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 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?

 

 

Apr 012014
 

Last week Tyler Dellow had a post titled “Two Graphs and 480 Words That Will Convince You On Corsi%” in which, you can say, I was less than convinced (read the comments). This post is my rebuttal that will attempt to convince you on the importance of Sh% in player evaluation.

The problem with shooting percentage is that it suffers from small sample size issues. Over small sample sizes it often gets dominated by randomness (I prefer the term randomness to luck) but the question I have always had is, if we remove randomness from the equation, how important of a skill is shooting percentage? To attempt to answer this I will look at the variance in on-ice shooting percentages among forwards as we increase the sample size from a single season (minimum 500 minutes ice time) to 6 seasons (minimum 3000 minutes ice time). As the sample size increases we would expect the variance due to randomness to decrease. This means, when the observed variance stops decreasing (or significantly slows the rate of decrease) as sample size increases we know we are approaching the point where any variance is actually variance in true talent and not small sample size randomness. So, without going on any further I present you my first chart of on-ice shooting percentages for forwards in 5v5 situations.

 

ShPctVarianceBySampleSize

Variance decline pretty much stops by the time you reach 5 years/2500+ minutes worth of data but after 3 years (1500+ minutes) the drop off rate falls off significantly. It is also worth noting that some of the drop off over longer periods of time is due to age progression/regression and not due to reduction in randomness.

What is the significance of all of this?  Well, at 5 years a 90th percentile player would have 45% more goals given an equal number of shots as a 10th percentile player. A player one standard deviation above average will have 33% more goals for given an equal number of shots as a player one standard deviation below average.

Now, let’s compare this to the same chart for CF/20 to get an idea of how shot generation varies across players.

 

CF20VarianceBySampleSize

It’s a little interesting that the top players show no regression over time but the bottom line players do. This may be because terrible shot generating players don’t stick around long enough. More importantly though is the magnitude of the difference between the top players and the bottom players.  Well, a 90th percentile CF20 player produces about 25% more shots attempts than a 10th percentile player and a one standard deviation above average CF20 player produces about 18.5% more than a one standard deviation below average CF20 player (over 5 years). Both of these are well below (almost half of) the 45% and 33% we saw for shooting percentage.

I hear a lot of ‘I told you so’ from the pro-corsi crowd in regards to the Leafs and their losing streak and yes, their percentages have regress this season but I think it is worth noting that the Leafs are still an example of a team where CF% is not a good indicator of performance. The Leafs 5v5close CF% is 42.5% but their 5v5close GF% is 47.6%. The idea that CF% and GF% are “tightly intertwined” as Tyler Dellow wrote is not supported by the Maple Leafs this season despite the fact that the Maple Leafs are the latest “pro-Corsi” crowds favourite “I told you so” team.

There is also some evidence that the Leafs have been “unlucky” this year. Their 5v5close shooting percentages over the past 3 seasons have been 8.82 (2nd), 8.59(4th), 10.54(1st) while this year it has dropped to 8.17 (8th). Now the question is how much of that is luck and how much is the loss of Grabovski and MacArthur and the addition of Clarkson (who is a generally poor on-ice Sh% player) but the Leafs Sh% is well below the past few seasons and some of that may be bad luck (and notably, not “regression” from years of “good luck”).

In summary, generating shots matter, but capitalizing on them matters as much or more.

 

Aug 022013
 

In Rob Vollman’s Hockey Abstract book he talks about the persistence and its importance when it comes to a particular statistics having value in hockey analytics.

For something to qualify as the key to winning, two things are required: (1) a close statistical correlation with winning percentage and (2) statistical persistence from one season to another.

More generally, persistence is a prerequisite for being able to call something a talent or a skill and how close it correlates with winning or some other positive outcome (such as scoring goals) tells us how much value that skill has.

Let’s look at persistence first. The easiest way to measure persistence is to look at the correlation of that statistics over some chunk of time vs some future chunk of time. For example, how well does a stat from last season correlate with the same stat this season (i.e. year over year correlation). For some statistics such as shooting percentages it may even be necessary to go with even larger sample sizes such as 3 year shooting percentage vs future 3 year shooting percentages.

