This morning, our very own Walter Foddis (@waltlaw69) had the glorious distinction of being interviewed by Oilogosphere royalty and the Godfather of Edmonton Oilers radio shows, Allan Mitchell aka Lowetide! They chatted about a plethora of things including Kris Versteeg’s potential impact on the Oilers’ roster, what might happen to Nail Yakupov, and should Brandon Davidson be used as trade fodder to bring in that elusive right-shooting defenceman?
I’ll be honest, I felt extremely proud after listening to the interview. I never thought that the site would reach the point to where TSN would be interviewing one of our contributors.
So Good you Walter! And thank you so much to Mr.Mitchell!
Here’s to many more appearances on radio shows, podcasts, etc. from the Beer League Heroes Family!
For the last 3 seasons, I’ve been putting together statistical power rankings of teams with a focus on tracking the Oilers’ progress, or lack thereof. Then at the end of the season, I have used these rankings to make Stanley Cup playoff predictions. The statistical model began by examining shot attempt (a.k.a. Corsi) differentials, but I have since revised the model to also include goal differentials. The analytics community refers to this model as Weighted Shots.
The basic idea is simple: Goals count more than shot attempts. In theory, Weighted Shots help account for shot quality on offense and for goaltender performance on defense. In the Weighted Shots model that I use, goals count for 5 points and shot attempts–which for the sake of simplicity I will refer to as shots–count for 1 point. Measuring Weighted Shots at even-strength (5v5) is the most important aspect. Perhaps surprising to many, but special teams (power-play & short-handed) count for little (about 20% of scoring), although I do apply the model to ranking special teams. In my mind, a huge difference in special teams between teams who have similar Weighted Shot differentials may help tip the scale. There are a few matches this playoffs in which special teams may play a factor.
Testing Weighted Shot Model over 9 Playoff Seasons
To figure out if my model is useful, I applied it to the last 9 playoff series (2006-2015). I used all regular season games (82) in arriving at my 5v5 and special teams ranking systems. I also included rankings for the last 25 games because some of claimed that these are useful in making predictions. I have not thoroughly tested this 25-game model, but I do know that last season it was horrible in making predictions. Perhaps when I have more time, I’ll revisit the 25-game model for the other 8 seasons. Important to note that my model does not account for injuries, especially to key players. For instance, Tampa Bay has 2 key players out: their 2nd best defenseman, Anton Stralman, and elite sniper, Steven Stamkos. I cannot help but think this will have a huge impact on their playoff performance, especially Stralman, whose shot-differential relative-to-team is +5.7 per hour, which is 2nd on the team (Hedman is first) and 23rd among NHL defensemen. In any case, without further delay, here is how my model performed over 9 playoff seasons.
As we can see, the accuracy rate varies a lot from season-to-season. A few times it is as high as 87% (13 out of 15) and other times as low as 53% (8 out of 15). The good news is that even 8 correct predictions is more than 50%. Even better, over the entire sample of 9 series, the accuracy rate is 70%.
What happened in those seasons in which only 8 predictions came true? In 2008, you can blame many failed predictions on upsets by the Montreal Canadiens and Pittsburgh Penguins, who were both riding high on hot goaltending, as well as for Pittsburgh, stellar offense by Sidney Crosby & Evgeni Malkin.
In 2011/12, goaltending was also a factor, but for different reasons. On the one hand, Henry Lundqvist carried the overachieving Rangers to the Eastern Conference finals. On the other hand, the Penguin’s–ranked 3rd–had Marc Andre Fleury losing his confidence and playing the worst hockey of his career. The Penguins lost to the Philadelphia Flyers in a memorable high-scoring and fight-filled first round series.
Then last season (2014/15), Lundquvist once again help carry the overachieving Rangers to the Conference Finals. Similarly, Carey Price helped the Montreal Canadiens upset the Ottawa Senators in the first round. Thus, despite the predictions, outstanding goaltending can change the outcome of a series. In the end, though, 14 out of the 18 Stanley Cup finalists ranked in the top-8. Moreover, the champions ranked in the top-4, except for Pittsburgh (ranked 15th) in 2009. Taking all this into account gives me enough confidence to keep using the model. In closely matched series, though, I think it’s important to pay attention to goaltending, as well as injuries and special teams. More on this below.
