- Dustin Byfuglien had the highest corsi percentage relative to his team at +9.1%
- Michael Grabner lead the league with 1.58 goals/60 at even strength
- Adam McQuaid had the league’s highest PDO at 106.0
- Christian Ehrhoff was on the ice for an astounding 50% of his team’s offensive zone faceoffs. The only player to surpass that since is Ryan Suter with 57% in 2013-14; Suter played 29 minutes/game while Ehrhoff played 24.
- Sergei Kostitsyn shot a league-high 24.7%; his 4.8 shots/60 ranked 308th
- Tim Thomas’ 94.8% even strength save percentage was best in the league; no regular goalie has topped it since
- Getzlaf, Lidstrom, and Perry faced the toughest quality of competition
- George Parros had the league’s highest on-ice save percentage at 96.1% yet his PDO was well under 100
- The Sharks lead the league with a 54.6% fenwick close
- The Senators had the lowest PDO at 97.4; no team has had a lower PDO in an 82-game season since
- The Ducks were the only team that didn’t control play when trailing by two or more
- At times the Kings had 98%, 96%, and 91% chances of winning game 3 of their first-round series with the Sharks, which they went on to lose 6-5 in overtime
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.
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.
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:
The 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|
|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|
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?
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.
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.
- Games played, goals, assists, and points
- Estimated time on ice per 60 minutes (eTOI/60) and points per 60 minutes (P/60)
- Even strength goals, assists, points, and points/60
- Even strength on-ice goals for, goals against, goals for percentage, and goals for percentage relative to team
- Even strength goals share (GShr), assists share (AShr), and points share (PShr): the percentage of goals a player was on the ice for that he was responsible for through scoring them, assisting them, or either
- Estimated percentage of team’s ice time at even strength (EVeTOI%), on the power play (PPeTOI%), and shorthanded (SHeTOI%)
- Quality of competition (QoC eTOI%) and quality of teammates (QoT eTOI%) based on estimated time on ice of opponents/teammates
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.)
- Top-scoring NHL draft eligible players (P/60)
- Top even strength scoring draft eligible players (EV P/60)
- Top-scoring 16-year-olds (P/60)
- Players who play the most of their team’s shorthanded time (SHeTOI%)
- Players involved in the most of their on-ice goals (PShr)
- Youngest players
- Forwards who play the most (eTOI/60)
- Players with the best even strength goals percentage relative to their team (EV GF% rel)
- Top-scoring players age 18 or younger
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:
Overall, 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:
Intuitively, 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.
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|
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.
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).
- Setup passes (SP): an estimate of passes that directly result in a shot attempt. Created by Rob Vollman, this is based on the assumption that the percentage of goals a player had the primary assist on is similar to the percentage of shot attempts the player set up. Here, SP = A1 / TMCSh%, where TMCSh% is teammate corsi shooting percentage (teammates’ goals / teammates’ shot attempts).
- Pass/shot ratio (PSR): measure of a player’s tendency to pass (vs. shoot). PSR = SP / CF, where CF is a player’s individual shot attempts.
- Takeaways (Tk) and giveaways (Gv), both most useful when limited to 5-on-5 road rate stats
- Relative on-ice shooting percentage (Sh% rel), on-ice teammates’ shooting percentage (TMSh%), relative save percentage (Sv% rel), and relative PDO (PDO rel)
- On-ice rate stats including CF/60, CA/60, CD/60, CD/60 rel, and more
- Share of goals, assists, points, shots, corsi, and fenwick (GShr, AShr, PShr, SShr, CShr, and FShr): the percentage of events a player was on the ice for that he was responsible for, e.g. GShr = G / on-ice G. The only caveat is that assist share excludes a player’s own goals, so AShr = A / TMG, where TMG is teammates’ goals.
