Apr 122013
 

Even though I think the idea of ‘usage’ and ‘tough minutes’ is a vastly over stated factor in an individual players statistics they are interesting to look at as it gives us an indication of how a coach views the player. So for all the usage fans, here is another usage statistic which I will call the Leading-Trailing Index, or LT Index for short.

LT Index = TOI% when leading / TOI% when trailing

where TOI% is the percentage of the teams overall ice time (in games that the player played in) that the player is on the ice (so a 5v5 TOI% of 20% means the player was on the ice for 20% of the time that the team was at 5v5). Thus, the LT index is a ratio of the players ice time when his team is leading to his ice time when his team is trailing adjusted for the overall ice time that the team is leading/trailing. Any number greater than 1.00 indicates the player gets a greater share of ice time when the team is leading and anything under 1.00 indicates the player gets a greater share of ice time when the team is trailing.  So, any players with an LT index greater than one is used more as a defensive player than an offensive one and anything less than one they are used more as an offensive player than a defensive one. Any player around 1.00 is a well balanced player. So, looking at this seasons data we have the following player usage:

Defensive Usage

Defenseman LT Index Forward LT Index
MICHAEL STONE 1.21 BJ CROMBEEN 1.66
KEITH AULIE 1.20 MATHIEU PERREAULT 1.45
RYAN MCDONAGH 1.19 CRAIG ADAMS 1.35
PAUL MARTIN 1.16 TRAVIS MOEN 1.33
BRYCE SALVADOR 1.15 BOYD GORDON 1.26
BRENDAN SMITH 1.14 JAMES WRIGHT 1.26
SCOTT HANNAN 1.14 MICHAEL FROLIK 1.23
ANDREJ SEKERA 1.13 BRIAN BOYLE 1.22
MIKE WEBER 1.13 MATT CALVERT 1.22
JUSTIN BRAUN 1.12 TANNER GLASS 1.20
BARRET JACKMAN 1.12 MATT MARTIN 1.19
ROBYN REGEHR 1.12 RUSLAN FEDOTENKO 1.19
CLAYTON STONER 1.12 STEPHEN GIONTA 1.19
ANTON VOLCHENKOV 1.11 CASEY CIZIKAS 1.19
RON HAINSEY 1.11 JEFF HALPERN 1.18
TIM GLEASON 1.11 DAVID JONES 1.17
ROSTISLAV KLESLA 1.11 NIKOLAI KULEMIN 1.17
ROB SCUDERI 1.10 ZACK KASSIAN 1.17
NIKLAS HJALMARSSON 1.10 RYAN CARTER 1.16
NICKLAS GROSSMANN 1.10 TORREY MITCHELL 1.16

Offensive Usage

Defenseman LT Index Forward LT Index
RYAN ELLIS 0.78 DEREK DORSETT 0.77
KRIS LETANG 0.84 RAFFI TORRES 0.77
MARK STREIT 0.86 TAYLOR HALL 0.79
KYLE QUINCEY 0.87 CORY CONACHER 0.79
MATT NISKANEN 0.87 JORDAN EBERLE 0.80
JUSTIN SCHULTZ 0.87 NAIL YAKUPOV 0.82
DOUGIE HAMILTON 0.88 RYAN NUGENT-HOPKINS 0.82
VICTOR HEDMAN 0.88 RICH CLUNE 0.82
DAN BOYLE 0.89 BLAKE COMEAU 0.82
KEVIN SHATTENKIRK 0.89 KYLE PALMIERI 0.84
ALEX PIETRANGELO 0.89 BRENDAN GALLAGHER 0.84
JOHN-MICHAEL LILES 0.90 CLAUDE GIROUX 0.86
JOHN CARLSON 0.90 VINCENT LECAVALIER 0.86
P.K. SUBBAN 0.90 DREW SHORE 0.86
LUBOMIR VISNOVSKY 0.91 TJ OSHIE 0.87
CODY FRANSON 0.91 ALEX OVECHKIN 0.87
JAMIE MCBAIN 0.91 JONATHAN HUBERDEAU 0.87
ROMAN JOSI 0.92 NICKLAS BACKSTROM 0.87
JARED SPURGEON 0.93 SCOTT HARTNELL 0.87
CHRISTIAN EHRHOFF 0.93 MARIAN HOSSA 0.87

Balanced Usage

Defenseman LT Index Forward LT Index
MICHAEL DEL_ZOTTO 0.99 BRYAN LITTLE 0.99
ERIC BREWER 0.99 MIKE FISHER 0.99
JAKUB KINDL 0.99 MIKKEL BOEDKER 0.99
ADRIAN AUCOIN 0.99 ALEXEI PONIKAROVSKY 0.99
ALEX GOLIGOSKI 0.99 JASON POMINVILLE 0.99
ERIK GUDBRANSON 1.00 CHRIS STEWART 0.99
DREW DOUGHTY 1.00 DANIEL BRIERE 1.00
THOMAS HICKEY 1.00 RADIM VRBATA 1.00
JOHNNY ODUYA 1.00 ALEX TANGUAY 1.00
SLAVA VOYNOV 1.00 GABRIEL LANDESKOG 1.00
MATT IRWIN 1.00 JIRI TLUSTY 1.00
FRANCIS BOUILLON 1.01 COLIN WILSON 1.00
JONAS BRODIN 1.01 PATRICK DWYER 1.00
BRENT SEABROOK 1.01 JADEN SCHWARTZ 1.01
JOSH GORGES 1.01 BRANDON SAAD 1.01
DUSTIN BYFUGLIEN 1.01 LEO KOMAROV 1.01
BRENDEN DILLON 1.01 DREW MILLER 1.01
GREG ZANON 1.01 DAVID PERRON 1.01
KRIS RUSSELL 1.02 TOM PYATT 1.01

It’s amazing how much more BJ Crombeem gets used protecting a lead than when trailing. You’d have to think that score effects could have a significant impact on his stats because of this. Not really a lot of surprises there though though in the case of a guy like Derek Dorsett him being in the ‘offensive usage’ category has more with the coach not wanting to use him defending a lead than hoping he will score a goal to get the team back in the game.

