2010-11 stats are up

Stats from the 2010-2011 NHL regular season and playoffs are now on Extra Skater, including stats for players, teams, and games. Here are some interesting numbers:

Bench scoring awards

It takes a lot of things going right for a player to score a goal in the NHL. It’s even harder to score one, or assist one, while riding the pine. I decided to look at how many goals and assists players racked up while watching the play from the sidelines. Or, in other words, how it’s possible to have a point share of over 100%.

Your 2013-14 Bench Scoring Champion is the Oilers’ Boyd Gordon. His one off-ice goal was one more than anyone else managed. Here’s his goal:

Sure, he hadn’t been on the ice for the 43 seconds before the goal. Sure, another Oiler or two touched the puck in the meantime. But Gordon gets credit for the goal and is your champion. The last Oiler to touch the puck was Marincin and it would have been his first career goal. Tyler Dellow speculates that the scorers noticed this and fudged the interpretation of the rules to let Marincin get a better first goal. Marincin didn’t score (again?) on the season.

Here is Gordon (#27) checking out his feat on the video board while his teammates are oblivious. As a bonus you can see that the CSN score graphic initially gave the goal to the Hawks.

Gordon celebrating goal from bench

Next, I looked into bench assists. Unfortunately, with assists we’re getting away from the true skill of off-ice scoring. It’s one thing to score from the bench and quite another to get someone else to score for you. But let’s see the results.

Your 2013-14 Bench Passing Champion is a three-way tie between Matt Carle, Justin Faulk, and Niklas Kronwall, each with 4 bench assists. Here’s a typical off-ice assist from Niklas Kronwall:

In total, there was 1 goal scored from the bench, 49 goals where the primary assister was on the bench, and 205 where the secondary assister was on the bench for 255 off-ice points, 173 for defencemen and 82 for forwards. 1.4% of all points were given to players on the bench.

On-ice shot distance

I’ve expanded the on-ice events report with three new stats to build on the existing on-ice shots for distance: on-ice shots against distance, relative on-ice shots for distance, and relative on-ice shots against distance. Note that these distance stats are actually ‘fenwick distance’: the average distance of goals, shots saved, and shots that miss the net.

I was curious to see on-ice shots against distance for defencemen in particular — I wondered if any defencemen were able to consistently force opponents to take shots from further out than their team could otherwise. Using the distance relative to their team’s when they’re not on the ice helps mitigate scorers’ bias in shot distance, which can be fairly substantial.

The result? On first look, not much. The list of defencemen with the highest relative on-ice shots against distance doesn’t really match what you’d expect. In 2013-14, Benoit, Hamonic, and N. Schultz had the biggest positive effect on opponents’ shot distances (i.e. forced shots from further out than their teams without them) and Sekera, Guenin, and Jackman had the biggest negative effect. Chara, a guy who you might expect to force shots from far out with his size, is in the middle of the pack at -0.3 ft. relative to the rest of the Bruins.

In addition to eyeballing the list, I wanted to check, statistically, if relative shot distance against was a skill for defencemen. If something’s a skill, it should persist year-to-year. Taking 298 defencemen season pairs from 2011-12 through 2013-14 (e.g. Niklas Kronwall in 2011-12 and 2012-13) I looked at how their relative shot distance on year 1 compared to the same in year 2:

Year 1 SADist rel vs. Year 2 SADist relThe correlation coefficient (R-squared) is 0.03 which is fairly low: on-ice shot against distance regresses 82% toward the mean year-to-year. To put that into perspective, Eric T found that goal scoring, something that certainly has a talent component, has a correlation of 0.22.

Another way of looking at this is to group players based on their year 1 shot distance and see how the groups fare in year 2:

Y1 SADist rel Group N Avg Y1 SADist rel Avg Y2 SADist rel
2+ 20 2.59 0.24
1.5 to 2 23 1.70 0.23
1 to 1.5 31 1.18 0.56
0.5 to 1 35 0.72 0.30
0 to 0.5 49 0.16 0.08
-0.5 to 0 50 -0.27 -0.03
-1 to -0.5 41 -0.79 0.09
-1.5 to -1 24 -1.28 -0.29
-2 to -1.5 12 -1.78 -0.32
<-2 13 -2.59 -0.55

If on-ice shot distance were completely random we wouldn’t see any trend reading the last column top to bottom. This isn’t the case; there is a trend, but it’s noisy.

When we look at these correlations, we’re capturing more than just a defenceman’s talent, of course: his teammates, zone starts, opponents, etc. are all mixed in. Taking everything into consideration I’d say that defencemen have little identifiable talent for forcing their opponents’ shots to come from further out or closer in. But it would be interesting to look into things deeper, perhaps with more seasons’ worth of data and only focussing on ‘open play’ shots.

