Jun 122013
 

Yesterday it came across my twitter feed a paper about using regularized logistic regression in estimating player contribution in hockey. I skimmed through the article but not enough to fully understand that article but found some of the conclusions at least mildly interesting. This post is neither a post in support or against the paper but rather a rebuttal to a rebuttal from Eric T at NHLNumbers.com.

To summarize the paper, the authors conducted a goal based analysis to estimate player contribution and to summarize Eric T’s rebuttal, Eric T applauded the effort but suggested a shot based analysis would be more appropriate because that is where ‘modern hockey thought’ currently stands.

 

I think my biggest concern is that by focusing exclusively on goals, you allow for shooting percentage variance to have a significant impact on a player’s calculated value. Even with four years of data, variance plays a large role in the shooting and save percentages with a given player on the ice.

This is why much of modern hockey analysis starts with shot-based metrics; the shooting percentages introduce a lot of variance which must be accounted for to get a reasonable assessment of talent. If you used shots for your model, I suspect you’d easily identify more than a mere 60 players who have significantly non-zero talent levels — and the model could be further refined from there (e.g. give each shot a weight based on the shooter’s career shooting percentage).

That is in essence Eric T’s argument.  Shooting percentages are unreliable so it is better to use a shot based approach (though I find it a little ironic that he then suggest incorporating shooting percentage again).

The “even with four years of data, variance plays a large role in shooting and save percentages with a given player on the ice” is the statement that I have the biggest problem with. It has been shown by myself many times that goal scoring rates are a better predictor of future goal scoring than shot rates are when dealing with multiple seasons of data. Furthermore, any study that uses sufficient amounts of data (either by using multiple seasons of data or by grouping similar players and using their aggregate shooting percentage) has concluded that shot quality (ability to sustain an elevated shooting percentage) exists and is significant. For example, we know that players that get a significant amount of ice time have significantly higher shooting percentages (see here and here and here) and just by looking at list of players sorted by their long-term on-ice shooting percentages we see that good offensive players rise to the top and poor offensive players fall to the bottom (in no way can anyone conclude that that list is random in nature). There is ample evidence to suggest that with 4 years of data goal based metrics should be the preferred tool over shot/possession based metrics.

Eric T brought up Dwayne Roloson, Kent Huskins, Sean O’Donnell, and others as examples of where he feels the evaluation system failed but pointing out a few counter examples is not enough to toss the analysis out completely. There will always be exceptions and outliers when attempting to build an all-encompassing evaluation metric. For the methodology in the paper maybe it is Roloson and Huskins but I can assure you than for any shot based metric it will be Tyler Kennedy and Scott Gomez.

The standard for which an all-encompassing metric should be tested against is not “is it perfect” and if it doesn’t pass that test toss it aside and ignore it forever. These metrics will never be perfect and should never be used as the final say on a players value. In truth, they should be used to spark conversation and discussion and further investigation, not end it. When we see strange results just as much as we shouldn’t assume they are true we shouldn’t assume the whole methodology is worthless.

Furthermore, making any argument against a new methodology because it doesn’t conform to “modern hockey thought” and suggesting they revise it to make it conform more to “modern hockey thought” is plainly the worst thing one can do. The best discoveries in the history of humanity typically arise when people don’t conform to current thought processes but rather do something different. You are free to make an argument against something but make sure that argument is something deeper than “it doesn’t conform to modern hockey thought.”

Finally, my biggest beef with many in the pro corsi/possession/shot differential crowd is the way in which many immediately and abjectly dismiss anything that strays from a corsi/possession/shot differential analysis. This is as fundamentally misguided as those that claim that corsi/possession/shot differential is meaningless and goals are the only tool one should use in player evaluation. The truth is, both methods provide value. The possession method primarily provides value when dealing with small sample sizes as it will reduce small sample size and random variance issues. Shot differential metrics are inherently a flawed metric though because shot differential isn’t the end goal of the player (goal differential is what matters in the win/loss column) and shot quality and ability to drive/suppress shooting percentages exists and are real. There is nothing wrong with using possession metrics as an evaluation tool so long as we are aware of this limitation just as there is nothing wrong with using goal based metrics as an evaluation tool so long as we are aware of its sample size, randomness and uncertainty limitations. Neither are perfect, both have their uses, both have their limitations and in reality both should be considered in any player evaluation.

(Note: Just to be clear, because apparently Tyler Dellow has a poor ability to interpret words properly, my critique of Eric T’s critique of the goal based all-encompassing player evaluation metric does not in any way mean that I believe Dwayne Roloson helps his team score goals. To be completely honest, I serious question how the authors of the paper incorporate goalies into the methodology and this is supported by the fact that in my own all-encompassing player evaluation metrics – goal or shot based – I assume goalies have no influence on a teams offensive production. Hope this clears the issue up for Tyler.)