 

Apr 122013
 

Now that I have added home and road stats to stats.hockeyanalysis.com I can take a look at how quality of competition differs when the team is at home vs when they are on the road. In theory because the home team has last change they should be able to dictate the match ups better and thus should be able to drive QoC a bit better. Let’s take a look at the top 10 defensemen in HARO QoC last season at home and on the road (defensemen with 400 5v5 home/road minutes were considered).

Player Name Home HARO QOC Player Name Road HARO QOC
GIRARDI, DAN 8.81 MCDONAGH, RYAN 6.73
MCDONAGH, RYAN 8.49 GORGES, JOSH 6.48
PHANEUF, DION 8.46 GIRARDI, DAN 6.03
GARRISON, JASON 8.27 SUBBAN, P.K. 5.95
GORGES, JOSH 8.25 PHANEUF, DION 5.94
GLEASON, TIM 8.21 GUNNARSSON, CARL 5.48
SUBBAN, P.K. 8.19 ALZNER, KARL 5.35
WEAVER, MIKE 7.92 STAIOS, STEVE 5.15
ALZNER, KARL 7.74 TIMONEN, KIMMO 4.95
REGEHR, ROBYN 7.72 WEAVER, MIKE 4.67

There is definitely a lot of common names in each list but we do notice that the HARO QoC is greater at home than on the road for these defensemen. Next I took a look at the standard deviation of all the defensemen with 400 5v5 home/road minutes last season which should give us an indication of how much QoC varies from player to player.

StdDev
Home 3.29
Road 2.45

The standard deviation is 34% higher at home than on the road which again confirms that variation in QoC are greater at home than on the road.  All of this makes perfect sense but it is nice to see it backed up in actual numbers.

 

 

Apr 112013
 

Stats.hockeyanalysis.com has just gotten even better! Several people have asked why I have zone start adjusted stats for team stats and it is a good question. The answer to that is that it was just easier from a programming point of view to have the same ‘situations’ for both the player level and the team level and since I was already calculating, for example, 5v5close zone start adjusted data for players it was east to add 5v5close zone start adjusted data for teams. Since it makes sense to have non-zone start adjusted data for teams it was on my todo list to get it implemented. So, now it is done, and so much more. The situations that you can access data for at both the player and team level are:

  • 5v5
  • 5v5 home
  • 5v5 road
  • 5v5 close
  • 5v5 tied
  • 5v5 leading
  • 5v5 trailing
  • 5v5 up 1 goal
  • 5v5 up 2+ goals
  • 5v5 down 1 goal
  • 5v5 down 2+ goals
  • 5v4 PP
  • 4v5 PK

In addition to all of the above, all of the above are also available in their Zone Adjusted forms except for the 5v4 PP and 4v5 PK situations. In total, there are now 24 different situations you can search for stats on.  Have at it and don’t blame me for any lost weekends (or lost productivity at work).

(As usual, if you find any issues with the new data please let me know. The stats should be correct but while I have done some testing on the new code to display the stats but it isn’t completely tested.)

 

Apr 112013
 

Every now and again someone asks me how I calculate HARO, HARD and HART ratings that you can find on stats.hockeyanalysis.com and it is at that point I realize that I don’t have an up to date description of how they are calculated so today I endeavor to write one.

First, let me define HARO, HARD and HART.

HARO – Hockey Analysis Rating Offense
HARD – Hockey Analysis Rating Defense
HART – Hockey Analysis Rating Total

So my goal when creating then was to create an offensive defensive and overall total rating for each and every player. Now, here is a step by step guide as to how they are calculated.

Calculate WOWY’s and AYNAY’s

The first step is to calculate WOWY’s (With Or Without You) and AYNAY’s (Against You or Not Against You). You can find goal and corsi WOWY’s and AYNAY’s on stats.hockeyanalysis.com for every player for 5v5, 5v5 ZS adjusted and 5v5 close zone start adjusted situations but I calculate them for every situation you see on stats.hockeyanalysis.com and for shots and fenwick as well but they don’t get posted because it amounts to a massive amounts of data.

(Distraction: 800 players playing against 800 other players means 640,000 data points for each TOI, GF20, GA20, SF20, SA20, FF20, FA20, CF20, CA20 when players are playing against each other and separate of each other per season and situation, or about 17.28 million data points for AYNAY’s for a single season per situation. Now consider when I do my 5 year ratings there are more like 1600 players generating more than 60 million datapoints.)

Calculate TMGF20, TMGA20, OppGF20, OppGA20

What we need the WOWY’s for is to calculate TMGF20 (a TOI with weighted average GF20 of the players teammates when his team mates are not playing with him), TMGA20 (a TOI with weighted average GA20 of the players teammates when his team mates are not playing with him), OppGF20 (a TOI against weighted average GF20 of the players opponents when his opponents are not playing against him) and OppGA20 (a TOI against weighted average GA20 of the players opponents when his opponents are not playing against him).

