How Well Do Scoring Events Reflect Ice Time?
Pretty simple question. Earlier today I wrote about Ryan Nugent-Hopkins, and one of the questions that I was trying to (at least partially) answer was the ice time question. Just how much of it did Nugent-Hopkins get? In trying to provide an answer I turned to scoring events, and noticed that Nugent-Hopkins was in a group of five forwards who led the Rebels in events per game, which led me to the conclusion that he was probably also one of five forwards who led the the Rebels in ice time per game. The fourth line players, meanwhile, didn't have many events five-on-five, which led me to the conclusion that they probably didn't play that much.
There were some objections to this methodology, but basically it boiled down to the fact (and it is a fact) that good players, in general, will have more events per unit of ice time than bad players. As such, we don't really know what it means when we see some of the Rebels' top players with more events per game than the poor ones. After the jump, I'll try to provide us with a frame of reference.
It would have been great to (as was suggested) compare the Rebels with other teams in the WHL. Unfortunately, the data isn't freely available. I tracked Red Deer's scoring events by hand, and don't really want to do that for the whole league! Fortunately, there are other leagues where this data is freely available, like the NHL, where, thanks to Gabriel Desjardins' behindthenet.ca, the five-on-five data is at our fingertips.
So... how well do five-on-five scoring events per game reflect five-on-five ice time per game at the NHL level?
It looks to me like there's a pretty strong relationship. Now, obviously every little blue dot (there are 454, one for every player in the 2010-11 season who had at least 10 five-on-five events) isn't right on top of the black line, but the Pearson correlation of 0.86 does in fact indicate that these two variables tend to move in lockstep, at least at the NHL level. That means that if we only have one variable, we can make a pretty good estimate for the other, and that if we used a four-minute range, we'd be right a whole lot more often than we'd be wrong.
Now let's take another look at those numbers for the Rebels, but this time we'll include an estimated ice time in the chart. Now, I did include 4-on-4 events with the Rebels, so we're looking at an estimate for all EV ice time per game here, not just 5-on-5, and the inclusion of 4-on-4 time might overstate the ice time a little in the final number. Still, it should provide a pretty good idea:
| Player | Qual Comp | Events / Game | Low EV Ice Time (Minutes) | High EV Ice Time (Minutes) |
| Adam Kambeitz | 0.66 | 0.86 | 9.15 | 13.15 |
| Andrej Kudrna |
0.67 | 1.20 |
11.37 | 15.37 |
| Brett Ferguson | 0.72 | 1.30 |
12.02 | 16.02 |
| Byron Froese | 0.72 | 1.22 |
11.50 | 15.50 |
| Chad Robinson |
0.44 | 0.27 | 5.32 | 9.32 |
| Colten Mayor |
0.58 | 0.51 | 6.88 | 10.88 |
| Daulton Siwak |
0.67 | 0.91 | 9.48 | 13.48 |
| John Persson |
0.65 | 1.13 |
10.92 | 14.92 |
| Josh Cowen |
0.75 | 0.67 | 7.92 | 11.92 |
| Lane Scheidl |
0.63 | 0.65 | 7.79 | 11.79 |
| Locke Muller |
0.59 | 0.43 | 6.36 | 10.36 |
| Ryan Nugent-Hopkins |
0.68 | 1.29 |
11.96 | 15.96 |
| Turner Elson |
0.68 | 0.84 | 9.03 | 13.03 |
| Tyson Ness |
0.55 | 0.48 | 6.69 | 10.69 |
Is this system ideal? No. The big assumption here is that junior hockey is going to have this relationship in about the same measure as the NHL. It's an assumption I'd rather not make, but for now, it beats the alternative (lots and lots of counting). Plus, the median guess fits quite well with what we've heard from others and plain old common sense. If running four lines means that the fourth line is playing about eight to nine minutes per night while the top guys play closer to fourteen plus special teams, that seems believable to me. I now feel more confident that Nugent-Hopkins was getting significantly more ice time than most players on his team (both at evens and overall), and also feel reassured that he wasn't getting run out there for close to twenty-five minutes per game either.