 

Jun 112013
 

Nathan Horton has been one of the stars of these NHL playoffs as will be an integral component of the Stanley Cup finals if the Bruins are going to beat the Chicago Blackhawks. Nathan Horton is also set to become an unrestricted free agent this summer so his good playoff performance is good timing. One of the things I have noticed about Horton while looking through the statistics is that he has one of the highest on-ice 5v5 shooting percentages over the past 6 seasons of any NHL forward (ranks 16th among forwards with >300 minutes of ice time).

Part of the reason for this is that he is a fairly good shooter himself (ranks 30th with a 5v5 shooting percentage of 12.25%) but this in no way is the main reason.  Let’s take a look at how Horton’s line mates shooting percentage have been over the past 6 seasons when playing with Horton and when not playing with Horton.

Sh% w/o Horton Sh% w/ Horton Difference
Weiss 11.28% 12.84% 1.56%
Lucic 13.03% 16.98% 3.95%
Krejci 11.41% 12.10% 0.68%
Booth 8.44% 11.26% 2.82%
Frolik 6.58% 10.84% 4.26%
Stillman 10.03% 15.38% 5.35%
Zednik 8.81% 13.56% 4.75%
Average 9.94% 13.28% 3.34%

Included are all forwards Horton has played at least 400 minutes of 5v5 ice time with over the past 6 seasons along with their individual shooting percentage when with Horton and when not with Horton. Every single one of them has an individual shooting percentage higher with Horton than when not with Horton and generally speaking significantly higher.  I have previously looked at how much players can influence their line mates shooting percentages and found that Horton was among the league leaders so the above table agrees with that assessment.

It is still possible that Horton is just really lucky but that argument starts to lose steam when it seems he is getting lucky each and every year over the past 6 years (he has never had a 5v5 on-ice shooting percentage at or below league average). Whatever Horton is doing while on the ice seems to be allowing his line mates to boost their own individual shooting percentages and the result of this is that he has the 9th highest on-ice goals for rate over the past 6 seasons. He is a massively under rated player and is this summers Alexander Semin of the UFA market.

 

May 212013
 

Last week there was a twitter discussion on the merits of playing a defensive shell game by limiting scoring chances against but also limiting scoring chances for, even if it meant the ration of goals for to goals against gets worse. The two sides of the debate are as follows:

Argument 1: It is always best to play a game where you are expected to out score the opposition regardless of the goals for/against rates.

Argument 2: When playing with a lead late in the game it is more important to reduce the goals against rate than maintain the goals for rate, even if it means the goals for to goals against ratio drops significantly.

To test each theory I simulated a number of games between teams T1 and T2 according to the following theories:

1. During normal play between teams T1 and T2, T1 will score at a rate of 2.75 goals/60 minutes and T2 will score at a rate of 2.50 goals/60 minutes. During this play it is expected that T1 will score approximately 52.4% of all the goals that are scored.

2. During play between T1 and T2 when T1 has a lead and is playing in defensive shell mode T1 score at a rate of 2.00 goals/60 and T2 will score at the same 2.00 goals/60 rate.

From there I simulated 1,000,000 games in which T1 is protecting a 1 goal lead for the remaining 2.5, 5, 7.5, 10, 12.5, 15, 17.5 and 20 minutes of a game under both normal style play and defensive shell style play. Here are the results at the end of regulation play.

Normal play

Wins Losses Ties RegWin% OTL Pts% PlayoffWin%
2.5mins 911132 4471 99307 96.08% 93.60% 96.32%
5mins 847011 15230 187894 94.10% 89.40% 94.54%
7.5mins 799667 28880 268711 93.40% 86.68% 94.04%
10mins 764672 44692 340642 93.50% 84.98% 94.31%
12.5mins 738696 59869 405525 94.15% 84.01% 95.11%
15mins 717679 75094 464680 95.00% 83.38% 96.11%
17.5mins 702071 88968 518004 96.11% 83.16% 97.34%
20mins 690638 102013 565261 97.33% 83.20% 98.67%

Defensive Shell

Wins Losses Ties RegWin% OTL Pts% PlayoffWinRate
2.5mins 926241 3011 79934 96.62% 94.62% 96.81%
5 mins 868285 10599 153384 94.50% 90.66% 94.86%
7.5mins 821835 21109 221668 93.27% 87.73% 93.79%
10mins 785935 32888 283819 92.78% 85.69% 93.46%
12.5mins 755920 46048 341509 92.67% 84.13% 93.48%
15mins 733346 58874 392918 92.98% 83.16% 93.92%
17.5mins 713419 72115 442202 93.45% 82.40% 94.50%
20mins 697687 85092 486930 94.12% 81.94% 95.27%

Wins, losses, ties are T1’s record after 60 minutes and regulation win% is the standard regulation winning percentage using 2 points for a win, 0 points for a loss and 1 point for a tie. PlayoffWinRate is the winning percentage of T1 in a playoff game assuming that they would win 52.4% of all overtime games. OTL Pts% is the current regular season system where you get 1 point for an overtime loss, 2 points for a win of any kind and zero points for a regulation loss (under this system for simplicity sake I assumed a 50% chance of winning an overtime game since we don’t know odds of winning a shoot out).

That is a lot of numbers, so lets look at these in nicer easier to read charts.

