the playoffs this season.

CFPctGFPctPlayoffPredictor

The above table shows last years 5v5close GF% and CF% and the teams in red are this years playoff teams.

There were 15 teams with a CF% above 50% last year, 9 of them made the playoffs this year while 6 missed. Of the remaining 15 teams that had sub 50% CF% last year, 7 of them made the playoffs this year while 8 missed. Seven of the top 10 CF% teams last year made the playoffs while 5 of the bottom 10 teams made the playoffs and 5 missed.

There were 18 teams last year with at least 50% GF% and 11 made the playoffs this season while 7 missed. Of the 12 teams that failed to reach 50% GF% last season, 5 made the playoffs and 11 missed. Seven of the top 10 GF% teams made the playoffs last season while 7 of the bottom 10 missed the playoffs.

Difficult to say one was significantly better than the other. Truth is, neither was particularly good but with 7 of the bottom 9 GF% teams last year missing the playoffs this year that might be enough to give GF% a slight edge. That said, the better predictor might have been last seasons point totals.

PtTotalsPlayoffPredictor

 

Apr 082014
 

The past few weeks while I have been shifting my website from one web host to another in an attempt to fight off the DDoS attacks I started thinking about how big my stats.hockeyanalysis.com database actually is. I was thinking about it because of how long it takes to upload the data to a new web host and how long it takes to set up the database again.

So, how many data points do I have in my database?  A lot. A data point is any single piece of data like the Leafs 2008-09 CF% or Jarome Iginla’s 2007-13 (6yr) individual Goals/60 or Jack Johnson’s CF% while playing with Drew Doughty during the 2008-09 season. Each of those is a single data point.

Here is a summary of all the data point totals by table type.

Database Table Type Total Records Datapoints/record Total Data points
Individual+OnIce Stats 595726 123 73274298
WOWY 3983667 54 215118018
“Against You” 10856454 38 412545252
Team Data 660 28 18480
Total 700956048

So yes, there are just over 700 million data points in my database not including things like player names, player positions, players team, etc. Once I add in all the multi-year data that includes this current season I estimate there will be over 900 million datapoints.

The majority, though not all (I’d estimate 70-80%), of these data points are accessible to you if you conduct the right searches. Which one of you is going to be the first to count them all?

Now, if I actually uploaded all the data I can generate (specifically WOWY and Against You data when players have played fewer than 5 minutes with/against each other) the number of data points would rise dramatically, probably several billion data points. This is why I don’t upload that data.

 

Apr 012014
 

Last week Tyler Dellow had a post titled “Two Graphs and 480 Words That Will Convince You On Corsi%” in which, you can say, I was less than convinced (read the comments). This post is my rebuttal that will attempt to convince you on the importance of Sh% in player evaluation.

The problem with shooting percentage is that it suffers from small sample size issues. Over small sample sizes it often gets dominated by randomness (I prefer the term randomness to luck) but the question I have always had is, if we remove randomness from the equation, how important of a skill is shooting percentage? To attempt to answer this I will look at the variance in on-ice shooting percentages among forwards as we increase the sample size from a single season (minimum 500 minutes ice time) to 6 seasons (minimum 3000 minutes ice time). As the sample size increases we would expect the variance due to randomness to decrease. This means, when the observed variance stops decreasing (or significantly slows the rate of decrease) as sample size increases we know we are approaching the point where any variance is actually variance in true talent and not small sample size randomness. So, without going on any further I present you my first chart of on-ice shooting percentages for forwards in 5v5 situations.

 

ShPctVarianceBySampleSize

Variance decline pretty much stops by the time you reach 5 years/2500+ minutes worth of data but after 3 years (1500+ minutes) the drop off rate falls off significantly. It is also worth noting that some of the drop off over longer periods of time is due to age progression/regression and not due to reduction in randomness.

What is the significance of all of this?  Well, at 5 years a 90th percentile player would have 45% more goals given an equal number of shots as a 10th percentile player. A player one standard deviation above average will have 33% more goals for given an equal number of shots as a player one standard deviation below average.

Now, let’s compare this to the same chart for CF/20 to get an idea of how shot generation varies across players.