- On-ice shot distance (SDist)
- On-ice faceoff wins, losses, and winning percentage (FOW, FOL, and FO%)
- On-ice icings drawn, taken, and drawn percentage (IceDr, IceT, and IceDr%)
- Relative offensive/defensive zone start percentage (ZS% rel): useful for normalizing zone starts between teams e.g. when comparing Dougie Hamilton and Justin Schultz
- Share of team’s offensive, neutral, and defensive zone starts (OZShr, NZShr, and DZShr)
- Relative quality of competition overall, against forwards, and against defencemen (QoC TOI% rel, QoC TOI% F rel, and QoC TOI% D rel)
- Relative quality of teammates overall, with forwards, and with defencemen (QoT TOI% rel, QoT TOI% F rel, and QoT TOI% D rel)
- Percent of shots on net, missed, and blocked (ON%, Miss%, Blk%)
- Goals on wrist shots, snap shots, and slap shots (WrtG, SnpG, SlpG)
- Shooting percentage on wrist shots, snap shots, and slap shots (WrtSh%, SnpSh%, SlpSh%)
- Goalies’ total corsi against and fenwick against (CA, FA)
- Goalies’ goal support (GF/60), goals against rate, shots against rate, corsi against rate, and fenwick against rate (GA/60, SA/60, CA/60, FA/60)
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|
Using these new stats, sorts, and situations, we can pull out some interesting stats from the 2013-14 season:
- Alex Ovechkin tops the list of ‘puck hogs’ (highest CShr) and the list is an odd mix of shooters and fourth liners
- Shea Weber faced the 38th toughest quality of competition overall but the 5th toughest relative to his team
- Joe Pavelski had the most setup passes at 726
- Pavel Datsyuk is a takeaway machine, and behind him on the list is Peter Holland
- Jaromir Jagr had the lowest on-ice shot attempt against rate (CA/60) by far, and the list is full of Devils and Kings
- Andrew Desjardins had a point share of 100% meaning he had a point on each of the 17 goals he was on ice for at 5 on 5
- Matt Stajan had the highest tendency to pass (highest PSR)
- Mike Ribeiro had the highest relative zone start percentage (ZS% rel), as he did ZS%, and Nail Yakupov was right behind him
- Tom Sestito was best at getting his shots through with a league-low 8.9% block percentage
- Boyd Gordon and Shea Weber were the only two players on ice for more than 50% of their team’s defensive zone faceoffs
- Niklas Kronwall had the highest icing drawn percentage (IceDr%) as he was on the ice for 136 icings drawn and 71 Red Wing icings
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.
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%|
|Detroit Red Wings||348||288||54.7%|
|Tampa Bay Lightning||338||292||53.7%|
|New Jersey Devils||347||319||52.1%|
|St. Louis Blues||317||302||51.2%|
|New York Rangers||332||319||51.0%|
|New York Islanders||312||310||50.2%|
|Columbus Blue Jackets||277||292||48.7%|
|Toronto Maple Leafs||310||357||46.5%|
|San Jose Sharks||317||373||45.9%|
Update 2014-05-03: numbers have been updated to include icings that coincided with another stoppage.
As a follow up to my lists of award contenders, here are my picks for the major individual awards.
Runners up: Getzlaf, Giroux, Pavelski, Kopitar
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)
Runners up: Varlamov, Price, Bobrovsky, Bishop
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)
Runners up: Palat, Johnson, Trouba, Krug
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)
Runners up: Weber, Karlsson, Suter, Niskanen
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)
Runners up: Bergeron, Getzlaf, Toews, Little
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.
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):
|Los Angeles Kings||3714||1307||1319||1088||54.6%|
|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%|
|Tampa Bay Lightning||3834||1254||1414||1166||51.8%|
|New York Islanders||3876||1250||1402||1224||50.5%|
|San Jose Sharks||4222||1327||1532||1363||49.3%|
|Columbus Blue Jackets||3684||1140||1347||1197||48.8%|
|Toronto Maple Leafs||4257||1111||1512||1634||40.5%|
Here are the players I see as contenders for the major awards, including those who merit consideration and those who will be considered due to reputation. Compare them in terms of production, possession, deployment, quality of competition, and more to make your own picks. Did I leave anybody off?
- Hart (MVP)
- Vezina (top goalie)
- Calder (top rookie)
- Norris (top defenceman)
- Selke (top defensive forward)