One mistake that many people make when doing this is conclude that the lack of correlation and thus lack of persistence means that the statistics is not a repeatable skill and thus, essentially, random. The thing is, the method for how we measure persistence can be a major factor in how well we can measure persistence and how well we can measure true randomness. Let’s take two methods for measuring persistence:

  1.  Three year vs three year correlation, or more precisely the correlation between 2007-10 and 2010-13.
  2.  Even vs odd seconds over the course of 6 seasons, or the statistic during every even second vs the statistic during every odd second.

Both methods split the data roughly in half so we are doing a half the data vs half the data comparison and I am going to do this for offensive statistics for forwards with at least 1000 minutes of 5v5 ice time in each half. I am using 6 years of data so we get large sample sizes for shooting percentage calculations. Here are the correlations we get.

Comparison 0710 vs 1013 Even vs Odd Difference
GF20 vs GF20 0.61 0.89 0.28
FF20 vs FF20 0.62 0.97 0.35
FSh% vs FSh% 0.51 0.73 0.22

GF20 is Goals for per 20 minutes of ice time. FF20 is fenwick for (shots + missed shots) per 20 minutes of ice time. FSh% is Fenwick Shooting Percentage or goals/fenwick.

We can see that the level of persistence we identify is much greater when looking at even vs odd minute correlation than when looking at 3 year vs 3 year correlation. A different test of persistence gives us significantly different results. The reason for this is that there are a lot of other factors that come into play when looking at 3 year vs 3 year correlations than even vs odd correlations. In the even vs odd correlations factors such as quality of team mates, quality of competition, zone starts, coaching tactics, etc. are non-factors because they should be almost exactly the same in the even minutes as the odd minutes. This is not true for the 3 year vs 3 year correlation. The difference between the two methods is roughly the amount of the correlation that can be attributed to those other factors. True randomness, and thus true lack of persistence, is essentially the difference between 1.00 and the even vs odd correlation. This equates to 0.11 for GF20, 0.03 for FF20 and 0.27 for FSh%.

Now, lets look at how well they correlate with a positive outcome, scoring goals. But instead of just looking at that lets combine it with persistence by looking at how well predict ‘other half’ goal scoring.

Comparison 0710 vs 1013 Even vs Odd Difference
FF20 vs GF20 0.54 0.86 0.33
GF20 vs FF20 0.44 0.86 0.42
FSh% vs GF20 0.48 0.76 0.28
GF20 vs FSh% 0.57 0.77 0.20

As you can see, both FF20 and FSh% are very highly correlated with GF20 but this is far more evident when looking at even vs odd than when looking at 3 year vs 3 year correlations. FF20 is more predictive of ‘other half’ GF20 but not significantly so but this is likely solely due to the greater randomness of FSh% (due to sample size constraints) since FSh% is more correlated with GF20 than FF20 is. The correlation between even FF20 and even GF20 is 0.75 while the correlation between even FSh% and even GF20 is 0.90.

What is also interesting to note is that even vs odd provides greater benefit for identifying FF20 value and persistence than for FSh%. What this tells us is that the skills related to FF20 are not as persistent over time as the skills related to FSh%. I have seen this before. I think what this means is that GMs are valuing shooting percentage players more than fenwick players and thus are more likely to maintain a core of shooting percentage players on their team while letting fenwick players walk. Eric T. found that teams reward players for high shooting percentage more than high corsi so this is likely the reason we are seeing this.

Now, let’s take a look at how well FF20 correlates with FSh%.

Comparison 0710 vs 1013 Even vs Odd Difference
FF20 vs FSh% 0.38 0.66 0.28
FSh% vs FF20 0.22 0.63 0.42

It is interesting to note that fenwick rates are highly correlated with shooting percentages especially when looking at the even vs odd data. What this tells us is that the skills that a player needs to generate a lot of scoring chances are a similar set of skills required to generate high quality scoring chances. Skills like good passing, puck control, quickness can lead to better puck possession and thus more shots but those same skills can also result in scoring at a higher rate on those chances. We know that this isn’t true for all players (see Scott Gomez) but generally speaking players that are good at controlling the puck are good at putting the puck in the net too.

Finally, let’s look at one more set of correlations. When looking at the the above correlations for players with >1000 minutes in each ‘half’ of the data there are a lot of players that have significantly more than 1000 minutes and thus their ‘stats’ are more reliable. In any given year a top line forward will get 1000+ minutes of 5v5 ice time (there were 125 such players in 2011-12) but generally less than 1300 minutes (only 5 players had more than 1300 minutes in 2010-11). So, I took all the players that had more than 1000 even and odd minutes over the course of the past 6 seasons but only those that had fewer than 2600 minutes in total. In essense, I took all the players that have between 1000 and 1300 even and odd minutes over the past 6 seasons. From this group of forwards I calculated the same correlations as above and the results should tell us approximately how reliable (predictive) one seasons worth of data is for a front line forward assuming they played in exactly the same situation the following season.