 

CF20VarianceBySampleSize

It’s a little interesting that the top players show no regression over time but the bottom line players do. This may be because terrible shot generating players don’t stick around long enough. More importantly though is the magnitude of the difference between the top players and the bottom players.  Well, a 90th percentile CF20 player produces about 25% more shots attempts than a 10th percentile player and a one standard deviation above average CF20 player produces about 18.5% more than a one standard deviation below average CF20 player (over 5 years). Both of these are well below (almost half of) the 45% and 33% we saw for shooting percentage.

I hear a lot of ‘I told you so’ from the pro-corsi crowd in regards to the Leafs and their losing streak and yes, their percentages have regress this season but I think it is worth noting that the Leafs are still an example of a team where CF% is not a good indicator of performance. The Leafs 5v5close CF% is 42.5% but their 5v5close GF% is 47.6%. The idea that CF% and GF% are “tightly intertwined” as Tyler Dellow wrote is not supported by the Maple Leafs this season despite the fact that the Maple Leafs are the latest “pro-Corsi” crowds favourite “I told you so” team.

There is also some evidence that the Leafs have been “unlucky” this year. Their 5v5close shooting percentages over the past 3 seasons have been 8.82 (2nd), 8.59(4th), 10.54(1st) while this year it has dropped to 8.17 (8th). Now the question is how much of that is luck and how much is the loss of Grabovski and MacArthur and the addition of Clarkson (who is a generally poor on-ice Sh% player) but the Leafs Sh% is well below the past few seasons and some of that may be bad luck (and notably, not “regression” from years of “good luck”).

In summary, generating shots matter, but capitalizing on them matters as much or more.

 

Mar 122014
 

I know I am in a bit of a minority but it is my opinion that one of the greatest failings of hockey analytics thus far is overstating the importance of Corsi at both the team and (especially) the individual level.

In a post yesterday about Luke Gazdic Tyler Dellow of mc79hockey.com wrote:

We care about Corsi% because it predicts future goals for/against better than just using goals for/against.

The problem is, this is only partly true and is missing an important qualifier at the end of the sentence. It should read:

We care about Corsi% because it predicts future goals for/against better than just using goals for/against when sample sizes are not sufficiently large.

We can debate what ‘sufficiently large’ sample sizes are but at the team level I’d suggest that it is something less than a full seasons worth of data and at the player level is probably between 500 and  750 minutes of ice time depending on shot rates based on some past research I have done.

In a post on the limits of Corsi at Arctic Ice Hockey Garret Hohl writes:

Winning in puck possession and scoring chances is important and will lead to wins but does not encompass the full game. The largest factors outside of possession and chances are luck (ie: bounces), special teams, and combination of goaltending and shot quality (probably in that order).

The problem with that paragraph is that there is no context of sample size. Sample size means everything when writing a sentence like that. If the sample was 3 games played by a particular team luck is quite probably the most important factor in determining how many of those 3 games the team wins. If the sample is 300 games luck is mostly irrelevant. Without considering sample size, there is no way of knowing what the ‘luck factor’ truly is. Furthermore, luck will mostly impact goaltending (save percentage) and shot quality (shooting percentage) so while goaltending and shooting talent can have minimal impact on winning over small sample sizes, it can’t be known what impact they have over the long haul without looking at larger sample sizes. Far too many conclusions about shot quality and goaltending have been made by looking at too small of sample sizes and far too few people have attempted to actually quantify the importance of shooting talent at the team level. As a result, far too often I hear statements like ‘Team X’s shooting percentage is unsustainable” when in reality it actually is.

Below is a chart of the top 5 and bottom 5 teams in terms of 5v5close shooting percentage over the 5 years from the 2007-08 season to 2011-12 season along with their shooting percentages from last year and this year through Saturday games.