DefensiveShellRegulationWinPct

DefensiveShellPlayoffWinRate

DefensiveShellOTLPointsPct

Under this constructed scenario the break even point for when to go into a defensive shell and when to continue playing normal hockey is at about 7-7.5 minutes for regulation win % and playoff win % systems and about 13 minutes for the point for an overtime loss system currently used during the regular season.

For some people this may not make sense intuitively. How can it be better to stop playing a system in which you are expected to out score your opposition and start playing a system in which you are expected to score the same as your opponent. The reason is simple and it comes down to that over a short period of time your are essentially dealing with small sample size issues and randomness becomes more important than long term skill. The reality is, over a short time one team is almost as likely to score as the other so which team scored next is close to random, if any team scores at all. The most important thing when protecting a lead is simply reducing the likelihood that your opponent will score because the cost of your opponent scoring is far greater than the benefit if you scoring (it is irrelevant whether you win 3-1 or 2-1, a win is a win in the standings).

What is interesting is the effect of awarding the point for an overtime loss is in reality providing additional incentive for teams to play the defensive shell game for longer periods of time because the cost of giving up a goal is not as great in that system because a tied at the end of regulation guarantees you one point with the possibility of 2 where as in the other systems it does not. This means teams can play the defensive shell for twice as long as they could otherwise.

Of course, this is only looking at one side of the equation. Typically the trailing team will get more offensively aggressive even if it means increasing the possibility of having a goal scored against them. This is why teams pull their goalie late in the game. At that point scoring a goal is the only thing that matters so you may as well risk giving one up to score. Over the last 5-10 minutes or so it probably makes sense for the trailing team to take more high risk high reward plays in the offensive zone because at that point scoring a goal has more benefit than the cost of giving up a goal.

 

 

May 152013
 

After last weeks untimely pinch by Dion Phaneuf in game 4 that led to an overtime goal and the Bruins taking a 3-1 lead in the first round series there was a lot of evaluation of Phaneuf as a defenseman both good and bad. I was intending to write an article to discuss the relative merits of Dion Phaneuf and attempt to get an idea of where he stands among NHL defensemen but in the process of researching that I came across some interesting Phaneuf stats that I think deserve their own post so here it is.

My observation was with respect to Phaneuf’s usage and performance when the Leafs are leading and when they are trailing over the previous 3 seasons. Let’s start of by looking at Phaneuf’s situational statistics over the past 3 seasons.

5v5 5v5close 5v5tied Leading Trailing
G/60 0.222 0.175 0.101 0.156 0.408
Pts/60 0.700 0.670 0.660 0.420 1.020
IPP 30.1% 31.1% 34.2% 20.0% 34.5%
GF20 0.773 0.721 0.640 0.692 0.986
GA20 0.841 0.760 0.943 0.865 0.714
GF% 47.9% 48.7% 40.4% 44.4% 58.0%
CF20 18.316 18.113 18.159 15.195 21.542
CA20 20.686 21.418 21.880 22.982 17.223
CF% 47.0% 45.8% 45.4% 39.8% 55.6%
OZ% 28.0% 26.7% 25.2% 24.2% 34.5%
DZ% 31.8% 30.3% 29.7% 37.5% 28.5%
NZ% 40.3% 43.0% 45.0% 38.3% 37.0%
DZBias 103.9 103.6 104.4 113.3 94.0
TeamDZBias 108.9 109 107 115.2 100.8
DZBiasDiff -5 -5.4 -2.6 -1.9 -6.8

Most of the stats above the regular readers should be familiar with but if you are not you can reference my glossary here. The one stat that I have not used before is DZBias. DZBias is defined as 2*DZ% + NZ% and thus anything over 100 indicates the player has a bias towards starting shifts in the defensive zone and anything under 100 the player has a bias towards starting in the offensive zone. I prefer this to OZone% which is OZStarts/(OZStarts+DZStarts) because it takes into account neutral zone starts as well. TeamDZBias is the zone start bias of the Leafs over the past 3 seasons and DZBiasDiff is Phaneuf’s DZBias minus the teams DZBias and provides a zone start bias relative to the team. Anything less than 0 indicates usage is more in the offensive zone relative to his teammates.

So, what does this tell us about Phaneuf.  Well, there isn’t a huge variation in either the zone start usage or the results during 5v5, 5v5close and 5v5tied situations so the focus should be on the differences between 5v5leading and 5v5trailing which are significant.

Typical score effects are when leading a team gives up more shots but of lower quality (defensive shells protect the danger zone in front of the net but allow more shots from the perimeter) and takes fewer shots but of higher quality (probably a result of more odd-man rushes due to pinching defensemen of the trailing team).  Phaneuf seems to take this concept to the extreme but more importantly Phaneuf seems to excel best in an offensive role and struggles in a defensive role. When the Leafs are trailing Phaneuf has  0.408G/60 (10th of 180 defensemen) and 1.02 points/60 (36th of 180 defensemen) but when leading Phaneuf falls to 0.156 G/50 (64th of 177 defensemen) and 0.42 points/60 (137th of 177 defensemen). Furthermore, Phaneuf’s involvement in the offensive zone drops off significantly when leading (IPP drops from 34.5% when trailing to 20.0% when leading).

