Tag Archives: Hockey Analytics

Edmonton Oilers, Fancy Stats, NHL, Walter Foddis

Recent Wins are Deceiving: Oilers’ progress after 30 games

Who doesn’t like to see their team winning? Does it matter why the team won? To the everyday fan, the ‘why’ doesn’t matter: Their team scored more goals than the opposition. End of story. Plus, wins leave you elated with the feeling that anything is possible. But if the fan wants to see a consistently competitive team, a team that is a regular playoff contender, how the team wins matters tremendously. When I focus on the question of why for the Oilers, I cannot help but conclude that the recent wins are deceiving.

With that in mind, I want to ease readers into my analysis of the Oilers’ progress compared to last season. Looking at the analysis one way, the results are promising, but in another way, the results can be slightly discouraging.

In a previous post, I explained how I computed my progress indices by comparing games to the respective 2014/15 season series. Why I used this comparative analysis is because simply looking at their current Weighted Shot (WghSh) or Shot Attempt (SAT) differentials, we don’t take into account the Oilers’ quality of competition. For instance, in their first 10 games, the Oilers played 7 games against top-5 teams (as measured by Weighted Shot differentials). We would not expect the Oilers’ shot metric differentials to be all that great against the best teams, so how would we know whether they’ve improved?

But if we compare their shot metrics from each game to their 2014/15 season series metrics (vs. the same team), we take care of quality of competition. For example, if last season the Oilers’ average SAT% against Los Angeles was 45%, but they achieved 48% this season, that would be a relative improvement, even though 48% is a poor differential in an absolute sense. If their SAT% against Buffalo was 54% last season, but only 51% this season, this would be a worse performance relatively speaking, even though 51% is a good differential in general.

Using data from war-on-ice, I compared 4 shot metrics from every game. For those unfamiliar to hockey analytics, the most confusing metric is Weighted Shots. I borrow from Matt Cane’s model that places 5 times more weight on goals versus shot attempts. That means both goals and shot attempts are part of Weighted Shots with goals getting 1 point and shot attempts receiving 0.2 points.

The 2nd metric is simply shot attempts (SAT), which includes blocked shots, missed shots, and shots on goal. SAT (a.k.a. Corsi) has been shown to be one the best predictors of a team’s future goal differential. Weighted Shots are also strong predictors, slightly better than shot attempts in fact, but to tease apart defense from goaltender performance, and offense from “puck luck,” (i.e., statistically high scoring rates) then shot metrics, like SAT, that exclude goals are useful. The 3rd metric is Scoring Chances (SC; defined by war-on-ice), which helps account for shot quality. The 4th metric is a proxy for the highest quality shot, which are scoring chances from the slot and are called High Danger Scoring Chances (HSC; defined by war-on-ice).

Shot Differentials

With our metrics defined, let’s have a look at their 2015/16-to-2014/15 comparative differentials using 5-game blocks.

To explain the graph a bit more, each differential is computed by dividing the ‘For’ metrics by the sum of the ‘For’ and ‘Against’ metrics. For example, SAT% = Shot Attempts For / (Shot Attempts For + Shot Attempts Against). For each 5-game block, I computed the average quality of competition using Weighted Shot differentials, which I put In parentheses. Teams greater than 50% are typically playoff-bound teams. So as you can see, the first 10 games were the toughest of the season.

Although little progress is evident in the first 5 games, albeit against some very tough competition, the progress indices are consistently positive up until game 25. That is, regardless of the quality of competition, the Oilers showed improvement in shot attempt and scoring chance differentials. One qualifier, though, is their inconsistency in High Danger Scoring Chances, which I’ll talk about more below when I look at defense and offense separately.

What happened after game 25? The Oilers’ winning streak of course! It makes perfect sense! Their differentials plunge below their levels of last season and yet, they win. For games 26-29, we can thank Anders “The Giant” Nilsson. Not much else to say there. Game 30, though, was nothing short of a shooting gallery for both the Oilers and New York Rangers. As a brief respite from the shellacking from the previous 4 games, the Oilers’ progress indices in the Rangers’ game were slightly positive. (This uptick is not seen in the graph because the 5-game average is negative.)

What’s going on after game 25? Their roster has been weakened by injuries and by diminished play of a few key players. Although the team maintained their improvement after McDavid broke his collarbone–you read that correctly, the Oilers were as good without McDavid–, the loss of McDavid’s linemates, Yakupov and Pouliot, appears to be straining the team’s offense.

Defensively, Fayne struggled mightily through this period. Prior to his re-assignment to the Condors, Fayne’s (score-adjusted) Relative SAT% for the previous 5 games was -11.7%.  Even worse, with Fayne on the ice, the Oilers’ Scoring Chance differential was 26.2%, which contrasts with the team’s respectable 49.8% SC% without Fayne. Finally, his Relative High Danger SC% was -27.7%. No matter how we slice the metrics, Fayne’s performance had deteriorated considerably.

