Sep 182014
 

Earlier this week TSN announced the creation of an Analytics team consisting of long-time TSN contributor Scott Cullen along with new TSN additions of Globe and Mail’s James Mirtle and hockey blogger Travis Yost. I am all for main stream media jumping on board with hockey analytics but once you go from independent hockey blogger to a significant contributor to TSN I think it opens the door to higher expectations and higher standards.  Scott Cullen has a long track record with TSN and I am confident James Mirtle will bring some intelligent insight as we are all familar with and respect his work. While I am fully aware of Yost and his blogging history I have to be honest in saying that I have not read a ton of his stuff so I was interested to see what he would offer. After reading his first two articles, I have to say I definitely think there is room for improvement.

Yost’s first article was a look at some trends as to how teams use players during 5 on 5 play. The point I think Yost was trying to make most is that teams are phasing out goons and other “specialists” and replacing them with guys that can play bigger minutes and at both ends of the rink. While this may very well be true I am not sure Yost’s evidence to support this is really valid. He produced a chart that showed that more players are getting more 5v5 ice time per game in 2013-14 than in 2007-08 and his conclusion was that this was evidence of teams moving away from goons and small ice time players.

The rightward shift here should seem apparent – a higher concentration of guys playing larger minutes now as opposed to seven years ago and fewer guys picking up scrap minutes in smaller roles. The number of forwards playing ten or less minutes a night has dropped from 109 in 2007, to 65 in 2014. And the number of forwards playing between 13 and 16 minutes a night has moved from 153 in 2007 to 231 in 2014. As a group, teams may still be leaning on their star players, but there’s also been a more balanced spread of total ice time than there was seven years ago.

First off, the rightward shift that Yost talks about is likely almost exclusively due to the fact that there were far fewer penalties and power plays in 2013-14 than there were in 2007-08 as Yost pointed out earlier. This lead to there being more even strength ice time to be doled out to the same number of players. This will almost certainly produce a right shift as observed. As for a more balanced spread in ice time, I don’t see that either. At least not to any significant extent. If one really wanted to look at this properly instead of looking at number of minutes of even strength ice time played one would want to look at percentage of a teams even strength minutes the player played. This would eliminate the difference in total even strength ice time and truly allow you to see whether teams are using a more balanced line up or not. At the very least one should adjust each players ES TOI by an appropriate amount for one of the seasons based on the ratio of league-wide ES TOI between the two seasons. I’d then be interested to see if a “right shift” occurs or whether there is a meaningful difference in the charts.

Yost’s second article for TSN.ca was about Marc-Edouard Vlasic and how he should probably be getting more recognition for how good he really is. Now that is a sentiment I can generally support but Yost’s supporting evidence for this is analytically unsound in my opinion. The first thing Yost does is identify a number of defensemen who are generally considered the leagues best that we should compare Vlasic too. This is a good start and Yost identified guys like Chara, Doughty, Karlsson, Pietrangelo, Subban, etc. What Yost did next is produce a bubble chart that plots even strength corsi% on the x-axis vs even strength goals % on the y-axis with bubble size representing scoring production. To be honest, I have no clue what the value of this chart is. Both corsi% and goal% are significantly  team driven but there was no accounting for quality of team and goal% has a certain amount of luck and randomness associated with it which was not discussed and I really have no idea what statistic was used for scoring production. The conclusion Yost drew from this chart was that Vlasic was right in the mix with some of the best defensemen in the league. Problem is I am certain I could find a number of other defensemen we generally consider mediocre that would be right there with Vlasic.

There are proper ways to do this kind of analysis and there is no way one can do this without taking into consideration quality of teammates. On my stats site I have teammate statistics (denoted by TM) and one can easily do a comparison of how the players on-ice stats compare to their teammates when their teammates are not playing with them. Doing this we get the following:

Player Name CF60 RelTM
ERIK KARLSSON 9.115
DUNCAN KEITH 8.597
ALEX PIETRANGELO 8.202
MARK GIORDANO 6.695
P.K. SUBBAN 6.152
MARC-EDOUARD VLASIC 5.87
SHEA WEBER 2.072
RYAN MCDONAGH 2.032
DREW DOUGHTY -0.448
ZDENO CHARA -0.55
RYAN SUTER -1.518

If we use CF60 as a proxy for offensive production we find the best offensive defensemen are Karlsson, Keith and Pietrangelo while the least offensive are Suter, Chara and Doughty. Vlasic is right in the middle and looks pretty good. One might be surprised at Doughty but the rest kind of make sense.

Now, let’s do the same for CA60.

Player Name CA60 RelTM
MARK GIORDANO -12.251
MARC-EDOUARD VLASIC -9.205
P.K. SUBBAN -4.586
ERIK KARLSSON -2.21
ZDENO CHARA -1.69
DREW DOUGHTY -1.585
ALEX PIETRANGELO -0.211
RYAN SUTER 0.953
DUNCAN KEITH 2.385
SHEA WEBER 4.34
RYAN MCDONAGH 4.468

For CA60 it is better to have a negative number as this indicates you are giving up fewer shot attempts than your teammates when they aren’t playing with you. Here Vlasic is second and looking pretty good.

Now we can combine these two stats by looking at CF% RelTM.

Player Name CF% RelTM
MARK GIORDANO 8.9%
MARC-EDOUARD VLASIC 6.6%
P.K. SUBBAN 4.8%
ERIK KARLSSON 4.6%
ALEX PIETRANGELO 3.7%
DUNCAN KEITH 2.3%
DREW DOUGHTY 0.7%
ZDENO CHARA 0.6%
SHEA WEBER -1.0%
RYAN SUTER -1.2%
RYAN MCDONAGH -1.2%

Out of this group, Vlasic is second best which is pretty good and is evidence that he probably deserves to be in the company of these guys. Now, with that said, this is just a cursory look and in no way a complete analysis. Not only are there limitations by just looking at corsi but there are a lot of other factors that need to be taken into consideration as well (for example, Giordano is probably not that good, only looks good because his Flames teammates are not very good relative to the teammates of the other players on this list). Overall though, this is how I think one should start an analysis of Vlasic and whether he deserves more credit for the player he is. To be fair to Yost, he gets into this a little bit by looking at a timeseries of Vlasic’s Relative Corsi% but in no way is this sufficient and he doesn’t compare it to any of the other defensemen he is comparing Vlasic to.