Something else I wanted to check was the relationship between shots against distance and save percentage. On-ice save percentage for defencemen is largely luck-driven by about as much as on-ice shot distance is. And distance and save percentage are closely related in general. So, maybe year-to-year changes in on-ice save percentage are driven by changes in shot distance?

SADist rel vs. SV% rel

This doesn’t appear to be the case. About 2% of a player’s on-ice relative save percentage can be explained by his on-ice shots against distance. Trying to understand this intuitively, we know that the league average shot distance is 36 ft., and the majority of defencemen are in the 35–37 range for shots against distance. The difference in save percentage between a 35 ft. shot and a 37 ft. shot just isn’t that big compared to other factors affecting save percentage.

Summing up, it appears that a) defencemen have little talent for forcing shots from further out, but further analysis could be done and b) there’s little relationship between relative on-ice save percentage and shot distance for defencemen. I haven’t looked at on-ice shots for distance for defencemen or any shot distances for forwards, but the data is now available to do so.

Introducing CHL statistics

It’s hard to do any kind of stat-based analysis of CHL players, including NHL draft prospects, since the only official statistics available are goals, assists, points, and penalty minutes. However, the three leagues do provide raw game-by-game data which means we can calculate more stats and make some assumptions to estimate more still. And that’s what I’ve done: Extra Skater now has statistics for CHL players for 2013-14. Check out the stats here or read on for details.

Stats available

Update 2014-06-23: I’ve removed all-situations on-ice stats because non-EV on-ice information is recorded too inconsistently across leagues to be useful

2014 NHL draft prospects

Choose ’17′ from the ‘age’ drop-down filter to show players eligible for the 2014 NHL Entry Draft. (Draft eligibility is a little more complicated than that but all the top CHL prospects are here.)

Interesting reports

Estimating time on ice

The CHL doesn’t track time on ice, so to use this metric we have to estimate it using the data they do make available. Luckily, many people have done this sort of thing including Iain Fyffe, Scott Reynolds, Eric T., and Rhys J/Josh Weissbock. The underlying assumption is that a player’s share of his team’s ice time is about the same as the share of goals he’s on the ice for. In other words, if a player is on the ice for 40% of the goals scored by or against his team, we assume he was on the ice for about 40% of the minutes (i.e. 24 minutes/60). This turns out to work fairly well.

I’ve refined this approach by separating the strengths (even strength, power play, and shorthanded), tracking how much time each team plays at each strength, and adjusting for time spent serving penalties. It’s by no means a perfect approach but it produces reasonable estimates (see the next section).

One important caveat about estimating time on ice for special teams: in the WHL, no arena records which players are on the ice for power play goals, and only 14 OHL arenas do (all in the QMJHL do). In the cases where we don’t have on-ice skater information, I’ve estimated time on ice based on players involved in goals as a scorer/assister. This method is very rough, especially for estimating shorthanded time on ice since there are so few shorthanded goals, and likely assigns more ice time than warranted to players involved in goals rather than just on the ice for them.

Accuracy of estimated metrics

To check the accuracy of time on ice estimates I used the same estimation method on NHL data. I chose 25 skaters with a range of ice time, from Colton Orr to Ryan Suter, and estimated their ice time using only data that would be available in the CHL (the QMJHL, to be specific, with all goals having on-ice players recorded). The results:

Graph of NHL TOI estimatesOverall, not too bad. If the estimates were 100% perfect all data points would lie on the black line where estimated TOI = actual TOI. While this method is certainly not perfect, it does a pretty good job ranking players by TOI, if not getting their TOI exactly correct. What is somewhat concerning is that TOI is underestimated for players with less than 15-17 minutes actual TOI/60 and overestimated for players above that mark. The effect is clearer in this graph of percentage error in estimates:

Graph of errors in NHL player TOI estimatesIntuitively, it makes sense for this to be the case. Good players—those who play more—generally have a higher on-ice goals rate (for + against), meaning that if they’re on the ice for 40% of their team’s goals for/against (est. 24 mins/60), their share of ice time is probably more like 33-35% (actual 20-22 mins/60). We could try to account for this effect by applying a curve to estimates but this isn’t something I’ve explored.

To check the accuracy of estimated QoC/QoT metrics, we could do a similar study to see how NHL players’ actual and estimated numbers differ, but I haven’t done this yet. When Eric T. did something similar the results lined up well, particularly for defencemen. A few people who watch the CHL closely that I’ve run these estimates by have said they’re generally in line with that they see and/or measure, again especially for defencemen.

To summarize: players’ ranks in TOI are probably fairly accurate, but high-TOI players are likely overestimated by up to a few minutes and low-TOI players likely underestimated by a similar margin. QoC and QoT are rough metrics by definition but generally reflective of reality and perhaps more accurate for defencemen than for forwards.