To add injury to injury, Klefbom was hurt in game 30 with a broken finger. Still not sure how long he’ll be out. Nikita Nikitin has been called up to replace him. I’m not optimistic that Nikitin will outperform Fayne, never mind Klefbom, but time will tell.

I wrote the above portion before watching and compiling data from games 31 and 32, which were against Boston and New York Rangers, respectively. In Boston, Talbot rescued the Oilers from being drubbed on the scoreboard with 47 saves. Different goalie, same story. The metrics indicated a worse performance compared to last season. However, against the Rangers, the metrics indicated a slight improvement over last season.

Offense

But back to the first 30 games, let me dig a little deeper and isolate offense from defense. The Oilers are scoring more this year, but is that because they generating more quality chances? Has their defense improved? First, I’ll examine the Oilers’ offense.

Improvement in offense has been inconsistent, and even though the indices (except for HSC), on average, are positive. The lack of improvement of High Danger Scoring Chances still suggests that, like previous seasons, the Oilers still struggle to create opportunities from the slot area, whether that’s by driving to the net, passing, or increased net traffic for deflections and rebounds. Games 21 to 25 were the height of their offensive improvement, which was followed by their weakest offense of the season from games 26 to 29.

Dissecting games 21-25, the Oilers did an excellent job of generating more shot attempts (+28/60 minutes compared to 14/15) and scoring chances (+18/60) against Washington, along with solid improvement in shot attempt generation (+5/60 on average) against Carolina, Detroit, & Pittsburgh. But this increased number of attempts didn’t necessarily result in boosting their scoring chance generation against Carolina (-2.8/60) and Pittsbrugh (-5/60). Against Detroit, though,  there was strong improvement in scoring chance generation (+11/60).

Breaking down offense to the level of lines (Note: These numbers are not relative to 2014/15, but simply 2015/16 figures.), the Hall–Draisaitl–Purcell combination was the primary offense-generating machine with a score-adjusted SAT% of 59.2% and a SC% of 55.1%.  In terms of individual performance, Hall had 18 and Draisaitl had 13 scoring chances to lead the way with Purcell firing 11 for a total of 42. That’s just over 8 scoring chances per game for this line. The Nugent-Hopkins–Eberle–Pouliot line combined for a score-adjusted SAT% of 51.1%, but only a 36% Scoring Chance differential. Ahead in the shot attempt game, but a lack of quality shots told a different story.

As to other forwards, Pakarinen was firing strong against Carolina & Detroit with 8 total scoring chances, but nothing at all in the other 3 games. Letestu and Korpikoski also had 8 each over 5 games. Yakupov was hurt by the 2nd game into this road-trip and had 2 scoring chances in one game. Lander recorded no scoring chances at all.

The Hall–Draisaitl–Purcell combination, then, appears to have been the primary catalyst to the improved offense during this 5-game stretch.

What changed on offense from games 26 to 29? The Oilers were without Yakupov and Pouliot due to injury. In effect, the Oilers 2nd forward line (McDavid–Pouliot–Yakupov) were all out. That meant more ice-time for marginal bottom-6 players, like Korpikoski and Gazdic, along with AHLers getting more ice-time (Pakarinen) and being called up (Juhar Khaira). From games 26 onward, the bottom-6 forward’s SAT% tells us the story: Gazdic – 44%, Lander – 43%, Pakarinen – 39%, Letestu – 34%, & Korpikoski – 32%.

To put this into perspective, the 20 worst SAT differentials (T.O.I. > 200 min) in the league range from 36% to 42%. For the record, Korpikoski happens to have the worst SAT% in the league. Ouch!

Defense

Next, let’s take a look at the comparative metrics for defense.

Defensive improvement, at least measured with Scoring Chances Against, had been consistent to game 25, but like the offense, dropped dramatically from game 26 onward. I already noted Fayne’s struggles, along with the entire loss of the 2L forwards to injury. These same roster deficiencies also help explain the team’s weakened defense. But is anyone else on defense that we can turn the analytical eye? This analysis won’t be popular, but here I go.

Nurse: Baptism by Fire

McLellan has been deploying Darnell Nurse as a top-2 defenceman. At first, things were going well. But as of late, Nurse’s game has soured a bit. Jonathan Willis gives his own take how Nurse’s play is not up to snuff. I would like to expand on that a bit. Willis’ article was not kindly received, if the comments section reflect the general fan-base.