In terms of on-ice stats, Phaneuf’s CF% drops from 55.6% when trailing (79th of 180 defensemen) to a very poor 39.8% when leading (164th of 177 defensemen).  Some may be thinking this is due to zone starts but Phaneuf is getting above average offensive zone starts both when trailing (ranks 100th of 180 defensemen) and when leading (ranks 154th of 177) and using even the most aggressive zone start adjustments in no way will account for the difference. Similar observations can be made with on-ice goal stats as well. Let’s look at how Phaneuf ranks among defensemen over the past 3 seasons.

Leading (of177) Trailing ( of 180)
GF20 109 25
GA20 125 71
GF% 126 36
CF20 128 31
CA20 174 154
CF% 164 79

That is a pretty significant improvement in rankings when trailing over when leading, especially in the offensive statistics (GF20, CF20). If zone starts aren’t a factor, might line mates be? He are Phaneuf’s most frequent defense partners:

Trailing:  Gunnarsson (364:33, 31.0%), Beauchemin(212:07, 18,0%), Aulie(162:09, 13.8%)

Leading: Gunnarsson (376:16, 32.5%), Aulie(234:17, 20.3%), Beauchemin(166:30, 14.4%)

Playing more with Beauchemin and less with Aulie when trailing ought to help, particularly ones offensive stats, but I doubt that is going to account for that much of a difference. Also, when leading Phaneuf has a 41.2CF% with Gunnarsson and when trailing that spikes to 54.6%. When leading Phaneuf and Beauchemin have a CF% of 37.3% and when trailing that spikes to 57.7%. With Aulie the difference is 36.6% vs 49.3%. Regardless of which defense partner Phaneuf is with, their stats dramatically improve when playing in catch up situation than when in trailing situations.

The same is true for forwards. When protecting a lead Phaneuf plays more with Grabovski and Kulemin but when playing catch up he plays a bit more with Kessel and Bozak but for all of those forwards Phaneuf’s numbers with them are hugely better when playing catch up than when protecting a lead and playing with Grabovski and Kulemin more when playing with a lead should only help his statistics as they are generally considered the Leafs better corsi players.

Let’s take a look at a chart of Phaneuf’s corsi WOWY’s when leading and when trailing.

Leading:

PhaneufLeadingCorsiWOWY201013

As you can see, when leading the majority of Phaneuf’s team mates are to the left of the diagonal line which means they have a better corsi% without Phaneuf than with.

Trailing:

PhaneufTrailingCorsiWOWY201013

When trailing the majority of Phaneuf’s team mates are near or to the right of the diagonal line which means they generally have better corsi% statistics when with Phaneuf than when apart.

So the question arises, why is this? It doesn’t seem to be zone starts. It doesn’t seem to be changes in line mates and it isn’t that the team as a whole automatically becomes a great corsi% team when trailing which Phaneuf could benefit from. When leading Phaneuf’s corsi% is 39.8% which is worse than the teams 41.2% and when trailing Phaneuf’s corsi% is 55.6% which is better than the teams 54.4%. It seems to me that the conclusion we must draw from this is that Phaneuf has been poor at protecting a lead relative to his team mates and we know his team mates have been poor at protecting a lead. Where Phaneuf excels is when he is asked to engage offensively be that when playing catch up hockey or when playing on the PP (Phaneuf’s PP statistics are pretty solid). From the first chart we know that Phaneuf has a slight bias towards more offensive zone starts (relative to his team mates) and when we dig into the numbers further it probably shows that he should be given even more offensive opportunities and given fewer defensive ones because he seems like a much better player when asked to be engaged offensively than when he is asked to be a shut down defenseman.

Acquiring a quality shut down defenseman (ideally two) this off season must be the #1 priority of Maple Leaf management and Phaneuf’s usage must shift further away from multi-purpose heavy work load defenseman to primarily an offensive usage defenseman.

 

May 012013
 

I brought this issue up on twitter today because it got me thinking. Many hockey analytics dismiss face off winning % as a skill that has much value but many of the same people also claim that zone starts can have a significant impact on a players statistics. I haven’t really delved into the statistics to investigate this, but here is what I am wondering.  Consider the following two players:

Player 1: Team wins 50% of face offs when he is on the ice and he starts in the offensive zone 55% of the time.

Player 2: Team wins 55% of face offs when he is on the ice but he has neutral zone starts.

Given 1000 zone face offs the following will occur:

Player 1 Player 2
Win Faceoff in OZone 275 275
Lose Faceoff in Ozone 275 225
Win Faceoff in DZone 225 275
Lose Faceoff in Dzone 225 225

Both of these players will win the same number of offensive zone face offs and lose the same number of defensive zone face offs which are the situations that intuitively should have the greatest impacts on a players statistcs. So, if Player 1 is going to be more significantly impacted by his zone starts than player 2 is impacted by his face off win % losing face offs in the offensive zone must still have a significant positive impact on the players statistics and winning face offs in the defensive zone must must still have a significant negative impact on the players statistics. If this is not the case then being able to win face offs should be more or less equivalent in importance to zone starts (and this is without considering any benefit of winning neutral zone face offs).