Overall I applaud TSN for wanting to jump on the analytics band wagon and I am certain Yost has the potential to provide a better analytical view than his first few posts which, to be honest, left me a little underwhelmed if not disappointed.

On the flip side, I saw some good stuff written recently by @MimicoHero that I think is worthy of mention. A recent blog post of his looked at Ryan Johansen’s value to the Blue Jackets and he, in my opinion, did a pretty good job of accounting for usage (i.e. QoT, QoC, zone starts) and comparing Johansen to his peers. I like the tables he produced and how he looked at offense and defense separately. Now I’d probably want to weight QoT far more heavily in the usage metric he came up with but overall a very good methodology for comparing players on different teams playing in different circumstances.

 

Aug 262014
 

I am sure many of you are aware that Corey Sznajder (@ShutdownLine) has been working on tracking zone entries and exits for every game from last season. A week and a half ago Corey was nice enough to send me the data for every team for all the games he had tracked so far (I’d estimate approximately 60% of the season) and the past few days I have been looking at it. So, ultimately everything you read from here on is thanks to the time and effort Corey has put in tracking this data.

As I have alluded to on twitter, I have found some interesting and potentially very significant findings but before I get to that let me summarize a bit of what is being tracked with respect to zone entries.

  • CarryIn% – Is the percentage of time the team carried the puck over the blue line into the offensive zone.
  • FailedCarryIn% – Is the percentage of the time the team failed to carry the puck over the blue line into the offensive zone.
  • DumpIn% – is the percentage of the time the team dumped the puck into the offensive zone.

The three of these should sum up to 100% (Corey’s original data treated FailedCarryIn% separately so I made this adjustment) and represent the three different outcomes if a team is attempting to enter the offensive zone – successful carry in, failed carry in, and dumped in.

I gathered all this information for and against for every team and put them in a table. I’ll spare you all the details as to how I arrived at this idea I had but here is what I essentially came up with:

  • Treat successful carry ins as a positive
  • Treat failed carry in attempts as a negative (probably results in a quality counter attack against)
  • Dump ins are considered neutral (ignored)

So, I then came up with NetCarryIn% which is CarryIn% – FailedCarryIn% and I calculated this for each team for and against to get NetCarryIn%For and NetCarryIn%Against for each team.

I then subtracted NetCarryIn%Against from NetCarryIn%For to get NetCarryIn%Diff.

In all one formula we have:

NetCarryIn%Diff = (CarryIn%For – FailedCarryIn%For) – (CarryIn%Against – FailedCarryIn%Against)

Hopefully I haven’t lost you. So, with that we now get the following results.

Team Playoffs? NetCarryIn%Diff RegWin%
Chicago Playoffs 12.2% 61.0%
Tampa Playoffs 6.1% 53.0%
Anaheim Playoffs 5.9% 64.6%
Colorado Playoffs 5.5% 59.1%
Detroit Playoffs 4.7% 51.2%
Minnesota Playoffs 4.1% 53.0%
Pittsburgh Playoffs 4.0% 59.8%
Dallas Playoffs 3.8% 51.8%
New Jersey . 3.4% 48.2%
Los Angeles Playoffs 1.7% 53.7%
Boston Playoffs 1.3% 67.1%
St. Louis Playoffs 1.2% 60.4%
Ottawa . 0.9% 47.6%
Columbus Playoffs 0.7% 51.8%
Edmonton . 0.7% 35.4%
NY Rangers Playoffs -0.1% 54.9%
Phoenix . -1.3% 48.8%
Montreal Playoffs -1.3% 53.0%
Vancouver . -1.7% 43.9%
Philadelphia Playoffs -1.8% 53.0%
Winnipeg . -1.8% 43.3%
San Jose Playoffs -2.3% 59.1%
NY Islanders . -3.0% 40.2%
Toronto . -4.8% 42.7%
Nashville . -6.0% 50.6%
Calgary . -6.4% 38.4%
Washington . -6.4% 46.3%
Florida . -6.7% 35.4%
Carolina . -6.8% 47.0%
Buffalo . -7.7% 25.6%

‘Playoffs’ indicates a playoff team and RegWin% is their regulation winning percentage (based on W-L-T after regulation time).

What is so amazing about this is we have taken the first ~60% of games and done an excellent job of predicting who will make the playoffs. The top 8 teams (and 11 of top 12) in this stat through 60% of games made the playoffs and all of  the bottom 8 missed the playoffs. That’s pretty impressive as a predictor. What’s more, the r^2 with RegWin% is a solid 0.42, significantly better than the r^2 with 5v5 CF% which is 0.31. Here are what the scatter plots look like.

CarryInPctDiff_vs_RegWinPct

CFPctDiff_vs_RegWinPct

I think what we are seeing is that if you are more successful at carrying the puck into the offensive zone, but not at the expense of costly turnovers attempting those carry ins, than your opponent you will win the neutral zone and that goes a long way towards winning the game. Recall that I have shown that shots on the rush are of higher quality than shots generated from zone play so an important key to winning is maximizing your shots on the rush and minimizing your opponents shots on the rush. To an extent this may in fact actually be measuring some level of shot quality.

Of course, why stop here. If it is in fact some sort of measure of shot quality, why not combine it with shot quantity? To do this I took NetCarryIn%Diff and add to it the teams Corsi% – 50%. This is what we get.

Team Playoffs? NetCarryIn%Diff – CF% over 50%
Chicago Playoffs 17.7%
Los Angeles Playoffs 8.5%
New Jersey . 7.8%
Tampa Playoffs 7.1%
Detroit Playoffs 6.2%
Anaheim Playoffs 5.7%
Boston Playoffs 5.2%
St. Louis Playoffs 4.3%
Dallas Playoffs 4.3%
Ottawa . 3.3%
Minnesota Playoffs 2.7%
Pittsburgh Playoffs 2.7%
Colorado Playoffs 2.5%
NY Rangers Playoffs 2.3%
San Jose Playoffs 1.4%
Columbus Playoffs 0.6%
Vancouver . -0.4%
Phoenix . -0.8%
Winnipeg . -1.7%
Philadelphia Playoffs -1.8%
NY Islanders . -3.6%
Montreal Playoffs -4.6%
Edmonton . -5.0%
Florida . -5.7%
Carolina . -6.5%
Nashville . -7.5%
Washington . -8.7%
Calgary . -10.1%
Toronto . -11.9%
Buffalo . -14.7%

New Jersey still messes things up but New Jersey is just a strange team when it comes to these stats. But think about this. If New Jersey and Ottawa made the playoffs over Philadelphia and Montreal it would have a perfect record in predicting the playoff teams. It was perfect in the western conference.