With metrics, I noted how poorly the team is doing with when the bottom-6 forwards are on the ice. Nurse’s numbers are no better. From game 26 onward, the team’s SAT% with Nurse is 34%. The goal-focused fan may not have noticed though. With the Nurse on the ice, the team’s goal-differential is +1.  It seems the gods of good fortune (referred to as PDO in hockey analytics) are smiling on Nurse and have blessed the team with a scoring rate of 24.2%! (The league-average scoring rate is 8%.)

With Sekera since game 26, Nurse’s numbers are not much better: Their combined SAT% is 35%. From the start of Nurse’s season, the team has allowed 28 Scoring Chances Against per hour. Relative to the other Oilers’ defense, only Fayne is worse at 29.6 per hour. In terms of Nurse’s overall Relative Scoring Chance differential (-5.3%), only Korpikoski (-9.6%) and Letestu (-6.4%) are worse. Not to imply that Nurse has been over his head the entire time. His SAT% prior to game 26 was a very respectable 48%.

G. Money, the blogger behind OilersNerdAlert.com, has developed an innovative (still tentative, but promising) metric to assess defenvemen that incorporates shot quality, namely, shot distance and shot type (e.g., slap shot, backhand, wrist shot). He calls the metric Dangerous Fenwick and it includes all unblocked shots. He has been tracking the Oilers’s metrics game-by-game. By his calculations, a Dangerous Fenwick Against (DFA) of under 39 (per hour) is “good” and conversely, greater than 39 is “bad.” From game 26 onward, Nurse’s DFA per hour is 49.9.

Finally, if we look at Nurse’s dCorsi score. Delta Corsi (dCorsi) is the difference between a player’s observed and expected SAT (a.k.a. Corsi). Expected SAT is the value an average defenceman would get in the same context (i.e., quality of competition, quality of teammates, and zone starts). Nurse’s his dCorsi is just over -8 per hour. Factor in his top-pairing ice-time, the impact on the team is a -50 shot attempt differential. In other words, he is performing well below what we would expect from an average defenceman with similar usage.

Because there is consistency in what all these metrics (Shot Attempts, Scoring Chances, Dangerous Fenwick, and dCorsi) tell us about Nurse, I believe this gives more weight to the conclusion that he is under-performing. I did not have intentions to discuss Nurse when I first started writing this article. But the more I wanted to explain why the Oilers’ woes after game 25, the more I had to break things down, which lead me to focus on a few individuals. Unfortunately, Nurse did not shine under the analytical spotlight.

The Oilers are in a defensive conundrum. Nurse is overwhelmed, and in my ideal world, should be sheltered by having him play against softer competition, if not returned to the AHL. Yet, Fayne has played so poorly that he was demoted to the AHL and now Klefbom is hurt. But if Nurse is sheltered or sent down, who takes his place? I don’t have an answer. Maybe McLellan doesn’t either. I can only assume that McLellan is deploying the best he can with who he has available to him. But the longer Nurse gets crushed in the shot counts, the more I would like to see him taken out of the limelight.

Summary

The downturn after game 25 has been substantial; wiping out improvement during first 25 games by over half. Still, shot attempt differential has improved by 2.2 per hour and scoring chance differential by 3.2 per hour (see table below). Despite the downswing, I am hopeful that first 25 games are a more reliable indicator of improvement. I believe that the Oilers’ recent struggles are mostly due to injuries rather than deteriorating play of individual players, although this is definitely part of it.

What do readers think? Are the wins enough for you? (For some, winning is enough.) Do you think the analysis above supports my conclusion, specifically, that the recent downturn is due more to injury than deteriorating play? What would you do with Nurse, if anything?

Data courtesy of war-on-ice and puckalytics.

Edmonton Oilers, Fancy Stats, Walter Foddis

The Analytics of Missing McDavid

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Connor McDavid has only played 13 games for the Oilers, but he has already made a large impact. He is the team’s 2nd leading scorer and 19th in the league (as of Nov. 3, 2015) with 12 points (5 G, 7 A), plays 2nd line center, the 2nd unit power-play, and even given some responsibility in taking on short-handed minutes.  No surprise, then, that having his clavicle broken after losing his balance (thanks to Flyers’ defenseman, Manning) and falling into the boards, undergoing surgery, and months of expected recovery has hit the team hard. The kid was impressing the hell out of everyone. I’m sure the disappointment in losing him is being felt throughout the locker room. Perhaps I’m rubbing salt into the team’s wounds, but I would like to go deeper into the analytics of missing McDavid.