Comparison Even vs odd
GF20 vs GF20 0.82
FF20 vs FF20 0.93
FSh% vs FSh% 0.63
FF20 vs GF20 0.74
GF20 vs FF20 0.77
FSh% vs GF20 0.65
GF20 vs FSh% 0.66
FF20 vs FSh% 0.45
FSh% vs FF20 0.40

It should be noted that because of the way in which I selected the players (limited ice time over past 6 seasons) to be included in this calculation there is an abundance of 3rd liners with a few players that reached retirement (i.e. Sundin) and young players (i.e. Henrique, Landenskog) mixed in. It would have been better to take the first 2600 minutes of each player and do even/odd on that but I am too lazy to try and calculate that data so the above is the best we have. There is far less diversity in the list of players used than the NHL in general so it is likely that for any particular player with between 1000 and 1300 minutes of ice time the correlations are stronger.

So, what does the above tell us? Once you factor out year over year changes in QoT, QoC, zone starts, coaching tactics, etc.  GF20, FF20 and FSh% are all pretty highly persistent with just one years worth of data for a top line player. I think this is far more persistent, especially for FSh%, than most assume. The challenge is being able to isolate and properly account for changes in QoT, QoC, zone starts, coaching tactics, etc. This, in my opinion, is where the greatest challenge in hockey analytics lies. We need better methods for isolating individual contribution, adjusting for QoT, QoC, usage, etc. Whether that comes from better statistics or better analytical techniques or some combination of the two only time will tell but in theory at least there should be a lot more reliable information within a single years worth of data than we are currently able to make use of.

 

Jun 112013
 

Nathan Horton has been one of the stars of these NHL playoffs as will be an integral component of the Stanley Cup finals if the Bruins are going to beat the Chicago Blackhawks. Nathan Horton is also set to become an unrestricted free agent this summer so his good playoff performance is good timing. One of the things I have noticed about Horton while looking through the statistics is that he has one of the highest on-ice 5v5 shooting percentages over the past 6 seasons of any NHL forward (ranks 16th among forwards with >300 minutes of ice time).

Part of the reason for this is that he is a fairly good shooter himself (ranks 30th with a 5v5 shooting percentage of 12.25%) but this in no way is the main reason.  Let’s take a look at how Horton’s line mates shooting percentage have been over the past 6 seasons when playing with Horton and when not playing with Horton.

Sh% w/o Horton Sh% w/ Horton Difference
Weiss 11.28% 12.84% 1.56%
Lucic 13.03% 16.98% 3.95%
Krejci 11.41% 12.10% 0.68%
Booth 8.44% 11.26% 2.82%
Frolik 6.58% 10.84% 4.26%
Stillman 10.03% 15.38% 5.35%
Zednik 8.81% 13.56% 4.75%
Average 9.94% 13.28% 3.34%

Included are all forwards Horton has played at least 400 minutes of 5v5 ice time with over the past 6 seasons along with their individual shooting percentage when with Horton and when not with Horton. Every single one of them has an individual shooting percentage higher with Horton than when not with Horton and generally speaking significantly higher.  I have previously looked at how much players can influence their line mates shooting percentages and found that Horton was among the league leaders so the above table agrees with that assessment.

It is still possible that Horton is just really lucky but that argument starts to lose steam when it seems he is getting lucky each and every year over the past 6 years (he has never had a 5v5 on-ice shooting percentage at or below league average). Whatever Horton is doing while on the ice seems to be allowing his line mates to boost their own individual shooting percentages and the result of this is that he has the 9th highest on-ice goals for rate over the past 6 seasons. He is a massively under rated player and is this summers Alexander Semin of the UFA market.

 

Apr 232013
 

With the win over the Ottawa Senators on Saturday night the Leafs have made the playoffs for the first time since the 2003-04 season and they are doing it largely on the backs of an elevated shooting percentage which currently sits at a lofty 10.52% (5v5 only). Here are all the teams with a 5v5 shooting percentage above 9.00% since 2007-08 season and how they have done in the playoffs.