Compared to Regulation Win Percentage we get:

CarryInPctDiff_CFPctDiff_vs_RegWinPct

That’s a pretty nice correlation and far better than corsi% itself.

Now, this could all be one massive fluke and none of this is repeatable but I am highly doubtful that will be the case. We may be on to something there. Will be interesting to see what individual players look like with this stat and I’ll also take a look at whether zone exits should somehow get factored in to this equation. I suspect it may not be necessary as it may be measuring something similar to Corsi% (shot quantity over quality).

 

Aug 092014
 

The other day over at PensionPlanPuppets.com there was a post by Draglikepull looking at zone exits by Maple Leaf defensemen for the first half of last season. If you haven’t seen it yet, definitely go read it. I wanted to compare the zone exit data to my rush shot data which I have calculated from play by play data as explained here. If we can find good correlations between zone entry/exit data and my rush shot data that would be an excellent finding because the zone entry/exit data need to be manually recorded and is very time consuming. Thankfully this is a project being undertaken by Corey Sznajder. If we can find useful correlations with data that can be automatically calculated we may not need to do this in the future and Corey can have a summer vacation next year.

Let’s first look at defensive zone exit percentage and how it correlates with rush and non-rush shots.

PlayerName RushCF/60 Non-Rush OtherCF/60 Exit%
MORGAN RIELLY 11.5 39.8 27.5
CARL GUNNARSSON 10.6 35.1 25.9
DION PHANEUF 10.1 37.9 25.5
JAKE GARDINER 11.2 37.7 24.8
JOHN-MICHAEL LILES 15.5 41.9 24
CODY FRANSON 10.5 36.9 23.8
PAUL RANGER 12.0 32.9 20.5
MARK FRASER 14.5 34.7 13.3

One thing to note is that my rush shot data is for the full season and the exit% data is for the first half of last year. Also, my rush shot data is only road data to eliminate arena bias and Liles and Fraser also includes their time with Carolina and Edmonton respectively.

Let’s look at some charts to more easily see if a correlation exits.

 

Leafs_dmen_DefZoneExitPct_vs_RushShotsFor

Ok, this is very counter-intuitive. The defensemen that have the best defensive zone exit percentage have a lower rush shot rate and a higher non-rush shot rate. On the surface this doesn’t make sense. If you are better at carrying the puck out of your own zone you should be able to generate more shots from the rush but that doesn’t seem to be the case. I think what is actually happening here is that to be able to carry the puck out of the defensive zone you have to be a skilled puck handler and if you are skilled with the puck you probably get more time in the offensive zone including more offensive zone starts and more ice time with offensive type forwards. Now, if you are not a good offensive defenseman you probably don’t get many offensive zone starts and get more defensive zone starts and maybe more importantly you play less with offensive minded forwards.

It should also be noted that Fraser is a bit of an anomaly here as his defensive zone exit percentage is well below anyone else’s and his rush shot rate is quite good. If we take Fraser out of the charts the relationship is much flatter and the correlations get weaker. We need to look at more defensemen to get more conclusive results though. Also, I think we will also find that we will get better results for forwards as I generally think it is forwards that drive the offense, not the defensemen.

Another factor in the non-relationship between defensive zone exits and rush shots for might be that often when a team exits the defensive zone they conduct a line change and maybe in particular a change in defensemen as the forwards are taking the puck up the ice. Defensemen may be able to get the puck out of their own end and initiate a rush but are on the bench before the benefits of the zone exit and follow-up rush have materialized. This could result in the lack of positive correlation between zone exits and rush shots. I need to create an “initiator of rush shots” statistic to account for this possibility.

In the comments of the pensionplanpuppets.com article Corey Sznajder provided statistics on  zone entries against each defenseman. Most defensemen would likely have significantly more control over zone entries against than they do for creating offense so we might find stronger correlations here.

PlayerName RushCA/60 OtherCA/60 Carry% Against Break-up %
MARK FRASER 18.9 44.2 71.4 7.1
JAKE GARDINER 14.9 42.4 67.7 6
MORGAN RIELLY 16.1 49.4 67.7 4.3
CARL GUNNARSSON 13.1 56.4 64.4 11.3
CODY FRANSON 14.8 46.6 64.1 5.7
JOHN-MICHAEL LILES 10.5 45.2 55.2 6.9
PAUL RANGER 14.8 49.2 54.7 17.4
DION PHANEUF 13.7 58.1 53.1 13.4

 

Leafs_dmen_RushShotsAgainst_vs_CarryInPctAgainst

Now this is a little closer to what we might expect. Those defensemen that have a high percentage of zone entries against being carry-in entries vs dump-ins give up rush shots at a higher rate while also giving up non-rush shots at a lower rate. There doesn’t appear to be any correlation between Carry In % Against and total corsi against per 60 (r^2=0.026) so it seems only the type of shot against is being impacted. I have observed that shots on the rush are significantly more difficult shots (shooting percentage on rush shots over last 7 seasons has been 9.56% vs 7.34% on non-rush shots making rush shots 30% more difficult on average) so players that can limit the frequency of carry-in rushes against and force dump-ins against instead are in fact likely to reduce average shot difficulty against.

The real counter-intuitive observation is that from a strategy/tactics point of view, it might be better to start your defensive defensemen (i.e. the ones that have the ability to limit rushes against) in the offensive zone (for the Leafs this would be Phaneuf  and Liles/Gleason last year) and start your strong offensive and weak against the rush defensemen (i.e. Rielly, Gardiner in particular) in the defensive zone . This is the opposite of what the Leafs did last season and generally opposite of what most normally consider doing. It makes sense though. When you are in your own zone you want defensemen who can get the puck and get it out and when you are in the oppositions zone you want defensemen who don’t give up high quality (often odd-man) rushes against. Defense should start in the offensive zone and offense should start in the defensive zone. The focus is generating offense on the rush and limiting the other teams ability to generate offense on the rush. It’s a bit counter-intuitive but might prove to be smart strategy.

I look forward to when the zone entry/exit tracking project gets completed and we can look at a much larger sample with more players from more teams but between that project and the rush shot data I have calculated we should gain significantly more insight into the game and how it is played. We might even come up with some new revolutionary on-ice strategies.

 

Jul 232014
 

Tyler Dellow has an interesting post on differences between the Kings and Leafs offensive production. He comes at the problem from a slightly different angle than I have explored in my rush shot series so definitely go give it a read. These two paragraphs discuss a theory of Dellow’s that is interesting.

