Jul 132011
 

Yesterday I described my player analysis method and used Brad Richards as an example.  Over the next little while I’ll apply my analysis method to a number of players so if there are any players you are interested in seeing my analysis for let me know.  First up is Tim Connolly.  The Leafs lost out on the Brad Richards sweepstakes so lets take a look at how Tim Connolly stacks up.

Let’s start off with a table of what I consider Tim Connolly’s most pertinent information – his 5v5 HARO+ (offense), HARD+ (defense) and HART+ (overall) ratings over the years.

Season(s) HARO+ HARO+ Rank HARD+ HARD+ Rank HART+ HART+ Rank
2007-11 (4yr) 1.171 18/310 0.985 152/310 1.078 31/152
2008-11 (3yr) 1.242 23/319 0.980 158/319 1.111 36/319
2009-11 (2yr) 1.169 67/319 0.975 170/319 1.072 85/319
2010-11 1.045 156/336 0.856 268/336 0.951 220/336
2009-10 1.289 32/338 1.082 100/338 1.185 34/338
2008-09 1.615 2/335 0.941 187/335 1.278 16/335
2007-08 1.322 38/328 0.974 159/328 1.148 55/328

Generally speaking Connolly’s offensive rankings have been well over 1.00 and ranking very highly among all forwards with at minimum 500 minutes of 5v5 time per season and his defensive rankings have been middle of the pack.

Based on Connolly’s offensive statistics he is legitimately a first line center though he has played against relatively weak defensive competition (232/310 in 4 yr OppGA20) as he has played behind Derek Roy in Buffalo.  Last year he played against somewhat tougher defensive competition than he did in 2008-09 and 2009-10 as Derek Roy was injured for more than half the season and he had his worst offensive (and defensive) season so that should be a bit of a concern for Leaf fans.  Still, one season is too short to draw any conclusions so it could just be an anomaly as well but it is something to watch for next season as he’ll likely be given top line duty in Toronto with Phil Kessel and Joffrey Lupul and play against the oppositions better defensive players.

Of interest to Leaf fans who have suffered through several years of poor PP and PK play is Connolly’s special team numbers.  Over the past 4 seasons Connolly has been played a significant role on Buffalo’s power play and the results have generally been good (his 4 year 5v4 HARO+ rating is 1.169).  Connolly has also played a fair amount (about 100 min/season) on the Buffalo PK unit and his performance has been better than what one would expect from his 5v5 defensive numbers.  His 4-year 4v5 PK HARD+ rating is a more than respectable 1.196 so maybe he can play defense when is he trying to stop the opposition from scoring as opposed to trying to produce offense himself.

Based purely on his performance over the past 4 seasons it seems Connolly is a more than reasonable gamble as one could argue he has legitimate first line offensive capabilities and is at least middle of the pack defensively.  The big question of course with Connolly is his health.  Has has played just 48, 48, 73 and 68 games over the past 4 seasons.  The good news is he hasn’t had a significant concussion in several years and his injuries over the past couple of seasons have been non-serious in nature.  If he can be healthy enough to play 70+ games I think a year from now we could look back and say that Connolly was one of the better free agent signings of the 2011 off season, even with a $4.75M cap hit.

Jul 122011
 

Over the past couple of weeks I have had several comment discussions regarding some of my recent posts on player evaluation and Norris and Hart trophy candidates which centered around which is a better method for evaluating players:  corsi vs goal based evaluation.  A lot of people, maybe the majority of those within the advanced hockey stat community, seem to prefer corsi based analysis while I prefer goal based analysis and I hope to explain why with this post.  I have explained much of this previously but hopefully this post will put it all into one simple easy to understand package.

There are two main objectives for a player when the coach puts him on the ice:  1.  Help his team score a goal.  2.  Help his team stop the opposing team from scoring a goal.  Depending on the situation and the player the coach may prioritize one of those over the other.  For example, a defensive player may be tasked primarily with shutting down an opposing teams offensive players and scoring a goal is really a very minor objective.  Late in a game when a team is down a goal the opposite is true and the primary objective, if not sole objective, is to score a goal.