Season Team 5v5 Sh% Playoff Result
2012-13 Maple Leafs 10.52 Made playoffs
2012-13 Stars 10.04 Fighting for playoff spot (10th)
2011-12 Lightning 9.73 Missed Playoffs
2009-10 Capitals 10.39 Lost in first round
2009-10 Canucks 9.14 Lost in second round
2008-09 Penguins 9.76 Won Stanley Cup
2008-09 Canucks 9.23 Lost in second round
2008-09 Bruins 9.15 Lost in second round
2008-09 Thrashers 9.02 Missed Playoffs
2007-08 Senators 9.03 Lost in first round

Prior to this season there have been 8 teams with a shooting percentage above 9.00%, 2 missed the playoffs, 2 lost in the first round, 3 lost in the second round and one team won the Stanley Cup. That isn’t very much success at all which is not a good sign for Leaf fans (myself included) hoping their team can go on a playoff run.

 

Apr 122013
 

The Toronto Maple Leafs shooting percentage has been predicted to fall for a couple of months now but it has held steady. I know that about 5-6 weeks ago the Leafs 5v5 shooting percentage was at 10.4% and I predicted it was sure to fall but as of this morning their 5v5 shooting percentage is even higher at 10.59%. Here is a graph of their 5v5 shooting percentage through out the season.

Toronto Maple Leafs 2012-13 Shooting %

Toronto Maple Leafs 2012-13 Shooting % (shots across x-axis)

League average 5v5 shooting percentage is normally just shy of 8% and the Leafs are about 33% higher than that which is incredibly high. Over the previous 5 seasons only one team has maintained a 5v5 shooting percentage above 10% over the course of an 82 game season and that was the Washington Capitals in 2009-10 when they shot at a 10.39% clip and only a handful of teams have managed to post a 5v5 shooting percentage above 9%. What the Leafs are doing is quite extraordinary even if it is a shortened season. Only 13.4% of the running 50 shot shooting percentage data points in the above graph fall below the typical league average of 8% so about 86.6% of the time they are at or above average in shooting percentage.

The only other team with a 5v5 shooting percentage above 10% this season is the Tampa Bay Lighting but they have been falling back a bit lately and in danger of falling below the 10% line as they currently sit at 10.01%.

Barring a collapse the Leafs should almost certainly end the season with a shooting percentage above 10% but it is difficult to know how much of it is luck/circumstance/randomness and how much is truly skill.

 

Feb 272013
 

The last several days I have been playing around a fair bit with team data and analyzing various metrics for their usefulness in predicting future outcomes and I have come across some interesting observations. Specifically, with more years of data, fenwick becomes significantly less important/valuable while goals and the percentages become more important/valuable. Let me explain.

Let’s first look at the year over year correlations in the various stats themselves.

Y1 vs Y2 Y12 vs Y34 Y123 vs Y45
FF% 0.3334 0.2447 0.1937
FF60 0.2414 0.1635 0.0976
FA60 0.3714 0.2743 0.3224
GF% 0.1891 0.2494 0.3514
GF60 0.0409 0.1468 0.1854
GA60 0.1953 0.3669 0.4476
Sh% 0.0002 0.0117 0.0047
Sv% 0.1278 0.2954 0.3350
PDO 0.0551 0.0564 0.1127
RegPts 0.2664 0.3890 0.3744

The above table shows the r^2 between past events and future events.  The Y1 vs Y2 column is the r^2 between subsequent years (i.e. 0708 vs 0809, 0809 vs 0910, 0910 vs 1011, 1011 vs 1112).  The Y12 vs Y23 is a 2 year vs 2 year r^2 (i.e. 07-09 vs 09-11 and 08-10 vs 10-12) and the Y123 vs Y45 is the 3 year vs 2 year comparison (i.e. 07-10 vs 10-12). RegPts is points earned during regulation play (using win-loss-tie point system).

As you can see, with increased sample size, the fenwick stats abilitity to predict future fenwick stats diminishes, particularly for fenwick for and fenwick %. All the other stats generally get better with increased sample size, except for shooting percentage which has no predictive power of future shooting percentage.

The increased predictive nature of the goal and percentage stats with increased sample size makes perfect sense as the increased sample size will decrease the random variability of these stats but I have no definitive explanation as to why the fenwick stats can’t maintain their predictive ability with increased sample sizes.

Let’s take a look at how well each statistic correlates with regulation points using various sample sizes.