So, let’s take a look at Alexander Steen’s 5v5 WOWY’s for 2011-12 to look at how TMGF20 is calculated. The columns we are interested in are the Teammate when apart TOI and GF20 columns which I will call TWA_TOI and TWA_GF20. TMGF20 is simply a TWA_TOI (teammate while apart time on ice) weighted average of TWA_GF20. This gives us a good indication of how Steen’s teammates perform offensively when they are not playing with Steen.

TMGA20 is calculated the same way but using TWA_GA20 instead of TWA_GF20. OppGF20 is calculated in a similar manner except using OWA_GF20 (Opponent while apart GF20) and OWA_TOI while OppGA20 uses OWA_GA20.

The reason why I use while not playing with/against data is because I don’t want to have the talent level of the player we are evaluating influencing his own QoT and QoC metrics (which is essentially what TMGF20, TMGA20, OppGF20, OppGA20 are).

Calculate first iteration of HARO and HARD

The first iteration of HARO and HARD are simple. I first calculate an estimated GF20 and an estimated GA20 based on the players teammates and opposition.

ExpGF20 = (TMGF20 + OppGA20)/2
ExpGA20 = (TMGA20 + OppGF20)/2

Then I calculate HARO and HARD as a percentage improvement:

HARO(1st iteration) = 100*(GF20-ExpGF20) / ExpGF20
HARD(1st iteration) = 100*(ExpGA20 – GA20) / ExpGA20

So, a HARO of 20 would mean that when the player is on the goal rate of his team is 20% higher than one would expect based on how his teammates and opponents performed during time when the player is not on the ice with/against them. Similarly, a HARD of 20 would mean the goals against rate of his team is 20% better (lower) than expected.

(Note: The OppGA20 that gets used is from the complimentary situation. For 5v5 this means the opposition situation is also 5v5 but when calculating a rating for 5v5 leading the opposition situation is 5v5 trailing so OppGF20 would be OppGF20 calculated from 5v5 trailing data).

Now for a second iteration

The first iteration used GF20 and GA20 stats which is a good start but after the first iteration we have teammate and opponent corrected evaluations of every player which means we have better data about the quality of teammates and opponents the player has. This is where things get a little more complicated because I need to calculate a QoT and QoC metric based on the first iteration HARO and HARD values and then I need to convert that into a GF20 and GA20 equivalent number so I can compare the players GF20 and GA20 to.

To do this I calculate a TMHARO rating which is a TWA_TOI weighted average of first iteration HARO. TMHARD and OppHARO and OppHARD are calculated in a similar manner. TMHARD, OppHARO and OppHARD are similarly calculated. Now I need to convert these to GF20 and GA20 based stats so I do that by multiplying by league average GF20 (LAGF20) and league average GA20 (LAGA20) and from here I can calculated expected GF20 and expected GA20.

ExpGF20(2nd iteration) = (TMHARO*LAGF20 + OppHARD*LAGA20)/2
ExpGA20(2nd iteration) = (TMHARD*LAGA20 + OppHARD*LAGF20)/2

From there we can get a second iteration of HARO and HARD.

HARO(2nd iteration) = 100*(GF20-ExpGF20) / ExpGF20
HARD(2nd iteration) = 100*(ExpGA20 – GA20) / ExpGA20

Now we iterate again and again…

Now we repeat the above step over and over again using the previous iterations HARO and HARD values at every step.

Now calculate HART

Once we have done enough iterations we can calculate HART from the final iterations HARO and HARD values.

HART = (HARO + HARD) /2

Now do the same for Shot, Fenwick and Corsi data

The above is for goal ratings but I have Shot, Fenwick and Corsi ratings as well and these can be calculated in the exact same way except using SF20, SA20, FF20, FA20, CF20 and CA20.

What about goalies?

Goalies are a little unique in that they only really play the defensive side of the game. For this reason I do not include goalies in calculating TMGF20 and OppGF20. For shot, fenwick and corsi I do not include the goalies on the defensive side of things either as I assume a goalie will not influence shots against (though this may not be entirely true as some goalies may be better at controlling rebounds and thus secondary shots but I’ll assume this is a minimal effect if it does exist). The result of this is goalies do have a HARD rating but no HARO, or shot/fenwick/corsi based HARD or HARO rating.

I hope this helps explain how my hockey analysis ratings are calculated but if you have any followup questions feel free to ask them in the comments.

 

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.

 

Apr 052013
 

Yesterday HabsEyesOnThePrize.com had a post on the importance of fenwick come playoff time over the past 5 seasons. It is definitely worth a look so go check it out. In the post they look at FF% in 5v5close situations and see how well it translates into post season success. I wanted to take this a step further and take a look at PDO and GF% in 5v5close situations to see of they translate into post season success as well.  Here is what I found:

Group N Avg Playoff Avg Cup Winners Lost Cup Finals Lost Third Round Lost Second Round Lost First Round Missed Playoffs
GF% > 55 19 2.68 2.83 5 1 2 6 4 1
GF% 50-55 59 1.22 1.64 0 2 6 10 26 15
GF% 45-50 52 0.62 1.78 0 2 2 4 10 34
GF% <45 20 0.00 - 0 0 0 0 0 20
FF% > 53 23 2.35 2.35 3 2 4 5 9 0
FF% 50-53 55 1.15 1.70 2 2 1 10 22 18
FF% 47-50 46 0.52 1.85 0 0 4 3 6 33
FF% <47 26 0.54 2.00 0 1 1 2 3 19
PDO >1010 27 1.63 2.20 2 2 2 6 8 7
PDO 1000-1010 42 1.17 1.75 1 0 5 7 15 14
PDO 990-1000 47 0.91 1.95 2 1 3 4 12 25
PDO <990 34 0.56 1.90 0 2 0 3 5 24

I have grouped GF%, FF% and PDO into four categories each, the very good, the good, the mediocre and the bad and I have looked at how many teams made it to each round of the playoffs from each group. If we say that winning the cup is worth 5 points, getting to the finals is worth 4, getting to the 3rd round is worth 3, getting to the second round is worth 2, and making the playoffs is worth 1, then the Avg column is the average point total for the teams in that grouping.  The Playoff Avg is the average point total for teams that made the playoffs.