2007-12 2012-13 2013-14
Pittsburgh 8.45 10.12 8.28
Philadelphia 8.30 8.96 8.14
Tampa 8.29 7.68 7.40
Edmonton 8.17 7.79 9.01
Toronto 8.16 10.52 8.05
Top 5 Avg 8.27 9.01 8.18
Bottom 5 Avg 7.14 6.51 6.68
NY Islanders 7.23 8.14 7.38
San Jose 7.19 6.59 7.23
New Jersey 7.14 6.35 6.65
Ny Rangers 7.11 5.99 6.11
Florida 7.05 5.49 6.05

What you will see is that the top 5 teams had an average 5-year shooting percentage 1.13% points higher than the bottom 5 teams. This is not insignificant either. It means that the top 5 teams will score almost 16% more goals than the bottom 5 teams just based on differences in their shooting percentage. If one looks at 5 year CF/60 you will find the top 5 teams are just over 17% higher than the bottom 5 teams so over a 5 year span. Thus, there is very little difference in the variation in shooting percentage and variation in corsi rates at the 5 year level.

Now, are shooting percentages sustainable?  Well, in the 2 seasons since, one lock out shortened and one not yet complete, the top 5 5-year teams have actually, on average, improved while the bottom 5 teams have, on average, gotten worse. Aside from the 2012-13 NY Islanders all the other bottom 5 teams remained well below average and nowhere close to any of the top 5 teams. There is no observable regression occurring here.

Based on these observations, one can conclude that when it comes to scoring goals at the team level shooting percentages is pretty close to being equally important as shot generation. I won’t show it here, but if one did a similar study at the player ‘on-ice’ level you will find the difference in the best shooting percentage players and worst shooting percentage players are significantly more important than the difference in shot generation.

I don’t quite know why hockey analytics got this all wrong and has largely not yet come around to the importance of shot quality (it is slowly moving, but not there yet) as there have been some good posts showing the importance of shot quality but they largely get ignored out by the masses. Part of the problem is certainly that some of the early studies in shot quality just looked at too small a sample size. Another reason is that 2009-10 seems to be a real strange year for shooting percentages at the team level. Toronto, Edmonton and Philadelphia (top 5 teams from above) ranked 25th, 23rd and 20th in shooting percentage while San Jose, NY Islanders and New Jersey (bottom 5 teams from above) ranked 6th, 10th, and 13th. These were anomalies for all those teams so any year over year studies that used 2009-10 probably resulted in atypical results and less valid conclusions. Finally, I think part of the problem is that analytics have followed the lead of a few very vocal people and dismissed some other important but less vocal voices.  Regardless of how we got here for hockey analytics to move forward we need to move past the notion that shot-based metrics are more important than goal based metrics.

Shot-based metrics are OK to use only when we don’t have a very large sample size. The thing is, this isn’t true for most players/teams. The majority of NHL players have played multiple seasons in the NHL and teams have a history of data we can look at. We can look at multiple years of data to see how sustainable a particular teams or players percentages are.  It isn’t that difficult to do and will tell us far more about the player than looking at his CF% this season.

When I am asked to look at a player that I am not particularly knowledgeable on, the first thing I typically do is open up my WOWY pages for that player at stats.hockeyanalysis.com, especially the graphs that will quickly give me an indication of how the player performs relative to his team mates. I’ll maybe look at a multi-year WOWY first, and then look at several single-year WOWY’s to see if there are any trends I can spot. I’ll primarily look at GF% WOWY’s but will consider CF% WOWY’s to and maybe even GF20/GA20/CF20/CA20 WOWY’s. I look for trends over time, not how the player did during any particular year. This is because the percentages can matter a lot for some players and it is important to know what players can post good percentages consistently from year to year. I then may look at that players individual numbers such as GF/60, Pts/60, Assists/60 as well as IPP, IGP and IAP to determine how involved they were in the offense while they were on the ice (and I’ll do this looking at several seasons, and multiple seasons combined). Then I’ll take a look at his line mates, quality of competition, and usage (zone starts, PP/PK ice time, etc.). Only then will I start to feel comfortable drawing any kind of conclusions about the player.