First, I present a table of McDavid’s contributions at even-strength (5v5), specifically, his individual production rate (points/60 min), Scoring Chances, and High-Danger Scoring Chances (i.e., scoring chances from the slot). His implied contributions are noted in his relative shot metrics, specifically, on-ice relative to off-ice (e.g., SAT% Rel) & relative-to-team average (e.g., SC% RelTM). To be clear, relative shot metrics show changes in the team’s shot metrics when the player is on the ice. Although relative metrics are group measures, we assume it measures a player’s individual contribution to the group measure.

McDavid leads the team in production at 2.7 points per hour, which also ranks 29th compared to league forwards. He is only 2nd to Hall in relative Shot Attempt differential (SAT%). Without him on the ice, the Oilers average SAT% is 46.8%. With him on the ice, the Oilers lead the possession game with a SAT% of 51.6%. Separating offense from defense, the team is generating more offense with McDavid on the ice, but he seems to have more of an impact on shot attempt suppression. In particular, compared to the team average, with McDavid on the ice the team generates 2.6 more shot attempts and suppresses 3.6 more shot attempts per hour. The team’s Scoring Chance  and High-Danger Scoring Chance differentials are also improved with McDavid.

However, his low individual Scoring Chance rate (5.4 per hour) does suggest that he is not shooting enough. If we look at regular shot rate–4.6 per hour–it is an abysmal 309th among forwards. Hall, who is among the top 30, has more than double that at 10.4 shots per hour. McDavid’s 5v5 scoring rate of 21.4% is unsustainable, so if he’s going to get 30+ goals, he’s going to have up his shot frequency.

WOWY (With-or-Without-You) tables from David Johsnon’s puckalytics.com provide useful information on how certain combinations of players perform when playing together.  Below we see how McDavid appears to effect Pouliot’s and Yakupov’s shot attempt metrics.

 

All 3 benefit in playing with each other. Their combined SAT% (52%) is greater than any of their own shot attempt differentials when playing apart from each other. Yakupov, in particular, improves by almost 6.7 percentage points (45.3% to 52%).

I was interested to see whether his improvement was more on the offensive side or defensive side. The green squares on the right highlight which improved most for each player. For McDavid, the Oilers have more offense–4.1 more shot attempts generated–when he plays with Pouliot and Yakupov then when he does not. But the most dramatic improvement is in Pouliot’s and Yakupov’s defensive metrics. When Pouliot is playing with McDavid (and Yakupov), the Oilers allow 11 fewer shot attempts per hour than when Pouliot is playing separately. Similarly, when Yakupov is on the ice with McDavid (and Pouliot), the Oilers are allowing 14 fewer shot attempts per hour. We all expected more offense with McDavid on the ice, but did we expect better defense? I know I didn’t.

McDavid’s impact on the team has not only been in individual production, in which he leads the team in even-strength scoring rate. Looking at shot metrics, he helps tilt the ice toward the offensive zone, but not only that, he appears to make an impact on defense, especially on his line-mates Yakupov and Pouliot. Losing McDavid does leave a big hole on the team, which will be most strongly felt by his line-mates.

Eberle returning to the line up will help immensely. Based on Ryan Stimson’s Passing Project data from last season, Eberle was Edmonton’s most effective passer, even better than Hall. As good as Eberle is, though, he is not a center. I’ll be interested to see how the team adjusts without McDavid. What we do know is that they won’t be the same.

Thanks for reading and please leave your comments below. What do you think will happen to the Oilers without McDavid?

Data courtesy of war-on-ice and puckalytics.

Edmonton Oilers, Fancy Stats, NHL, Walter Foddis

Are the Oilers measurably better?

Recently, Jonathan Willis of the Edmonton Journal’s Cult of Hockey, argued that there is no evidence that the Oilers have improved from previous seasons. Based on the shot metric comparisons he used, that was a logical conclusion. However, there are other ways to assess progress, which I indicated in an earlier post, especially early in the season. In particular, a Progress Index can be derived by comparing the shot metrics of a game, or series of games, to the previous season’s series against a specific team. For metrics, I compare Weighted Shots (WghtSh%; 1 point for goals, 0.2 points for shot attempts); Shot Attempts (SAT%; blocked, missed, and shots on goal), Scoring Chances (SC%; defined by war-on-ice), and High-Danger Scoring Chances (HSC%; i.e., shots from the slot area). (All data is collected from war-on-ice.)

Direct comparison to the previous season series accounts for quality of competition. For instance, against an elite possession team like Los Angeles, you would not expect the Oilers to improve from a dismal 45% shot attempt differential to a respectable 50% SAT (i.e., break-even). Rather, you would expect something more incremental, such as improving to a 47% or 48% SAT. In this prior post, I show how progress indices are computed for the Oilers first two games. I have done this analysis for every game to answer the question, “Are the Oilers measurably better?” The table below shows with coloured bars whether a shot metric improved (blue), worsened (red), or did not change significantly (no colour).

