Feb 012013
 

Last week I introduced player TOI usage charts and one use I thought they had was to look at how a players usage changed during the downside of their careers. Today I will do just that by looking at Nicklas Lidstrom’s TOI charts over the last 5 seasons. Consider this an extension to my earlier article where I took a look at Lidstrom’s last few seasons of his career. Let’s get right at it with his 5v5 chart.

LidstromTOIChart

 

Lidstrom’s last big season was clearly 2007-08 and every year since he has been below his 2007-08 levels in terms of 5v5 ice time. What is interesting to note is how little (relatively) ice time he had during the 2010-11 season, the year he won the Norris Trophy. I think it was a big mistake that he was awarded the trophy that season and this is just a little more evidence of that. In fact, Lidstrom was 4th on the Red Wings in ESTOI/Game by defensemen which is why his TOI% in the chart above were so low that year. Rafalski retired in the summer of 2011 which meant Lidstrom would get a boost to his ice time in 2011-12.

So, what about his special teams play?

LidstromPPPKTOIChart

On the powerplay, Lidstrom maintained his level of playing ~60% of his teams 5v4 power play minutes but his penalty kill ice time dropped significantly over the final 2 seasons of his career.

Based on the above charts, the last year I think you could consider Lidstrom a true heavy work load stud of a defenseman was in 2007-08. He was still awfully good for a couple more years and quite good until he retired but his slow decline in ice time had begun.

 

Jan 302013
 

For those familiar with my history, I have been a big proponent that there is more to the game of hockey than corsi and that players can certainly drive on-ice shooting percentage. I have not done much work at the team level, but now that I have team stats up at stats.hockeyanalysis.com I figured I’d take a look.

Since shooting percentages can vary significantly over small sample sizes, my goal was to use the largest sample size possible.  As such, I used 5 years of team data (2007-08 through 2011-12) and looked at each teams shooting and save percentages over that time. During those 5 years Vancouver led all teams in 5v5 ZS adjusted save percentage shooting at 10.69% while Columbus trailed all teams with a 8.61% shooting percentage. What’s interesting to note is the top 6 teams are Vancouver, Washington, Chicago, Philadelphia, Boston and Pittsburgh, all what we would consider the teams with the best offensive talent in the league. Meanwhile, the bottom 5 teams are Columbus, Los Angeles, Phoenix, Carolina, and Minnesota, all teams (except maybe Carolina) more associated with defensive play and a defense-first system.

As far as save percentage goes, Phoenix led the league with a 91.83% save percentage while the NY Islanders trailed with an 89.04% save percentage. The top 5 teams were Phoenix, Boston, Anaheim, Nashville, and Montreal.  The bottom 5 teams were NY Islanders, Tampa, Toronto, Chicago and Ottawa. Not surprises there.

As far as sample size goes, teams on average had 7,627 shots for (or against) over the course of the 5 years which gives us a reasonable large sample size to work with.

Now, in order to not use an extreme situation, I decided to compare the 5th best team to the 5th worst team in each category and then determine the probability that their deviations from each other are solely due to randomness.  This meant I was comparing Boston to Minnesota for shooting percentage and Montreal to Ottawa for save percentage.

TeamShootingPercentageComp

As you can see, there isn’t a lot of overlap, meaning there isn’t a large probability that luck is the reason for the difference between these two teams 5 year save percentages.  In fact, the intersecting area under the two curves amounts to just a 6.2% chance that the differences are luck driven.  That’s pretty small and the differences between the teams above Boston and below Minnesota would be greater. I think we can be fairly certain that there are statistically significant differences between teams 5 year shooting percentages and considering how much player movement and coaching changes there are over the span of 5 years it makes it that much more impressive. Single seasons differences could in theory (and probably likely are) more significant.

TeamSavePercentageComp

The save percentage chart provides even stronger evidence that there are non-luck factors at play.  The intersecting area under the curves equates to a 2.15% chance that the differences are due to luck alone. There is easily a statistically significant differences between Ottawa and Montreal’s 5 year save percentages. Long-term team save percentages are not luck driven!

