Dec 162010
 

In the Hockey Statistical Analysis world Tomas Vokoun is an interesting case study because depending on how he gets evaluated he either shows up as an very good goalie or in some cases a true elite goalie in the NHL.  Most ways we evaluate goalies has to do with save percentages.  We either look at overall save percentage or even strength save percentage or even even strength game tied save percentage.  Under all of these scenarios Vokoun excels to various degrees.  A recent Behind the Net Hockey Blog post asked several hockey statistic analysts to discuss “elite goalies” and Tomas Vokoun’s name came up frequently.  What is dumbfounding to me is Vokoun’s record because his won-loss record (79-80-25) is notably worse over the past 3 seasons than his backups (32-22-8).  That can’t be a sign of an elite goalie, even if his backups have been relatively good (i.e. Craig Anderson).  One may postulate it is due to facing tougher competition as backup goalies often get the to play against weaker teams or one may postulate it is just due to bad luck.  Or maybe, he just isn’t a great goalie.

Since shots totals and shooting/save percentage is often affected by game score I’ll focus on 5v5 even strength game tied statistics to balance everything out.  Over the last 3 seasons (2007-08 to 2009-10) there are 35 goalies with 1500 or more 5v5 game tied minutes.  Of these goalies, Tomas Vokoun ranks 8th in 5v5 game tied save percentage which may not be elite, but still very good.  Jonas Hiller tops the list with a .942 save % with Vokoun at .933 and Chris Osgood trails the list with a .906 save %.  So, Vokoun looks pretty good.

But, Tomas Vokoun ranks just 23rd in goals against average which isn’t great and probably average at best.  Those who are in love with fenwick numbers will note that Vokoun has the second highest fenwick against of any goalies with 1500+ 5v5 tied minutes and he gives up so many goals because Florida gives up so many shots and scoring chances.  Of course, I believe that not all shots against are equal and shot totals can be influenced by style of play as much as talent.  If you don’t believe style of play affects shot totals and scoring chances, ask yourself why there are score effects on shot/corsi totals?  The answer is depending on the score, teams play differently.  But teams play differently when the score is tied as well.  Some teams play a defense first style, even when game is tied, and others play a more wide open offensive style.  Florida, without any true elite offensive stars, probably plays more of a defensive game which would naturally lead to more shots against, but not necessarily more quality scoring chances against.

So yes, Florida gives up a lot of shots, but how good is Tomas Vokoun’s competition really.  He does play in the weakest division in the NHL and yet he can’t produce a good won-loss record.  Just looking at Vokoun’s opposition, his opponents rank dead last in goals for per 20 minutes so compared to other goalies he is playing against relatively weak opponents offensively.  His oppositions GF% (goals for / goals for + against) is also fourth worst so overall so he plays against very weak opposition in terms of scoring goals and stopping goals.  For those who prefer Fenwick, his opposition has a FF% (fenwick for / fenwick for + against) of .499, good for 27th among the 35 goalies.  So his opposition isn’t good and his performance in goals against average isn’t good either.  That isn’t a good combination if you want to be considered an elite level goalie.

How about a direct comparison with his backups.  In 2007-08 his goals against average per 20 minutes was significantly worse than Craig Anderson’s (0.949 for Vokoun, 0.538 for Anderson) while Anderson’s opponents had a slightly better goals for per 20 minutes (0.678 vs 0.671).  In 2008-09 Vokoun had a much better season giving up 0.697 goals per 20 minutes compared to Anderson’s 0.896 though Anderson played against slightly better offensive competition.  In 2009-10 Vokoun had a much better goals against than Clemmensen (0.621 vs 1.058) but played against weaker competition as well (OppGF20 of .714 vs 0.743 for Clemmensen’s opponents).  Generally speaking Tomas Vokoun had a very weak 2007-08 season but much better 2008-09 and 2009-10 seasons even though he always seemed to play against weaker offensive opponents.

In terms of my Hockey Analysis Ratings, Tomas Vokoun ranked 16th out of 35 goalies in 2007-10 HARD and 18th in 2007-10 HARD+ rankings.  Middle of the pack.  The seasonal breakdown positioned him 35th of 38 in HARD+ for goalies with 500+ minutes in 2007-08, 19th of 35 in 2008-09, and 6th of 37 in 2009-10.  So far this season he is closer to the bottom again.

Is Tomas Vokoun an elite goalie, or even great goalie?  Probably not.  He just posts good save percentages because his team gives up a lot of shots, but not necessarily quality scoring chances, and he plays against weak offensive competition.

Dec 152010
 

I have been pretty quiet here recently not because of a lack of things I want to write about but because I needed to get my stats site up and running first so I can reference it in my writings.  Plus, getting my stats site up has been on my todo list for a real long time.  There will be a lot more stats to come including my with/against on ice pairing stats which I had up a season or two ago and many of you found interesting as well as team stats but for now let me explain what is there.

What you will find there now is my player rating system which produces the following ratings:

HARD – Hockey Analysis Rating – Defense

HARO – Hockey Analysis Rating – Offense

HART – Hockey Analysis Rating – Total

HARD+ – Hockey Analysis Rating – Defense

HARO+ – Hockey Analysis Rating – Offense

HART+ – Hockey Analysis Rating – Total

HARD is the defensive rating and is calculated by taking expected goals against while on the ice and dividing it by actual goals against while on the ice.  The expected goals against is calculated by taking the average of a players team mates goals against per 20 minutes (TMGA20) and averaging it with the players opposition goals for per 20 minutes (OppGF20).  Similarly HARO is calculated by taking a players actual goals for while on the ice and dividing it by the expected goals against while on the ice.  For both, a rating above 1.00 means that the player helped the team perform better than expected when he was on the ice where as a rating below 1.00 means the player hurt the teams performance when he was on the ice.  HART is just an average of HARD and HARO.

