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.

 

Dec 092011
 

Gabe Desjardins of Arctic Ice Hockey asks the question about whether a player can influence his teammates shooting percentage.  To answer this question he took a look at the Pittsburgh Penguins shooting percentages with and without Mario Lemieux.  The conclusion:

I’d posit that Lemieux’s playmaking contribution is about as large as we’re going to consistently find – something on the order of 7-8% – and we can use it to bound the impact that a player can truly have on the quality of his teammates’ scoring chances.

Since I have the numbers handy I figured I’d take a look at some more recent examples but instead of looking at straight shooting percentage I looked at corsi shooting percentage (since I had corsi data more available).  Corsi shooting percentage is simply goals for divided by corsi for.  I’d consider Joe Thornton one of the premiere playmaking centers in the league today so let’s take a look at how some players performed while playing with, and without, him.

CSH% With Thornton CSH% Without Boost
Marleau 4.93% 3.88% 27.02%
Setogutchi 5.09% 3.80% 33.82%
Heatley 5.59% 4.32% 29.50%

I included the past 4 years with Marleau, 3 years with Setogutchi and 2 years with Heatley.  That would indicate Thornton has an approximately 30% boost in corsi shooting percentage to his teammates.  Certainly far more than the 8% Gabe predicted as the upper bound.

Now, let’s take a look at another great player, Sidney Crosby.

CSH% With CSH% Without Boost
Malkin 7.30% 5.15% 41.89%
Dupuis 5.73% 4.06% 40.95%
Fleury 5.75% 4.22% 36.15%

All players are using 4 years of data.  I included Fleury in the list because it provides a good proxy of the Penguins shooting percentages when Crosby is on the ice vs when he is not.  This would seem to indicate that Crosby is worth a nearly 40% boost in his teams shooting percentage.  That’s significantly more than even Thornton and a massive amount more than Gabe’s estimated upper bound.  Maybe we should revise the upper bound to be 40%, not 8%.

For interest sake, here how much Crosby influenced his teammates corsi rates.

Boost in CF20
Malkin 21.15%
Dupuis 18.15%
Fleury 15.59%

While a ~20% boost is significant, it is at best only half the boost he provided to corsi shooting percentage.  Driving shooting percentage is a more significant reason why Crosby is so good offensively than driving corsi events.

 

Update:  Eric over at Broad St. Hockey has an interesting post looking at individual shooting percentages as opposed to on-ice shooting percentages as I did above.  Four of the players he looked at are H. Sedin, Crosby, Thornton and Datsyuk and for each he looked at a number of teammates with at least 30 shots with and without.  Taking it a step further I think it is necessary to average across players to get a better idea of what is happening.  If you do that, this is what you get:

With Without Boost
Sedin 11.19% 6.81% 64.28%
Crosby 9.14% 7.55% 21.17%
Thornton 9.69% 7.27% 33.19%
Datsyuk 9.36% 6.98% 34.09%

Wow, that might make Sedin the best playmaker in the league, by a significant margin.  Crosby doesn’t look quite as good as my “on-ice” analysis but that is because much of the reason why Crosby improves his linemates on-ice shooting percentage is because he is such a great shooter himself.

The point still stands, without considering shooting percentages we aren’t getting anywhere close to having a complete analysis of a players impact on the game.