That’s the sort of thing that can affect a team’s shooting percentage. To take it to an extreme, teams shot 6.2% in the ten seconds after an OZ faceoff win this year; the league average shooting percentage at 5v5 is more like 8%. Of course, when you win an offensive zone draw, you start with the puck but the other team has five guys back and in front of you.

I wonder whether there isn’t something like that going on here that explains LA’s persistent struggles with shooting percentage (as well as those of New Jersey, another team that piles up Corsi but can’t score – solving this problem is one of the burning questions in hockey analytics at the moment). It’s a theory, but one that seems to fit with what Eric’s suggested about how LA generates the bulk of their extra shots. It’s hard for me to explain the Leafs scoring so many more goals in the first 11 seconds after a puck has been carried in, particularly given that I suspect that LA, by virtue of their possession edge, probably enjoyed many more carries into the offensive zone overall.

Earlier today I posted some team rush statistics for the past 7 and past 3 seasons. Let’s look in a little more detail how the Leafs, Kings and Devils performed over the past 3 seasons.

Team RushGF RushSF OtherGF OtherSF RushSh% OtherSh% Rush%
New Jersey 45 540 103 1675 8.33% 6.15% 24.4%
Toronto 66 523 128 1675 12.62% 7.64% 23.8%
Los Angeles 53 609 112 1978 8.70% 5.66% 23.5%

The Leafs scored the most goals on the rush despite the fewest rush shots due to a vastly better shooting percentage (nearly 50% better than the Devils and Kings) on the rush. They do not generate more shots on the rush, but do seem to generate higher quality shots.

The Kings generate by far the most shots in non-rush situations but have the poorest shooting percentage and thus do not score a ton of goals. The Devils don’t generate many non-rush shots and don’t have a great non-rush shooting percentage either and thus posted the fewest goals. The Leafs have had the same number of shots as the Devils but a significantly higher shooting percentage than the Devils and thus scored significantly more non-rush goals.

The Leafs scored 34% of their goals on the rush compared to 32% for the Kings and 30% for the Devils.

Are the Leafs a good rush team? Well, only Boston has scored more 5v5 road rush goals than the Leafs so probably yes but it is mostly because of finishing talent, not shot generating talent. They are 4th last in 5v5 road rush shots.

The Ducks have very similar offense to the Leafs. They don’t get many rush shots but post a really high rush shooting percentage. Anaheim generate a few more non-rush shots than the Leafs but they are very similar offense.

The Kings are a slightly better rush team than the Devils but neither are good and both are weak shooting percentage teams regardless of whether it is a rush or non-rush shot. The Kings make up for this though by generating a lot of shots from offensive zone play where as the Devil’s don’t.

 

Jul 232014
 

There was some discussion of rush shots over at Pensburgh.com and I realized I haven’t really provided much in the way of team rush stats other than some in chart form. So, here are some tables with 7 year and 3 year data in 5v5 road situations (sorted by RushSh% or RushSv%).

7 year Shooting Percentages

Team RushSh% OtherSh% Rush%
Toronto 11.02% 7.75% 24.44%
Los Angeles 10.37% 5.93% 23.16%
Anaheim 10.26% 7.95% 22.57%
Chicago 10.20% 7.31% 21.78%
Winnipeg 10.19% 7.47% 24.00%
St. Louis 10.11% 7.22% 24.24%
Nashville 10.05% 7.65% 25.13%
Columbus 10.01% 6.70% 24.48%
Washington 9.89% 7.59% 23.93%
Philadelphia 9.85% 7.53% 24.58%
Pittsburgh 9.83% 7.94% 24.92%
Dallas 9.82% 8.01% 22.19%
Ottawa 9.82% 7.04% 24.62%
Tampa Bay 9.77% 7.31% 24.35%
Buffalo 9.66% 6.95% 22.95%
Minnesota 9.63% 6.34% 23.37%
Detroit 9.57% 6.99% 22.36%
Edmonton 9.42% 8.08% 22.70%
Colorado 9.35% 7.39% 24.51%
NY Rangers 9.31% 6.54% 25.72%
Vancouver 9.27% 7.29% 24.56%
Boston 8.96% 7.63% 26.37%
Carolina 8.76% 6.85% 25.09%
Montreal 8.59% 7.38% 23.78%
San Jose 8.57% 6.54% 23.78%
Phoenix 8.24% 6.71% 23.96%
Calgary 8.14% 7.37% 24.78%
Florida 8.02% 7.16% 25.03%
New Jersey 7.38% 5.93% 23.82%
NY Islanders 7.16% 6.71% 23.80%
  • RushSh% is shooting percentage on rush shots.
  • OtherSh% is shooting percentage on shots we can’t identify as rush shots.
  • Rush% is the percentage of shots taken by the team that were on the rush.

7 year Save Percentages

Team RushSv% OtherSv% Rush%Against
Anaheim 91.92% 92.88% 22.24%
Phoenix 91.80% 93.20% 22.64%
New Jersey 91.66% 92.23% 23.92%
Boston 91.55% 93.34% 24.73%
NY Rangers 91.39% 93.01% 23.43%
Washington 91.32% 92.58% 23.71%
Philadelphia 91.10% 92.67% 24.00%
Los Angeles 90.82% 92.74% 24.13%
Vancouver 90.79% 92.97% 23.65%
Calgary 90.68% 91.50% 23.17%
Minnesota 90.65% 93.02% 22.77%
Nashville 90.50% 92.49% 21.44%
San Jose 90.48% 92.52% 22.53%
Pittsburgh 90.46% 92.33% 23.93%
Carolina 90.43% 92.65% 22.21%
Detroit 90.40% 92.73% 22.84%
Toronto 90.38% 91.88% 22.88%
Ottawa 90.35% 91.87% 23.61%
St. Louis 89.92% 92.37% 23.39%
Dallas 89.83% 92.21% 22.19%
Montreal 89.80% 92.68% 23.28%
Buffalo 89.80% 92.86% 23.54%
Colorado 89.52% 92.41% 20.71%
Edmonton 89.47% 92.21% 23.18%
Columbus 89.38% 92.27% 23.15%
Chicago 89.21% 92.47% 22.69%
NY Islanders 89.13% 91.93% 20.97%
Florida 89.12% 92.86% 23.61%
Winnipeg 89.08% 92.31% 23.19%
Tampa Bay 86.54% 91.89% 23.44%
  • RushSh% is save percentage on rush shots.
  • OtherSh% is save percentage on shots we can’t identify as rush shots.
  • Rush%Against is the percentage of shots given up by the team that were on the rush.