So, the next question is, how much does it matter?  Well, the average team takes approximately 1500 5v5 ZS adjusted shots each season. The differences in shooting percentage between the 5th best team and the 5th worst team is 1.27% so that would equate to a difference of 19 goals per year during 5v5 ZS adjusted situations. The difference between the 5th best and 5th worst team in save percentage is 1.5% which equates to a 22.5 goal difference. These are not insignificant goal totals and they are likely driven solely by the percentages.

Now, how does this equate to differences in shot rates? If we take the team with the 5th highest shot rate and apply a league average shooting percentage and then compare it to the team with the 5th lowest shot rate we would find a difference of 17.5 goals over the course of a single season. This is slightly lower than what we saw for shooting and save percentages.

What is interesting is this (the percentages being more important than the shot rates) is not inconsistent with what we have seen at the individual level. In Tom Awad’s “What makes Good Players Good, Part I” post he identified 3 skills that good players differed from bad players. He identified the variation in +/- due to finishing as being 0.42 for finishing (shooting percentage), 0.08 for shot quality (shot location) and 0.30 for out shooting which would equate to out shooting being just 37.5% of the overall difference. I also showed that fenwick shooting percentage is more important than fenwick rates by a fairly significant margin.

Any player or team evaluation that doesn’t take into account the percentages or assumes the percentages are all luck driven is an evaluation that is not telling you the complete story.

 

Jan 252013
 

The last few days I have been looking at the percentage of a teams ice time for a given situation that a particular player is on the ice for.  So for instance, what percentage of the Leafs 5v5 even strength ice time was Joffrey Lupul on the ice in games in which Joffrey Lupul played. When I write a new program to calculate these numbers I need to to some testing to make sure the results are correct.  The first test is always the standard sniff test.  When the program runs I look at the output and ask myself “does the output make sense?”. When I first looked at the output the other day one of the numbers surprised me so much that I had to do some double checking to make sure it made sense. That number was the percentage of his teams power play ice time that Ilya Kovalchuk was on the ice for. That number was 87.25%.

That’s insane I thought so off to NHL.com to check and see if it could be at all possible. I first checked and noticed that the Devils had 439:59 minutes of PP ice time last year, including 420:36 minutes of 5v4 ice time. Next I checked out much PP ice time Kovalchuk had last year and see that he had 379:08 minutes of PP time. I do not know his exact 5v4 PP ice time numbers but 379:08 is about 86% of 439:59 so my calculation of Kovalchuk being on the ice for 87.25% of his teams PP ice time is perfectly within reason.

To me this seems like a crazy high number.  It means for every 2 minute penalty Kovalchuk is on the ice for 1:44 of it. That just makes me say “WOW!” but Kovalchuk is not alone in getting big PP minutes.  Here are some other players who have played in >70% of his teams 5v5 PP minutes (in games he played in) over the past 5 seasons.

Player 5v4 TOI%
Ilya Kovalchuk 87.25%
Alex Ovechkin 83.08%
Mike Green 76.86%
Mark Streit 75.35%
Sergei Gonchar 74.76%
Evgeni Malkin 73.83%
Sidney Crosby 73.01%
Dan Boyle 72.78%

I knew some players played a lot of PP ice time, but that still astonishes me. Oh, and for the record, in addition to being on the ice for 87.25% of his teams 5v4 PP ice time, Kovalchuk was on the ice for 89.66% of his teams 5v4 PP goals.

On the other end of things, over the last 5 years Willie Mitchell has played a whopping 59.2% of his teams 4v5 PK ice time which is might actually be more impressive considering how much more demanding playing on the PK is.

 

Jan 242013
 

The other day I introduced a new way of visualizing player time on ice and usage and today I am taking that one step further by superimposing a players performance on those charts.