I think we can all agree on the previous paragraph.  Goals are what matter in hockey so right there we have the #1 reason why goals should be used in player evaluation.  The problem is, goals are a relatively rare event and thus ‘luck’ can have a serious impact on our player analysis results due to the small sample size that goals provide.  This brought on the concept of corsi which is nothing more than shot attempts and is used as a proxy for scoring chances.  The benefit of corsi is that shot attempts occur about 10 times often as goals which gives us a larger sample size to evaluate players.

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Jun 012011
 

There seems to be some confusion, or lack of clarity, about my post on corsi vs shooting percentage vs shooting rate the other day so let me clear it up in as straight forward a way as I can.

“Hawerchuk” over at BehindTheNetHockey.com writes the following:

“I’m not totally sure what he’s getting at. People use Fenwick because it’s persistent, and PDO because it’s not. Over the course of a single season, observed shooting and save percentage drive results, but they are not persistent.”

Dirk Hoag over at OnTheForecheck.com writes:

“Here’s an example of when NOT to use correlation as a tool in statistical analysis (when the variables in question are linked by definition). David makes a bad blunder here, by looking at scoring leaders, seeing a bunch of high shooting percentages, and concluding that shooting percentage is the true “talent”. The problem is that shooting percentage swings wildly from season to season, whereas shooting rates are much more consistent.”

The great advantage of corsi/fenwick has over goals as an evaluator of talent is the greater sample size associated with it.  The greater the sample size the more confidence we can have in any results we conclude from it and the less chance that ‘luck’ messes things up.  Year over year shooting percentage fluctuates a lot, but that doesn’t necessarily mean that it isn’t a talent or doesn’t have persistence, it could mean that the sample size of one year is too small.  The four year shooting percentage leader board seems to identify all the top offensive players so it can’t be completely random.  So what happens if we increase the sample size?  Here are correlations of fenwick shooting percentages while on ice in 5v5 even strength situations for forwards:

Year(s) vs Year(s) Corrolation
200708 vs 200809 0.249
200809 vs 200910 0.268
200910 vs 201011 0.281
200709 vs 200911 (2yr) 0.497

As you can see, there isn’t a lot of persistence year over year but for 2 years over 2 years we are starting to see some persistence.  Still not to the level of corsi/fenwick, but certainly not non-existant either, and the greater correlation with scoring goals makes fenwick shooting percentage on par with fenwick as a predictor of future goal scoring performance when we have 2 seasons of data as I pointed out in my last post.

For the record, year over year correlation for fenwick for rate is approximately 0.60 depending on years used  and 2 year vs 2 year correlation is 0.66.

But as I pointed out in my previous post, you would probably never use shooting percentage as a predictor because you may as well use goal rate instead which has the same sample size limitations as shooting percentage but also factors in fenwick rate.  Year over year correlation of GF20 (goals for per 20 minutes) is approximately 0.45 depending on years used and the 2 year vs 2 year correlation is 0.619 so GF20 has persistence and has a 100% correlation with itself making it as reliable (or more) a predictor of future goal scoring rates as fenwick rate with just one year of data and a better predictor when using 2 years of data.  Let me repost the pertinent table of correlations:

Year(s) vs Year(s) FenF20 to GF20 GF20 to GF20
200708 vs 200809 0.396 0.386
200809 vs 200910 0.434 0.468
200910 vs 201011 0.516 0.491
Average 0.449 0.448
200709 vs 200911 (2yr) 0.498 0.619
200709 vs 200910 (2yr vs 1yr) 0.479 0.527

The conclusion is, when dealing with less than a years worth of data, fenwick/corsi is probably the better metric to identify talent and predict future performance, but anything greater than a year goals for rate is the better metric and for one years worth of data they are about on par with each other.

Note:  This is only true for forwards.  The same observations are not true about defensemen where we see very little persistence or predictability in any of these metricts, I presume because the majority of them don’t drive offense to any significant degree.

May 302011
 

The general consensus among advanced hockey statistic analyzers and is that corsi/fenwick stats are the best statistic for measuring player and team talent levels.  For those of you who are not aware of corsi and fenwick let me give you a quick definition.  Corsi numbers are the number of shots directed at the goal and include shots, missed shots and blocked shots.  Fenwick numbers are the same except it does not included blocked shots (just shots and missed shots).  I generally look at fenwick and will do that here but fenwick and corsi are very highly correlated to the results would be similar if I used corsi.