As HabsEyesOnThePrize.com found, 5v5close FF% is definitely an important factor in making the playoffs and enjoying success in the playoffs. That said, GF% seems to be slightly more significant. All 5 Stanley Cup winners came from the GF%>55 group while only 3 cup winners came from the FF%>53 group and both Avg and PlayoffAvg are higher in the GF%>55 group than the FF%>53 group. PDO only seems marginally important, though teams that have a very good PDO do have a slightly better chance to go deeper into the playoffs. Generally speaking though, if you are trying to predict a Stanley Cup winner, looking at 5v5close GF% is probably a better metric than looking at 5v5close FF% and certainly better than PDO. Now, considering this is a significantly shorter season than usual, this may not be the case as luck may be a bit more of a factor in GF% than usual but historically this has been the case.

So, who should we look at for playoff success this season?  Well, there are currently 9 teams with a 5v5close GF% > 55.  Those are Anaheim, Boston, Pittsburgh, Los Angeles, Montreal, Chicago, San Jose, Toronto and Vancouver. No other teams are above 52.3% so that is a list unlikely to get any new additions to it before seasons end though some could certainly fall out of the above 55% list. Now if we also only consider teams that have a 5v5close FF% >50% then Toronto and Anaheim drop off the list leaving you with Boston, Pittsburgh, Los Angeles, Montreal, Chicago, San Jose and Vancouver as your Stanley Cup favourites, but we all pretty much knew that already didn’t we?

 

Apr 012013
 

I have been on a bit of a mission recently to push the idea that quality of competition (and zone starts) is not a huge factor in ones statistics and that most people in general over value its importance. I don’t know how often I hear arguments like “but he plays all the tough minutes” as an excuse as to why a player has poor statistics and pretty much every time I do I cringe because almost certainly the person making the argument has no clue how much those tough minutes impact a players statistics.

While thinking of how to do this study, and which players to look at, I was listening to a pod cast and the name Pavel Datsyuk was brought up so I decided I would take a look at him because in addition to being mentioned in a pod cast he is a really good 2-way player who plays against pretty tough quality of competition. For this study I looked at 2010-12 two year data and Datsyuk has the 10th highest HART QoC during that time in 5v5 zone start adjusted situations.

The next step was to look how Datsyuk performed against various types of opposition. To do this I took all of Datsyuk’s opponent forwards who had he played at least 10 minutes of 5v5 ZS adjusted ice time against (you can find these players here) and grouped them according to their HARO, HARD, CorHARO and CorHARD ratings and looked at how Datsyuk’s on-ice stats looked against each group.

OppHARO TOI% GA20
>1.1 46.84% 0.918
0.9-1.1 34.37% 0.626
<0.9 18.79% 0.391

Lets go through a quick explanation of the above table. I have grouped Datsyuk’s opponents by their HARO ratings into three groups, those with a HARO >1.1, those with a HARO between 0.9 and 1.1 and those with a HARO rating below 0.9. These groups represent strong offensive players, average offensive players and weak offensive players. Datsyuk played 46.84% of his ice time against the strong offensive player group, 34.37% against the average offensive player group and 18.79% against the weak offensive player group. The GA20 column is Datsyuk’s goals against rate, or essentially the goals for rate of Datsyuk’s opponents when playing against Datsyuk. As you can see, the strong offensive players do significantly better than the average offensive players who in turn do significantly better than the weak offensive players.

Now, let’s look at how Datsyuk does offensively based on the defensive ability of his opponents.

OppHARD TOI% GF20
>1.1 35.39% 1.171
0.9-1.1 35.36% 0.994
<0.9 29.25% 1.004

Interestingly, the defensive quality of Datsyuk’s opponents did not have a significant impact on Datsyuk’s ability to generate offense which is kind of an odd result.

Here are the same tables but for corsi stats.

OppCorHARO TOI% CA20
>1.1 15.59% 15.44
0.9-1.1 77.79% 13.78
<0.9 6.63% 10.84

 

OppCorHARD TOI% CF20
>1.1 18.39% 15.89
0.9-1.1 68.81% 18.49
<0.9 12.80% 22.69

I realize that I should have tightened up the ratings splits to get a more even distribution in TOI% but I think we see the effect of QoC fine. When looking at corsi we do see that CF20 varies across defensive quality of opponent which we didn’t see with GF20.

From the tables above, we do see that quality of opponent can have a significant impact on a players statistics. When you are playing against good offensive opponents you are bound to give up a lot more goals than you will against weaker offensive opponents. The question remains is whether players can and do play a significantly greater amount of time against good opponents compared to other players. To take a look at this I looked at the same tables above but for Valtteri Filppula, a player who rarely gets to play with Datsyuk so in theory could have a significantly different set of opponents to Datsyuk. Here are the same tables above for Filppula.

