Category Archives: Walter Foddis

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

Edmonton Oilers, Fancy Stats, NHL, Walter Foddis

Brief Analysis of Justin Schultz’s 2014/15 Season

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When I analyzed the seasons of Mark Fayne & Andrej Sekera, my analyses were dense. I was still learning about the new data–data that went beyond the “first wave” of analytics, like shot-attempt differential (a.k.a. Corsi). I was still deciphering what “right” analytical pieces were needed for a comprehensive, yet readable player profile. Recently, an Oilers’ fan asked me if I could give an analytical overview of Schultz’s season. The overview had to be brief because I was posting to a Facebook thread. Much to my surprise, I think I managed to pull it off. I include this analysis below. I am curious to hear from readers if they find the analysis, as brief as it is, understandable and informative.

Brief Analysis of Justin Schultz’s 2014/15 Season

Take-Home Point: The Oilers were only slightly worse defensively with Schultz on the ice, but he appears to contribute enough on offense to compensate.

I begin with the broadest measure, shot attempts (SAT), and then refine the analysis to Scoring Chances (SC) and High-Danger (i.e., the slot area) Scoring Chances (as defined by war-on-ice). Unless otherwise stated, all player data originates from war-on-ice.

Shot Attempt Metrics

I computed the Oilers’ score-adjusted shot attempts generated (SAG) and suppressed (SAS) per 60 minutes, relative to the team’s average (RelTM). Combining SAG and SAS, I also computed a shot-attempt differential per 60 minutes (SAT60), Schultz lead the Oilers’ defense with a SAT60 RelTM of +7.2. Next was Oscar Klefbom with +4.5. To clarify, +7.2 means that with Schultz on the ice, the Oilers generated just over 7 more shot attempts per 60 minutes compared to the team’s average. Defensively, Schultz’s SAS60 RelTM was -2.2, which was third lowest (lower is better) behind Klefbom (-3.7) and Jeff Petry (-2.2).

Scoring Chances & Net Goals

In terms of scoring chances, defensively Schultz was only slightly better than the team’s average with the Schultz-iced team allowing 0.14 fewer scoring chances (SC) per 60 minutes. Offensively, though, Schultz again lead Oilers’ defenders with the team generating 3.5 more scoring chances per 60 minutes. Marincin was second with 3.25 and Klefbom was third with 2.8.

In terms of the High-Danger Zone shots (the slot), the Oilers allowed 14% more shots than league average. With Schultz on the ice, the team allowed 2% more shots from the slot. So when it comes to allowing high quality shots, Schultz is a little worse than the team. Again, though, Schultz tends to compensate with more offense. Indeed, the team generated 16% more shots from the slot with Schultz on the ice. Because of the additional offense with Schultz on the ice, the team is actually expected to out-score the opposition. Including all scoring zones, when Schultz was playing, the team’s expected Net Goals was +0.32 per 60 minutes, which translates into +8 goals.

Passing Metrics

A common pattern in Schultz’s shot metrics is his apparent ability to generate more offense. Because of Ryan Stimson’s Passing Project, we have direct measures of a player’s passing and shot metrics. Here’s the glossary for the graph below.

  • CC% and CC/60 = Corsi Contribution (or Shot Attempt Contribution), which are individual shot attempts, primary passes leading to shot attempts, and secondary passes leading to shot attempts. These are given as a percentage (i.e., proportion of shot attempts a player is involved in when on the ice) and per sixty minutes. These metrics tell you how much offense goes through that player while on the ice and also how often they contribute.
  • Composite SAG and SG represent the total number of shot attempts and shots a player generated from both primary and secondary passes per sixty minutes. SAG/60 is solely for the player’s primary passing contributions.
  • Entry Assists represent the number of controlled entries a player assisted on. This is determined by the number of passes in transition (prior to entering the offensive zone) that was recorded for each player.
  • SC Contribution% and SCC/60 are identical CC% and CC/60, but represent only the scoring chances a player was involved in. Passing data for scoring chances was combined with War-on-Ice’s scoring chance (link to definition) data to arrive at a player’s total number of scoring chance contributions. SC SAG/60 represents the number of scoring chances set up from a player’s primary passes.