So, with the TOI usage charts I presented the other day you can see how frequently a player was on the ice in any particular situation relative to how frequently the team plays during that situation.  So, a player might be on the ice for 30% of the teams 5v5 game tied minutes.  The next logical step is to take a look at his production during those situations relative to his teams production. If a player is on the ice for 30% of his teams 5v5 game tied minutes but he was only on the ice for 25% of the teams 5v5 game tied goals, that isn’t a good thing.  The team under-produced during his ice time relative to when he was not on the ice. We can also do the same for goals against and the resulting chart might look like this one for Zdeno Chara over the past 5 seasons.

The blue is Chara’s TOI usage percentages, the green is his goals for percentages and the red is his goals against percentages. You will notice that I have removed special teams play. The reason for this is because GA is not significant on power plays and GF is not significant on penalty kill so the chart ends up looking odd but in theory you could include them.

In an ideal situation the red box is smaller than the blue box (give up fewer goals than expected) and the green box is bigger than the blue box (give up more goals than expected). For Chara his results are a little mixed. When trailing he is very good having more goals for than expected and fewer goals against than expected when he is on the ice. His goals against relative to his teammates rises significantly when leading. I am not certain why, but maybe it has to do with his defense pairings when protecting a lead or opposing teams pressure him more when they are trailing.

Let’s take a look at another player who has been in the news lately, for both a contract signing and an injury.  Joffrey Lupul.

Strangely, almost the opposite of Chara. Lupul’s ‘leading’ stats are better than Chara’s while Chara is better when trailing. I am thinking maybe matchups are a factor here. When leading coaches are more diligent in matching Chara up against the opposing teams top line and keeping Lupul away from the opposing teams top line. Something to investigate further.

That said though, for Leaf fans if the Leafs get a better team that spends more time leading than trailing, Lupul’s numbers should, at least according to the chart above, get better. Especially goals against numbers.

Let’s finish off with one more superstar player, Sidney Crosby.

That is the chart of an offensively dominant player. Crosby’s offense is through the roof. Like Chara though, he is much weaker protecting a lead than any other situation.

As I said in my previous post, I am not sure where I will go with these radar charts, but they seem to be a valuable way of visualizing data so when appropriate I will attempt to make use of them. For example, it might be interesting to take a look at how a players usage and performance changes from year to year. In particular it might be interesting to see how ice time and performance changes for young players as they slowly improve or older players who are on the downsides of their careers.

 

Jan 172013
 

Yesterday evening James Mirtle from the Globe and Mail posted an article on The Curious case of Tim Connolly and the Leafs.  It’s worth a read so go read it but the premise of the article is how the narrative around Tim Connolly in training camp is he had a poor year last year and he needs to perform better this year.  Makes sense from most peoples view points but Connolly tries to present a different perspective.

Connolly can be prickly to deal with and wasn’t particularly interested in talking about last season, but when pressed, you could tell he felt he did more of value than the narrative – that he’s been an unmitigated bust in Toronto – would suggest.

Here was his answer when asked (maybe for the second or third time) about needing to “rebound” this season.

“Even strength, I think I had my second highest career points last year,” Connolly said. “I’d like to improve my play on the power play and maybe play a bigger role. Penalty killing, I think, my individual percentage was 89 per cent I read somewhere. I was able to lead the forwards in blocked shots.”

He makes two points in there.  The first is that he had his second highest even strength points last year and the second was something about individual percentage was 89 percent. Lets deal with the first one first by looking at his even strength points since the first lockout.

Season Goals Assists Points
2011-12 11 20 31
2010-11 7 16 23
2009-10 9 27 36
2008-09 12 16 28
2007-08 3 20 23
2005-06 9 20 29

(Note: Connolly only played 2 games in 2006-07 so I have omitted it from the table and discussion)

Tim Connolly is actually correct.  His best even strength point total came in 2009-10 when he had 36 points followed by his 31 even strength points last year.  But let’s take a look at those point totals relative to even strength ice time.

Season ESTOI Points TOI/Pt
2011-12 940:12 31 30:20
2010-11 840:31 23 36:33
2009-10 966:41 36 26:51
2008-09 631:26 28 22:33
2007-08 603:18 23 26:14
2005-06 708:47 29 24:26

The last column is time on ice per point, or time on ice between points.  Last year he was on the ice for an average of 30 minutes and 20 seconds between each of his even strength points. This was his second worst since the locked out season. So, while Connolly was technically correct in saying that he had his second highest even strength point total last season, it was a somewhat misleading representation of his performance.