3 year Shooting Percentages

Team RushSh% OtherSh% Rush%
Toronto 12.62% 7.64% 23.79%
Anaheim 11.50% 8.21% 22.51%
Columbus 11.17% 6.29% 24.17%
Chicago 11.01% 7.37% 21.02%
Tampa Bay 10.47% 7.71% 23.00%
Boston 10.18% 7.64% 27.00%
Ottawa 10.11% 6.72% 24.36%
NY Rangers 10.09% 6.40% 26.57%
Philadelphia 10.02% 7.59% 24.76%
Detroit 9.98% 6.55% 22.57%
Minnesota 9.96% 5.68% 25.64%
Vancouver 9.78% 6.94% 25.21%
Montreal 9.54% 7.59% 24.98%
Dallas 9.45% 7.70% 22.66%
St. Louis 9.38% 6.56% 24.76%
Edmonton 9.23% 8.29% 24.37%
NY Islanders 9.21% 6.89% 25.14%
Nashville 9.17% 8.13% 26.05%
Phoenix 8.86% 6.91% 24.21%
San Jose 8.72% 5.62% 24.36%
Los Angeles 8.70% 5.66% 23.54%
Pittsburgh 8.69% 8.39% 26.53%
Colorado 8.69% 7.04% 25.16%
Washington 8.64% 7.60% 24.26%
Winnipeg 8.43% 7.67% 24.50%
New Jersey 8.33% 6.15% 24.38%
Buffalo 8.05% 6.58% 23.63%
Florida 7.65% 6.75% 24.75%
Carolina 7.62% 6.69% 24.91%
Calgary 7.46% 6.77% 25.24%

3 year Save Percentages

Team RushSv% OtherSv% RushA%
Anaheim 93.49% 91.92% 22.39%
Minnesota 92.76% 93.04% 23.43%
NY Rangers 91.90% 93.02% 23.66%
Ottawa 91.86% 93.20% 22.85%
Washington 91.79% 92.22% 23.05%
Toronto 91.68% 92.94% 22.49%
Los Angeles 91.36% 93.12% 25.88%
Dallas 91.00% 92.39% 23.58%
Boston 90.59% 92.62% 24.60%
Chicago 90.38% 91.70% 22.45%
St. Louis 90.25% 93.15% 23.90%
Phoenix 90.23% 93.44% 23.64%
New Jersey 90.17% 91.73% 25.34%
Philadelphia 90.13% 92.15% 24.98%
Nashville 89.98% 92.01% 21.62%
Carolina 89.95% 93.04% 21.58%
Columbus 89.82% 92.92% 23.39%
Detroit 89.72% 93.10% 24.49%
San Jose 89.67% 92.65% 21.22%
Pittsburgh 89.66% 92.03% 24.59%
Colorado 89.54% 93.26% 21.96%
Winnipeg 89.43% 92.38% 24.00%
Vancouver 89.37% 92.89% 23.89%
Buffalo 89.15% 93.36% 23.44%
Calgary 89.12% 91.90% 22.78%
Edmonton 88.91% 93.22% 24.29%
Florida 88.32% 91.93% 26.41%
Montreal 88.07% 92.85% 21.97%
NY Islanders 87.64% 92.26% 21.16%
Tampa Bay 87.29% 92.45% 23.62%

I will look into making more data available (single season, individual player, etc.) on stats.hockeyanalysis.com or possibly as a google document in the future.

 

Jul 182014
 

If you haven’t read my previous posts on rush shots or want to learn more about how I determine what is and what is not a rush shot please go back and read the series.

So far I have only looked at team data but I have now calculated rush shots by players and so I will take a look at rush shots by forwards.

I am restricting my analysis to forwards who have been on the ice for >1500 total shots (shots for plus shots against) over the past 7 seasons. There were 307 such forwards so basically we are dealing with top 9 type players. Here is a list of the 30 players with the best, and worst, on-ice shooting percentage on rush shots.

Rank Player RushSh% Rank PlayerName RushSh%
1 TEDDY PURCELL 16.02% 307 SAMUEL PAHLSSON 4.35%
2 J.P. DUMONT 16.00% 306 MIKE GRIER 4.69%
3 ARTEM ANISIMOV 14.29% 305 SHAWN THORNTON 4.73%
4 JONATHAN TOEWS 14.10% 304 JAMIE LANGENBRUNNER 5.00%
5 BRAD MARCHAND 13.80% 303 JOHN MADDEN 5.38%
6 STEVE DOWNIE 13.79% 302 TIM JACKMAN 5.41%
7 DREW STAFFORD 13.38% 301 DAVID STECKEL 5.43%
8 THOMAS VANEK 13.21% 300 NATE THOMPSON 5.46%
9 COLIN WILSON 13.11% 299 MAXIM LAPIERRE 5.48%
10 PATRICK KANE 13.10% 298 KYLE WELLWOOD 5.48%
11 KRISTIAN HUSELIUS 13.08% 297 MIKE KNUBLE 5.52%
12 JASON POMINVILLE 13.08% 296 DANIEL WINNIK 5.56%
13 NICKLAS BACKSTROM 13.08% 295 BOYD GORDON 6.06%
14 CLARKE MACARTHUR 13.07% 294 CRAIG ADAMS 6.06%
15 JAMES VAN_RIEMSDYK 13.06% 293 PETER MUELLER 6.31%
16 MIKHAIL GRABOVSKI 13.06% 292 BJ CROMBEEN 6.41%
17 PATRICE BERGERON 12.97% 291 TRAVIS MOEN 6.45%
18 PAVEL DATSYUK 12.95% 290 COLIN GREENING 6.45%
19 CHRIS STEWART 12.93% 289 JERRED SMITHSON 6.48%
20 TYLER SEGUIN 12.93% 288 MATTHEW LOMBARDI 6.52%
21 ALEX TANGUAY 12.90% 287 ROB NIEDERMAYER 6.63%
22 VACLAV PROSPAL 12.87% 286 JAMAL MAYERS 6.67%
23 PATRICK O’SULLIVAN 12.86% 285 VLADIMIR SOBOTKA 6.69%
24 DEREK STEPAN 12.77% 284 MASON RAYMOND 6.76%
25 MARCUS JOHANSSON 12.69% 283 RYAN CARTER 6.77%
26 SIDNEY CROSBY 12.64% 282 SERGEI KOSTITSYN 6.80%
27 EVANDER KANE 12.46% 281 ANTTI MIETTINEN 6.83%
28 WAYNE SIMMONDS 12.38% 280 SHAWN MATTHIAS 6.88%
29 TODD BERTUZZI 12.38% 279 VERNON FIDDLER 6.92%
30 TJ OSHIE 12.20% 278 BILL GUERIN 6.94%

