Practical Concerns: “The Blind Side”, Intangibles and My Off-Season Plan At McGill

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(Photo credit: Derek Drummond)

At VANHAC, I was asked by a few people about how we use analytics in our program. Every season is different, and to gain a full appreciation of my intentions this summer, it’s worth digging into the central thesis of a football book.

What Really Drives Results?

“[Quarterback Joe Montana, wide receiver Jerry Rice and running back Roger Craig] are stars. They accumulated the important statistics: yards, touchdowns, receptions, completions. [Left tackle Steve] Wallace is not considered a producer. He has no statistics.” – The Blind Side: Evolution of a Game (Michael Lewis, 2006)

While Michael Lewis’ Moneyball did much to improve the popular understanding of analytics in sports, I happen to think that The Blind Side can help bridge the gap between traditionalist and numbers-driven analysts just as much as Moneyball did.

If you peel away the diverse storylines in The Blind Side, this is the central question behind Lewis’ book: What does a good left tackle do for his quarter-back (and by extension, their team)? And how much is that worth?

Very valuable, as it turned out. Unless an NFL team wanted your multi-million dollar quarterback seriously maimed by an opposing pass-rusher, it had better hire a left tackle with the size, speed and sense to keep up. The problem is, if this player does his job well, nothing happens that can directly be attributed to him – he has no statistics.

But conceptually, his impact on the game is not all that hard to identify. A good left tackle provides a safe, productive (and dare I say, fun) work environment for his teammates. By paying attention to the process of football, you can probably come up with a few good ways to account for that.

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(credit: Derek Drummond)

Building A Bridge

When you arrive to this conclusion about football players, it becomes a lot easier to see why the idea of “being a good teammate” and “having intangibles” matters to people working in hockey. I’ve alluded to this elsewhere, but there are really two aspects to creating that good working environment for other people – one can’t be expressed in numbers conveniently, but I reckon the other already can be. Both matter a great deal to the end result, and to how people feel in the process to getting there.

I didn’t have time to really dig into this during my talk at VANHAC, but this is probably the most important realization I’ve had in two years working for the McGill Martlets hockey program.

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How Can We Quantify Power Play Performance In Formation?

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Last week I wrote about a new metric, ZEFR Rate, which measures zone entry success on the power play and is relatively repeatable and predictive of future goal scoring efficiency. The metric was based around the idea that getting into formation efficiently — most frequently a 1-3-1 — is a catalyst for power play success.

But now let’s say you’re a team that has perfected your entry scheme, and you find yourself setting up in formation at a consistent rate. What now? How can one maximize one’s use of possession in formation to score goals at the highest possible rate?

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ZEFR Rate: A New and Better Way to Evaluate Power Plays

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Some day we will reach the point where we can comprehensively analyze which power plays are the best, which players drive that success, and most elusively, what roles to place players in to maximize a unit’s output, but statistically, our special teams cupboard is pretty bare. This season, as many of you know, I took on the long and arduous task of hockey tracking in the interest of trying to get us even one step closer to our objective: how can we better evaluate and predict power play success? So let’s dive right in. Continue reading

Matt Hunwick, Martin Marincin and Quality of Competition

During the offseason, the Toronto Maple Leafs made two small additions to their blueline that were lauded by many in the analytics community. At the draft they traded a fourth round pick and a low-tier prospect for Martin Marincin and on the first day of free agency they signed Matt Hunwick to a low money two-year deal.

Both players had very similar trajectories over the previous three seasons. Marincin had a relative shots percentage of +4.3 while playing 15.7 minutes per night while Hunwick landed at +2.8 percent playing 15.3 minutes. Looking at just the 2014-15 season, Hunwick had the edge at +5.1 in 14.3 minutes to Marincin’s +2.4 in 16.1 minutes. Basically, the Leafs acquired two decent and under-appreciated defensemen who have shown ability to push play in the right direction and for a relatively low cost too.

Flash forward to the culmination of their first seasons as Leafs and opinions of the two couldn’t be more different. Marincin is praised regularly while Hunwick is seen as a proverbial boat anchor.

