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?

 

 

Jul 042014
 

The other day I put up a post on Mike Weaver’s and Bryce’s Salvador’s possible ability to boost their goalies save percentage and I followed it up with a post on the Maple Leafs defensemen where we saw Phaneuf, Gunnarsson, Gleason and Gardiner all seemingly able to do so as well while Robidas had the reverse effect (lowering goalie save percentage). This got some fight back from the analytics community suggesting this is not possible. My question to them is, why not?

Their answer is that if you do year over year analysis of a players on-ice save percentage or a year over year analysis of a players on-ice save percentage relative to their teams you will find almost no correlation. While this is true I claim that this is not sufficient to prove that such a talent does not exist. Here is why.

We Know Players Can and Do Impact Save %

The most compelling argument that players can and do impact save % is that we see it happening all the time and it is fully accepted among the hockey analytics community. It is known as score effects. Score effects are a well entrenched concept in hockey analytics.  It is why we often look at 5v5 “close” or 5v5 tied statistics instead of just 5v5 statistics. Generally speaking, the impact score effects have is that the trailing teams usually experiences an increase in shot rate along with a decrease in shooting percentage while the team protecting the lead experiences a decrease in shot rate but an increase in shooting percentage. The following table shows the Boston Bruins shooting and save percentages when tied, leading and trailing over the past 7 seasons combined.

. Tied Leading Trailing
Shooting% 7.27% 9.14% 7.66%
Save% 93.36% 93.86% 92.53%

The difference in the Bruins save percentage between leading and trailing is 1.33%. This is the difference between a .923 save percentage goalie and a .910 save percentage goalie which is the difference between an elite goalie and a below average goalie. That is not insignificant. Is this the goalies fault or does it have something to do with the players in front of him? The latter seems most likely. It makes sense that when protecting the lead the players take fewer risks in an attempt to generate offense and in return give up fewer good scoring chances against albeit maybe more chances in total. Conversely, the team playing catch up take more offensive risks so they end up giving up more quality scoring chances against. This is reflected in their teams save and shooting percentages when leading and trailing.

So, now if a team can play a style that boosts the team save percentage when they are protecting a lead, why is it so inconceivable that a player could see the same impact in his on-ice save percentage if that player plays that style of hockey all the time? If Mike Weaver and Bryce Salvador play the same style all the time that teams play when protecting a lead, why can they not boost on-ice save percentage? There is no reason they can’t.

It is Difficult to Detect because Individual Players Don’t Have a lot of Control of Outcomes

The average player’s individual ability to influence of what happens on the ice is actually fairly small as there are also 9 other skaters and 2 goalies on the ice with him. At best you can say the average player has a ~10% impact on outcomes while he is on the ice. That isn’t much. Last week James Mirtle tweeted a link to Connor Brown’s hockeydb.com page as evidence why +/- is a useless statistic. Over the course of three OHL season’s Brown’s +/- went from -72 to -11 to +44. I suggested to Mirtle that if this is the criteria for tossing out stats we can toss out a lot of stats including corsi% because most stats are highly team/linemate dependent. When challenged that this dramatic of reversal is not seen in corsi% I cited David Clarkson as an example.  In 2012-13 Clarkson was 4th in CF% but in 2013-14 he was 33rd (of 346) in CF%. From one year to the next he went from 4th best to 14th worst. Why is this? WEll, Clarkson essentially moved from playing with good corsi players on a good corsi team to playing with bad corsi players on a bad corsi team. No matter how much puck possession talent Clarkson has (or hasn’t) his talent doesn’t dominate over the talent level of the 4 team mates he is on the ice with.

Now think about how many players change teams from one year to the next and think about how many players get moved up and down a line up and change line mates from one season to next. It is not an insignificant number. TSN’s UFA tracker currently has 109 UFA’s getting signed starting July 1st, the majority of them changing teams. There are only ~800 NHL players (regulars and depth players) in a season so that is pretty significant turnover. Some teams turn over a quarter to half their line up while others stay largely the same. With that much roster turnover and with so little ability for a single player to drive outcomes it should be expected that the majority of statistics see relatively high “regression”. Regression doesn’t mean lack of individual talent though.