The top 30 consists for forwards we’d mostly consider good to excellent offensive players though many of the truly elite offensive players are notably absent. Guys like Malkin (65th), Getzlaf (46th), Perry(75th), Giroux (84th), Parise (216), St. Louis (93rd), Kessel (62nd), etc. Some of those guys you’d think would thrive on end to end rushes so it is kind of strange to not see them there. The list also seems to have as many good 2-way players (Bergeron, Grabovski, Datsyuk, etc.) as elite offensive talents.

The bottom of the list is dominated mostly with 3rd line defensive types which is no surprise. Not really a lot of offensive talent in that group.

How about non-rush shots. Which players are best and worst at converting non-rush shots into goals.

Rank Player OtherSh% Rank PlayerName OtherSh%
1 SIDNEY CROSBY 11.00% 307 CAL CLUTTERBUCK 3.70%
2 THOMAS VANEK 9.98% 306 SHAWN THORNTON 4.03%
3 TAYLOR HALL 9.95% 305 JERRED SMITHSON 4.22%
4 CORY STILLMAN 9.95% 304 RYAN CARTER 4.28%
5 JAMIE BENN 9.89% 303 TORREY MITCHELL 4.67%
6 JORDAN EBERLE 9.81% 302 ZACK SMITH 4.73%
7 BRENDEN MORROW 9.74% 301 DEREK DORSETT 4.85%
8 JAROME IGINLA 9.71% 300 MARTY REASONER 4.91%
9 MARTIN ST._LOUIS 9.69% 299 SEAN BERGENHEIM 5.01%
10 DAVID DESHARNAIS 9.68% 298 PATRICK EAVES 5.12%
11 ALEX TANGUAY 9.63% 297 NATHAN GERBE 5.13%
12 NATHAN HORTON 9.60% 296 BRIAN BOYLE 5.18%
13 DAVID KREJCI 9.54% 295 BJ CROMBEEN 5.18%
14 MARC SAVARD 9.49% 294 TIM JACKMAN 5.20%
15 MARIAN GABORIK 9.47% 293 CHAD LAROSE 5.21%
16 ALEXANDER SEMIN 9.45% 292 BRIAN ROLSTON 5.28%
17 PATRICK SHARP 9.44% 291 DREW MILLER 5.31%
18 BOBBY RYAN 9.39% 290 JOHN MADDEN 5.35%
19 MIKE RIBEIRO 9.36% 289 DAVID CLARKSON 5.35%
20 STEPHEN WEISS 9.34% 288 DAVID STECKEL 5.37%
21 MATT DUCHENE 9.28% 287 PATRICK O’SULLIVAN 5.42%
22 JASON SPEZZA 9.26% 286 SAMUEL PAHLSSON 5.42%
23 STEVEN REINPRECHT 9.26% 285 TRAVIS ZAJAC 5.46%
24 STEVEN STAMKOS 9.24% 284 MICHAL HANDZUS 5.47%
25 BRENDAN MORRISON 9.24% 283 RYAN CALLAHAN 5.49%
26 LOUI ERIKSSON 9.21% 282 JAMIE LANGENBRUNNER 5.58%
27 MICHAEL RYDER 9.20% 281 JAMAL MAYERS 5.61%
28 JONATHAN TOEWS 9.14% 280 MARTIN HANZAL 5.61%
29 RYAN GETZLAF 9.13% 279 JORDIN TOOTOO 5.64%
30 COLBY ARMSTRONG 9.10% 278 BLAKE COMEAU 5.64%

The list of top players in non-rush shooting percentage is definitely a stronger list in terms of elite level players. One reason could possibly be that the greater sample size (there are 3-4 times as many non-rush shots as rush shots) allows for an improved ability to identify shooting talent. Another reason could be that the skills necessary to generate offense in the offensive zone is different than the skills to generate offense from the rush. To generate offense in the offensive zone you have to have elite puck handling and shooting skills because you are attempting to generate scoring chances with 5 defenders and a goalie in a small portion of the ice surface. To generate offense on the rush it may be more about being able to force turnovers and generating odd man rushes may be more important than pure puck handling or shooting skills. More rugged guys like Downie (63rd in non-rush Sh%), Stafford (106th), Bertuzzi (175th) and Marchand (149th) may be good at forcing turnovers to generate good rush opportunities but may not be so good at generating offense in the offensive zone.

Once again the worst shooting percentages on n0n-rush shots is dominated by 3rd line defensive/role playing types with a few other interesting names included like O’Sullivan who had the 23rd best rush shooting percentage. Clarkson and Callahan are two interesting names on this list. Clarkson ranked 122nd (probably pretty good for a Devil player) in on-ice rush shooting percentage lending support to the idea that the rugged types are better at generating good scoring chances on the rush than in offensive zone play. Callahan ranked 236th though.

The following table shows the number of players that had the highest percentage of their on-ice shots being rush shots.