As I recently wrote and article suggesting hockey analytics is hard and the above explains why. There is no single stat we can look at to find an answer. A goal-based analysis has flaws. A corsi-based analysis has flaws. Looking at just a single season has flaws. Looking at multiple seasons has flaws. There are score effects and quality of teammates and quality of opponents and zone starts that we need to consider not to mention sample sizes. Coaching/style of play is another area where hockey analytics has barely touched and yet it probably has a significant impact on statistics and results (maybe especially significant on corsi statistics). Hockey Analytics is hard and corsi doesn’t have all the answers so it is important not to reduce hockey analytics to looking up some corsi stats and drawing conclusions. I fear that hockey analytics has over-hyped the importance of corsi at the expense of other important factors and that is unfortunate.

 

Feb 092014
 

There is a recently posted article on BroadStreetHockey.com discussing overused and overrated statistics. The first statistic on that list is Plus/Minus. Plus/minus has its flaws and gets wildly misused at times but it doesn’t mean it is a useless statistics if used correctly so I want to defend it a little but also put it in the same context as corsi.

The rational given in the BroadStreetHockey.com article for plus/minus being a bad statisitcs is that the top of the plus/minus listing is dominated by a few teams. They list the top 10 players in +/- this season and conclude:

Now there are some good players on the list for sure, but look a little bit closer at the names on the list. The top-ten players come from a total of five teams. The top eight all come from three teams. Could it perhaps be more likely that plus/minus is more of a reflection of a team’s success than specific individuals?

Now that is a fair comment but let me present you the following table of CF% leaders as of a few days ago.

Player Name Team CF%
MUZZIN, JAKE Los_Angeles 0.614
WILLIAMS, JUSTIN Los_Angeles 0.611
KOPITAR, ANZE Los_Angeles 0.611
ERIKSSON, LOUI Boston 0.606
BERGERON, PATRICE Boston 0.605
TOFFOLI, TYLER Los_Angeles 0.595
TOEWS, JONATHAN Chicago 0.592
THORNTON, JOE San_Jose 0.591
MARCHAND, BRAD Boston 0.591
ROZSIVAL, MICHAL Chicago 0.590
TARASENKO, VLADIMIR St.Louis 0.589
KING, DWIGHT Los_Angeles 0.589
BROWN, DUSTIN Los_Angeles 0.586
DOUGHTY, DREW Los_Angeles 0.584
BURNS, BRENT San_Jose 0.583
BICKELL, BRYAN Chicago 0.582
HOSSA, MARIAN Chicago 0.581
KOIVU, MIKKO Minnesota 0.580
SAAD, BRANDON Chicago 0.579
SHARP, PATRICK Chicago 0.578
SHAW, ANDREW Chicago 0.578
SEABROOK, BRENT Chicago 0.576

Of the top 22 players, 8 are from Chicago and 7 are from Los Angeles. Do the Blackhawks and Kings have 68% of the top 22 players in the NHL? If we are tossing +/- aside because it is “more of a reflection of a team’s success than specific individuals” then we should be tossing aside Corsi as well, shouldn’t we?

The problem is not that the top of the +/- list is dominated by a few teams it is that people misinterpret what it means and don’t consider the context surrounding a players +/-. No matter what statistic we use we must consider context such as quality of team, ice time, etc. Plus/minus is  no different in that regard.

There are legitimate criticisms of +/- that are unique to +/- but in general I think a lot of the criticisms and subsequent dismissals of +/- having any value whatsoever are largely unfounded. It isn’t that plus/minus is over rated or over used it is that it is often misued and misinterpreted and to be honest I see this happen just as much with Corsi and the majority of other “advanced” statistics as well. It isn’t the statistic that is the problem, it is the user of the statistic. That, unfortunately, will never change but that shouldn’t stop us who know how to use these statistics properly from using them to advance our knowledge of hockey. So please, can we stop dismissing plus/minus (and other stats) as a valueless statistics just because a bunch of people frequently misuse it.

The truth is there are zero (yes, zero) statistics in hockey that can’t and aren’t regularly misused and used without contextualizing. That goes from everything from goals and point totals to corsi to whatever zone start or quality of competition metric you like. They are all prone to be misused and misinterpreted and more often than not are. It is not because the statistics themselves are inherently flawed or useless its because hockey analytics is hard and we are a long long way from fully understanding all the dynamics at play. Some people are just more willing to dig deeper than others. That will never change.