1 year 2 year 3 year 4 year 5 year
FF% 0.3030 0.4360 0.5383 0.5541 0.5461
GF% 0.7022 0.7919 0.8354 0.8525 0.8685
Sh% 0.0672 0.0662 0.0477 0.0435 0.0529
Sv% 0.2179 0.2482 0.2515 0.2958 0.3221
PDO 0.2956 0.2913 0.2948 0.3393 0.3937
GF60 0.2505 0.3411 0.3404 0.3302 0.3226
GA60 0.4575 0.5831 0.6418 0.6721 0.6794
FF60 0.1954 0.3058 0.3655 0.4026 0.3951
FA60 0.1788 0.2638 0.3531 0.3480 0.3357

Again, the values are r^2 with regulation points.  Nothing too surprising there except maybe that team shooting percentage is so poorly correlated with winning because at the individual level it is clear that shooting percentages are highly correlated with goal scoring. It seems apparent from the table above that team save percentage is a significant factor in winning (or as my fellow Leaf fans can attest to, lack of save percentage is a significant factor in losing).

The final table I want to look at is how well a few of the stats are at predicting future regulation time point totals.

Y1 vs Y2 Y12 vs Y34 Y123 vs Y45
FF% 0.2500 0.2257 0.1622
GF% 0.2214 0.3187 0.3429
PDO 0.0256 0.0534 0.1212
RegPts 0.2664 0.3890 0.3744

The values are r^2 with future regulation point totals. Regardless of time frame used, past regulation time point totals are the best predictor of future regulation time point totals. Single season FF% is slightly better at predicting following season regulation point totals but with 2 or more years of data GF% becomes a significantly better predictor as the predictive ability of GF% improves and FF% declines. This makes sense as we earlier observed that increasing sample size improves GF% predictability of future GF% while FF% gets worse and that GF% is more highly correlated with regulation point totals than FF%.

One thing that is clear from the above tables is that defense has been far more important to winning than offense. Regardless of whether we look at GF60, FF60, or Sh% their level of importance trails their defensive counterpart (GA60, FA60 and Sv%), usually significantly. The defensive stats more highly correlate with winning and are more consistent from year to year. Defense and goaltending wins in the NHL.

What is interesting though is that this largely differs from what we see at the individual level. At the individual level there is much more variation in the offensive stats indicating individual players have more control over the offensive side of the game. This might suggest that team philosophies drive the defensive side of the game (i.e. how defensive minded the team is, the playing style, etc.) but the offensive side of the game is dominated more by the offensive skill level of the individual players. At the very least it is something worth of further investigation.

The last takeaway from this analysis is the declining predictive value of fenwick/corsi with increased sample size. I am not quite sure what to make of this. If anyone has any theories I’d be interested in hearing them. One theory I have is that fenwick rates are not a part of the average GMs player personal decisions and thus over time as players come and go any fenwick rates will begin to vary. If this is the case, then this may represent an area of value that a GM could exploit.

 

Jan 302013
 

For those familiar with my history, I have been a big proponent that there is more to the game of hockey than corsi and that players can certainly drive on-ice shooting percentage. I have not done much work at the team level, but now that I have team stats up at stats.hockeyanalysis.com I figured I’d take a look.

Since shooting percentages can vary significantly over small sample sizes, my goal was to use the largest sample size possible.  As such, I used 5 years of team data (2007-08 through 2011-12) and looked at each teams shooting and save percentages over that time. During those 5 years Vancouver led all teams in 5v5 ZS adjusted save percentage shooting at 10.69% while Columbus trailed all teams with a 8.61% shooting percentage. What’s interesting to note is the top 6 teams are Vancouver, Washington, Chicago, Philadelphia, Boston and Pittsburgh, all what we would consider the teams with the best offensive talent in the league. Meanwhile, the bottom 5 teams are Columbus, Los Angeles, Phoenix, Carolina, and Minnesota, all teams (except maybe Carolina) more associated with defensive play and a defense-first system.

As far as save percentage goes, Phoenix led the league with a 91.83% save percentage while the NY Islanders trailed with an 89.04% save percentage. The top 5 teams were Phoenix, Boston, Anaheim, Nashville, and Montreal.  The bottom 5 teams were NY Islanders, Tampa, Toronto, Chicago and Ottawa. Not surprises there.