OppHARO TOI% GA20
>1.1 42.52% 1.096
0.9-1.1 35.35% 0.716
<0.9 22.12% 0.838

 

OppHARD TOI% GF20
>1.1 32.79% 0.841
0.9-1.1 35.53% 1.197
<0.9 31.68% 1.370

 

OppCorHARO TOI% GA20
>1.1 12.88% 19.03
0.9-1.1 78.20% 16.16
<0.9 8.92% 14.40

 

OppCorHARD TOI% GF20
>1.1 20.89% 15.48
0.9-1.1 64.94% 17.16
<0.9 14.17% 19.09

Nothing too exciting or unexpected in those tables. What is more important is how the ice times differ from Datsyuk’s across groups and how those differences might affect Filppula’s statistics.

We see that Datsyuk plays a little bit more against good offensive players and a little bit less against weak offensive players and he also plays a little bit more against good defensive players and a little bit less against weak defensive players. If we assume that Filppula played Datsyuk’s and that Datsyuk’s within group QoC ratings was the same as Filppula’s we can calculate what Filppula’s stats will be against similar QoC.

Actual w/ DatsyukTOI
GF20 1.135 1.122
GA20 0.905 0.917
GF% 55.65% 55.02%
CF20 17.08 17.09
CA20 16.37 16.49
CF% 51.05% 50.90%

As you can see, that is not a huge difference. If we gave Filppula the same QoC as Datsyuk instead of being a 55.65% GF% player he’d be a 55.02% GF% player. That is hardly enough to worry about and the difference in CF% is even less.

From this an any other study I have looked at I have found very little evidence that QoC has a significant impact on a players statistics. The argument that a player can have bad stats because he plays the ‘tough minutes’ is, in my opinion, a bogus argument. Player usage can have a small impact on a players statistics but it is not anything to be concerned with for the vast majority of players and it will never make a good player have bad statistics or a bad player have good statistics. Player usage charts (such as those found here or those found here) are interesting and pretty neat and do give you an idea of how a coach uses his players but as a tool for justifying a players good, or poor, performance they are not. The notion of ‘tough minutes’ exists, but are not all that important over the long haul.

 

 

Mar 202013
 

I generally think that the majority of people give too much importance to quality of competition (QoC) and its impact on a players statistics but if we are going to use QoC metrics let’s at least try and use the best ones available. In this post I will take a look at some QoC metrics that are available on stats.hockeyanalysis.com and explain why they might be better than those typically in use.

OppGF20, OppGA20, OppGF%

These three stats are the average GF20 (on ice goals for per 20 minutes), OppGA20 (on ice goals against per 20 minutes) and GF% (on ice GF / [on ice GF + on ice GA]) of all the opposition players that a player lined up against weighted by ice time against. In fact, these stats go a bit further in that they remove the ice time the opponent players played against the player so that a player won’t influence his own QoC (not nearly as important as QoT but still a good thing to do). So, essentially these three stats are the goal scoring ability of the opposition players, the goal defending ability of the opposition players, and the overall value of the opposition players. Note that opposition goalies are not included in the calculation of OppGF20 as it is assume the goalies have no influence on scoring goals.

The benefits of using these stats are they are easy to understand and are in a unit (goals per 20 minutes of ice time) that is easily understood. GF20 is essentially how many goals we expect the players opponents would score on average per 20 minutes of ice time. The drawback from this stat is that if good players play against good players and bad players play against bad players a good player and a bad player may have similar statistics but the good players is a better player because he did it against better quality opponents. There is no consideration for the context of the opponents statistics and that may matter.

Let’s take a look at the top 10 forwards in OppGF20 last season.

Player Team OppGF20
Patrick Dwyer Carolina 0.811
Brandon Sutter Carolina 0.811
Travis Moen Montreal 0.811
Carl Hagelin NY Rangers 0.806
Marcel Goc Florida 0.804
Tomas Plekanec Montreal 0.804
Brooks Laich Washington 0.800
Ryan Callahan NY Rangers 0.799
Patrik Elias New Jersey 0.798
Alexei Ponikarovsky New Jersey 0.795

You will notice that every single player is from the eastern conference. The reason for this is that the eastern conference is a more offensive conference. Taking a look at the top 10 players in OppGA20 will show the opposite.

Player Team OppGF20
Marcus Kruger Chicago 0.719
Jamal Mayers Chicago 0.720
Mark Letestu Columbus 0.721
Andrew Brunette Chicago 0.723
Andrew Cogliano Anaheim 0.723
Viktor Stalberg Chicago 0.724
Matt Halischuk Nashville 0.724
Kyle Chipchura Phoenix 0.724
Matt Belesky Anaheim 0.724
Cory Emmerton Detroit 0.724

Now, what happens when we look at OppGF%?

Player Team OppGF%
Mike Fisher Nashville 51.6%
Martin Havlat San Jose 51.4%
Vaclav Prospal Columbus 51.3%
Mike Cammalleri Calgary 51.3%
Martin Erat Nashville 51.3%
Sergei Kostitsyn Nashville 51.3%
Dave Bolland Chicago 51.2%
Rick Nash Columbus 51.2%
Travis Moen Montreal 51.0%
Patrick Marleau San Jose 51.0%

There are predominantly western conference teams with a couple of eastern conference players mixed in. The reason for this western conference bias is that the western conference was the better conference and thus it makes sense that the QoC would be tougher for western conference players.

OppFF20, OppFA20, OppFF%

These are exactly the same stats as the goal based stats above but instead of using goals for/against/percentage they use fenwick for/against/percentage (fenwick is shots + shots that missed the net). I won’t go into details but you can find the top players in OppFF20 here, in OppFA20 here, and OppFF% here. You will find a a lot of similarities to the OppGF20, OppGA20 and OppGF% lists but if you ask me which I think is a better QoC metric I’d lean towards the goal based ones. The reason for this is that the smaller sample size issues we see with goal statistics is not going to be nearly as significant in the QoC metrics because over all opponents luck will average out (for every unlucky opponent you are likely to have a lucky one t cancel out the effects). That said, if you are doing a fenwick based analysis it probably makes more sense to use a fenwick based QoC metric.