 

(Note: This isn’t intended to be a critique of the Broad Street Hockey article because the gist of the article is true. The premise of the article is really about statistics needing context and I agree with this 100%. I just wish it wasn’t limited to stats like plus/minus, turnovers, blocked shots, etc. because advanced statistics are just as likely to be misused.)

 

Oct 012013
 

It appears that Phil Kessel’s is on the verge of signing an 8 year, $8M/yr contract with the Leafs so this is a good time to compare this contract to a couple other elite wingers who have signed contracts in the past year or so. Corey Perry and Zach Parise. I have also chosen to include Rick Nash in the discussion because he is a comparable goal scoring winger with a comparable salary even though he signed his contract several years ago. Before we get into contracts though, let’s take a look at production levels by age.

KesselGoalsPerGameByAge

 

In terms of goal production, both Nash and Kessel got their careers started earlier than Perry or Parise and both had their best goal production years earlier int heir careers. Kessel of course had his best goal production year playing a significant amount of time with one of the best playmakers in the league at the time, Marc Savard. He has yet to match that level in Toronto but of course he is playing with Tyler Bozak in Toronto. Aside from Perry’s career year at age 25 he has generally been at or below the production level of the other three at the same age while Nash has generally been the more productive player. Note that I have removed Parise’s Age 25 season as he missed the majority of the year to injury. Nash’s age 20 season was lost due to a lockout. Ages are based on draft year (first season after draft year is age 18)

 

KesselPointsPerGameByAge

Not really a lot different in the points/game chart which kind of makes sense because all these players are wingers and more goal scorers than play makers. Parise once again had his peak season at age 23 while Perry again had his at age 25. Nash has maintained a little more consistency fluctuating between 0.8 and 1.0 since his age 21 season though one should remember that Nash’s age 21 season was 2005-06 when goal production was inflated due to obstruction crackdown and far more power plays. Kessel appears to still be on the upswing and he has shown more play making ability with Lupul or van Riemsdyk on the other wing and the absence of a play maker at center.

Age Length Total$
Parise* 27 8 $80M
Perry 27 8 $69M
Kessel 25 8 $64M
Nash 25 8 $62.4M

*Parise’s salary over the first 8 years of his contract.

Parise’s salary is a little wonky as he signed his contract under the old CBA which was a back diving contract in which he earns $94M over the first 10 years and $4M over the final 3. Perry is the easiest to compare with as he is the most recent contract signing while Nash signed several years ago when the salary cap was lower. All things considered Kessel’s contract is at least fairly priced if not a slight bargain.

In conclusion, even though the others may have had higher ‘peak’ seasons (though it is certainly possible, maybe likely, that Kessel hasn’t reached his peak) it is fair to suggest that Kessel is deserving to be considered similarly talented to the other three which makes his $8M/yr salary not only fair but maybe a slight bargain.

 

Sep 212013
 

In a series of recent posts at mc79hockey.com, Tyler Dellow discussed a new concept (to me anyway) that he called ‘open play’ hockey. In a post on “The Theory of the Application of Corsi%” he wrote:

I have my own calculation that I do of what I call an open play Corsi%. I wipe out the faceoff effects based on some math that I’ve done as to how long they persist and look just at what happened during the time in which there wasn’t a faceoff effect.

This sounds strangely similar to my zone start adjusted statistics where I eliminate the first 10 seconds after an offensive or defensive zone face off as I have found that beyond that the effect of the face off is largely dissipated. I was curious as to how in fact these were calculated and it seemed I wasn’t the only one.

As far as I can tell, the tweet went unanswered.

In a followup post “New Metrics I” the concept of open play hockey was mentioned again.

I’m calculating what I call an open play Corsi% – basically, I knock out the stuff after faceoffs and then the stuff I’m left with, theoretically, doesn’t have any faceoff effects. It’s just guys playing hockey.

In the comments I asked if he could define more precisely what “stuff after faceoffs” meant but the question went unanswered. Dellow has subsequently referenced open play hockey in his New Metrics 2 post and in a follow up post answering questions about these new metrics. What still hasn’t been explained though is how he actually determines “open play” hockey.