When it came to generating shot attempts and shots, Schultz was pretty close to the average defender (e.g., Corsi Contributions/60, Composite Shots-on-Goal/60). However, in terms of directly contributing to Scoring Chances through primary passes (SC SAG/60), Schultz reached an “elite” level. He ranked 8th overall among NHL defenders. Moreover, looking at scoring chance contributions overall (i.e., the last 3 metrics), Schultz ranked among the best in the league (over 90th percentile). Thus, there is not only more offense with Schultz, but higher quality offense.

Summary

Relative to the team, Schultz’s defense metrics suggest that he isn’t a terrible liability, and in fact, not that bad at all. This contrasts with his defensive gaffs in highlight reels. I also vaguely remember Schultz’s blunders, but I also know that human memory is unreliable and biased. (That’s why I rely on analytics to rescue my fallible memory with objective data.) Although Schultz is not that bad defensively, relative to the team, we all know the team’s defense was bad overall (e.g., 26th in score-adjusted Scoring Chances Against). In short, he’s average as a defender on a defensively weak team. Still, Schultz’s offensive ability compensated for his middling defense, which we clearly saw in the team’s expected Net Goals (+8) and increased Scoring Chances with Schultz on the ice.

Recommendations for Schultz to Succeed

What does Schultz need to succeed? First, he needs to be more consistent in executing his defensive responsibilities. At times, it seemed like he fell back into old habits. Easier said than done when under pressure, but I think he needs to work on the mental discipline to execute what he intuits to be the “right” play, which includes the discipline to implement what McLellan and his coaching staff will have taught him. This is more of a psychological battle than mistakenly being labelled as “lazy.” When a player is overwhelmed, they tend to feel their options are limited. But if they have enough practiced learning in different situations, this would give them confidence when a similar game-situation occurs.

Second, I would suggest avoid matching him up against tougher competition. On the road, though, this may be next to impossible.

Third is what I consider the must-fix-above-all-else element: Schultz needs to improve his ability to read plays. This is possibly linked to my first point about mental discipline. I recall seeing him look lost at times and wondered how the coaching staff was going to help him correct it. Then I would see this “lost in the wilderness” situation repeatedly, which lead me to wonder about Schultz’s “Hockey IQ.” Specifically, I questioned whether he was cognitively capable enough to read developing plays, decide where he should go, and what he should to to be most effective. Can McLellan and crew help him break through this apparent psychological barrier? (Disclaimer: I don’t know what media pundits and hockey people mean by “Hockey IQ.” If someone can define it for me in a formal way; that is, a way that it can be measured reliably, I would be a happy analytics camper.)

Fourth, Schultz cannot be hesitant to use his body. (His physical hesitation, accompanied by a compensatory stick-reach while bent over, has lead to the derogatory term, Jultzing.) I think being more physical is what Schultz was alluding to when he spoke of “playing with an edge.” By physicality, I don’t expect him to separate opponents from the puck with big hits, but I do expect and want him to make it more difficult for opponents to execute their shots and passes. Duncan Keith of Chicago had 16 hits last season. You’d think he get more hit by random chance alone. Yet, he is one of the best defensemen in the league. Victor Hedman of Tampa Bay also hits very little; less than 1 hit per game. Thus, effective defense is possible without splattering players along the boards.

Finally, being partnered with an effective defensive defender would be ideal. Klefbom is on his way to being a strong defender (as well as an offensive contributor), but we also have to remember he only has 60 NHL games under his belt. Despite being in the league for over 200 games, Schultz still needs a mentor! Pairing him with a veteran like Fayne in certain situations (e.g., softer competition), could help Schultz progress.

Thanks for reading and please let me know what you thought of this brief analysis. (At least, briefer than what I normally post.) Also, what do you believe needs to happen for Schultz to succeed?

Fancy Stats, NHL, Walter Foddis, Zach Laing

Is Ryan Nugent-Hopkins a top center in the NHL?

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Thanks to BLH writer Walter Foddis for helping contribute to this article. 