So what’s changed exactly?

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The Shift: Breaking Down The L.A. Kings’ Secrets To Success

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By virtue of their 5vs5 shot differential, the Los Angeles Kings are the best team in hockey. As of Saturday night, the Kings are rolling along at 56.1% Corsi – #1 in the NHL by a long shot. In fact, the 3% gap between the Kings and the No. 2 Anaheim Ducks is the same as the one between the Ducks and the No. 15 Philadelphia Flyers.

So why are the King so good?

The simple answer is that they have good players executing a sound game plan developed by a good coaching staff. But how exactly does this manifest itself?

On March 26th, the Kings were beating up on the Edmonton Oilers in the middle of the second period when, in the span of 45 seconds, they put together – in my mind – a perfect, representative shift of everything that makes them a superior hockey team.

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Can We Accurately Predict Which PK Units Will Score Shorthanded?

Last week I went on Montreal radio and talked about how dangerous the Ottawa Senators’ penalty kill units are. Led by speedy forwards like Curtis Lazar, Jean-Gabriel Pageau and Mark Stone, and with help from puck moving genius Erik Karlsson, the team has feasted on opposing power plays this year to the tune of the highest GF/60 minutes shorthanded in the league since at least 2007-2008. When considering the team’s league worst GA/60 — mixed with a little bit of film — it becomes clear that the Senators yield chance against in exchange for opportunities for on the break. It may not have been intentional at first, but once the team started capitalizing on its rushes, it seems likely coach Dave Cameron gave his players the green light to go, to try and come out on top on aggregate. The result? While being last in GA/60 shorthanded, the Senators are third in GF%. The problem with GF% when it comes to special teams though is that volume matters more when the ice is tilted. Two goals for and Eight goals against isn’t the same as Four goals for and 16 goals against. So goal differential per 60 is a more accurate measure of success on special teams. The Sens are 30th in GD/60 shorthanded, so it’s hard to say the strategy has been that much of a positive for the team (unless, say they’re down a goal and shorthanded near the end of a game).

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Does aggressive play on the penalty kill pay off?

Late last week, Arik Parnass pointed out a particular peculiarity about the Ottawa Senators’ penalty killing so far this year.

While the Sens may be an extreme example, their numbers tell the story of a constant struggle that teams are faced with when killing a penalty: do you focus solely on your own end and do whatever it takes to prevent a goal, or do you allow your forwards to take the play to your opponents, trying for a shorthanded goal and forcing them to defend in a situation where they may not be expecting it.

This risk-reward question is one that’s central to the value of hockey analytics. It’s very easy to make decisions based on personal experience which is so often dominated by memories of things that are out of the ordinary – a coach will likely remember watching his winger get caught deep trying for a shorthanded goal, while forgetting the 2-on-1 opportunity he generated earlier in the game. It’s just as easy, however, for a fan to complain that his favourite team won’t put out their best forwards to aim for a go-ahead shorthanded goal without any data to back up their argument. The challenge for analysts then is to dig through the available data to figure out what past experience has taught us about the overall net impact of playing for a goal on the penalty kill, so that we can make an informed judgement as to what the potential costs and benefits are.

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A New Passing Project Data Visualization

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Back when the season started, I started playing around with the idea of how best to visualize our passing data. There will be plenty of time to arrange it in a viz to evaluate players like we did last season (Forwards, Defensemen), but there’s another way to present this data and that is within the realm of tactics and opposition analysis. Last December, I wrote a little preview of what we can do with this data by focusing on tendencies of how and where teams generate offense. If you haven’t yet, I encourage to read these pieces (all are linked in the beginning of that piece I just linked) for the background of what I’ve been imagining for this data since I added in lane concepts last summer.

We already know that passing is a skill and an important one at that, so there is always the importance for the descriptive and predictive levels of analytics (what has happened, what will happen), but one we don’t often discuss is the prescriptive level (what should we do). Combining data visualization of these events and video analysis is the best way forward. In this post, I’ll go over exactly how to use our new viz to pinpoint areas of the game to analyze. If you simply want to go to the viz, scroll to the bottom of this piece.

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