Next, I provide the overall rankings of the teams using 4 measures. As a reminder, the one I am using is the first green column (i.e., Weighted Shots using 82 regular season games). The other rankings are there as secondary predictions. As I mentioned above, I would like to test the model on the last 25 games of the season, so might as well include it below. For special teams, I took the difference between Power Play and Short-Handed Weighted Shots. This rank, I think, should only be used in a series in which the 5v5 numbers are very close. What is close? This season it’s easy. The Anaheim Ducks and Nashville Predators only have a WghSh% difference of 0.01%! Then in the 2nd round, assuming the St. Louis Blues and Dallas Stars beat their respective opponents, the difference is only 0.1%.
What is obvious is that, regardless of the ranking system, the top 2 teams are Los Angeles and Pittsburgh. Next, I’ll show my predictions and explain predictions that go against my model. Teams in green are the predicted winners of each series. The value in parentheses is the even-strength Weighted Shot differential.
My main upset is the Detroit Red Wing over the Tampa Bay Lightning. Although Tampa Bay is ranked 3rd overall, recall that they have injuries to two key players: Stralman and Stamkos. Although I believe the Lightning are a pretty good team even without those two, in a 7-game series, I do see Detroit as able to push passed them. I don’t expect it to be easy, though.
Another “upset” is the Anaheim Ducks over the Nashville Predators. Their respective WghSh% values are practically identical. Anaheim’s special teams, which are ranked 1st, are much stronger than Nashiville’s, which are ranked 14th. Thus, I give the advantage to the Ducks.
The final upset is St. Louis over Dallas (assuming they both make it to the 2nd round). Their respective WghSh% values are also nearly identical. Although the Stars’s special teams are slightly stronger–7th vs 11th–I don’t think this difference is that substantial. More importantly, I think goaltending and defense will be a factor. Dallas are double-teaming veterans Kari Lehtonen (90.6 save%; all situations) and Antti Niemi (90.5%). Neither has performed as a #1 goaltender. Also, despite Dallas’s strong offense–ranked 2nd–their defense is rather porous for a playoff team, ranking 17th. In contrast, St. Louis is solid both offensively and defensively, ranking 7th and 6th, respectively. Although there tends to be some variance in save% within a season, St. Louis’s save% (93.2%; rank=4th) exceeds that of Dallas’s (91.8%; rank=27th) by a large margin. I think there is more than variance going in this difference. With the Blues having the advantage in defense and goaltending, I favour St. Louis in this series.
Speaking of St. Louis, what of Chicago, the defending Stanley Cup champions? Unfortunately, their WghSh% rank is not even top-10. Last season, they were 2nd and favoured to win the Cup because Los Angeles (ranked 1st) failed to make the playoffs. Will intangibles such as “playoff experience” and “knowing how to win” matter? Maybe. But what the model shows, over 9 seasons of data, it that is sure helps when a team is better at out-shooting their opposition.
For the finals, I have seen a few models predict Pittsburgh over Los Angeles, which I don’t think is unreasonable. Pittsburgh has been the hottest team since January showing an improvement of 20th to 2nd in Weighted Shots over the last 40 games. I have not seen such an improvement within a season since I’ve been tracking these metrics. Plus, Pittsburgh is my 2nd favourite team. But then Pittsburgh without their #1 goaltender, Fleury, who is injured, but day-to-day, could make it a rough road for the Penguins to go deep into the playoffs.
There you go, folks! My predictions for the 2015/16 Stanley Cup playoffs. Please share your thoughts and predictions.
After the turnover, Eberle was hustling hard until he believed Oesterle had taken control of the puck, then slowed down with the assumption that play was going to turn around. It’s a split-second decision. His willingness to persist on the back-check, even if it puts him out of position for a quick transition, is what he didn’t show. Is this a pattern? Does he routinely give up on back-checks because of an apparent offense-first mentality? I don’t know.