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Very interesting stuff Scott (both articles). I wonder how much of this sort of information plays into draft position.
If RNH was playing 14 EV min./night and someone like Couturier or Strome or whomever was playing 20 EV min./night that puts a completely different context on their numbers.
My rough math indicates that if the spread was that big then all of sudden RNH would have a high EVP/60 than Couturier even though Couturier’s EVP/game was significantly higher. It is a shame this information isn’t available.
I don’t think anyone was trying to claim the 4th line played the same number of minutes, just that they got used quite a bit more than most 4th lines.
What happens to the correlation if you run it for EV events per 60 minutes rather than per game.
It’s the rate of events that seems to throw a kink into this idea. It would be impossible for TOI not to correlate to some level, but the question is how does it indicate ice time and how does it account for lower event rates? It’s hard to know which factor is more important for individual players.
You indicated your belief that he got around 1.5 times the EV ice time as the 4th liners, but when you look at the rates of events I looked at in the previous post it suggests that it would actually be possible, or even likely, for those numbers to occur with ice time significantly closer than that.
by TigerUnderGlass on Aug 22, 2011 5:52 PM MDT reply actions
Yeah, I think that “rolling four lines” is pretty ambiguous. Some people hear that and think four lines over the boards all game long, others hear it and think the fourth line is getting at least ten minutes, and others probably think seven or eight is enough. At this point, I’m not convinced that the Rebels play their fourth line more than most NHL clubs, but I don’t really know what’s standard in the WHL.
As for finding a correlation between EV events per sixty minutes and EV ice time, I’m not sure what you think the payoff is there. We don’t have EV events per sixty minutes for junior players, so even if the correlation is strong, it doesn’t help us at all. Or maybe I’m misunderstanding your point here?
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by Scott Reynolds on Aug 22, 2011 7:19 PM MDT up reply actions
I think my only point it that I am not convinced the correlation is a strong one because of the nature of per game data which is partially based on ice time by definition.
A per 60 rate is based on how often events occur when players are one the ice.
It’s kind of like saying goals scorers score because they play more often. The problem is that they play more often because they score more goals, so the statement can be true in some circumstances and false in others.
If you look at the Oiler players I referenced in the last thread the top 6 types had around double the number of events per 60, but they had around triple the number of events per game.
So I wonder how much of the time on ice correlation comes from high event player types just getting more playing time. A portion of it come from the playing time, but that portion is integral with any per game reading and needs to be weeded out.
It hardly matter since it seems like we have come to pretty similar conclusions, but it may matter if we want to use this measure in the future.
by TigerUnderGlass on Aug 22, 2011 7:47 PM MDT reply actions
I think my only point is that I am not convinced the correlation is a strong one because of the nature of per game data which is partially based on ice time by definition.
I’m not sure why per game data “is partially based on ice time by definition” except that more ice time will allow players to record more events and thus higher per game rates. This is exactly what makes per game data a useful proxy for ice time! I’m using goal events because it happens to be what we have the most of recorded at the individual level in the WHL. If we had shot data, that would be even better.
I also don’t understand how you’re not convinced of a strong correlation between these two measures. The correlation is strong, at least at the NHL level. Do you think the WHL is just significantly different? That it’s strong for more than one reason (i.e. more events = more ice time; high event players get more ice time) is a feature. Why on earth would we want to weed one of the things making it strong out?
The biggest fanana of the Havana Bananas.
by Scott Reynolds on Aug 22, 2011 9:09 PM MDT up reply actions
To improve the statistical view of Ryan Nugent-Hopkins.
Editor of The Copper & Blue, and leader of The Cult Of Hartikainen.
Can’t I question his methodology without having an agenda? Anytime anyone has a thought different from yours you point immediately to some agenda they must have. Who cares if I said in my post that Scott and I had arrived at similar conclusions right?
You always have so many good points to make on most hockey subjects that I can’t understand why you resort to this type of inane comment so often.
I have no interest in “improving the statistical view” of anybody. What do I care how he looks statistically? It doesn’t matter anymore, the draft is over. I can’t talk them out of it now, and I have no interest in telling everybody in 5 years how I was right that time 5 years ago.
by TigerUnderGlass on Aug 23, 2011 12:00 AM MDT up reply actions
It’s not useful unless you can effectively quantify how much of the difference is related to playing time. It seems much more heavily related to the type of player on the ice.