The belief by many that support corsi and fenwick is that by looking at fenwick +/- or fenwick ratio (i.e. fenwick for /(fenwick for + fenwick against)) is an indication of which team is controlling the play and the team that controls the play more will, over time, score the most goals and thus win the most games.  There is some good evidence to support this, and controlling the play does go a long way to controlling the score board.  The problem I have with many corsi/fenwick enthusiasts is that they often dismiss the influence that ability to drive or suppress shooting percentage plays in the equation.  Many dismiss it outright, others feel it has so little impact it isn’t worth considering except when considering outliers or special cases.  In this article I am going to take an in depth look at the two and their influence on scoring goals on an individual level.

I have taken that last 4 seasons of 5v5 even strength data and pulled out all the forwards that have played at minimum 2000 minutes of 5v5 ice time over the past 4 seasons.  There were a total 310 forwards matching that criteria and for those players I calculated the fenwick shooting percentage (goals / fenwick for), fenwick for rate (FenF20 – fenwick for per 20 minutes of ice time) and goal scoring rate (gf20 – goal for per 20 minutes ice time) while the player was on the ice. What we find is shooting percentage is more correlated with goal production than fenwick rate.

Shooting % vs GF20 R^2 = 0.8272
FenF20 vs GF20 R^2 = 0.4657
Shooting % vs FenF20 R^2 = 0.1049

As you can see, shooting percentage is much more highly correlated with goal scoring rate than fenwick rate is which would seem to indicate that being able to drive shooting percentage is more important for scoring goals than taking a lot of shots.

Here is a list of the top 20 and bottom 10 players in fenwick shooting percentage and fenwick rate.

Rank Player FenSh% Player FenF20
1 MARIAN GABORIK 8.07 HENRIK ZETTERBERG 16.7
2 SIDNEY CROSBY 7.83 ALEX OVECHKIN 16.3
3 ALEX TANGUAY 7.64 PAVEL DATSYUK 16.15
4 HENRIK SEDIN 7.63 TOMAS HOLMSTROM 16.05
5 BOBBY RYAN 7.60 NICKLAS BACKSTROM 16.05
6 STEVE DOWNIE 7.58 ERIC STAAL 16.04
7 EVGENI MALKIN 7.57 RYANE CLOWE 15.91
8 DANIEL SEDIN 7.55 ALEXANDER SEMIN 15.85
9 ILYA KOVALCHUK 7.49 SCOTT GOMEZ 15.8
10 NATHAN HORTON 7.44 ZACH PARISE 15.8
11 J.P. DUMONT 7.43 ALEXEI PONIKAROVSKY 15.79
12 JASON SPEZZA 7.39 JOHAN FRANZEN 15.78
13 PAUL STASTNY 7.36 JIRI HUDLER 15.74
14 PAVOL DEMITRA 7.33 DAN CLEARY 15.71
15 DANY HEATLEY 7.30 SIDNEY CROSBY 15.71
16 RYAN MALONE 7.29 JUSTIN WILLIAMS 15.68
17 JONATHAN TOEWS 7.28 CHRIS KUNITZ 15.61
18 THOMAS VANEK 7.24 MIKHAIL GRABOVSKI 15.56
19 SERGEI KOSTITSYN 7.24 JOE PAVELSKI 15.43
20 DREW STAFFORD 7.24 MIKAEL SAMUELSSON 15.39
301 BLAIR BETTS 4.20 CHUCK KOBASEW 11.34
302 ERIC NYSTROM 4.12 TRAVIS MOEN 11.31
303 SAMUEL PAHLSSON 4.10 IAN LAPERRIERE 11.23
304 SHAWN THORNTON 3.99 ERIC NYSTROM 11.21
305 TRAVIS MOEN 3.89 ROB NIEDERMAYER 10.94
306 TODD MARCHANT 3.88 TODD MARCHANT 10.91
307 NATE THOMPSON 3.75 SAMUEL PAHLSSON 10.87
308 FREDRIK SJOSTROM 3.70 JERRED SMITHSON 10.76
309 CRAIG ADAMS 3.52 JAY PANDOLFO 10.74
310 STEPHANE VEILLEUX 3.49 BLAIR BETTS 10.67