Other questions: Is Eberle simply a poor reader of plays from the defensive side of things? Is it a lack of effort and/or persistence issue? Can these things be learned, or is Eberle too inflexible in not learning them? We all know if that we don’t practice something regularly, it doesn’t become automatic in a game situation. Is Eberle not practicing hard backchecking? Is more prone to transitioning to offense “too early”? Tonnes of questions and you need a lot of video data, let’s say a 20-game sample (1 in 4 games, for e.g.,) to be clear on what’s going on.
We need to be careful of bias and sample size. In this case, recency bias and a sample size of one! To make radical roster-altering decisions on these irrational bases is asking for trouble. We need to look at a player’s body of work over a season. Are the problem areas–patterns that have been meticulously tracked each game–consistent? That is, is the player repeating the same problem despite coaching and video feedback? If so, why is that? The “why” is the key. The why will tell us whether it’s time to trade the player, or if the problems (clearly defined and persistent over time), can be fixed.
David Staples of the Edmonton Journal does analysis along these lines by tracking contributions to Grade A scoring chances, as well as mistakes on Grade A scoring chances against. According to Staples’ tracking, Eberle’s mistakes measure is on the lower side, relative to the other Oilers’ forwards.
Outside of video-tracked data, like that of Staples’s, we don’t have reliable measures of a player’s defensive abilities, but we a few have rough ones. One of them is Shot Attempts Against Relative-to-Teammates. This metric tells us, relative to his teammates, how many more, or fewer, shot attempts occur while the player is on the ice. From 2013-16 (3 seasons), Eberle’s ranking on this measure relative to the Oilers’ other forwards is 8th (+.78) out of 14 forwards. In other words, this suggests Eberle is a middling defensive forward relative to his teammates. Half the Oilers’ forwards are better and half are worse.
As fans, we watch only what the cameras show us. We don’t fixate on a single player and watch his every move. Because of that, I don’t have the knowledge or confidence to answer the questions I posed above. One failed back-check may suggest a pattern; a pattern that needs to be checked with thorough analysis. Or it may be simply a mental error that the player rarely makes. In this case, it turns out to be a goal and an opportunity for Kelly Hrudey (on Hockey Night in Canada) to rant about Eberle’s failure as a complete hockey player.
On Twitter recently, I was told that I shouldn’t be writing a hockey blog simply because I referred to Taylor Hall as an elite player. I couldn’t let this ironic opportunity pass me by, so here is why, from an analytics perspective, I think Hall is an elite player.
First, let me define elite player. Statistically, I define elite as any measure that is about 2 standard deviations above the mean, or in simpler terms, 98% better than all other comparison players. Typically, we would compare forwards with forwards, and defensemen with defense. Also possible to be more specific by comparing centers and wingers separately, but in presenting evidence for Hall, I’m going long and comparing him to all forwards.
Second, what would be a reasonable time-line? I think 4 seasons gives pretty reliable measurement over time. Goal-scoring can be highly variable from season to season because of shooting percentage variability, but after 4 seasons, shooting percentage approaches a “true” or reliable value.
Third, I focus on even-strength (5v5) metrics because 80% of the game is determined at 5v5 and moreover, it is more challenging to produce as compared to the power-play. These 2 factors, then, would be my basis for arguing that for 5v5 represents a more accurate assessment of a player’s ability.
Fourth, what measures would I use as evidence, that is, what are nuts and bolts of an elite player? The 6 measures (5v5; per 60 minutes) are :
Goals
Primary Assists
Primary Points
Points
GF% Relative-to-Team (i.e., team’s goal differential with the player on the ice vs. the team’s average goal differential)
Individual Proportion of Points
Why primary, but not secondary assists? Recent analysis has shown that Primary Assists are repeatable over time, but Secondary are more random. Thus, primary Assists tend to represent a player’s “true” skill as a playmaker. This then, in theory, affects which points are the more reliable points. By excluding secondary assists, we are left with primary points (goals + primary assists). But does that mean secondary assists are meaningless in assessing a player’s ability? I would argue, no.
In the sample (297 forwards, 2012-16), I computed the correlation between primary & secondary assists and found a statistically significant correlation of .49 (p < .001). This moderate correlation suggests to me that players who are better playmakers–because they are likely strong passers in general–tend to have more secondary assists. The points metric, then, is included because it captures three meaningful metrics: goals, primary assists, & secondary assists. The whole is greater than the sum of the individual parts, or something like that.