HARO QoC, HARD QoC, HART QoC

As stated above, one of the flaws of the above QoC metrics is that there is no consideration for the context of the opponents statistics. One of the ways around this is to use the HockeyAnalysis.com HARO (offense), HARD (defense) and HART (Total/Overall) ratings in calculating QoC. These are player ratings that take into account both quality of teammates and quality of competition (here is a brief explanation of what these ratings are).The HARO QoC, HARD QoC and HART QoC metrics are simply the average HARO, HARD and HART ratings of players opponents.

Here are the top 10 forwards in HARO QoC last year:

Player Team HARO QoC
Patrick Dwyer Carolina 6.0
Brandon Sutter Carolina 5.9
Travis Moen Montreal 5.8
Tomas Plekanec Montreal 5.8
Marcel Goc Florida 5.6
Carl Hagelin NY Rangers 5.5
Ryan Callahan NY Rangers 5.3
Brooks Laich Washington 5.3
Michael Grabner NY Islanders 5.2
Patrik Elias New Jersey 5.2

There are a lot of similarities to the OppGF20 list with the eastern conference dominating. There are a few changes, but not too many, which really is not that big of a surprise to me knowing that there is very little evidence that QoC has a significant impact on a players statistics and thus considering the opponents QoC will not have a significant impact on the opponents stats and thus not a significant impact on a players QoC. That said, I believe these should produce slightly better QoC ratings. Also note that a 6.0 HARO QoC indicates that the opponent players are expected to produce a 6.0% boost on the league average GF20.

Here are the top 10 forwards in HARD QoC last year:

Player Team HARD QoC
Jamal Mayers Chicago 6.0
Marcus Kruger Chicago 5.9
Mark Letestu Columbus 5.8
Tim Jackman Calgary 5.3
Colin Fraser Los Angeles 5.2
Cory Emmerton Detroit 5.2
Matt Belesky Anaheim 5.2
Kyle Chipchura Phoenix 5.1
Andrew Brunette Chicago 5.1
Colton Gilles Columbus 5.0

And now the top 10 forwards in HART QoC last year:

Player Team HART QoC
Dave Bolland Chicago 3.2
Martin Havlat San Jose 3.0
Mark Letestu Columbus 2.5
Jeff Carter Los Angeles 2.5
Derick Brassard Columbus 2.5
Rick Nash Columbus 2.4
Mike Fisher Nashville 2.4
Vaclav Prospal Columbus 2.2
Ryan Getzlaf Anaheim 2.2
Viktor Stalberg Chicago 2.1

Shots and Corsi based QoC

You can also find similar QoC stats using shots as the base stat or using corsi (shots + shots that missed the net + shots that were blocked) on stats.hockeyanalysis.com but they are all the same as above so I’ll not go into them in any detail.

CorsiRel QoC

The most common currently used QoC metric seems to be CorsiRel QoC (found on behindthenet.ca) but in my opinion this is not so much a QoC metric but a ‘usage’ metric. CorsiRel is a statistic that compares the teams corsi differential when the player is on the ice to the teams corsi differential when they player is not on the ice.  CorsiRel QoC is the average CorsiRel of all the players opponents.

The problem with CorsiRel is that good players on a bad team with little depth can put up really high CorsiRel stats compared to similarly good players on a good team with good depth because essentially it is comparing a player relative to his teammates. The more good teammates you have, the more difficult it is to put up a good CorsiRel. So, on any given team the players with a good CorsiRel are the best players on team team but you can’t compare CorsiRel on players on different teams because the quality of the teams could be different.

CorsiRel QoC is essentially the average CorsiRel of all the players opponents but because CorsiRel is flawed, CorsiRel QoC ends up being flawed too. For players on the same team, the player with the highest CorsiRel QoC plays against the toughest competition so in this sense it tells us who is getting the toughest minutes on the team, but again CorsiRel QoC is not really that useful when comparing players across teams.  For these reasons I consider CorsiRel QoC more of a tool to see the usage of a player compared to his teammates, but is not in my opinion a true QoC metric.

I may be biased, but in my opinion there is no reason to use CorsiRel QoC anymore. Whether you use GF20, GA20, GF%, HARO QoC, HARD QoC, and HART QoC, or any of their shot/fenwick/corsi variants they should all produce better QoC measures that are comparable across teams (which is the major draw back of CorsiRel QoC.

 

Mar 142013
 

I often see people using zone starts and/or quality of competition as a way to justify any players unexpectedly poor or unexpectedly good play. Player X has a bad goal or corsi ratio because he plays all the tough minutes (i.e. the defensive zone starts and against the oppositions best lines). I am pretty certain that quality of competition is vastly over emphasized (everyone plays against everyone to some extent) and is vastly overshadowed by individual skill and quality of teammates, and I think zone starts do as well.

Eric Tulsky at NHL Numbers.com posted a good review of the research into the zone start effects on corsi statistics and I recommend people give that a read. I want to look into the issue a little further though. Most of the attempts to identify the impact of zone starts on a players stats have been inferred by looking at the league-wide correlations or by actual counting of how many shots are taken after a zone face off. Both of these have their faults. As Eric Tulsky pointed out, taking a correlation of every players corsi with their zone start stats doesn’t take into account that it is the top line players that usually get the offensive zone starts and thus this likely over estimates the impact as these players do take more shots regardless of their zone start. Eric Tulsky also took the time to count the number of fenwick events that occur between an offensive zone face off and the time the puck leaves the offensive zone and estimated that to be 0.31. This would imply that every extra offensive zone start a player takes is worth 0.31 fenwick events. Of course, this doesn’t take into account that the best offensive players in the league typical get more  offensive zone starts but it also doesn’t consider what happens after the puck leaves the zone. If the puck leaves the zone under the opposing teams control there is probably a negative fenwick effect for the next several seconds of play reducing the 0.31 number further.