Doing a search on Dellow’s website for “open play” we find that this concept has been mentions a couple times previously. In a post titled Big Oilers Data IX: Neutral Zone Faceoff Wins we might get an answer to exactly what ‘open play’ actually is.

As those of you who have been reading this series as I’ve gone along will be aware, I’ve been kind of looking at things on the basis of eight different kinds of 5v5 shift: Open Play (no faceoff during shift), six types of shift with one faceoff (OZ+, OZ-, NZ+, NZ-, DZ+, DZ-) and multi-faceoff shifts. The cool thing with seven of those types of shift is that I can get a benchmark of a type by looking at how the Oilers opposition did in the same situation.

So, as best I can determine, open play is basically any shift that doesn’t have  a face off.

The next question I’d like to answer is, how different is ‘open play’ from my 10 second adjustment. This is an interesting question because I have had this debate with many people that suggest that my 10 second adjustment isn’t adequate and that zone start effects are far more significant than my 10 second adjustment suggests. I have even had debates with Tyler Dellow about this (See here, here and here) so I am really curious as to what impact open play hockey has on a players statistics. Unfortunately, I don’t have much ‘open play’ data to go with but in the posts that Dellow has discussed it he has mentioned a few players open play corsi% statistics so I will work with what I have. Here is a comparison of Dellow’s open play stats and my 10-second zone start adjusted stats.

Player Year OpenPlay Corsi% ZSAdj CF% OZ% DZ%
Fraser 2012-13 50.8% 50.4% 40.1 25.3
Fraser 2011-12 52.8% 53.2% 31.1 35.5
Fraser 2010-11 45.2% 42.2% 30.4 35.1
Fraser 2009-10 59.2% 57.7% 29.2 40.5
Fraser 2008-09 51.8% 52.6% 30.9 37
O’Sullivan 2011-12 44.3% 42.0% 35.7 26
O’Sullivan 2010-11 45.2% 45.6% 29.4 34
O’Sullivan 2009-10 43.9% 44.1% 31 32.2
O’Sullivan 2007-08 45.5% 46.5% 29.9 29.4
Eager 2012-13 34.4% 35.6% 40.5 32.8
Eager 2011-12 42.0% 43.0% 29.6 30.7
Eager 2009-10 54.4% 54.5% 18.3 39.1
Eager 2008-09 52.9% 53.9% 22.6 37.4

I have incldued OZ% and DZ% which is the percentage of face offs (including neutral zone face offs) that the player had in the offensive and defensive zone. These statistics along with ZSAdj CF% can be found on stats.hockeyanalysis.com.

If it isn’t obvious to you that there isn’t much difference between the two, let me make it more obvious by looking at this in graphical form.

OpenPlayvsZSAdjustedCorsiPct

That’s a pretty tight correlation and we are dealing with some player seasons that have had fairly significant zone start biases. Ben Eager had a very significant defensive zone start bias in both 2008-09 and 2009-10 but a sizable offensive zone bias in 2012-13. Colin Fraser had sizable defensive zone bias in 2009-10 but a sizable offensive zone bias in 2012-13. Patrick O’Sullivan had a heavy offensive zone bias in 2011-12. There is no compelling evidence here that ‘open play’ statistics are any more reliable or better than my 10-second zone start adjusted data. There is essentially no difference which reaffirms to me (yet again) that my 10-second adjustment is a perfectly reasonable method to adjust for zone starts which ultimately tells us that zone starts do not have a huge impact on a players statistics. Certainly not anywhere close to what many once believed, including Dellow himself. Any impact you see is more likely due to the quality of players one plays with if one gets a significant number of defensive zone starts.

Update: For Tyler Dellow’s response, or lack there of, read this.  Best I can tell is he doesn’t want to publicly say what open play is or how it shows zone starts affect players stats beyond my 10-second adjustment because I might interpret what he says as thinking I am right despite him clearly thinking the evidence proves me wrong. I guess rather than have me make a fool of myself by misinterpreting his results so I can believe I am right he is going to withhold the evidence from everyone. I feel so touched that Dellow would choose to save me from such embarrassment as misinterpreting results over letting everyone know the real effect of zone starts have on a players statistics and why ‘open play’ is what we should be using to negate the effect of zone starts. Truthfully though, I am willing to take the risk  of embarrassing myself if it furthers our knowledge of hockey statistics.