Now for the individual PK percent. It generated a bit of twitter conversation last night questioning what it actually is.

One might think it is the penalty kill percentage when he was on the ice but that seems like a strange thing to calculate.  Is it goals per 2 minutes of PK time?  Is it goals per PK he spent any amount of time killing?  I really didn’t know so I dug into the numbers deeper by looking at the Leafs PK percentages on my stats site and noticed that Connolly had the best on-ice save percentage (listed as lowest opposition shooting percentage) of any Leaf last season during 4v5 play and that save percentage while he was on the ice was just shy of 89% (88.68%). It seems that maybe what Connolly meant to say was that he had an on-ice PK save percentage of 89%.

How good is an 89% save percentage on the PK?  Well, of the 100 forwards with at least 100 4v5 minutes of ice time last year, Connolly ranks 42nd in the league so league wide it isn’t that impressive but considering the Leafs weak goaltending it might actually be fairly good.

Here is the thing though. Single season PK save percentage is so fraught with sample size issues that it is next to useless as a stat for goalies let alone forwards.

One could evaluate Connolly based on PK goals against rate in which he came up 3rd on the Leafs (trailing Lombardi or Kulemin) but that is still fraught with sample size issues. More fairly we probably should evaluate Connolly’s PK contribution based on shots against rate or maybe even more fairly fenwick or corsi against rates. In each of those categories he ranked 5th among Leafs with at least 50 minutes of 4v5 ice time with only Joey Crabb being worse. Furthermore, among the 110 players with 100 minutes of 4v5 PK ice time last year, Connolly ranked 99th in fenwick against rate.

I don’t mean for this article to be a Connolly bashing article. I actually do think Connolly was a little misused and would probably do better with a more well defined role and not bounced around in the line up so much so in that sense I agree with the premise of what Connolly is saying. With that said though, it probably is fair to say that he didn’t have a great season and if he wants a regular role in the top six with time on the PP and PK he needs to perform better as his use of stats to attempt to show he had a good season is really just evidence to how statistics can be misused to support almost any narrative you want.  As they say, there are lies, damn lies, and then there are statistics.

 

Nov 082012
 

Eric T. over at NHL Numbers had a post last week summarizing the current state of our statistical knowledge with respect to accounting for zone start differences.  If you haven’t read it definitely go read it because it is not only a good read but because it concludes that how the majority of people have been doing is is wrong.

Overall, no two estimates are in direct agreement, but the analyses that are known to derive from looking directly at the outcomes immediately following a faceoff converge in the range of 0.25 to 0.4 Corsi shots per faceoff — one-third to one-half of the figure in widespread use. It is very likely that we have been overestimating the importance of faceoffs; they still represent a significant correction on shot differential, but perhaps not as large as has been previously assumed.

In the article Eric refers to my observation that eliminating the 10 seconds after a zone start effectively removes any effect that the zone start had on the game.  From there he combined my zone start adjusted data found at stats.hockeyanalysis.com with zone start data from behindthenet.ca and came up with an estimate that a zone start is worth 0.35 corsi.  He did this by subtracting the 10 second zone start adjusted corsi from standard 5v5 corsi and then running a regression against the extra offensive zone starts the player had.  In the comments I discussed some further analysis I did on this using my own data (i.e. not the stuff on behindthenet.ca) and came up with similar, though slightly different, numbers.  In any event I figured the content of that comment was worthy of its own post here.

So, when I did the correlation between extra offensive zone starts and difference between 5v5 and 5v5 10 second zone start adjusted corsi I got the following (using all players with >1000 minutes of ice time over last 5 seasons):

My calculations come up with a slope of 0.3043 which is a little below that of Eric’s calculations but since I don’t know the exact methodology he used that might explain the difference (i.e. not sure if Eric used complete 5 years of data, or individual seasons).