Rank Player RushS% Rank PlayerName RushS%
1 SHAWN THORNTON 29.87% 307 VALTTERI FILPPULA 19.62%
2 RYAN JONES 29.56% 306 PATRICK KANE 19.73%
3 TIM JACKMAN 29.21% 305 SAKU KOIVU 19.92%
4 RUSLAN FEDOTENKO 28.24% 304 TEEMU SELANNE 20.06%
5 BRANDON PRUST 28.14% 303 DANIEL ALFREDSSON 20.22%
6 CAL CLUTTERBUCK 28.07% 302 DAN CLEARY 20.23%
7 BRAD MARCHAND 28.05% 301 PATRICK SHARP 20.57%
8 BRIAN BOYLE 28.04% 300 THOMAS VANEK 20.64%
9 GREGORY CAMPBELL 28.02% 299 ANDREW COGLIANO 20.71%
10 CHRIS NEIL 27.78% 298 JONATHAN TOEWS 20.77%
11 DEREK STEPAN 27.73% 297 DUSTIN PENNER 20.80%
12 CHUCK KOBASEW 27.70% 296 SHAWN HORCOFF 20.84%
13 CODY MCLEOD 27.68% 295 BRIAN ROLSTON 20.90%
14 JERRED SMITHSON 27.52% 294 TROY BROUWER 20.91%
15 BRANDON SUTTER 27.38% 293 HENRIK ZETTERBERG 20.94%
16 JORDIN TOOTOO 27.25% 292 ANDREW BRUNETTE 21.02%
17 RYAN CALLAHAN 27.20% 291 DEREK ROY 21.05%
18 MATT COOKE 27.15% 290 ERIC BELANGER 21.18%
19 MAXIM LAPIERRE 27.14% 289 MIKE RIBEIRO 21.19%
20 DAVID JONES 27.10% 288 JOHN TAVARES 21.21%
21 DANIEL PAILLE 27.10% 287 TEDDY PURCELL 21.23%
22 ERIC NYSTROM 27.07% 286 PAVEL DATSYUK 21.33%
23 CRAIG ADAMS 26.99% 285 TOMAS HOLMSTROM 21.38%
24 TOM KOSTOPOULOS 26.88% 284 CLAUDE GIROUX 21.43%
25 ARTEM ANISIMOV 26.78% 283 ALES HEMSKY 21.45%
26 DREW MILLER 26.71% 282 JIRI HUDLER 21.55%
27 LARS ELLER 26.68% 281 PAUL STASTNY 21.55%
28 MIKAEL BACKLUND 26.67% 280 JAROMIR JAGR 21.61%
29 JANNIK HANSEN 26.62% 279 RYAN GETZLAF 21.64%
30 J.P. DUMONT 26.57% 278 BRAD BOYES 21.68%

It is probably not surprising but there are a lot of defensive type players with high percentage of their shots on the rush while a number of good offensive players have a lower percentage of shots coming from the rush. Zone starts would have a significant impact on this and the ability (and desire) for elite offensive players to maintain offensive zone time and offensive shots is a factor too.

I am generally a believer that the next great leap in hockey analytics is getting away from overall statistics such as on-ice shot differentials/ratios and isolating individual skills and knowing what skills are best used in what situations and what skills complement each other to maximize line combinations. I think some of the results we see above take us a step closer in that direction though there is still a lot to learn and understand about the game. Better data will go a long way to achieving that but  until then I think there is more we can extract from the data we have.

 

Jul 152014
 

Before I get into rush shots of individual players I am going to look at some teams. I am starting with the Columbus Blue Jackets which was suggested for me to look at by Jeff Townsend who was interested to see impact the decline of Steve Mason and then the transition to Bobrovsky had. Before we get to that though, let’s first look at the offensive side of things (and if you haven’t read my introductory pieces on rush shots read them here, here and here).

ColumbusRushShPct

The League data is league average over the past 7 seasons.

There is a lot of randomness happening here, particularly the rush shot shooting percentages. This could be due to randomness as sample size for single season 5v5 road data is getting pretty small, particularly for rush shot data. Having looked at a number of these charts I think sample size is definitely going to be an issue. They key will be looking for trends above and beyond the variability.

Now for save percentages.

ColumbusRushSvPct

This chart is definitely a little more stable. Steve Mason’s excellent rookie season was 2008-09 where he actually had a below average non-rush 5v5road save percentage but an above average rush save percentage. Columbus never again posted a rush save percentage anywhere close to league average until this past season. Interestingly, despite Bobrovsky’s good season in 2012-13 his 5v5road save percentage that year was somewhat average (at home it was outstanding though which just goes to show you how variable these things can be).

Let’s take a look at the percentage of shots that were rush shots for and against.

ColumbusRushPct

Not really sure what to read into that, but I thought I toss it out there for you.

Something that I haven’t looked at before is PDO which is the sum of shooting and save percentages. There is no reason we can’t do this for rush and non-rush shots so here is what it looks like for Columbus.

ColumbusRushPDO

Again, I am not sure what we can read into this PDO table. PDO is kind of an odd stat in my opinion. PDO typically gets used as a “luck” metric which it can be if it deviates from 100.0% significantly which is certainly the case for a couple of seasons of Rush Shot PDO.

I am still trying to figure out how useful any of this rush/non-rush information is. Certainly I think we hit some serious sample size issues when looking at a single seasons worth of road-only data and I think that puts some of the usefulness in question. I have done some year over year correlations and truthfully they aren’t very good. I think that is largely sample size related but I still think playing style and roster turnover can have significant impacts too. All that said, there is a clear difference between the difficulty of rush and non-rush shots and teams that can maximize the number of rush shots they take and minimize the number of rush shots against will be better off.

 

Jul 142014
 

I have written a couple of posts (here and here) on rush shots as it relates to shooting percentages and I investigate this further at a later date. First though, I wanted to take a look at save percentages on rush and non-rush shots. Let’s start by looking at teach teams 5v5 road save percentages for the past 7 seasons combined.

RushvsNonRushSavePct_5v5road

A few observations:

  1. Whoa Tampa! That’s a dreadful save percentage on the rush, 2.5% below anyone else. More on this later.
  2. The teams with the best save percentages on the rush are Anaheim, Phoenix, New Jersey, and Boston.
  3. r^2 between rush save % and non-rush save % is just 0.23 which is below what I might have expected.
  4. There is significantly less variability in non-rush save percentage than rush save percentage. The standard deviation in rush save percentage is 1.09% while this standard deviation in non-rush save percentage is 0.43%. All but 7 teams have a non-rush save percentage between 92.2% and 93.0% (a range of 0.8%) while only 21 teams lie in the range from 89.4% to 91.4% (a range of 2.0%). This could be due to greater variation in the smaller sample size of rush shots but the difference in variability is greater than we see with shooting percentage (standard deviation of 0.91% on rush shots and 0.57% on non-rush shots). Could ability to make saves on rush shots be the larger factor in goaltending talent variability?

Which teams give up the highest percentage of “rush” shots? Well, this chart will provide you that answer.

PercentageOfShotsAgainstOnRush_5v5road

Boston and Los Angeles. Who’d have guessed that? Both are teams we would consider good defensive teams and yet a higher percentage of their shots against are of the tougher rush shot variety. Meanwhile Colorado, NY Islanders and Nashville give up the smallest percentage of shots on the rush. Certainly wouldn’t have predicted that for the Islanders and maybe not Colorado either. There doesn’t seem to be any trends that can be extracted from that chart as good teams and bad teams are spread throughout.