Now, I realize that there is a greater variance in zone start deployment than face off winning percentage, but if a 55% face off percentage is roughly equal to a 55% offensive zone start deployment and a 55% face off win% has a relatively little impact on a players statistics then a 70% zone start deployment would have a relatively little impact on the players statistics times four which is still probably relatively little.

I hope to be able to investigate this further but on the surface it seems that if face off win% is of relatively little importance it is supporting of my claim that zone starts have relatively little impact on a players statistics.

 

Apr 252013
 

I am hoping to get playoff stats on stats.hockeyanalysis.com but it is going to take some work especially if I am to do game by game and series by series stats including “with you” and “against you” stats. As such I have decided to start a crowd funding project at RocketHub.com (because unlike Kickstarter they support Canadians) to help justify the time I will have to put in to getting these stats up in a relatively short time frame. Below is the description of the project and what I hope to achieve and if you are interested in contributing you can do that at the project page at RocketHub.com. Your contributions are greatly appreciated and I think you will enjoy what I have planned for stats.hockeyanalysis.com.

—————————————

Hello. This is David Johnson from HockeyAnalysis.com and creator of the popular advanced hockey statistics website Stats.HockeyAnalysis.com. Much of my work on hockey analytics has been at the macro level, or more specifically evaluating players over 1, 2, or more years. This works great for the regular season and for evaluating a players overall talent level which is where my interest mostly lies but there seems to be a strong demand for more micro level stats such as how players or teams perform in a single game or over a short stretch of games (i.e. after the trade deadline, before and after a coach got replaced, etc.) and this is especially true during the Stanley Cup playoffs.

The problem is, much of my existing code base that I use for stats.hockeyanalysis.com is designed for macro level stats and to revamp it to calculate stats on a per game or per playoff series basis and make these available on the web will take a significant redesign and rewrite of large portions of the code.

My goal for this project is to make some of those changes so I can get some playoff stats up for those that are interested and down the road make per game and per groups of game data available for regular season data starting next season. Here is what I am hoping to generate for these playoffs:

  • Team stats by series and playoffs overall
  • Player stats by series and playoffs overall
  • Game by game team stats
  • “with you” stats by game, series and playoffs overall so you can see how the team performed with various pairs of players on the ice.
  • “against you” stats by game, series and playoffs so you can see which players were successful at scoring on or shutting down their opponents.
  • For each of the above I will be adding goal, shot, fenwick and corsi data (totals and possibly %’s).
  • Will add zone start data to “with you” and “against you” data as time permits.
  • Will start with just looking at 5v5 situations but will add other situations if time permits.

My intent is to start by adding playoff stats similar to the existing regular season stats and then as development progresses I’ll be adding the other features with hopefully the majority of them being added by the end of round 1 if not sooner.

I am looking for some funding so I can justify the significant time over the next few weeks that it will take to rewrite my code and make game by game playoff stats available. I figure if each of the regulars that use stats.hockeyanalysis.com contributes between $10 and $50 (larger donations certainly welcome though) it will be easy to reach my funding goal. Any additional funding beyond my goal will be devoted towards adding similar game by game features to the regular season data for the start of next season.

Apr 232013
 

With the win over the Ottawa Senators on Saturday night the Leafs have made the playoffs for the first time since the 2003-04 season and they are doing it largely on the backs of an elevated shooting percentage which currently sits at a lofty 10.52% (5v5 only). Here are all the teams with a 5v5 shooting percentage above 9.00% since 2007-08 season and how they have done in the playoffs.

Season Team 5v5 Sh% Playoff Result
2012-13 Maple Leafs 10.52 Made playoffs
2012-13 Stars 10.04 Fighting for playoff spot (10th)
2011-12 Lightning 9.73 Missed Playoffs
2009-10 Capitals 10.39 Lost in first round
2009-10 Canucks 9.14 Lost in second round
2008-09 Penguins 9.76 Won Stanley Cup
2008-09 Canucks 9.23 Lost in second round
2008-09 Bruins 9.15 Lost in second round
2008-09 Thrashers 9.02 Missed Playoffs
2007-08 Senators 9.03 Lost in first round

Prior to this season there have been 8 teams with a shooting percentage above 9.00%, 2 missed the playoffs, 2 lost in the first round, 3 lost in the second round and one team won the Stanley Cup. That isn’t very much success at all which is not a good sign for Leaf fans (myself included) hoping their team can go on a playoff run.