Think of this scenario. We have a player with an average ability to boost on-ice save percentage and he has been playing on a team with a number of players who are good at boosting on-ice save percentage but generally speaking he doesn’t play with those players. Under this scenario it will appear that the player is poor at boosting on-ice save percentage because he is being compared to  players who are good at it. Now that player moves to another team who isn’t very good at boosting on-ice save percentage. Now that same average player will look like he is a good player because he has a better on-ice shooting percentage than his teammates. The result is little year over year consistency but that doesn’t mean there aren’t talent differences among players.

Hockey is not like baseball which is a series of one-on-one matchups between pitcher and batter or isolated attempts to make a fielding play on a hit ball. Outcomes in hockey are completely interdependent on up to 12 other players on the ice. QoT is the largest driver of a players statistics in hockey. Only when we factor out QoT completely can we truly be able to identify every players talent level for any metric we measure. This is a kind of like the chicken and an egg problem though because to identify a players talent level we need to know the talent level of their team mates which in turn required knowledge of his own talent level. We can’t just look at year over year regression to isolate talent level.

Comments

The “team” aspect in hockey is more significant than any other sport and any particular players statistics are largely driven by the quality of his team mates. Even more than teammates, style of play can be a significant factor in a players statistics. The quality of the players that a particular player plays with is a function of both the team the plays on and the role (offensive first line vs defensive third line) he is playing on the team and this is maybe the greatest driver of a players statistics. This is why David Clarkson can be a Corsi king in New Jersey and a Corsi dud in Toronto. It also accounts for why James Neal can be a 25 goal guy playing on the first line in Dallas to a 40 goal guy in Pittsburgh (and probably back to a 25 guy guy in Nashville next year).  This also accounts for why year over year correlation in many stats is not very good despite there being measurable differences in the talent that that stat is measuring. Significant statistical regression is not sufficient, in my opinion, to conclude insignificant controllable talent if no significant attempt to completely isolate individual contribution to team results has been successfully made.

Just for fun, here is a chart of Lidstrom’s on-ice save percentage vs team save percentage. It is pretty outstanding that an offensive defenseman can do this too.

LidstromOnOffSavePct

 

Jul 022014
 

The other day I looked at the effect that Mike Weaver and Bryce Salvador had on their teams save percentage (if you haven’t read it, definitely go give it a read) when they were on the ice versus when they weren’t on the ice. Today I am going to take a look at the Maple Leaf defensemen to see if there are any interesting trends to spot. We’ll start with the new acquisitions.

Stephane Robidas

RobidasOnOffSavePct

(Blue line above orange is good in these charts, opposite is not good)

Aside from 2008-09 he has had a negative impact on his team save percentage. In 2007-08, 2009-10 and 2010-11 his main defense partner was Nicklas Grossman but in 2008-09 his main defense partner was Trevor Daley. Did this have anything to do with his poor effect on save percentage in 2008-09? Well, aside from last season Daley’s on-ice save percentage has been at or better than the team save percentage so there might be something to that.

Roman Polak

PolakOnOffSavePct

Not really a lot happening there except in 2011-12 when he was worse than the team (and the team had significantly better goaltending). Rembember though, the Blues have a pretty good defense so it is quite possible that not being worse than the rest of them is a good thing. Will be interesting to see how he does in a Leaf jersey this season.

Dion Phaneuf

PhaneufOnOffSavePct

Aside from 2008-09 there has been a slight positive impact on save percentage when he is on the ice. In 2008-09 he didn’t have a regular defense partner. At 5v5 he played a total of 1348:08 in ice time and his main defense partners were Giordano (364:56), Vandermeer (342:47), Pardy (304:27), Leopold (163:47), Regehr (85:08) and Sarich (77:41). That variety in defense partners can’t be a good thing. But, maybe Phaneuf has a slight positive impact on save percentage.

Cody Franson

FransonOnOffSavePct

So, he was good for a few years and then he was bad. What happened? Well, he was traded to the Leafs. For the 2009-10 and 2010-11 seasons his main defense partner was Shane O’Brien and he also spent significant time with Hamhuis. This could be a case of him playing “protected” minutes as he had really easy offensive QoC but I generally don’t think QoC has anything near as significant an impact as other factors so I am not sure what is going on. He has had pretty weak QoC the last couple seasons too so who knows.

Jake Gardiner

GardinerOnOffSavePct

It is only 3 seasons of data but so far so good for Gardiner. He has been a boost to the teams save percentage and that is on top of his good possession numbers. In my opinion, Gardiner is quite likely the best defenseman. I’ll drop the “quite likely” from that statement when he repeats his success but against tougher QoC as that will remove any doubt.