I want to get beyond these issues by taking a look at how zone starts affect individual players. I have previously argued that after 10 seconds of an offensive/defensive zone face off the majority of the benefit (or penalty) of an offensive (or defensive zone) face off has worn off. I wanted to take it a bit further to be sure that there is no residual effect and chose to conduct this analysis using a 45 second cut off. So, any time within 45 seconds of an offensive or defensive zone face off with no other stoppages in play will be eliminated in my face off adjusted data. This should eliminate pretty much every second of every shift that started with an offensive or defensive zone face off leaving just the play that occurred after a neutral zone face off or on the fly changes. I am going to call this ice time F45 ice time and it will represent ice time that is not in any way affected by zone starts. With this in mind, I will take a look at the differences between straight 5v5 stats and the F45 stats and the differences will give me an indication of how significant zone starts impact a players stats.

To do this I will look at both corsi for and corsi against stats on a per 20 minutes of ice time basis. It should be noted that corsi rates are about 7.5% higher during the f45 play (goal rates are ~15% higher!) so I will reduce the f45 corsi rates by 7.5% to account for this and conduct a fair comparison (previous zone start studies may have been impacted by this as well). Now, let’s take a look at eight players (Manny Malhotra, Dave Bolland, Brian Boyle, Jay McClement, Tanner Glass, Brandon Sutter, Adam Hall, and Taylor Pyatt) with an excess of defensive zone starts.

OZ% DZ% OZ%-DZ% FF20 FA20 FF%
Malhotra 12.2 54.6 -42.4 -3.09% 1.09% -1.0%
Bolland 19.8 40.5 -20.7 8.94% -5.25% 3.5%
B. Boyle 21.0 40.2 -19.2 2.87% 8.74% 0.3%
McClement 24.8 41.9 -17.1 -0.31% 1.34% -0.4%
Glass 20.5 37.1 -16.6 4.39% -6.00% 2.6%
Sutter 23.1 36.6 -13.5 -2.67% 2.32% -1.2%
Hall 20.7 33.9 -13.2 -4.06% 4.59% -2.2%
Pyatt 24.0 36.4 -12.4 0.38% -0.25% 0.2%
Average 20.8 40.2 -19.4 0.81% 0.82% 0.23%

The FF20 and FA20 columns show the % change in from 5v5 play to F45 play and the FF% column shows the 5v5 FF% – F45 FF%. The averages are a straight average, not weighted for ice time or zone starts. For players that have a significant defensive zone bias we would expect their F45 play to exhibit an increase in FF20 and a decrease in FA20 resulting in an increase in FF%. In bold are the circumstances where this in fact did happen. As you can see, this isn’t the majority of the time. It is actually kind of surprising that these heavily defensive zone start biased players didn’t see a significant and systematic improvement in their fenwick rates.

Now, let’s take a look at eight players (Henrik Sedin, Patrick Kane, Maian Gaborik, Justin Abdelkader, Kyle Wellwood, Tomas Vanek, John Tavares, Jason Arnott) who had a heavy offensive zone start bias.

OZ% DZ% OZ%-DZ% FF20 FA20 FF%
H. Sedin 49.3 16.2 33.1 -3.72% 1.81% -1.4%
P. Kane 41.4 20.3 21.1 5.94% 4.66% 0.3%
Gaborik 39.0 22.8 16.2 0.60% 2.32% -0.4%
Abdelkader 37.5 26.0 11.5 3.93% 3.49% 0.1%
K. Wellwood 36.9 27.6 9.3 4.54% -2.32% 1.7%
Vanek 36.2 27.2 9.0 -3.39% 1.06% -1.1%
Tavares 35.8 27.2 8.6 -2.39% 1.83% -1.0%
Arnott 36.4 28.0 8.4 -3.41% 1.81% -1.3%
Average 39.1 24.4 14.7 0.26% 1.83% -0.39%

For offensive zone start biased players we would expect to see their FF20 decrease, FA20 increase and FF% decrease when we remove their zone start bias. This is mostly true for FA10 (only Wellwood deviated from expectations) but less true for FF20 and FF% and overall the adjustments were relatively minor. Henrik Sedin had the greatest negative impact to his FF% but it only took him from a 55.2% fenwick player to a 53.8% fenwick player which is still pretty good. This could very well be an upper bound on the benefit of excessive offensive zone starts.

Eric Tulsky also presented a paper at the recent Sloan Sports Analytics Conference in which he suggested that a successful zone entry via carrying the puck in is worth upwards of 0.60 fenwick and upwards of 0.28 fenwick on a dump in. As pointed out earlier, Eric Tulsky counted o.31 fenwick between an offensive zone face off and the puck clearing the zone so and if the other team is clearing the zone with control of the puck, it is certainly possible that they will generate almost as many shots on their subsequent counter-rush essentially negating much of the benefit of the offensive zone start. Without studying zone exits and how frequently zone exists result in successful zone entries into opposing teams end we won’t know for sure, but the data shown above indicates that this might be the case.