Reading the bottom two rows, we see that the average Progress Indices turn out positive! Although the Weighted Shot metric improved, I find it difficult to describe in straightforward language, but I can describe the other metrics. On average, the Oilers have increased their shot attempt differential (SAT%) by almost 6 per 60 minutes (+3%) compared to the 2014/15 season series against these teams. With regard to higher quality shots, the Oilers increased their Scoring Chance differential by just over 6 per hour (+6%). Finally, there is a slight improvement in the highest quality shot, High-Danger Scoring Chances, of 0.5 per hour.

Although we see improvement in overall shot metrics, what we don’t know is if the improvement is that more offense is being generated, or better defense is involved, or both. To tease apart offense and defense, we look at shot metrics for and against, respectively. An increase in shot metrics “for” means the Oilers are finding ways to generate more shots, especially quality shots, which will translate into more goals. A decrease in shot metrics “against” means the Oilers, as a team, are doing a better job in suppressing the team’s offense. So which is more responsible for the improvement: Offense or defense? My intuition was offense, but I was wrong!

Although the average progress indices for offense has improved a little (+1.1 SAT/60; +1.55 SC/60), most of the improvement in the differentials is coming from defense! In particular, compared to last season against these same teams, the Oilers have allowed close to 5 (4.78) fewer shot attempts, 4.62 fewer scoring chances, as well as 1.29 fewer high-danger scoring chances per hour. Are you surprised? I was. So it seems that the combination of new personnel and McLellan’s systems have made more of a difference defensively than offensively, although both have improved. This is something any Oilers fan wants to see. We all know that the Oilers are not a playoff team, and that are greatest weakness is our defensive corps, but given that our team defense has improved, that’s good news!

Special Teams

The above analysis is equal-strength (5v5) data, which is about 80% of the game. What about the other 20%; special teams? Early in the season, special teams are best measured using Shot Attempts For in assessing the power-play and Shot Attempts Against to measure the penalty kill. From 2012 to 2015, the Oilers’ power-play has ranked 27th as measured by goal differential and 24th as measured by Shot Attempts For (SAT_F = 89.4 per hour). Notably, though, under coach Todd Nelson for the latter part of the 2014/15 season, their PP goal differential was in the top 10.  This young season, the Oilers’s PP units are generating shot attempts at rate of 93.5 per hour, which ranks ranks 19th. In terms of quality scoring chances (high-danger zone), the power-play ranks 11th with 20.6 high-danger scoring chances per hour. Thus, compared to previous 3 seasons combined, this season’s PP looks to be generating more offense.

Curious to see whether the 1st unit (Nugent-Hopkins/Hall) or 2nd unit (Mcdavid/Yakupov) is performing better, I looked at the their respective shot atttempt generation per hour. The 1st unit is generating more offense, with the Oilers pumping out shot attempts at rate of just over 106 per hour with Hall & Nugent-Hopkins on the ice. With McDavid and Yakupov on the ice, the Oilers are generating about 90 shot attempts per hour. When comes to high quality shots, Hall, Nugent-Hopkins, and McDavid have similar metrics with with a high-danger scoring chance rate of about 25 per hour. With Yakupov on the ice, this metric drops substantially to 13 per hour.

Last season, the Oilers’ penalty kill–as measured by Shot Attempts Against–ranked 12th (SAT Against/60 = 95). This season, the Oilers allowed shot attempt rate is worse at 99, which ranks 21st. Our top penalty killers (by ice-time) last season were Gordon, Hendricks, Fayne, & Ference with a SAT Against of about 99 per hour. This season, the top 4 are Letestu, Lander, Klefbom, & Sekera with a combined SAT Against of 107 per hour.

Still too early to evaluate the goal-tenders because of too small a sample size, I’ll say a few tentative words. Unfortunately, to this point, the Oilers’ goal-tending tandem has not performed well, despite their strong starts. Talbot’s adjusted save% is ranked 24th (among goalies with a minimum of 7 games played) and Nilsson’s adjusted save% is ranked 42nd out of all 60 goalies. This means that despite the team’s improved defensive, the below-average goal-tending hasn’t allowed the Oilers to capitalize with fewer goals against.

Final Notes

I was surprised by the improved team defense and the poor goal tending performances. With Talbot, I was very hopeful that the Oilers’ goalie woes were behind them, but it seems this is still a question. Time will tell. Let’s hope the improved defense, which I attribute mostly to coaching, will continue as players internalize further the systems they’ve been taught.

Hope you found this informative. Please leave any comments or questions below. Thanks for reading.

Edmonton Oilers, NHL, Walter Foddis

What counts as a successful season for the Oilers?