What is interesting is that when I explored things further, I noticed that the results varied across positions, but varied very little across talent levels.  Here are some more correlations for different positions and ice time restrictions.

Position Slope r^2
All Players >1000 min. 0.30 0.55
Skaters >1000 min. 0.28 0.52
Forwards >1000 min. 0.26 0.50
Defensemen >1000 min. 0.33 0.57
Goalies >1000 min. 0.44 0.73
Forwards >500 min. 0.26 0.50
Forwards >2500 min. 0.26 0.52
Forwards 500-2500 min. 0.26 0.39

Two observations:

1.  The slope for forwards is less than the slope for defensemen which is (quite a bit) less than the slope for goalies.

2.  There is no variation in slope no matter what restrictions we put on a forwards ice time.

There isn’t really much to say regarding the second observation except that it is nice to see consistency but the first observation is quite interesting.  Goalies, who have no impact on corsi, see the greatest zone start influences on corsi of any position.  It is a little odd but I think it addresses one of the concerns that Eric had pointed out in his article:

The next step would be to remove the last vestige of sampling bias from our analysis. The approaches that focus on the period immediately after the faceoff reduce the impact of teams’ tendency to use their best forwards in the offensive zone, but certainly do not remove it altogether.

I think that is exactly what we are witnessing here, but maybe more importantly teams put out their best defensive players and, maybe more importantly, their best face off guys for defensive zone face offs. If David Steckel, who is an excellent face off guy, is getting all the defensive zone face offs, it is naturally going to suppress the corsi events immediately after the defensive zone face off because he is going to win the draw more often than not.  There is probably more line matching done for the zone face offs than during regular play so the line matching suppresses some of the zone start impact.  It is more difficult to line match when changing lines on the fly so a good coach can more easily get favourable line matches. The result is normal 5v5 play offensive players might see a boost to their corsi (because they can exploit good matchups) and during offensive zone face offs they see their corsi suppressed because they will almost always be facing good defensive players and top face off guys.  Thus, the boost to corsi based on a zone start is not as extreme as should be for offensive players.  The opposite is true for defensive players.

Defensemen are less often line matched so we see their corsi boost due to an offensive zone face off a little higher than that of forwards, but it isn’t near as high as goalies because there are defensemen that are primarily used in offensive situations and others that are primarily used in defensive situations.

Goalies though, tell us the real effect because they are always on the ice and they are not subject to any line matching.  In the table above you will notice that goalies have a significantly higher slope and an impressively high r^2.  I feel I have to post the chart of the correlation because it really is a nice chart to look at.

I have looked at a lot of correlations and charts in hockey stats but very few of them are as nice with as high a correlation as the chart above.

I believe that this is telling us that an offensive zone start is worth 0.44 corsi, but only when a player is playing against similarly defensively capable players as he would during regular 5v5 play which I speculate above is not necessarily (or likely) the case.  The 0.44 adjustment really only applies to an idealistic situation that doesn’t normally occur for any players other than goalies.  So where does that leave us?  Should we use a zone start adjustment of 0.44 corsi for all players, or should we use something like 0.33 for defensemen and 0.26 for forwards?  The answer isn’t so simple.  One could argue that we should apply 0.44 to all players and then make some sort of QoC adjustment and that would make some sense.  But if we are not intending to apply a QoC adjustment, does that mean we should use 0.33 and 0.26?  Maybe, but that is a little inconsistent because it would mean you are using a QoC adjustment only for the zone start adjustment of a players stats, and not for all his stats.  The answer for me is what I have been doing the past little while and not even attempt to adjust a players stats based on zone starts differences and rather simply just ignore the the portion of play that is subject to being influenced by zone starts – the 10 seconds after a zone start face off.  To me it seems like the simplest and easiest thing to do.