 

Related Articles:

Face offs and zone starts, is one more important than the other?

Tips for using Hockey Fancy Stats

 

 

Sep 162013
 

Let’s imagine a sport where two factors are equally correlated with winning so that FactorA is 50% correlated with winning and FactorB is 50% correlated with winning. Now for years general managers in this sport only ever knew that FactorA existed and when choosing how to build their team they only ever considered FactorA. Now let’s assume that in this idealist, yet uninformed about FactorB, world every general manager of every team allocated their financial resources perfectly based on their knowledge of Factor A. On top of that, every team is working under the same financial constraints meaning they spend the exact same amount of money.

The result is, in this fictional world, FactorA becomes perfectly evenly distributed across every team. Strangely though, even after accounting for luck, teams have statistically significant differences in winning percentages.

Now, along comes a smart individual who discovers the existence of FactorB and finds out that FactorB correlates 100% with winning percentage (after factoring out luck) and concludes that General Managers were wrong all along and that FactorB is all that matters to winning and FactorA is irrelevant (has to be since it has zero correlation with winning). Upon discovering this he gets hired to become a General Manager of a team and while every other GM was only signing FactorA players he chose to go out and sign solely FactorB players. He made signing FactorB players his goal. Strangely, despite FactorB seemingly showing a 100% correlation with winning, his team didn’t win any more than anyone else.

The reason for this is that FactorA is in fact important. It just doesn’t seem important because everyone knows about FactorA and FactorA is getting evenly spread out across teams. Ignoring FactorA for FactorB is equally wrong as ignoring FactorB for FactorA. Upon learning of the existence of FactorB and its high correlation with winning, the goal of a General Manager is not to optimize his team for FactorB but to recognize that there is undiscovered value in players that have FactorB as a skill while not ignoring other skills that we previously knew existed.

Bringing this back to hockey, lets call FactorA shooting percentage and FactorB shot generation. Teams have typically doled out contracts based on shooting percentage but not based on corsi as shown by Eric T. His conclusion was:\

most teams don’t give out contracts because of Corsi. But a team that does will get more wins out of their budget than a team that follows the conventional path and overvalues finishing talent.

My response is, not if it comes at the expense of ignoring finishing talent. Based on Tom Awad’s work, finishing talent is probably at least 50% of out scoring your opposition (note that shooting percentage is a combination of out finishing and shot quality in Awad’s terminology).

So, if teams have been doling out contracts based on, effectively, shooting percentage then it is perfectly reasonable to assume that shooting percentage talent is more evenly distributed across teams than corsi-talent is. Under these circumstances corsi would be highly correlated with winning percentage because that is where the differences lie between teams. This doesn’t mean that corsi is the main factor in out scoring the opponent though and valuing corsi at the expense of shooting percentage will be a detriment to any General Manager.

Furthermore, if General Managers as a whole started paying primarily for corsi we will start to find that corsi talent becomes more evenly distributed across teams and thus shooting percentage would become much more highly correlated with winning (even after adjusting for luck). Furthermore, paying players based on corsi would potentially lead to players altering their style of play to optimize their corsi statistics to the detriment of the ultimate goal, out scoring the opponent.

It is certainly possible in the current hockey universe in which players are paid more by shooting percentage than corsi that they play a style of game to optimize shooting percentage at the expense of winning so it is not unreasonable to see the flip side occur of corsi because a metric by which general managers dole out contracts.

Ultimately, the goal of any General Manager is to optimize his line up for out scoring the opposition, not out shooting percentage-ing them and not out corsi-ing them. Corsi or possession should never be considered the goal just as shooting percentage or any other identifiable skill shouldn’t be. The goal has been, is, and always will be out score the opposition and it’s the General Managers job to find the right balance of all the identifiable skills, not just those that seemingly correlate with winning.