David Johnson

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.

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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)

 

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
 

The Toronto Maple Leafs shooting percentage has been predicted to fall for a couple of months now but it has held steady. I know that about 5-6 weeks ago the Leafs 5v5 shooting percentage was at 10.4% and I predicted it was sure to fall but as of this morning their 5v5 shooting percentage is even higher at 10.59%. Here is a graph of their 5v5 shooting percentage through out the season.

Toronto Maple Leafs 2012-13 Shooting %

Toronto Maple Leafs 2012-13 Shooting % (shots across x-axis)

League average 5v5 shooting percentage is normally just shy of 8% and the Leafs are about 33% higher than that which is incredibly high. Over the previous 5 seasons only one team has maintained a 5v5 shooting percentage above 10% over the course of an 82 game season and that was the Washington Capitals in 2009-10 when they shot at a 10.39% clip and only a handful of teams have managed to post a 5v5 shooting percentage above 9%. What the Leafs are doing is quite extraordinary even if it is a shortened season. Only 13.4% of the running 50 shot shooting percentage data points in the above graph fall below the typical league average of 8% so about 86.6% of the time they are at or above average in shooting percentage.

The only other team with a 5v5 shooting percentage above 10% this season is the Tampa Bay Lighting but they have been falling back a bit lately and in danger of falling below the 10% line as they currently sit at 10.01%.

Barring a collapse the Leafs should almost certainly end the season with a shooting percentage above 10% but it is difficult to know how much of it is luck/circumstance/randomness and how much is truly skill.

 

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.