Entering his fifth full season in the NHL, Ryan Nugent-Hopkins has yet to disappoint. Nugent-Hopkins has put in the necessary work to begin to turn into one of the top two-way centers in the NHL. Personally, I feel that he is often overlooked by people outside of Edmonton as the limelight can often fall on the other players like Taylor Hall or Jordan Eberle.

With Connor McDavid coming to town, the Oilers are finally on their way to having two top centers. That’s something the team hasn’t been able to boast since the 2005-2006 when a 26-year old Shawn Horcoff and a 23-year old Jarret Stoll were our potent one-two punch that helped lead us to the cup finals. When you look around the league, the teams that often most successful have not one, but two centers.

Pittsburgh has Sidney Crosby and Evgeni Malkin. Anaheim has Ryan Getzlaf and Ryan Kesler. St. Louis has David Backes and Paul Stastny. The New York Islanders are on their way with John Tavares and Ryan Strome. Dallas has Tyler Seguin and Jamie Benn.

Granted not all of these teams are the elites in the NHL, but all of these one-two punches create an absolute nightmare for opposing teams. Soon, the Oilers will have Nugent-Hopkins and Connor McDavid running show.

[youtube http://www.youtube.com/watch?v=6FOUqQt3Kg0]

In all situations last year, Nugent-Hopkins has some good comparables.

[table id=15 /]

stats c/o war on ice and hockeyanalysis.com

What do we see here? We see that Ryan Nugent-Hopkins is in good company. Ryan O’Reilly has been widely considered to be the best, if not one of the best two-way centers in the NHL today and last season the Nuge beat him out in almost all of the categories listed.

David Johnson listed the statistics for GF60Rel combined with CA60Rel for a good chunk of NHL centers over the last five years. What this combined metric shows is a player’s implied (1) contribution to goal production and (2) ability to suppress shot attempts, relative to his team’s average on these metrics. It also is a good reader for a player’s ability in the two-way game. Sidney Crosby (1.34), Pavel Datsyuk (1.21) and Jonathan Toews (1.14) are the leaders in said category. Nugent-Hopkins’ closest comparables for that statistic were John Tavares (.55), Jason Spezza (.56), David Backes (.57) and Ryan Getzlaf (.64).

I am very impressed that how despite the fact he is tied for third among NHL centers in time-on-ice per game, he still drew 13 more penalties than he took. That is some discipline.

Ryan Stimpson’s passing project suggests that Ryan Nugent-Hopkins is performing at the level of a 2nd line center (34th to 66th percentile) for most metrics.

  • CC% and CC/60 = Corsi Contribution (or Shot Attempt Contribution), which are individual shot attempts, primary passes leading to shot attempts, and secondary passes leading to shot attempts. These are given as a percentage (i.e., proportion of shot attempts a player is involved in when on the ice) and per sixty minutes. These metrics tell you how much offense goes through that player while on the ice and also how often they contribute.
  • Composite SAG and SG represent the total number of shot attempts and shots a player generated from both primary and secondary passes per sixty minutes. SAG/60 is solely for the player’s primary passing contributions.
  • Entry Assists represent the number of controlled entries a player assisted on. This is determined by the number of passes in transition (prior to entering the offensive zone) that was recorded for each player.
  • SC Contribution% and SCC/60 are identical CC% and CC/60, but represent only the scoring chances a player was involved in. Passing data for scoring chances was combined with War-on-Ice’s scoring chance (link to definition) data to arrive at a player’s total number of scoring chance contributions. SC SAG/60 represents the number of scoring chances set up from a player’s primary passes.

Throughout his career, Nugent-Hopkins has steadily improved in almost every facet of his game. Last season, he was given more defensive responsibilities than he had in previous season, including more time on the Oilers penalty kill than in previous seasons. He also tied a career high in points (56) and set a career high in goals with 24.

Overall, I think it is fair to say Nugent-Hopkins is well on his way to being a top-flight NHL center.

Thanks for reading. Drop a comment below and let me know if you think Nugent-Hopkins is as good as his statistics say.