As far as sample size goes, teams on average had 7,627 shots for (or against) over the course of the 5 years which gives us a reasonable large sample size to work with.

Now, in order to not use an extreme situation, I decided to compare the 5th best team to the 5th worst team in each category and then determine the probability that their deviations from each other are solely due to randomness.  This meant I was comparing Boston to Minnesota for shooting percentage and Montreal to Ottawa for save percentage.

TeamShootingPercentageComp

As you can see, there isn’t a lot of overlap, meaning there isn’t a large probability that luck is the reason for the difference between these two teams 5 year save percentages.  In fact, the intersecting area under the two curves amounts to just a 6.2% chance that the differences are luck driven.  That’s pretty small and the differences between the teams above Boston and below Minnesota would be greater. I think we can be fairly certain that there are statistically significant differences between teams 5 year shooting percentages and considering how much player movement and coaching changes there are over the span of 5 years it makes it that much more impressive. Single seasons differences could in theory (and probably likely are) more significant.

TeamSavePercentageComp

The save percentage chart provides even stronger evidence that there are non-luck factors at play.  The intersecting area under the curves equates to a 2.15% chance that the differences are due to luck alone. There is easily a statistically significant differences between Ottawa and Montreal’s 5 year save percentages. Long-term team save percentages are not luck driven!

So, the next question is, how much does it matter?  Well, the average team takes approximately 1500 5v5 ZS adjusted shots each season. The differences in shooting percentage between the 5th best team and the 5th worst team is 1.27% so that would equate to a difference of 19 goals per year during 5v5 ZS adjusted situations. The difference between the 5th best and 5th worst team in save percentage is 1.5% which equates to a 22.5 goal difference. These are not insignificant goal totals and they are likely driven solely by the percentages.

Now, how does this equate to differences in shot rates? If we take the team with the 5th highest shot rate and apply a league average shooting percentage and then compare it to the team with the 5th lowest shot rate we would find a difference of 17.5 goals over the course of a single season. This is slightly lower than what we saw for shooting and save percentages.

What is interesting is this (the percentages being more important than the shot rates) is not inconsistent with what we have seen at the individual level. In Tom Awad’s “What makes Good Players Good, Part I” post he identified 3 skills that good players differed from bad players. He identified the variation in +/- due to finishing as being 0.42 for finishing (shooting percentage), 0.08 for shot quality (shot location) and 0.30 for out shooting which would equate to out shooting being just 37.5% of the overall difference. I also showed that fenwick shooting percentage is more important than fenwick rates by a fairly significant margin.

Any player or team evaluation that doesn’t take into account the percentages or assumes the percentages are all luck driven is an evaluation that is not telling you the complete story.

 

Sep 032012
 

A month and a half ago Eric T at NHLNumbers.com had a good post on quantifying the impact on teammate shooting percentage.  I wanted to take a second look at the relative importance the impact on teammate shooting percentage can have because I disagreed somewhat with Eric’s conclusions.

For a very small number of elite playmakers, the ability to drive shooting percentage can be a major component of their value. For the vast majority of the league, driving possession is a more significant and more reproducible path to success.

It is my belief that it is important to consider impact on shooting percentage for more than a “very small number of elite playmakers” and I’ll attempt to show that now.

The method that Eric used to identify a players impact on shooting percentage is to compare that players line mates shooting percentages with him to their overall shooting percentage.  As noted in the comments the one flaw with this is that their overall shooting percentage is impacted by the player we are trying to evaluate which will end up underestimating the impact.  In the comments Eric re-did the analysis using a true “without you” shooting percentage and the impact of driving teammate shooting percentages was greater than initially expected but he concluded the conclusions didn’t  chance significantly.

Overall average for the top ten is a 1.2% boost (up from 0.9% in story) and 5 goals per year (up from 4.5). I don’t think this changes the conclusions appreciably.

In the minutes that a player is on the ice with one of the very best playmakers in the league, his shooting percentage will be about 1% better. For a player who gets ~150-200 shots per year and plays ~40-60% of his ice time with that top-tier playmaker, that’s less than a one-goal boost. It’s just not that big of a factor.

He also suggested that using the “without you” shooting percentage instead of the “overall shooting percentage” would probably result in “more accurate but less precise” analysis.  This is because a guy like Daniel Sedin would get very few shots when playing apart from Henrik Sedin because they rarely play apart and this small “apart” sample size might be subject to significant small sample size errors.

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