Finally, the last 2 metrics attempt to the player’s impact on the team. GF% Relative-to-Team answers the question: Relative to his teammates, how well does the team do in outscoring the opposition with the player on the ice? Individual percentage of points tells us the what proportion of points is the player directly involved in when the team scores.
So how many players are in the 98th percentile of any given metric? Using the last 4 seasons, including the current (2012-16), with players over 2000 minutes of ice-time there are 297 forwards, which represents the top-9 forwards of all teams (i.e., 270 players). There is a few ways to compute 98th percentile. The simplest way, which I think makes intuitive sense for the non-statistical-minded person, is to look at the upper 2% of players on any metric. Thus 2% X 297 = 6. In short, we want to know the top-6 players on a metric.
So how does Hall rank in each metric? I present the rank plus the metric in parentheses (per 60 minutes; except where otherwise noted):
Goals: 58th (0.82)
Primary Assists: 3rd (1.68)
Primary Points: 7th (1.91)
Points: 3rd (2.5)
GF% RelTM 17th (+9.2%)
IPP 1st (87.6%)
Thus, in 3 of 6 metrics, Hall is in the upper 2% of the league: Primary Assists, Points, & Individual Proportion of Points. In fact, he ranks 1st overall in his proportional contribution to the team’s points while on the ice. His Primary Points rank of 7th is so close that I might as well call it upper 2%. So that’s 4 metrics that rank as elite (as I have defined it). Next, he ranks 17th in GF% RelTM, which is about 95th percentile, and 58th in goals, which is about 80th percentile.
Overall, 4 out 6 of Hall’s metrics are at the elite level, the GF% RelTM metric has Hall performing better than 95% of forwards, and the goal metric has him scoring at a rate better than 80% of forwards. For me, I think that’s a pretty solid case for being an elite forward. Others may disagree, but if you’re going to post your disagreement, I would kindly ask for your rationale. (Remember, the measures do not include the power-play.)
Thanks for reading and please post your comments below.
Walter
Data courtesy of David Johnson’s stats.hockeyanalysis.com
The short story (spoiler alert) is that the Oilers have improved over last season. If that’s all you wanted to know about the Oilers’ progress, there you have it. If you want to know what specific ways they have improved, read on.
2014/15 Season Series Comparison
To account for quality of competition, I compared even-strength (5v5) shot metrics from each Oilers’ game with metrics from the 2014/15 season series against the same team. I referred to the difference in metrics as a Progress Index. As you might expect, a positive Progress Index indicates improvement over last season, whereas a negative Progress Index indicates the metric is worse relative to 2014/15.
Over the long run, the quality of competition balances out in a team’s schedule. The 4 metrics I tracked were:
(1) Weighted Shots (1 shot attempt = 1 point; 1 goal = 5 points),
(2) Shot Attempts (in following analytics blogger, Micah McCurdy (@IneffectiveMath), I will now refer to as ‘Shots’),
(3) Scoring Chances, &
(4) High Danger Scoring Chances (i.e., Scoring Chances from the slot).
(These last two metrics were developed by war-on-ice to help account for shot quality.)
In my report after game 30, I noted that the Oilers had improved significantly to game 25. But then because of injuries (e.g., McDavid, Pouliot, & Yakupov), certain players struggling (e.g., Fayne & Nurse), and other factors (e.g., the team stagnating in their execution of systems), the team’s metrics were trending downward to levels worsethan last season. The good news: Although requiring 10-15 games, the Oilers recovered! Let’s take a look at the differential shot metrics below.
Shot Metric Differentials
As we can see, from games 26-35 the Oilers were performing worse than last season on all metrics. But then something happened from game 36 onward, especially from games 41-45, in which they played some of their best hockey of the season. Sure, Pouliot and Yakupov returned from injury–and Yakupov only very recently. But they also lost their best defenseman, Klefbom, at game 30. What changed?