 

Jul 112012
 

I have been wondering about the benefits of using 5v5 close data instead of 5v5 when we do player analysis and player comparisons.  The rationale for comparing players in 5v5close situations is that we are comparing players under similar situations.  When teams have a comfortable lead they go into a defensive shell resulting in fewer shots for but with a higher shooting percentage and more shots against, but a lower shooting percentage.  The opposite of course is true when a team is trailing.  But what I have been thinking about recently is whether there is a quality of competition impact during close situations.  My hypothesis is that teams that are really good will play more time with the score close against other good teams and less time with the score close against significantly weaker teams.  Conversely, weak teams will play more minutes with the score close against other weak teams than against good teams.

My hypothesis is that players on good teams will have a tougher QoC during 5v5 close situations than during overall 5v5 situations and players on weak teams will have weaker QoC during 5v5 close situations than during overall 5v5 situations.  Let’s put that hypothesis to the test.

The first thing I did was to select one key player from each of the 30 teams to represent that team in the study.  Mostly forwards were chosen but a few defensemen were chosen as well.  From there I looked at the average of their opponents goals for percentage (goals for / [goals for + goals against]) over the past 3 seasons in zone start adjusted 5v5 situations as well as zone start adjusted 5v5 close situations and then compared the difference to the players teams record over the past three seasons.  The table below is what results.

Player Team GF% 5v5 GF% Close Close – 5v5 3yr Pts Avg. Pts
Doan Phoenix 50.3% 50.6% 0.3% 303 101.0
Chara Boston 50.7% 50.9% 0.2% 296 98.7
Toews Chicago 50.4% 50.6% 0.2% 310 103.3
Datsyuk Detroit 50.8% 51.0% 0.2% 308 102.7
Weber Nashville 50.5% 50.7% 0.2% 303 101.0
Backes St. Louis 50.8% 51.0% 0.2% 286 95.3
E. Staal Carolina 50.4% 50.5% 0.1% 253 84.3
Ribeiro Dallas 50.5% 50.6% 0.1% 272 90.7
Gaborik Ny Rangers 50.1% 50.2% 0.1% 289 96.3
Malkin Pittsburgh 50.1% 50.2% 0.1% 315 105.0
Ovechkin Washington 49.9% 50.0% 0.1% 320 106.7
Enstrom Winnipeg 50.1% 50.2% 0.1% 247 82.3
Weiss Florida 50.3% 50.3% 0.0% 243 81.0
Plekanec Montreal 50.4% 50.4% 0.0% 262 87.3
Tavares NY Islanders 50.3% 50.3% 0.0% 231 77.0
Hartnell Philadelphia 50.1% 50.1% 0.0% 297 99.0
J. Thornton San Jose 50.9% 50.9% 0.0% 314 104.7
Kessel Toronto 50.1% 50.1% 0.0% 239 79.7
H. Sedin Vancouver 50.0% 50.0% 0.0% 331 110.3
Nash Columbus 50.9% 50.8% -0.1% 225 75.0
J. Eberle Edmonton 50.6% 50.5% -0.1% 198 66.0
Kopitar Los Angeles 50.6% 50.5% -0.1% 294 98.0
M. Koivu Minnesota 50.7% 50.6% -0.1% 251 83.7
Parise New Jersey 50.8% 50.7% -0.1% 286 95.3
Getzlaf Anaheim 51.0% 50.8% -0.2% 268 89.3
Roy Buffalo 50.3% 50.1% -0.2% 285 95.0
Stastny Colorado 50.3% 50.1% -0.2% 251 83.7
Spezza Ottawa 50.6% 50.4% -0.2% 260 86.7
Stamkos Tampa 50.2% 50.0% -0.2% 267 89.0
Iginla Calgary 50.5% 50.2% -0.3% 274 91.3
50.4% 50.5% >0 97.3
50.3% 50.3% =0 91.3
50.6% 50.4% <0 86.6

The list above is sorted by the difference between the oppositions 5v5 close GF% and the oppositions 5v5 GF%.  The bottom three rows of the last column is what tells the story.  These show the average point totals of the teams for players whose opposition 5v5 close GF% was greater than, equal to and less than the opponents 5v5 GF%.  As you can see, the greater than group had a team average 97.3 points, the equal to group had a team average of 91.3 points and the less than group had a team average of 86.6 points.  This means that good teams have on average tougher 5v5 close opponents than straight 5v5 opponents and weak teams have tougher 5v5 opponents than 5v5 close opponents which is exactly what we predicted.  It is also not unexpected.  Weak teams tend to play close games against similarly weak teams while strong teams play close games against similarly strong teams.