During the pre-season, Connor McDavid was asked what he would consider to be success. His response: a “winning season.” I couldn’t agree more. Elliote Friedman of Sportsnet believes the Oilers being a playoff team is “crazy.” McDavid’s expectation, though, is realistic. For instance, if the Oilers finish with 9 overtime losses (ties), 36 regulation time losses, and 37 wins, that’s 83 points; 12 more wins and 19 more points than last season. With average goaltending, I believe the Oilers can do that.

However, because there is a lot of luck to winning games (38%), and sometimes significant randomness in goals even over an entire season, I would want a more reliable measure of success, which would be using the team’s shot metrics. As of last season, there is a new and improved Shot Attempt differential (SAT% or Corsi%), which war-on-ice blogger, Matt Cane, refers to as Weighted Shot differential. This metric involves giving more weight to goals than to shot attempts. At this point, the weight given to goal is an estimate. Specifically, we would attribute 5 times more weight to a goal compared to a shot attempt. Based on Kane’s analysis, this estimated weight seems to work well in predictive models of future success; out-predicting SAT differentials by a small, but statistically significant margin.

Last season, the Oilers had an even-strength score-adjusted SAT differential of 47.3% (ranked 24th). (Score-adjusted accounts for how shot differentials change based on the score of the game. Once teams get a lead, they typically generate fewer shots relative to teams who are behind.) Adding goals to the SAT differential, we have Weighted Shot differential of 51.5% (ranked 23rd). The Weighted Shot differential of teams ranked 14th to 18th ranged from 55.2% to 54.2%. If the Oilers were to move up 5 places, this would be about a 50% score-adjusted SAT% and a 54% Weighted Shot differential. I would consider these values a successful season regardless of the win-loss record. In terms of how close this is to a playoff team, teams with a SAT% of 52.5% have a 90% chance of making the playoffs.

But what about assessing the team throughout the season? Early in the season, I think a plausible index of improvement, or Progress Index, can be derived by comparing the shot metrics of a game to the previous season’s series. Specifically, we  can compare Weighted Shots (WghSh%; 1 point for goals, 0.2 points for shot attempts); Shot Attempts (SAT%; blocked, missed, and shots on goal), Scoring Chances (SC%; as defined by war-on-ice), and High-Danger Scoring Chances (HSC%; i.e., shots from the slot area). As usual, all the data is collected from war-on-ice.

In fact, I did this sort of comparison already in assessing the Oilers first two games.  The table below is example of this analysis. (For a detailed interpretation, follow this link.) What’s helpful about this analysis is that it shows the team’s performance relative to previous performance. For example, if the Oilers’ SAT% after a game is 48%, in absolute terms, this is an “unsuccessful” game. But if last season the Oilers averaged only a SAT% of 44% against the team, 48% turns out to be a 4% improvement (i.e., Progress Index = 4%).

[table id=17 /]

Once the Oilers play more games, we can compare different shot metrics to the previous season’s number of games in increments of 10 games. For instance, after game 10, we would compare the same shot metrics after game 10 of last season, and so on.

Like last season, about 2 to 3 times a month, I will be updating my Fancy Stat Power Rankings using Weighted Shot differentials at even-strength (80% of the ranking), along with a Shot-Attempts For (per 60 minutes) for power-play, and Shot-Attempts Against (per 60 min) for penalty kill. Special teams each account for about 10% of the ranking.  I post these rankings on Twitter, if you’d like to follow.

The everyday fan loves to see wins. Who doesn’t love to win? But unfortunately, because of the luck factor, we can’t use win/loss as a primary indicator of improvement. Instead, by using shot metrics, we have a more reliable measure of improvement; one that will predict future success and tell us if the Oilers are truly on the right track.

Thanks for reading. Please leave any comments or questions below. Question to the reader: Where do you think the Oilers will finish this season?

Edmonton Oilers, Edmonton Oilers Post Game Report, Fancy Stats, NHL, Walter Foddis

Post-Game Analytics: St. Louis and Nashville

After the first 2 games, I’m sure there are OIlers fans disappointed with the lack of goal production (and wins), but I want to assure the Oilers faithful that–so far–this is not the same Oilers team of previous season (or the season before that, and so forth). The team made much-needed changes to management and coaching, as well as making acquisitions such as Connor McDavid, Top-4 defenseman, Andrej Sekera, and the New York Ranger’s former backup goalie, Cam Talbot. Coach Todd McLellan is developing the team from the ground up and there will be growing pains. I believe that based on his success in San Jose and internationally, that he’s clearly demonstrated that he is a successful coach and for that reason, he’s ready to take on the task of molding a competitive team. Rebuild 3.0 (or whatever it is; I lost count) is a work in progress and despite the losses, I’m liking what I’m seeing from an analytics perspective. I also like what I’m seeing when watching the game, besides the giveaways that lead to goals.