I think the answer is in Todd McLellan’s observation that the Oilers had plateaued in December. He stated that he looked forward to practice at home in January to get the team back on track. With these critical practice sessions to get everyone back on the same page, which includes a rehabilitated (overly dramatic, I know) Fayne, who was down in Bakersfield (their AHL affiliate) to get his game and confidence back. the team responded extremely well. There was even a period of 6 consecutive games (Jan 8-18/16) in which they sported a 54% (scored-adjusted) shot differential (Reminder: I use “shot” instead of “shot attempt.”) and a top-5 shot suppression rate.
Seems to me this dramatic improvement kind of went unnoticed by the regular fan because the Oilers were not winning much. But for numbers person, this kind of progress leaves me feeling hopeful and not only because the team is performing better. More importantly, when the team is off track, the coaching staff is able to correct what is going wrong and correct it in a major way. Even after losing Davidson, who has surprisingly earned his way into being a top-4 defenseman, the team has stayed the course.
Not let me breakdown where the improvements have happened, namely, separating offense from defensive.
Offense
Generally, shot generation has been a roller-coaster ride. Still, from game 36 onward, the progress indices have been positive. Over the last 15 games, compared to last season, they have generated +5.9 shots and +3.3 scoring chances per hour. Great to see! On average (over 50 games), the Oilers have generated 2.6 shots and 2 scoring chances more per hour.
In addition to practice time, the return of Pouliot and Yakupov, as well as the addition of Zack Kassian, has helped the team’s offense. Nugent-Hopkins and Eberle had also picked up the pace; both who were struggling, possibly due to slow recovery from illness and injury, respectively. Unfortunately, Nugent-Hopkins was recently injured blocking a shot with his hand and breaking it. At least McDavid will be back next week, which means that even without the Nuge, the Oilers will still have a competitive group of top-6 forwards. (We can thank Draisaitl and his breakout season for that luxury.) What about defense?
Defense
In contrast to offense, progress on defense has been linear. The progress indices in defense was fairly stable over the first 25 games, but then dropped substantially over 15 games (games 26-40). Losing Klefbom at game 30 didn’t help. Perhaps this is the “plateau” (to put it kindly) that McLellan observed. McLellan and crew then got to work, serviced the defensive engine, and it’s been full speed ahead since game 41.
Overall, the improvement in defense appears to be minor because of that terrible 15-game slump. Specifically, the average progress indices per hour are only +1.3 shots, +1.5 scoring chances, and +0.5 high danger chances. But if we focus on the positive, over the last 10 games the Oilers have improved–relative to last season–in suppressing more 6.3 shots, 5.8 more scoring chances, and 1.8 more high danger chances per hour. These rates are an amazing improvement. I don’t expect the numbers to to last, but if the trend stabilizes to half that for the remainder of the season, I’ll be happy.
Altogether, the team’s metrics have improved, especially on defense!
Score Situations
Teams play differently depending on whether they have a lead or are trailing. Leading teams tend to play safer, whereas trailing teams tend to be more aggressive. This means trailing teams, more often than not, out-shoot their opposition during the time they are behind. So I wanted to examine the Oilers in different score situations. Also important to note is that weaker teams tend to be trailing more often than tied or leading, which makes intuitive sense.
Compared to last season, I was interested in knowing three things: Did the Oilers spend less time trailing? When trailing, were they more aggressive? (The Oilers, by their own admission, tended to get down on themselves when trailing last season.) When leading, how well was the team able to hold the leads?
Games when Trailing
Last season, the Oilers trailed their opposition almost half the time: 49.7%. This season, they’ve trailed much less: 36.1%. In particular, they spent less time trailing by 2+ goals, dropping from 20% to 12% of the time. Although their time trailing by 1 goal increased by 5% (19% to 24%), their time with the score tied increased from 36.5% to 42.9% (i.e., 4% more). So if you believe the Oilers are closer in more games this season, your belief is correct! Unfortunately, this hasn’t translated into more time leading–which is the same as last season (about 23%)–but one step at a time.