I will go back to the Oilers example just to try and make the point more clearly. Based on that data the differences in per game rates was about 2/3 based on who the player was, not how long they were on the ice. My reason for asking about the correlation of per 60 instead of per game is because I would like to see how the 2/3 correlates instead of the 1/3.
I hope this is making sense, I probably misspoke when I said I don’t buy the correlation, the correlation was completely predictable – I guess what I am saying is that the per game rate is too small a part of the equation. Of course more events means more TOI, but player type has more to do with it than TOI. Why would we not want to see the rate correlation instead when it may have more impact?
If a 4th liner has 30% the number of events as a 1st liner is it because he played less or because he had a lower event rate? He probably played less, but we know that. Knowing the per 60 might help tell us how much less.
Now of course this is all based on a small sample I looked at, but I found it interesting that I only quickly looked at 2 teams and both teams demonstrated the same effect.
by TigerUnderGlass on Aug 22, 2011 11:34 PM MDT up reply actions
I think the reason for my earlier confusion may have been that we seem to have a different opinion on the importance of “why”. I don’t care that much whether the correlation is strong because high-event players generally get more ice time or because more events per game is in fact a result of more ice time. The important thing to me is that the results are consistent, and it seems to me that the correlation is so strong because both variables exist: guys with more ice time generally get more events and the high-event guys get more ice time.
Anyroad, it’s not hard to check for the correlation between TOI/60 and Events/60, so I ran it up, and the answer for the 2010-11 NHL season was 0.45, which isn’t terrible or anything, but is obviously substantially weaker.
As for the guys at the bottom of the lineup being lower event, I think you’ll see that effect league-wide. Fourth liners play against other fourth liners and they all suck offensively, which will depress the number of events they see per unit of ice time. But play them against good players, and you’ll see their rate shoot upward.
The biggest fanana of the Havana Bananas.
by Scott Reynolds on Aug 23, 2011 10:12 AM MDT up reply actions
I should also point out that I do understand why you used that data – there is nothing else to work with. The problem is that the results obtained could have happened if Red Deer’s 4th line played 5 minutes or 15. It doesn’t really give us any clearer picture of playing time because we can assume the top lines play more, we just don’t know how much more without more data.
by TigerUnderGlass on Aug 23, 2011 12:08 AM MDT up reply actions
Although I suppose it’s possible that the results could have been achieved with them playing either five or fifteen minutes, it’s extremely unlikely that you’d get a result at the upper end. In the NHL, there were 26 forwards with fifteen minutes per game and the lowest events per game was 1.20; there were 70 forwards with at least fourteen minutes per game, and only one had less than 1.00 events per game (0.95). I agree that the data only provides us with a range as opposed to an exact number, but I think the range is significantly smaller than ten minutes, especially if you’re willing to allow for some error (i.e. 9 out of 10 times, his ice time will be “x”).
The biggest fanana of the Havana Bananas.
by Scott Reynolds on Aug 23, 2011 9:54 AM MDT up reply actions
By my count, Couturier had 120 EV events (+86, -34) in 68 games (including both regular season and playoffs)
That’s 1.76 events/game.
Looking at your chart, that number resides at the very top right corner of your dataset. On that black line, it translates to about 17 minutes/game (compared to RNH’s 14 minutes/game)
So accordingly to your methodology, there’s strong evidence to suggest that Couturier did in fact receive a significantly greater amount of EV ice time than RNH.
17.01 actually! That was some impressive chart reading!
The biggest fanana of the Havana Bananas.
by Scott Reynolds on Aug 23, 2011 10:15 AM MDT up reply actions
So, if we go back to this scout, it seems that his take was pretty accurate. 17ish minutes of ES, 4ish mins of PP, 4ish mins of PK, or 25 minutes a night.
So all of the hullabaloo about “30 minutes a night” was….hullabaloo.
Editor of The Copper & Blue, and leader of The Cult Of Hartikainen.