Randomness Influences Goals & Wins

Because of randomness is a major statistical factor in NHL goal-scoring, assessing a team’s progress using goals plus shot metrics (a.k.a. Weighted Shots) is a more reliable measure of future performance. There is also significant randomness in winning any particular NHL game; with 38% being one of the widely accepted estimates. Hard to believe for the average fan, but that’s the nature of statistics. The numbers are not always intuitive, but they are what they are. I think there would be less hair-pulling among fans if they accept the reality of statistical facts then to deny or dismiss them. First fact: There is a lot of luck in winning a hockey game! Second fact: Weighted Shot (and shot attempt; a.k.a. Corsi) differentials at even-strength (5v5) are the strongest predictors of a team’s performance in the regular season, as well as the playoffs.

Of course, despite the luck factor in any single game, a team can improve their chances at winning over the long-term.  What’s the best way of doing that? Simply put, generating more shots and suppressing more shots, especially quality shots. If a team out-shoots their opponents more often than not (assuming league-average goal-tending), over time, they will win more games than they lose. The Oilers have been unable to accomplish this positive shot differential over the last 9 seasons. This season, though, we may see this shot tide turning.

Predictive Strength of Shot Metrics

Split-season analysis (i.e., using half of a season to predict the remainder of the season) has shown that shot-attempt differentials (SAT%) and Weighted Shot differentials (WghSh%) are better predictors than goal differentials. Goals are important, but because of randomness, just because a team is scoring more goals does not mean they are generating more shots than their opponents. The team could simply be getting lucky with unrealistically high shooting percentages (e.g., Calgary Flames’ shooting% last season and the Colorado Avalanche’s shooting% the season previous). Any time a team is shooting way above the mean (i.e., 8% shooting efficiency at 5v5) in any season, we can expect their shooting% to regress toward the mean the next season. We sat that with Colorado last season and Toronto the season before that. We can also expect to see that regression to the mean with Calgary this season.

With that preamble out of the way, this leads me to how I will be evaluating the Oilers progress at this early point. In this post-game analytics, I will comparing key shot metrics from the games against St. Louis (Oct. 7/15) and Nashville (Oct. 10/15) to their respective 2014/15 season series numbers. Specifically, I will compare Weighted Shots (WghSh%; 1 point for goals, 0.2 points for shot attempts); Shot Attempts (SAT%; blocked, missed, and shots on goal), Scoring Chances (SC%; as defined by war-on-ice), and High-Danger Scoring Chances (HSC%; i.e., shots from the slot area). As usual, all my data originates from war-on-ice.

[table id=17 /]

Progress?

If we look at offense (Weighted Shots – For) from both games, and compare each game to their respective 2014/15 series, we see little difference, as indicated by the progress indices. (Improvement is indicated on the Progress Index with a plus sign, both for offense and defense. Needless to say, worsening is shown with a negative sign.) On offense, it looks like nothing has changed. The defensive metric, though, shows some improvement. But this improvement is hard to grasp because the Weighted Shot figure incorporates shot attempts and goals; wherein goals are weighted 5 times more heavily than shot attempts. What does +1.4 mean? Frankly, I couldn’t tell you.

St. Louis

To help give this improvement some clarity, we need to unpack shots from goals. Thus, examining shot attempts, scoring chances, and high-danger scoring chances becomes useful.  We see that offense has improved as measured by shot attempts (SAT-For), relative to the season series, by 6.3 shot attempts/60 min. Moreover, their defense also improved by allowing 3.6 fewer shot attempts per 60 minutes. Combining these figures, we see an improvement of +4.8% in their SAT differential. That is a very good sign. If it was around 2%, let’s say, I wouldn’t be doing a happy dance unless this improvement was consistent across 20 games. But not all shots are equal. How about the quality of shots?

War-on-ice analysts have developed a refined metric of shot quality, which they refer to as Scoring Chances. The definition is quite long, so I’ll refer you to this link if you want to understand what it entails. In short, Scoring Chances have a higher chance of going in the net than simply shot attempts. Looking the progress indices, here we see that the Oilers improved both in offense (+3.6/60) and slightly on defense (+1.5/60), which results in an improved Scoring Chance differential of +5.3%.

Finally, we look at the highest quality shots; those shots that are generated from the slot. Again, if you follow the war-on-ice link above, you’ll see a diagram of the slot area. The scoring rate from the high-danger zone, if we just factor in shots on goal (excluding missed and blocked shots), is 20%! On average, a team scores on 8% of their shots on goal. This helps explain how Tampa Bay was the highest scoring team last season. They easily lead the league in shots from the slot with 25% above league-average. In generating offense, the Oilers performed slightly worse relative to the season series (-2.5 high-danger scoring chances/60). In contrast, defensively, the Oilers allowed 5 fewer high-danger scoring chances per 60 minutes.