 

Apr 192013
 

Tyler Dellow has a post at mc79hockey.com looking at zone starts and defensemen and if you read it the clear conclusion is that zone starts seem to matter quite a bit. In the third chart you can see that defensemen who get the most extreme defensive zone starts have an average corsi% of 44.7% while the average corsi% for defensemen with the most extreme offensive zone starts is 53.3%. This would seem to indicate that for defensemen zone starts can impact your corsi% anywhere from -5.3% to +3.3%. This is far more significant than I have estimated myself using a different methodology so I pondered that part of the reason for this is that when you start in the defensive zone you are playing with weaker quality of teammates than when you start in the offensive zone. My reasoning is that players that get used primarily in the defensive zone are often weak offensive players as if you are a good offensive player you will be given offensive opportunities. I wanted to explore this concept further and that is what I present to you here.

Unlike Tyler Dellow I used forwards in my analysis but it is unlikely that this will have a major impact in the analysis as forwards and defensemen are always on the ice together. One difference between my analysis and Tyler Dellow’s is I used data from stats.hockeyanalysis.com where as Tyler used stats from behindthenet.ca. Behindthenet.ca includes goalie pulled situations in their data and this has the potential to greatly emphasize the impact of zone starts. I feel it is important to eliminate this factor so I have it removed from the data. I also only used 2011-12 data but that shouldn’t have a major impact on the results.

So, my theory is that players who start in the defensive zone are weaker players overall. The challenge to this is that players who start with players that start frequently in the defensive zone likely start frequently in the defensive zone themselves and thus their stats are subject to zone start effects so if they have weak stats we don’t know whether they are due to the zone starts or because they are weak players. My solution was to look at the players zone start adjusted stats that I have on stats.hockeyanalysis.com. These stats ignore the first 10 seconds after a zone face off as it has been shown that the majority of the benefit/penalty of a zone face off has largely dissipated after 10 seconds. I understand that it may seem weird to use zone start adjusted data in a study that attempts to estimate the impact of zone starts but I don’t know what else to do.

I want to also point out that I will be using ZS adjusted FF% team mates when the team mates are not on the ice with the player and this may also mitigate the ZS impact on the teammates stats. My reasoning is, if a player has an extensive number of defensvie zone starts, it is quite possible that when his team mates are not playing with him their zone starts are more neutral or maybe even offensive zone biased. It if there ever was a way to get a non-zone start impacted FF% to use as a QoT metric this is probably the best we can do.

Ok, so what I did was compare a players 5v5 FF% (fenwick %) and zone start adjusted 5v5 TMFF% (zone start adjusted FF% of teammates when team mates are not playing with him) and came up with the following:

FFPct_vs_TMFFPct_by_ZS

As you can see, TMFF% does seem to vary across zone start profiles as I had hypothesized though to a lesser extent than the players zone start influenced FF% which is to be expected. So, if we subtract TMFF% from FF% we get the following chart:

FFPct-TMFFPct_by_ZS

This chart indicates that the zone start impact on forwards once adjusted for quality of teammates (as best we can) ranges from -2.5% to +2.15% which is significantly lower than the -5.3% to +3.3% estimate that Tyler Dellow came up with for defensemen without adjusting for quality of teammates and using goalie pulled situations included in the data. That said, this is still more significant than my own estimates when I compared 5v5 data to 5v5 data with the first 10 seconds after a zone start ignored. When I did that I calculated the impact on H. Sedin’s FF% due to his heavy offensive zone starts to be +1.4% to his FF% and considered this an upper bound. To investigate this further I plotted the average difference between 5v5 FF% and my 5v5 zone start adjusted FF% and I get the following:

FFPct-ZSAdjFFPct_by_ZS

The above is an estimate of the average impact of zone starts using my zone start adjustment methodology which ignores the first 10 seconds after a zone face off. This is significantly lower than either of the previous 2 estimates as we can see in this summary table:

Methodology ZS Impact Estimate
T. Dellow’s estimate for defensemen -5.3% to +3.3%
My TM Adjusted estimate for forwards -2.5% to +2.15%
My 10 second after Zone FO adjustment for forwards -0.5% to +0.41%

I am pretty sure none of what I have said above will put an end to the impact of zone starts on a players statistics debate but at the very least I hope it sheds some light on some of the issues involved. For me personally, I have the most confidence in my zone start adjustment method which removes the 10 seconds after a zone face off. My reasoning is studies have shown that the effect of a zone face off is largely eliminated within the first 10 seconds (see here or here) and also because it is the only methodology that compares a player to himself under similar playing conditions (i.e. same season, almost identical QoT, QoC and situation profiles) eliminating most of the opportunity for confounding factors to influence the results. If this is the case, the impact of zone starts on a players stats is fairly small to the point of being almost negligible for the majority of players.

 

Apr 172013
 

Even though I am a proponent of shot quality and the idea that the percentages matter (shooting and save percentage) puck control and possession are still an important part of the game and the Maple Leafs are dreadful at it. One of the better easily available metrics for measuring possession is fenwick percentage (FF%) which is a measure of the percentage shot attempts (shots + shots that missed the net) that your team took. So a FF% of 52% would mean your team took 52% of the shots while the opposing team took 48% of the shots. During 5v5 situations this season the Maple Leafs have a FF% of 44.4% which is dead last in the NHL. So, who are the biggest culprits in dragging down the Maple Leafs possession game? Let’s take a look.