Another important observation is how little deviation from 50% there is in each players opposition GF% metrics.  The range for the above players is from 49.9% to 51.0%.  That is an incredible tight range and reconfirms to me the small importance QoC has an a players performance, especially when considering longer periods of time.

I also conducted the same study using fenwick for percentage as the QoC metric instead of goals for percentage but the results were less conclusive.  The >0 group had an average of 93.2 team points int he standings, the =0 group had 93.4 team points in the standings and the <0 group had 83.25 team points in the standings.  Furthermore there was even less variance in opposition FF% than GF% and only 12 teams had any difference between opposition 5v5 and opposition 5v5 close FF%.  For me, this is further evidence that fenwick/corsi are not optimal measures of player value.

Finally, I looked at the difference in player performance during 5v5 situations and found no trends among the different performance levels.  For GF% almost every player had their 5v5 close GF% within 4% of their of their 5v5 GF% (r^2 between the two was 0.7346) and for FF% every player but Parise had their 5v5 close FF% within 1.7% of their 5v5 GF% (r^2 = 0.945).  Furthermore, there was consistency as to which players saw an improvement (or decrease) in their 5v5 close GF% or FF% so it seems it might be luck driven (particularly for GF%) or maybe coaching factors.

So what does this all mean?  It means that in 5v5 close situations good teams have a bias towards tougher QoC than weak teams do.  Does it have a significant factor on player performance?  No, because the QoC metrics vary very little across players or from situation to situation (from my perspective QoC can be ignored the majority of the time).  Does it mean that we should be using 5v5 close in our player analysis?  I am still not sure.  I think the benefits of doing so are still probably quite small if there is any at all as 5v5 close performance metrics mirror 5v5 performance metrics quite well and in the case of goal metrics using the larger sample size of 5v5 data almost certainly supersedes any benefits of using 5v5 close data.

 

Jun 042012
 

I have a ton of information on my stats website stats.hockeyanalysis.com but one of the things I have always wanted to do is to make it more visual and I’d like to announce the first step in that process.  Thanks to google and their cool google chart api I have now added bubble charts when you do a stats search that returns no more than 30 players (more than 30 players makes the bubble charts too cluttered).  For example, if you did a search of all Maple Leaf Skaters with 500 minutes of 5v5 zone start adjusted ice time this past season you will see a nice bubble chart at the bottom plotting each players defensive rating (i.e. HARD+) along the horizontal axis and their offensive rating along the vertical axis (i.e. HARO+).  Or you can see the same thing using corsi ratings (i.e. CorHARD+ vs CorHARO+) if you are one of those people who prefer corsi based ratings.  Or, if you prefer, you can even look at multi-year goal ratings such as 3 year 5v5 zone start adjusted goal ratings for the Toronto Maple Leafs (though still not perfect, I believe 3 year goal ratings are the best indicator of a players value).

In the charts, forwards and defensemen are differentiated by different colors and the size of the bubble is indicative of the amount of time the player was on the ice for (the largest bubbles for the players with the most ice time and the smallest bubbles for the players with the least).  As always with my ratings, any value over 1.00 is above average and any rating below 1.00 is below average and these ratings take into account quality of teammates and quality of opposition and the players on-ice statistics.  This means players with bubbles to the right side of the chart are stronger defensive players and players with bubbles towards the top of the chart are stronger offensive players.  The best players are good at both and thus have their bubbles in the upper right quadrant.   Players with bubbles in the lower right quadrant are the worst performing players.  The nice thing about these charts is it gives a very easy to read visual representation of every player on a team.

I am hoping that this is just a start of things to come with more charting enhancements (and others as well) to be implemented in the future.  As always, if you have any suggestions submit a comment below or drop me a message.