Earlier we saw that Tampa had a downright dreadful save percentage on the rush. I wanted to take a look and see if there has been any improvement over the years, particularly last year when they got some good goaltending for the first time since probably Khabibulin.

TampaBaySavePct_Rush_5v5road

For the first time in the past 7 seasons Tampa goaltending provided a league average save percentage on rush shots. Not sure one season is enough to definitively declare their goaltending problems over, but they seem to be on the right path after years of dreadful goaltending.

Since I am a Toronto fan I wanted to take a look at the Leafs as well to see how their goaltending has improved over the years with Reimer and now Bernier.

TorontoSavePct_Rush_5v5road

The Leafs had a good “rush shot” save percentage in 2007-08 but a poor save percentage on other shots with Toskala playing the majority of the games backed up by Raycroft. Everything was bad in 2008-09 though when Toskala once again had the majority of the starts while Curtis Joseph, Martin Gerber and Justin Pogge shared backup duties. Since then things have slowly gotten better, particularly when Reimer came on board the second half of the 2010-11 season and the Leafs have been a better than average team on both rush and non-rush shots the past couple seasons.

I’ll look at some other teams in future posts. If you have any teams that you’d like me to look at (i.e. teams that are particularly interesting due to change in goalies or whatever) let me know and I’ll take a look.

 

Jul 102014
 

Yesterday I introduced the concept of rush shots which are basically any shot we can identify as being a shot taken subsequent to a rush up the ice which can be determined by the location of previous face off, shot, hit, giveaway or takeaway events. If you haven’t read the post from yesterday go give it a read for a more formal definition of what a rush shot is. Today I am going to take a look at how rush shots vary when teams are leading vs trailing as well as investigate home/road differences as arena biases in hits, giveaways and takeaways might have a significant impact on the results.

Leading vs Trailing

One hypothesis I had is that a team defending a lead tends to play more frequently in their own zone and thus have the potential to generate a higher percentage of shots from the rush. Here is a table of leading vs trailing rush shot statistics.

Game Situation Rush Sh% Other Sh% Overall Sh% % Shots on Rush
Leading 10.43% 8.03% 8.62% 24.3%
Trailing 9.36% 7.15% 7.63% 22.0%
Leading-Trailing 1.07% 0.89% 0.98% 2.28%

As expected, teams get a boost in the percentage of overall shots that are rush shots when leading (24.3%) compared to when trailing (2.28%). This higher percentage of shots being rush shots would factor in to the higher shooting percentages but it actually doesn’t seem to be all that significant. The more significant impact still seems to be that teams with the lead experience boosts in shooting percentage on both rush and non-rush shots. The hypothesis that teams have a higher shooting percentage when leading due to the fact that they have more shots on the rush doesn’t seem to be true. It’s just that they shoot better. Note that empty net situations are not considered and thus the shooting percentages when leading are not a result of empty net goals.

 Home vs Road

My concern with home stats is the various arena game recorders dole out hits, giveaways and takeaways at different rates. I determine what is a rush and what isn’t based in part on those events so there is the potential of significant arena biases in rush shot stats. To investigate I looked at the percentage of shots that were rush shots at home and on the road for each team. Here is what I found.

RushShotPercentage_Home_vs_Road

That is about as conclusive as you can get. The rush shot percentage at home is far more variable than on the road with higher highs and lower lows. It is possible that last change line matching usage tactics that coaches can more easily employ at home could account for some of the added variability but my guess is it is mostly due to arena scorer biases. From the chart above I suspect Buffalo, Minnesota, New Jersey  and Pittsburgh don’t hand out hits, giveaways and takeaways as frequently as other arenas. This chart takes a look at last years real time stats (the above chart is for last 7 seasons combined).

HitsGiveawaysTakeaways_Home_vs_Road

Most teams have more hits+giveaways+takeaways on home ice than on the road. The teams that have more on the road than at home are Buffalo, Minnesota, New Jersey, Pittsburgh and St. Louis. Despite comparing a 7-year chart with a 1-year chart the two charts seem to align up fairly well. There does seem to be significant arena biases in rush shot statistics so when looking at team and player stats it is certainly best to consider road stats only.

 

Jul 092014
 

I have been pondering doing this for a while and over the past few days I finally got around to it. I have had a theory for a while that an average shot resulting from a rush up the ice is more difficult than a shot than the average shot that is generated by offensive zone play. It makes sense for numerous reasons:

  1. The rush may be an odd-man rush
  2. The rush comes with speed making it more difficult for defense/goalie to defend.
  3. Shots are probably take from closer in (aside from when a team wants to do a line change rarely do they shoot from the blue line on a rush).

To test this theory I defined a shot off the rush as the following:

  • A shot within 10 seconds of a shot attempt by the other team on the other net.
  • A shot within 10 seconds of a face off at the other end or in the neutral zone.
  • A shot within 10 seconds of a hit, giveaway or takeaway in the other end or the neutral zone.

I initially looked at just the first two but the results were inconclusive because the number of rush events were simply too small so I added giveaway/takeaway and hits to the equation and this dramatically increased the sample size of rush shots. This unfortunately introduces some arena bias into the equation as it is well known that hits, giveaways and takeaways vary significantly from arena to arena. We will have to keep this in mind in future analysis of the data and possibly consider just road stats.

For now though I am going to look at all 5v5 data. Here is a chart of how each team looked in terms of rush and non-rush shooting percentages.

Rush_vs_NonRush_ShootingPct_2007-14b

So, it is nice to see that the hypothesis holds true. Every team had a significantly higher shooting percentage on “rush” shots than on shots we couldn’t conclusively define as a rush shot (note that some of these could still be rush shots but we didn’t have an event occur at the other end or neutral zone to be able to identify it as such). As a whole, the league has a rush shot shooting percentage of 9.56% over the past 7 seasons while the shooting percentage is just 7.34% on shots we cannot conclusively define as a rush shot. Over the 7 years 23.5% of all shots were identified as rush shots while 28.6% of all goals scored were on the rush.

In future posts over the course of the summer I’ll investigate rush shots further including but not limited to the following:

  • How much does the frequency of rush shots drive a teams/players overall shooting/save percentages?
  • Are score effects on shooting/save percentages largely due to increase/decrease in rush shot frequency?
  • Are there teams/players that are better at reducing number of rush shots?
  • Can rush shots be used to identify and quantify “shot quality” in any useful way?
  • How does this align with the zone entry research that is being done?