Acknowledgements and feedback

I’d like to thank those who took a look at this project as it developed and gave invaluable input and feedback on the data, estimates, functionality, presentation, and more.

If you have feedback or ideas about Extra Skater’s new CHL stats, I’d love to hear it. Leave a comment below or contact me.

Closest series

I was wondering which series were the “closest” these playoffs without going by just the length. So I ran some numbers showing the percentage of time each series was within a goal. The full results are below; but first, some findings.

The closest series is Blues-Blackhawks from first round, which was within one goal 91% of the time. That this series is the closest is unsurprising since 4 of the 6 games went to OT. The least close series so far is the ongoing Canadiens-Rangers conference final that has featured a blowout and a game where the Rangers took a 3-1 lead halfway through and didn’t give it up.

Interestingly, although the Avalanche-Wild series was the most lopsided in terms of possession, it is the second closest series overall, and for only 56 seconds did either team hold a lead of three or greater.

Series % of time within 1 goal
Blues-Blackhawks 91%
Avalanche-Wild 90%
Blackhawks-Wild 86%
Lightning-Canadiens 76%
Blackhawks-Kings 73%
Penguins-Rangers 70%
Ducks-Kings 70%
Penguins-Blue Jackets 69%
Bruins-Canadiens 68%
Rangers-Flyers 67%
Bruins-Red Wings 66%
Sharks-Kings 61%
Ducks-Stars 59%
Canadiens-Rangers 56%
Average 72%

Upgrades to player stats

I’ve made some big upgrades to player stats on Extra Skater. There are new reports, new stats, and new names. Plus you can now sort everything and get stats in more situations (5v5 tied, home, PP, etc.). Read on to learn more, skip to ‘cool stats’ to see some examples, or check out the new stats now.

New reports

The ‘on-ice’ report has been expanded and renamed ‘possession’. This makes room for a new ‘on-ice events’ report with new stats like shares and on-ice shot distance (see below for more).

New stats

New names

I’ve tweaked some stat acronyms to make them more intuitive:

Stat Old name New name
Percentage of team’s time that player is on ice for TotTm% TOI%
Percentage of team’s EV/PP/SH time player is on ice for EVTm%/PPTm%/SHTm% EVTOI%/PPTOI%/SHTOI%
Quality of competition/quality of teammates based on TOI TotTm% QoC/TotTm% QoT QoC TOI%/QoT TOI%
Penalties drawn/taken/differential PenD/Pen/Pen +/- PenDr/PenT/PenD
Hit differential Hit +/- HitD
Zone starts (on-ice faceoffs) FO ZS
Offensive/neutral/defensive zone start percentage OZSt%/NZSt%/DZSt% OZS%/NZS%/DZS%
Zone start percentage O/DSt% ZS%
Average shot distance ShDst SDist

Cool stats

Using these new stats, sorts, and situations, we can pull out some interesting stats from the 2013-14 season:

Thanks

And finally, a big thank you to everyone who suggested, gave feedback on, and helped test these upgrades. Let me know if you find anything that seems amiss.

Team icing stats

Here’s how often each team drew an icing and took an icing at 5v5 in 2013-14, and their “icing drawn percentage”:

Team Icings drawn Icings taken Icing drawn%
Los Angeles Kings 344 255 57.4%
Washington Capitals 319 250 56.1%
Detroit Red Wings 348 288 54.7%
Tampa Bay Lightning 338 292 53.7%
Boston Bruins 349 302 53.6%
Winnipeg Jets 316 276 53.4%
Carolina Hurricanes 381 338 53.0%
Colorado Avalanche 338 301 52.9%
New Jersey Devils 347 319 52.1%
Ottawa Senators 290 276 51.2%
St. Louis Blues 317 302 51.2%
Chicago Blackhawks 364 349 51.1%
Minnesota Wild 321 308 51.0%
New York Rangers 332 319 51.0%
Pittsburgh Penguins 271 263 50.7%
New York Islanders 312 310 50.2%
Philadelphia Flyers 312 314 49.8%
Anaheim Ducks 379 387 49.5%
Florida Panthers 296 311 48.8%
Columbus Blue Jackets 277 292 48.7%
Phoenix Coyotes 294 311 48.6%
Dallas Stars 298 323 48.0%
Calgary Flames 307 348 46.9%
Buffalo Sabres 302 344 46.7%
Nashville Predators 296 340 46.5%
Toronto Maple Leafs 310 357 46.5%
San Jose Sharks 317 373 45.9%
Edmonton Oilers 265 314 45.8%
Vancouver Canucks 282 366 43.5%
Montreal Canadiens 287 381 43.0%

Update 2014-05-03: numbers have been updated to include icings that coincided with another stoppage.