Forwards

Player Name FF% TMFF% OppFF% FF% – TMFF% FF%-TMFF%+OppFF%-0.5
MACARTHUR, CLARKE 0.485 0.44 0.507 0.045 0.052
KESSEL, PHIL 0.448 0.404 0.507 0.044 0.051
KOMAROV, LEO 0.475 0.439 0.508 0.036 0.044
KADRI, NAZEM 0.478 0.444 0.507 0.034 0.041
GRABOVSKI, MIKHAIL 0.45 0.424 0.508 0.026 0.034
VAN_RIEMSDYK, JAMES 0.456 0.433 0.508 0.023 0.031
FRATTIN, MATT 0.475 0.448 0.504 0.027 0.031
LUPUL, JOFFREY 0.465 0.445 0.502 0.02 0.022
BOZAK, TYLER 0.437 0.453 0.508 -0.016 -0.008
KULEMIN, NIKOLAI 0.421 0.454 0.51 -0.033 -0.023
ORR, COLTON 0.401 0.454 0.5 -0.053 -0.053
MCLAREN, FRAZER 0.388 0.443 0.501 -0.055 -0.054
MCCLEMENT, JAY 0.368 0.459 0.506 -0.091 -0.085

FF% is the players FF% when he is on the ice expressed in decimal form. TMFF% is an average of the players team mates FF% when they are not playing with the player in question (i.e. what his team mates do when they are separated from them, or a quality of teammate metric). OppFF% is an average of the players opponents FF% (i.e. a quality of competition metric). From those base stats I took FF% – TMFF% which will tell us which players perform better than their teammates do when they aren’t playing with him (the higher the better). Finally I factored in OppFF% by adding in how much above 50% their opposition is on average. This will get us an all encompassing stat to indicate who are the drags on the Leafs possession game.

Jay McClement is the Leafs greatest drag on possession. A few weeks ago I posted an article visually showing how much of a drag on possession McClement has been this year and in previous years. McClement’s 5v5 FF% over the past 6 seasons are 46.2%, 46.8%, 45.3%, 47.5%, 46,2% and 36.8% this season.

Next up are the goons, Orr and McLaren which is probably no surprise. They are more interested in looking for the next hit/fight than they are the puck. In general they are low minute players so their negative impact is somewhat mitigated but they are definite drags on possession.

Kulemin is the next biggest drag on possession which might come as a bit of a surprise considering that he has generally been fairly decent in the past. Looking at the second WOWY chart here you can see that nearly every player has a worse CF% (same as FF% but includes shots that have been blocked) with Kulemin than without except for McClement and to a much smaller extent Liles. This is dramatically different than previous seasons  (see second chart again) when the majority of players did equally well or better with Kulemin save for Grabovski. Is Kulemin having an off year? It may seem so.

Next up is my favourite whipping boy Tyler Bozak. Bozak is and has always been a drag on possession. Bozak ranks 293 of 312 forwards in FF% this season (McClement is dead last!) and in the previous 2 seasons he ranked 296th of 323 players.

Among forwards, McClement, McLaren, Orr, Kulemin and Bozak appear to be the biggest drags on the Maple Leafs possession game this season.

Defense

Player Name FF% TMFF% OppFF% FF% – TMFF% FF%-TMFF%+OppFF%-0.5
FRANSON, CODY 0.469 0.437 0.506 0.032 0.038
GARDINER, JAKE 0.463 0.44 0.506 0.023 0.029
KOSTKA, MICHAEL 0.459 0.435 0.504 0.024 0.028
GUNNARSSON, CARL 0.455 0.437 0.506 0.018 0.024
FRASER, MARK 0.461 0.445 0.506 0.016 0.022
LILES, JOHN-MICHAEL 0.445 0.443 0.503 0.002 0.005
PHANEUF, DION 0.422 0.455 0.509 -0.033 -0.024
HOLZER, KORBINIAN 0.399 0.452 0.504 -0.053 -0.049
O_BYRNE, RYAN 0.432 0.505 0.499 -0.073 -0.074

O’Byrne is a recent addition to the Leafs defense so you can’t blame the Leafs possession woes on him, but in Colorado he was a dreadful possession player so he won’t be the answer to the Leafs possession woes either.

Korbinian Holzer was dreadful in a Leaf uniform this year and we all know that so no surprise there but next up is Dion Phaneuf, the Leafs top paid and presumably best defenseman. In FF%-TMFF%+OppFF%-0.5 Phaneuf ranked a little better the previous 2 seasons (0.023 and 0.003) so it is possible that he is having an off year or had his stats dragged down a bit by Holzer but regardless, he isn’t having a great season possession wise.

 

 

Apr 162013
 

If you follow me on twitter you know I am not a fan of Tyler Bozak and I have written about him in the past. As a Leaf fan I want to keep writing about his poor play because I really do not want to see him re-signed in Toronto. He isn’t a good player and simple does not deserve it, especially if he is going to be making upwards of $4M/yr on a 4+ year long contract.  Let’s take a look at how he ranks in a variety of categories over the previous 3 seasons combined as well as this season.