Actually this suggests his EV TOI was lower than that scout’s take considering you quoted him as saying, “He [Couturier] played 30 minutes, maybe more in tight games, sure.”
A lot of PK time mixed in there could never be a bad sign though.
by TigerUnderGlass on Aug 23, 2011 5:27 PM MDT up reply actions
I would assume in tight games, he was over the boards nearly every other shift.
Editor of The Copper & Blue, and leader of The Cult Of Hartikainen.
Are we reading that the same?
I see:
Normal game = 30 minutes
tight game = more than 30 minutes.
It sort of seems like you are reading it as he only plays 30 minutes in tight games.
Doesn’t really matter though I suppose. Either way he likely didn’t actually play 30 minutes, but the general idea that he likely played a fair bit more than Hopkins still stands.
by TigerUnderGlass on Aug 24, 2011 9:15 AM MDT up reply actions
Where do you get 4 min of PP and 4 of PK a night from? Is that just an educated guess or am I missing something from the article you linked to?
The PK numbers are in there, I just extrapolated the same PP time. If he’s your #1 offensive asset, he’s going to be out there a whole bunch.
Editor of The Copper & Blue, and leader of The Cult Of Hartikainen.
So all of the hullabaloo about "30 minutes a night" was….hullabaloo.
Except not, when compared to RNH’s icetime. The actual icetime Couturier ended up getting doesnt really matter, especially in a case where BOTH players played less at 5V5 than anticipated, adjusted he still played somewhere in the range of 7-8 minutes a night more on average. Which, over the course of a year, is a huge discrepancy in TOI (Enough to nullify the “Couturier would’ve outscored RNH if he played the full schedule” argument because their total TOI would be roughly even).
And on top of that, 25 minutes a night for a forward, no matter who he is, is a lot of icetime. So the view of the scouting community that he had a lot of icetime and didnt do a lot with it is still very valid.
So the view of the scouting community that he had a lot of icetime and didnt do a lot with it is still very valid.
The part before the conjunction seems quite right to me. The part after, not so much, both in that it seems wrong, and in that I don’t remember scouts saying that. He was, after all, named the QMJHL player of the year. In terms of comparison to Nugent-Hopkins with regard to P/60, well, we haven’t done that part yet.
The biggest fanana of the Havana Bananas.
by Scott Reynolds on Aug 24, 2011 8:00 AM MDT up reply actions
I need to learn how to use the quote button, but...
The part after, not so much, both in that it seems wrong, and in that I don’t remember scouts saying that.
I don’t really know if it was in any scouting reports, and I’m not in the mood to go run around and check, but, the “he doesn’t do much with his icetime” bit was talked about a little bit by the Avalanche scouts in their pre-draft video they produced.
While I agree the events are there to justify the icetime, I also didn’t ever really get a chance to watch the kid play either so it’s hard to reconcile that position from my own experience. I’m just going off the tidbits I’ve picked up here and there. Althought in the interest of fairness, I think it is pretty valid to say Couturier is still a very good prospect (he is, I had him #2) and that he suffered a lot of criticism due to high expectations due to his dominance last year, and the fact that he is an older player as well as a 3rd year Jr.
What’s the formula for the line, Scott?
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by Jonathan Willis on Aug 23, 2011 3:21 AM MDT reply actions
I’m using an excel formula for it. If you’d like a copy of the excel file, drop me an email and I’ll send it off. If you’d just like some reference points:
0.25 = 7.19
0.50 = 8.82
0.75 = 10.44
1.00 = 12.07
1.25 = 13.70
1.50 = 15.32
1.75 = 16.95
2.00 = 18.57
The biggest fanana of the Havana Bananas.
by Scott Reynolds on Aug 23, 2011 10:20 AM MDT up reply actions
I’d imagine junior to be somewhat more skewed than the NHL. We’re aware of cases like Robbie Schremp Hockey where he played a zillion minutes a game in London, and I’m sure he’s far from alone. Still, without proper ice-time data, this is what we’ve got, so as long as we all acknowledge up front that it’s an imperfect dataset, we can still interpret the data, albeit with a lower degree of confidence than NHL data.
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