Award picks

As a follow up to my lists of award contenders, here are my picks for the major individual awards.

Hart (MVP)

Winner: Crosby

Runners up: Getzlaf, Giroux, Pavelski, Kopitar

Stats comparison

Crosby runs away with this one. His 103 points is gaudy in today’s NHL and he played most often with non-stars Kunitz, Dupuis, and Stempniak. He played a huge number of minutes, drew penalties, played against the other team’s best — he did it all. Pavelski may be a surprise on the runners up list, but he really shouldn’t: he scored 40 goals, was strong defensively, and played in all situations.

Vezina (top goalie)

Winner: Rask

Runners up: Varlamov, Price, Bobrovsky, Bishop

Stats comparison

This was a tight choice between Rask and Varlamov but I gave the edge to Rask’s better 5-on-5 save percentage. (Assessing goalies is always difficult.)

Calder (top rookie)

Winner: MacKinnon

Runners up: Palat, Johnson, Trouba, Krug

Stats comparison

MacKinnon lead all contenders in points, shots, and penalty differential, but this wasn’t an easy selection. I can see cases for Palat with his excellent production on tough minutes or Trouba for doing well while forgoing the sheltered minutes usually given to rookie defencemen.

Norris (top defenceman)

Winner: Giordano

Runners up: Weber, Karlsson, Suter, Niskanen

Stats comparison

The only thing that made this a tough call for Giordano was the time he missed. He had sparkling numbers (+13% GF% rel and +10% CF% rel) while playing the toughest minutes in all situations on a bad team. Some may be surprised with Niskanen as a runner up but he performed very well playing mostly with a rookie (Maatta) and two boat anchors (Scuderi and Engelland). Despite not playing as many minutes as other D his +30 5-on-5 goal differential is best among Norris contenders.

Selke (top defensive forward)

Winner: Kopitar

Runners up: Bergeron, Getzlaf, Toews, Little

Stats comparison

This was a two-horse race between Bergeron and Kopitar. What pushed me over towards Kopitar is his higher quality of competition, more ice time (3 mins/game), and ridiculous +20% GF% rel. Just to spell that last point out: while he was on the ice at 5 on 5, the Kings scored 56 goals and allowed 25 (69%). While he was on the bench, they were 75-78 (49%). It doesn’t hurt that he also had 70 points.

Team zone starts

One stat that you can see on the site for players but not teams in zone starts: in which zones of the ice (offensive/neutral/defensive) players/teams start. This indicates which zone teams most often play in and can help anchor evaluations of players’ zone start stats. A few people have asked about these stats so I compiled the data for the 2013-14 season. Here it is ordered by O/DZS%, which is OZ starts / (OZ + DZ starts):

Team EV ZS OZS NZS DZS O/DZS%
Chicago Blackhawks 3910 1358 1474 1078 55.7%
Los Angeles Kings 3714 1307 1319 1088 54.6%
Boston Bruins 4004 1388 1430 1186 53.9%
Detroit Red Wings 3800 1299 1389 1112 53.9%
New Jersey Devils 3663 1181 1446 1036 53.3%
St. Louis Blues 3831 1281 1388 1162 52.4%
New York Rangers 3935 1322 1407 1206 52.3%
Carolina Hurricanes 4030 1360 1415 1255 52.0%
Tampa Bay Lightning 3834 1254 1414 1166 51.8%
Phoenix Coyotes 3881 1237 1466 1178 51.2%
Winnipeg Jets 3793 1201 1447 1145 51.2%
Philadelphia Flyers 3801 1219 1417 1165 51.1%
Dallas Stars 3973 1277 1474 1222 51.1%
Anaheim Ducks 4161 1369 1474 1318 50.9%
Ottawa Senators 4026 1285 1499 1242 50.9%
New York Islanders 3876 1250 1402 1224 50.5%
Washington Capitals 3884 1236 1434 1214 50.4%
Minnesota Wild 3736 1176 1357 1203 49.4%
Florida Panthers 3879 1206 1435 1238 49.3%
San Jose Sharks 4222 1327 1532 1363 49.3%
Pittsburgh Penguins 3750 1114 1489 1147 49.3%
Colorado Avalanche 3916 1249 1375 1292 49.2%
Columbus Blue Jackets 3684 1140 1347 1197 48.8%
Calgary Flames 3924 1204 1433 1287 48.3%
Vancouver Canucks 3945 1222 1403 1320 48.1%
Nashville Predators 3989 1239 1397 1353 47.8%
Montreal Canadiens 3969 1117 1491 1361 45.1%
Edmonton Oilers 3847 1077 1448 1322 44.9%
Buffalo Sabres 3812 1092 1336 1384 44.1%
Toronto Maple Leafs 4257 1111 1512 1634 40.5%