Overall, then, the Oilers’ offense improved both in terms of shot volume and to a degree, in shot quality, except for high-danger scoring chances. Defensively, they showed improvement in all 3 shot metrics. True, in the game itself, the Blues outperformed the Oilers on all these metrics, but the upshot is that relative to their former selves, the Oilers peformed better. That’s something. Isn’t that what we ought to aspire to in life? To better our current selves relative to our past selves?

Nashville

How about the Nashville game? Again, the Weighted Shot metrics show little change to offense with a slight improvement on defense. But if we examine shot metrics, we see a different and, dare I say, optimistic story. In generating offense, the Oilers outperformed their season series on every metric: shot attempts (+4.6/60), scoring chances (+5.3/60), and high-danger scoring chances (+3/60). That’s what I expected with McLellan. In his time with the Sharks, they were not only one of the best power-play teams, they were one of the best offensive teams. (Of course, it helps to have an elite playmaker like Joe Thornton.)

How about defensively? Again, relative to last season, the Oilers improved by allowing fewer shot attempts (+6.0/60) and scoring chances (+5.6/60).  However, the Oilers did allow more high-danger scoring chances (-3.7/60).

Overall, the Oilers shot attempt differential (SAT%) improved by 4.9%, their Scoring Chance differential (SC%) improved by a whopping 11.6%, and their High-Danger Scoring Chance differential (HSC%) remained about the same (+0.3%). As a bonus, the Oilers actually won two of the shot metric battles in the game: SAT% = 52.5% and SC% = 54.3%. 52.5% is actually the playoff “magic” number. Teams who average a 52.5% shot-attempt differential have a 90% chance of making the playoffs. (Not to mean I believe the OIlers are a playoff team, but I thought this was an interesting tidbit to share.)

Summary

Despite the losses, then, I think these metrics show that the Oilers are on the right track. The everyday fan wants to see wins, which is understandable, but the solipsistic focus on wins can blind us to what is happening on the ice. What we’re seeing is that the team has performed better defensively, and importantly, independently of goaltending (more on goaltending below). They appear to be doing a better job at suppressing shot attempts and scoring chances, although they are still weak when it comes to allowing shots from the slot.

Offensively, the Oilers showed signs of improvement in both games, especially the Nashville game. McLellan seems to have a system in place that is helping them generate more offensive opportunities. If this continues, we will see the goals coming. Our bad puck luck cannot continue forever. Looking at the player stats of the Nashville game, Hall was on fire with 7 scoring chances! (5 is a high, Ovechkin-like number, so 7 is exceptional.) The McDavid/Yakupov tandem also looked good  and during their brief time together (TOI=6:23), their line had a shot-attempt differential of 66.7% (8 for; 4 against). I’m curious to see if McLellan keeps them together. I’ll be at the game in Dallas tomorrow (Oct. 13/15) and I’ll be rocking my Hall jersey. Hope to see McDavid pot his first one. That would be memorable.

Goaltending?

Analytically, goal tending is almost impossible to evaluate over a few games. Still, by the eye, Talbot has looked solid. One metric that I will be tracking is high-danger zone saves. Analysis by Stephen Burtch (unpublished; so I’m taking his word on Twitter) has shown that this save percentages from this zone are the most reliable over time. Thus, it appears to be a good indicator of goaltender ability. Against St. Louis, Talbot saved 5 of 6 high-danger shots and against Nashville, he stopped 7 of 9. Combining both games, he stopped 12 of 15 for high-danger zone save% of 80%. How did our goaltenders do last year against these teams? Scrivens and Fasth had a combined high-danger zone save% of 78.8%. A small improvement by Talbot, but as I mentioned, it’s still too early to evaluate. But even a 1% improvement is meaningful. Extrapolate that to 6 high-danger shots/game over 60 games, that’s 360 shots and saving 1.2% more of these is 4 fewer goals. For the record, Talbot’s high-danger save% last season was 86.17%, which ranked him 8th overall.

One thing I failed to mention is that St. Louis and Nashville were two of the best teams last season from the toughest division, the Central. They finished 2nd and 3rd, respectively, in the Western conference. In fact, St. Louis tied Anaheim with 109 points, but was relegated to 2nd place because of they had one fewer win, which didn’t go to shootout.  In other words, these are elite teams. If the Oilers can show improvement against the best teams in the NHL, regardless of the losses, I’m left feeling hopeful.

Thanks for reading. Please leave any comments or questions below.

Walter