HARD+, HARO+ and HART+ are enhanced ratings which result from an iterative process that iteratively feeds HARD and HARO ratings into an algorithm to refine the ratings.  For the most part this iterative process produced a nice stable state but sometimes the algorithm goes haywire and things fail (i.e. for a particular season or seasons).  For this reason I am calling the + ratings experimental but if you don’t see anything wacky (i.e. large differences in every players ratings) they should be considered reliable and probably better ratings than the straight HARD, HARO and HART ratings.  Anything better than 1.00 should be considered better than the average player and anything less than 1.00 should be considered below average.

Continue reading »

Dec 032010
 

(Updated to include 3 seasons of data as I now realize that more luck data was available)

The other day there was a post on the Behind the Net Blog which used betting odds to estimate how lucky a team was during the 2009-10 season.  In many ways it is quite an ingenious way to evaluate a teams luck and I recommend those who have not read it go take a look.  Last night I was watching, sadly, the Leafs-Oilers game and thinking about luck in a hockey game and whether a team has any control over the luck they experience.   It got me thinking, does a team which controls the flow of the play mean that team is more likely to have more ‘good luck’ stuff happen to them than ‘bad luck’ stuff.

I defined luck as being how many standard deviations their actual point totals were from their expected point totals as defined in the document referenced in the Behind the Net blog post and in an updated document with 4 years of data.  I have only included 3 seasons in this analysis since I have only been working with 3 seasons of data recently and I was too lazy to go back and calculate a fourth season right now.

The most used stat to indicate how well a team controls the play is corsi or fenwick percentage which is basically the number of shots a team directs at the goal divided by the number of shots that they and their opponents teams directed at the goal.  I’ll be using Fenwick % here which includes shots and missed shots but not blocked shots.  So how does Fenwick % correlate with luck?

The correlation is fairly low but a correlation exists.  Maybe good teams can generate their own luck.  Here is a table of a teams luck and fenwick% for 2009-10.

Team Luck Fen%
Chicago Blackhawks 0.777 0.578
Detroit Red Wings 0.395 0.541
Boston Bruins -0.534 0.536
Pittsburgh Penguins -0.156 0.530
Toronto Maple Leafs -1.282 0.528
New Jersey Devils 0.459 0.522
St. Louis Blues 0.186 0.519
Phoenix Coyotes 2.092 0.515
Nashville Predators 1.225 0.514
Calgary Flames -0.590 0.513
Washington Capitals 1.883 0.512
San Jose Sharks 1.020 0.512
Philadelphia Flyers -1.157 0.511
Ottawa Senators 0.083 0.508
Los Angeles Kings 1.040 0.498
Buffalo Sabres 0.302 0.496
Atlanta Thrashers -0.347 0.496
New York Rangers -0.753 0.495
Vancouver Canucks 0.471 0.495
Carolina Hurricanes -0.555 0.491
New York Islanders -0.201 0.490
Columbus Blue Jackets -0.855 0.488
Dallas Stars -0.212 0.480
Anaheim Ducks -0.087 0.467
Tampa Bay Lightning -0.604 0.466
Florida Panthers -0.726 0.465
Montreal Canadiens 0.052 0.464
Minnesota Wild -0.486 0.459
Colorado Avalanche 0.599 0.449
Edmonton Oilers -1.993 0.446

When I was looking through the table something caught my attention.  Of the bottom 15 teams in Fenwick%, only four teams had positive luck.  These were Buffalo, Vancouver, Montreal and Colorado.  Generally speaking, these four teams had good to very good goaltending.  Of the top 15 teams in Fenwick%, only five teams had negative luck.  These were Boston, Pittsburgh, Toronto, Calgary and Philadelphia.  Boston and Calgary had good to very good goaltending (especially once Boston switched mostly to Rask) but Philadelphia, Pittsburgh and Toronto had mediocre to poor goaltending.  That got me to wondering whether goaltending correlated with luck at all so I took a look at the correlation between 5v5 game tied shooting and save percentages with luck.

Like fenwick%, there is an indication of a small correlation between shooting percentage and luck and there is a bit more of a correlation with save percentage.  Next I looked at combining all three factors.  Initially I was going to look at combining all three through some sort of average but then decided to look at goals for percentage instead (goals for divided by goals for plus goals against) since that basically encompasses everything anyway and we find that combined we get a relatively strong correlation with luck.

Now we are getting into correlation that might actually mean something, but what does it all mean?  To be honest, I am not sure.  Regardless of what ‘skill’ we look at there does seem to be a small positive correlation between how good a team is and how good their luck is (as calculated from the betting lines).  Does this mean that a bad team and especially a team with bad goaltending opens itself up to more bad luck than good teams or teams with good goaltending, or does it mean that luck manifests itself mostly in bad goals against or does it simply mean that the people who bet on hockey games trend towards betting the underdog which would push their expected winning percentage up and good teams expected winning percentage down which would result in a poor estimation of luck?  I am not sure how you determine what the exact cause of the correlation is but if it is the latter I have a word of advice, always bet the favourite.