Now, let’s take a look at a couple of departing Leaf defensemen.

Carl Gunnarsson

GunnarssonOnOffSavePct

Save for 2010-11 Leaf save percentage has been better whith Gunnarsson on the ice. His two main defense partners that year were Luke Schenn and Mike Komisarek so maybe we can forgive him. In 2009-10 his defense partner was mainly Beauchemin or Kaberle and starting in 1011-12 it has mainly been Phaneuf.

Tim Gleason

GleasonOnOffSavePct

Tim Gleason gets a lot of criticism from Leaf fans, the analytics community, and maybe pretty much everyone but his teams have generally had a positive boost in save % when he is on the ice and in some cases a significant boost.

Based on the loss of Gunnarsson and Gleason, two defenseman who seem to be able to boost on-ice save percentage, and the addition of Robidas who has a negative impact and Polak who has more neutral impact it is quite possible the Leafs suffer a drop off in save percentage this season.

That said, I am not certain what to make of the impact we see and why they occur. Of the 9 defenseman I have presented charts for the past few days (the 7 above as well as Weaver and Salvador in my previous post) it seems that the majority of them have all but one or two of their seasons consistently boosting or inhibiting their teams save percentage. More investigation is needed as to why but I am becoming fairly confident that this is a repeatable talent. There is just too much consistency to consider it purely random.

 

Jul 012014
 

The other day I commented on twitter that I would be happy if the Leafs signed defenseman Mike Weaver because I think he is a defensive defenseman that I think the Leafs could really use. I have thought of Mike Weaver as a premier defensive defenseman for quite some time now. I always seem to get a little flak over it but that’s fine, I can handle it. For example, as a response to my Weaver comment on twitter Eric Tulsky thought it would be prudent to point out a “flaw” in my thought process.

 

And of course, Tyler Dellow never passes up an opportunity to take a jab at me (or anyone who he disagrees with) took the opportunity to re-tweet it.

Now, of course I had thought of responding with a tweet to the effect of “Florida’s save percentage was probably is a bit of a factor in that regression” but I didn’t want to get into a twitter debate at that moment and I was confident I could come up with more concrete evidence. So here is that evidence.

SavePercentageWeaverOnOffIce

The above chart shows the save percentage of Weaver’s team when Weaver is on the ice vs when Weaver is not on the ice including only games in which Weaver has played in (i.e. it is better than just using team save percentage for that season and also allows us to combine his time in Florida and Montreal last season). As you can see, there has only been one season in the last 7 in which his team had a worse save percentage when he is on the ice than not. That is reasonably compelling evidence. It’s difficult to say what happened that season but his main defense partners were a young Dmitry Kulikov and Keaton Ellerby so maybe that was a factor. An investigation of Kulikov’s and Ellerby’s impact on save percentage over the years may help us identify why Weaver slipped that year. It could have been a nagging injury as well. Or, it could just be randomness associated with save percentage.

Regardless of the “reason” for the slide in 2011-12 it is pretty difficult to argue that there has been significant “regression” the past 3 seasons as Tulsky and Dellow so eagerly wanted to point out as the past 2 seasons Weaver has seemingly had a significant positive impact on his teams save percentage. Since I made that statement there has been one seasons of “regression” so to speak and two seasons in support of my claim. I guess that means it is 2-1 in my favour. It continues to appear that Weaver is a good defenseman who can suppress shot quality against.

Another defenseman I have identified as a defenseman who possibly can suppress opposition save percentage is Bryce Salvador. Here is Salvador’s on/off save percentage chart similar to Weaver’s above (2010-11 is missing as Salvador missed the season due to injury).

SavePercentageSalvadorOnOffIce

Salvador’s on-ice save percentage has been better than the teams save percentage every year since 2007-08. Regression? Doesn’t seem to be.

To summarize, there are a lot of instances where if we simply do a correlation of stats from one year to the next or  make observations of future performance relative to past performance we see the appearance of regression. In fact, the raw stats do in fact regress. That doesn’t necessarily mean the talent doesn’t exist, just that we haven’t been able to properly isolate the talent. The talent of the individual player is only a small factor in what outcomes occur when he is on the ice (a single player is just one of 12 players on the ice during typical even strength play) so it is difficult to identify without attempting to account for these other factors (quality of team mates in particular).

Possession and shot generation/suppression is important, but ignore the percentages at your peril. They can matter a lot in player evaluation.