So they’ve spend less time trailing, but have they been better at pulling themselves back into games? When trailing by 2+ goals, no question! Last season when trailing by 2 goals, their 52.4% shot differential and 42% goal differential was near the bottom of the league. This season, their 58.9% shot differential is 11th best with an even stronger goal differential of 63%. When the Oilers are really down, they are not laying down. There’s more push back, much more than last season, or any season since 2007. In fact, from 2007-2015, the Oilers have the worst shot and goal differentials–yes, even worse than Buffalo–when trailing by 2 goals or more.
They’ve also improved when trailing by 1 goal, although not as much. Their shot differential has only improved by 0.6% to 53.5%, but their goal differential is much better at 50% (42% last season). The difference in goals is due to much better goaltending with save percentage improving to a decent 91.8% from a horrible 87.9% in 2014/15.
The Oilers are a better team when trailing! They are also spending more time tied in games. Both indicators of an improved team, especially the psychological element of not letting their confidence and effort drop when trailing. Ultimately, this motivation is within the players themselves, especially the leaders of the team, but McLellan’s influence is felt here too. In post-game interviews, I have heard him speak of observing the bench feeling down after a goal or two (e.g., shoulders drooped, heads down) and taking steps to motivate them, whether on the bench or between periods. Sounds like a coach who is attuned to the psychology of the team and has constructive ways to encourage the players.
Games when Tied & Leading
This is all well and good, but the team unfortunately has not improved when tied or leading. They give up leads this season as easily as they did last season. When leading, their shot differential of 42% ranks 20th and their goal differential of 33% ranks last. Both the team, who has allowed a boatload of shots (65 per hour; 25th in the league), and poor goaltending (89.1 save%), have lead to this awful goal differential.
Being in a tied game, which I noted above, is a situation the Oilers find themselves in more often. Yet, when tied, they have a rough time gaining a lead. Their shot differential is slightly worse this season than last–46.5% vs 47.7%–as is their goal differential (41% vs 43%). Goaltending cannot be blamed. When the score is tied, the save percentage is a very respectable 92.4%.
Indeed, on top of a poor shot differential, the team’s shot percentage is extremely low (5.2%; whereas 8% is more typical). To see if this low percentage is due, in part, to decreased shot quality, I looked more deeply into the numbers. Indeed, compared to last season, when the game is tied, their scoring chances dropped by 3 per hour. Most of that is due their decreased ability to generate high danger chances, which fell by 2 per hour.
I really don’t have an explanation for why the Oilers have not improved in tied games or when leading, except to repeat the obvious: The roster is weak, especially on defense and among the bottom-6 forwards. Although there’s been improved coaching, individual breakout seasons (i.e., Draisaitl, Davidson, McDavid, and Yakupov, especially with McDavid, although he is playing well without him) and more stable goaltending with Talbot, there are roster deficiencies.
Lander’s promise under coach Todd Nelson in 2014/15 has not developed under McLellan. Schultz has not flourished either. With Korpikoski on the ice, the team’s shot metrics are bad as last season’s Buffalo, which is to say monumentally bad. As I noted in a previous blog, Nurse is playing over his head. Fayne has struggled, although he seems to have found his way back onto the team recently and playing well. Still, he is a not top-2 defender.
Summary
We haven’t seen a full, healthy line-up all season, including long spells without some of our top-end players, yet we have seen improvement, especially when it comes to team defense. Players appear to be responding to the coaches, and vice versa. For instance, Fayne being put on waivers was a clear message that no contract is safe. Everyone is expected to pull their weight. Todd McLellan expects accountability from his players, and in turn, he has shown he is accountable to them when the team is under-performing.
We’re all hoping that General Manger, Peter Chiarelli, is able to accomplish two things before next season: (1) Create depth in the forward line-up (Kassian looks to be a promising piece) and (2) strengthen the blue-line with two legitimate top-2 defenders. This will likely happen during off-season with names like Travis Hamonic (my favourite), Dustin Byfuglien, Kevin Shattenkirk, and Sami Vatanen being thrown out there.
In the meantime, let’s enjoy the rest of the season without the anxiety of playoff expectations. We’ll have the pleasure of seeing our future top-2 centers, McDavid and Draisaitl, taking on their roles early. It will be a primer for the future.
Thanks for reading and please comment below. What do you think of the season so far?