For both lists, the players are the top of the list are for the most part considered top offensive players and the players at the bottom of the list are not even close to being considered quality offensive players.  So, it seems that both shooting percentage and fenwick do a reasonable job at identifying offensively talented players.  That said, the FenF20 list includes 7 players (Zetterberg, Datsyuk, Holmstrom, Franzen, Hudler, Cleary and Samuelsson) who have played mostly or fully with the Detroit Red Wings and it seems unlikely to me that 7 of the top 20 offensive players are Red Wing players.  Furthermore, the fenwick list also includes guys like Ponikarovsky, Samuelsson, Hudler, Cleary, Williams, etc. who would probably be considered secondary offensive players at best.  From just this cursory overview it seems to confirm what we saw with the correlations – Shooting Percentage is a better indicator of offensive talent than Fenwick For rates.

It is actually no surprise that the Red Wings dominate the fenwick rate leader board because the Red Wings organizational philosophy is all about puck control.

“It’s funny because our game looks at numbers just like other games,” says Red Wings general manager Ken Holland, “but as much value as we assign to puck possession and how essential it is to winning, we really don’t have a numerical value for it that everyone can agree on. Remember when [A’s general manager] Billy Beane started emphasizing on-base percentage in baseball? It wasn’t just a curious number; it changed the game. It redefined the type of player you wanted on your team. It’s coming in hockey; we just have to figure out how.”

This got the pro-corsi crowd riled up a bit as they said “Umm, yeah, we have that stat and it is called corsi” and were a bit bewildered at why NHL GMs didn’t make that recognition.  But anyway, what the above shows is that an organization that focuses on puck control dominates the corsi for statistic so I guess what that shows is that corsi/fenwick probably is a good measure of puck control.  But, as we have seen, fenwick (i.e. puck control) doesn’t automatically translate into goals scored.  There are no Red Wing players among the top 20 in fenwick shooting percentage and Datsyuk is the only Red Wing player in the top 20 in goals for per 20 minutes so while they take a lot of shots (or at least shot attempts), they aren’t the best at converting them into goals.

For me, and I am sure many others, the above is enough to conclude that shooting percentage matters a lot in scoring goals, but for the staunch corsi supporters they will argue that corsi is more persistent from season to season and thus is a better predictor of future performance.  So which is the better predictor of future performance?  The following table shows the correlation between shooting percentage and fenwick rate with the following seasons goal scoring rate.

Year(s) vs Year(s) FenSh% to GF20 FenF20 to GF20
200708 vs 200809 0.253 0.396
200809 vs 200910 0.327 0.434
200910 vs 201011 0.317 0.516
Average 0.299 0.449
200709 vs 200911 (2yr) 0.479 0.498
200709 vs 200910 (2yr vs 1yr) 0.375 0.479

Note:  For the above season(s) vs season(s) correlation calculations, only players with at least 500 5v5 even strength minutes in each of the four seasons are included.  This way the same players are included in all season(s) vs season(s) correlation calculations.

As you can see, when dealing with a single season of data the correlation with GF20 is much better for fenwick rate than for fenwick shooting percentage.  The gap closes when using 2 seasons as the predictor of a single season and is almost gone when using 2 seasons to predict the following 2 seasons.  It seems that the benefit of using corsi over shooting percentage diminishes to near zero when we have multiple seasons of data and though I haven’t tested it shooting percentage probably has an edge in player evaluation with 3 years of data.

Of course, you would never want to use shooting percentage as a predictor of future goal scoring rate when you could simply use past goal scoring rate as the predictor.  Past goal scoring rate has the same ‘small sample size’ limitations as shooting percentage (both use goals scored as it sample size limitation) but scoring rate combines the prediction benefits of shooting percentage and fenwick rate.  The table below is the same as above but I have added in GF20 as a predictor.