The next question that might be worth exploring is, if there is no significant benefit to starting your offensive players in the offensive zone, is there a penalty? For example, might it be better for the Canucks to start the Sedin’s solely in the defensive and neutral zones on the theory that their talent with the puck will allow them to more frequently carry the puck into the offensive zone which, as Eric Tulsky showed, more frequently results in shots and goals. I am not certain of that but might be worthy of further investigation.  I suspect again any benefit/penalty of any zone start deployment will largely be overshadowed by the players individual ability and the quality of their line mates. The ability to win puck battles, control the puck and move it up the ice is the real driver of stats, not usage of the player.

All of this is to say that coaching strategy (at least player usage strategy) is probably not a significant factor in the statistical performance of the players or the outcomes of games and I suspect, as I previously found, the majority of the benefit of an offensive zone start is those situations where you win a face off, take a shot resulting in a goal or the goalie catching it or covering it for another face off.  If the play goes beyond that individual talent (puck retrieval for example) takes over and the opposition will get an opportunity to counter attack. This is why, as I previously determined, eliminating the first 10 seconds after a face off is sufficient for eliminating the majority of the effects of a zone start and even then, the effects are probably not as significant as we think they should be.

 

Mar 082013
 

Cam Charron has an interesting post on the state of Hockey Analytics over at The Score and how hockey executives are a step behind the hockey analytics bloggers but I have to disagree with one statement that Charron made.

There’s a reference in the Friedman piece to Craig MacTavish walking around looking for the “Aha!” moment when it comes to hockey analytics. I don’t think MacTavish has realized that half the hockey world is a step ahead of him in that regard. The “Aha!” moment comes when you realize that shots are a hell of a lot more predictive than goals for determining future events. As soon as you realize that hockey is a game between two teams trying to take shots on goal, I think the rest of it falls into place.

The problem with that thinking is that the minute we think hockey is all about shots and not goals the whole system could fall apart.  We know that shot quality exists. It’s a fact of life. A 45′ shot is generally not nearly as tough as a 10′ shot. A shot from 20′ after a cross ice pass is more difficult than a shot from 20′ on a two on two rush before the guys turn back for a line change. A screened shot from the point is more difficult than an unscreened shot from the point. Shot quality in that sense exists and is undisputed. The only reason shot analytics work is if over a large enough sample the quality of shots averages out such that the average quality of shot for one team is more or less equal to the average quality of shot for another team. I differ with some the extent that this is the case, but for this discussion I’ll go along with that premise. Now the problem is, when hockey starts to incentivize shots rather than goals I am not certain that that premise will hold up. There are lots of time a player could shoot the puck, but chooses not to because it is not a good scoring chance. If we start rewarding players on the basis of shot totals and that player starts shooting in those bad scoring chance situations the premise by which shot analytics is based on falls apart. Hockey at its core is, and always will be, about scoring goals. The fact that shot differentials correlate highly with winning is an interesting observation, and maybe even a useful one, but to change the focus of the game to shot differentials from goals differentials is not likely a strategy that will work in the long run.

Positive shot differentials is a result of good play and not because a team chose shot differentials as their goal and achieved it. The reality is, to generate positive shot differential you need to:

  1. When you have control of the puck you generate an offensive opportunity from that puck possession more frequently and you give up control of the puck less frequently.
  2. When you do not have control of the puck you force the opposing team to give up the puck more frequently and generate an offensive opportunity less frequently.
  3. You gain possession more frequently than the opposition be that through winning face offs or winning the puck battles after shot attempts.

If you can win the puck battles, give away the puck less frequently and force the opposition to turnover the puck more you should win the shot differential contest. I suspect shot differential is highly correlated with winning because good teams do those three things better than their opposition and not choose to shoot more often than their opposition. We really need metrics to measure those three things but unfortunately we don’t have them. The work being done on carry the puck into the offensive zone vs dumping the puck in is valuable because it hits at the heart of those good attributes (i.e. what is the best way to generate an offensive opportunity when we have possession of the puck).

This isn’t to suggest that looking at shot totals is a bad thing. So long as we live in a world where driving shots is not the primary goal, shots totals can act as a proxy for identifying players who might have some of those other good attributes and since we have no good metric for measuring them. We just have to be careful that we aren’t identifying systems that result in more shots but not more good shots. Again, shots is not the goal, goals are.

Furthermore, it is quite possible that shot differential analytics can result in a value proposition for GMs. In my post last week about the declining predictive value of corsi/fenwick I showed that as sample sizes increase corsi/fenwick does a poorer job of predicting future events at the team level than with smaller sample sizes where as the percentages and goal metrics maintain or improve their predictive value. In that post I deliberately was careful about drawing any conclusions about what it meant because, to be honest, I am not completely sure what it means though I do have a couple of theories. One is that it could mean that corsi/fenwick is largely driven by the depth of the team and for many teams the second and third lines have a fair bit of turnover over the course of 2-3 years (where as elevated shooting percentage or save percentage is largely driven by the elite players who don’t change teams nearly as often). If GMs aren’t evaluating second-tier players using shot differential metrics they may not be replacing the players with similarly talented (shot differential-wise) players. If this were true, it could mean that this is a flaw in current thinking and that a smart GM could exploit this flaw but again by filling his second and third lines with positive shot differential players. This could give his team the depth it needs to win. It is just a theory but one worth exploring more.

In the end though, hockey is all about out scoring the opponent, not out shooting them. Always has been, always will be, and that is the way it should be. Realizing that that shot differentials is highly correlated with winning is not the ‘aha’ moment in the sense that all of hockey should change focus to out shooting over out scoring at the cost of shot quality because that won’t work. The focus always has to be how to generate more shots from good scoring plays, not just generating more shots.