Statistic 3yr 2012-13
5v5 G/60 219/324 130/310
5v5 A/60 168/324 144/310
5v5 Pts/60 199/324 139/310
5v5 IGP 265/324 195/310
5v5 IAP 202/324 221/310
5v5 IPP 288/324 268/310
5v5 FF20 155/324 173/310
5v5 FA20 319/324 309/310
5v5 FF% 275/324 291/310
5v4 G/60 116/155 57/147
5v4 A/60 144/155 98/147
5v4 Pts/60 150/155 89/147
5v4 IGP 76/155 66/147
5v4 IAP 131/155 110/147
5v4 IPP 139/155 114/147

The above are his rankings among other forwards (i.e. 219/324 means 219th among 324 forwards with >1500 5v5 3yr minutes, >300 5v5 2012-13 minutes, >400 5v4 3yr minutes and >75 5v4 2012-13 minutes.  2012-13 stats for games up to but not including last nights).  For 5v5 ice time we are essentially talking the top 10-11 forwards on each team, or their regulars and on the power play we are talking the top 5 forwards in PP ice time per team.

In 3-year 5v5 goals, assists and points per 60 minutes of play Tyler Bozak is ranking approximately the equivalent of a good 3rd line player. The thing is, he is doing that while playing on the first line but his terrible IGP, IAP, and IPP numbers indicate he is doing a terrible job keeping pace with his fellow first line mates.  If you look at his 3 year fenwick numbers (FF20, FA20 and FF%) which are on-ice stats you see when Tyler Bozak has been on the ice the Leafs have been mediocre at shot generation and terrible at shot prevention. Only a handful (literally, just 5 players) have a worse shot prevention record when they are on the ice.

On the power play things aren’t much better. He is second powerplay unit material at best but he is near the bottom of the pack in every assist and point generation and only a bit better in goal production.

Overall his numbers look a little better in 2012-13 but they certainly aren’t much to write home about, especially his IGP, IAP and IPP. He still looks to be a 3rd line offensive player with terrible defensive ability.

Another thing we can look at is his WOWY numbers with his most frequent line mate Phil Kessel.

Bozak w/Kessel Bozak wo/ Kessel
3yr GF20 0.874 0.648
3yr GA20 0.995 1.297
3yr GF% 46.8% 33.3%
3yr CF20 19.60 17.43
3yr CA20 20.89 20.82
3yr CF% 48.4% 45.6%
2012-13 GF20 0.956 0.000
2012-13 GA20 0.918 0.419
2012-13 GF% 51.0% 0.0%
2012-13 CF20 19.50 8.38
2012-13 CA20 21.53 25.55
2012-13 CF% 47.5% 24.7%

When Phil Kessel and Tyler Bozak are on the ice together they are not even breaking even. When Tyler Bozak is on the ice without Kessel they are significantly worse. Individually, Tyler Bozak has scored just 3 of his 26 5v5 goals (11.5%) and 8 of his 68 points (11.8%) over the previous 3 seasons when separated from Kessel despite playing nearly 20% of his ice time apart from Kessel. When not with Kessel his goal and point production drops significantly and as we know from above it wasn’t all that impressive to start with.

Not shown are Phil Kessel’s numbers when he isn’t playing with Tyler Bozak but they are generally better than when they are together. Phil Kessel when not playing with Tyler Bozak has a GF% of 50.4% and a CF% of 51.5% over the previous 3 seasons. Tyler Bozak appears to be a drag on Kessel’s offense.

The only argument you can for keeping Bozak is that the Kessel-Bozak-Lupul/JVR line has been productive and is working so why break them up. To me that argument only works when Bozak is making $1.5M and is not a significant drag on the salary cap but you can’t be paying a player $3.5-4M to essentially be a place holder between Kessel and Lupul/JVR.

Related News Article: James Mirtle wrote an article on the tough decision Leaf management has regarding the re-signing of Tyler Bozak.

(I am going to try and include a glossary in my posts for advanced statistics mentioned in the post so those not familiar with advanced stats can find out what they mean but a full glossary can also be found here).

Glossary

  • G/60 – Goals scored per 60 minutes of play
  • A/60 – Assists per 60 minutes of play
  • Pts/60 – Points per 60 minutes of play
  • IGP – Percentage of teams goals while player was on ice that were scored by the player
  • IAP – Percentage of teams goals while player was on the ice that the player had an assist on
  • IPP – Percentage of teams goals while player was on the ice that player scored or had an assist on
  • FF20 – Fenwick (shots + missed shots) by team per 20 minutes of ice time
  • FA20 – Fenwick (shots + missed shots) against team per 20 minutes of ice time
  • FF% – % of all shot attempts (shots + missed shots) while on ice that the players team took – FF/(FF+FA)
  • GF20, GA20, GF% – same as FF20, FA20, FF% except for goals
  • CF20, CA20, CF% – same as FF20, FA20, FF% but also includes shot attempts that were blocked (corsi)