Year(s) vs Year(s) FenSh% to GF20 FenF20 to GF20 GF20 to GF20
200708 vs 200809 0.253 0.396 0.386
200809 vs 200910 0.327 0.434 0.468
200910 vs 201011 0.317 0.516 0.491
Average 0.299 0.449 0.448
200709 vs 200911 (2yr) 0.479 0.498 0.619
200709 vs 200910 (2yr vs 1yr) 0.375 0.479 0.527

The above table tells you everything you need to know.  When looking at single seasons both GF20 and FenF20 perform similarly at predicting next seasons GF20 with fenwick shooting percentage well behind but when we have 2 years of data as the starting point, GF20 is the clear leader.  This means, when we have at least a full seasons worth of data (or approximately 500 minutes ice time), goal scoring rates are as good or better than corsi rates as a predictor of future performance and beyond a years worth of data the benefits increase.  When dealing with less than a full season of data, corsi/fenwick may still be the preferred stat when evaluating offensive performance.

So what about the defensive side of things?

Year(s) vs Year(s) FenA20 to GA20 GA20 to GA20
200708 vs 200809 0.265 0.557
200809 vs 200910 0.030 0.360
200910 vs 201011 0.120 0.470
Average 0.138 0.462
200709 vs 200911 (2yr) -0.037 0.371
200709 vs 200910 (2yr vs 1yr) 0.000 0.316

Defensively, fenwick against rate is very poorly correlated with future goals against rate and it gets worse, to the point of complete uselessness, when we consider more seasons.  Past goals against rate is a far better predictor of future goals against rate.

Where it gets interest is unlike offense correlation drops when you consider more seasons which seems a bit strange.  My guess is the reason we are seeing this is because I am just looking at forwards and defense is more driven by goaltending and defensemen and as more time passes the greater the difference are in goalie and defensemen teammates.  Furthermore, forward ice time is largely driven by offensive ability (and not defensive ability) so many of the quality defensive forwards may be removed from the study because of the 500 minute per season minimum I am using (i.e. the group of players used in this study are biased towards those that aren’t focusing on defense).  Further analysis is necessary to show either of these as true though but the conclusion to draw from the above table that, for forwards at least, goals against rates are by far the better indicator of defensive ability.

In summary, it should be clear that we cannot simply ignore the impact of a players ability to drive or suppress shooting percentage in the individual player performance evaluation and so long as you have a full year of data (or > 500 or more minutes ice time) the preferred stat for individual player performance evaluation should be goal scoring rate.  Corsi/fenwick likely only provide a benefit to individual performance evaluation when dealing with less than a full year of data.

Sep 292010
 

Ok, let me start by stating that Wade Redden is not worth $6.5M.  He may be never was and the contract (6 years, $39M) the Rangers gave Redden was one of the worst ever handed out in NHL history.  In part because he is not worth that and in part because there is no evidence that any other team had any interest in offering anything close to that amount so the Rangers were bidding against themselves and still paid well over market value.  But that isn’t the point of this article.  The point I want to make is that Redden was, and still is a good defenseman that should be in the NHL.

Ok, now for some straight forward stats:

Year Team GP G A PTS Pts/GM
2005-6 OTT 65 10 40 50 0.77
2006-7 OTT 64 7 29 36 0.56
2007-8 OTT 80 6 32 36 0.45
2008-9 NYR 81 3 23 26 0.32
2009-10 NYR 75 2 12 14 0.19

As you can see there is a clear drop off in his offensive production.  The question is, can Redden be fully blamed for that dropoff?  Here are his teams goals per game production during that time compared to Redden’s points per game production.

Year Redden Team
2005-6 0.77 3.76
2006-7 0.56 3.45
2007-8 0.45 3.11
2008-9 0.32 2.39
2009-10 0.19 2.64

Clearly Redden’s offensive production, up until last year, was in part due to the fact that his teams overall offensive production dropped.  In Ottawa it was due to losing some quality talent off the team as well as becoming a more defensive team than an offense first team.  Then he went to New York where the Rangers offense was awful because they played a defensive style and had no real elite offensive players.  Not all of Redden’s offensive production drop off can be explained by team influences but a good chunk of it can.

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