The Deployment Dilemma: How Fourth Lines Can Maximize Team Output

The season is finally here, which is great for most of us. But for some young up-and-coming players, it means getting cut from NHL rosters (whether now or following brief tryouts) while players who nobody would argue possess equal skill occupy the league’s fourth line spots. It’s an interesting dilemma, and one that until now nobody has to my knowledge managed to quantify. I’m only going to take the first step here, and speak in very broad generalities, but I hope that this piece will frame the debate over the use of the fourth line a little better, and present some evidence that maybe it’s time for change.

But first, a little perspective. Hockey wasn’t always about toughness or grit. In Canada, hockey’s roots lie in lacrosse, where early Canadians sought to find a winter alternative to their favored summer pastime. They implemented elements of rugby (like the no-forward pass), which certainly brought an element of ruggedness to the idea, but keeping the puck – or in the earliest days, ball – was basically as important as it’s seen to be by analytic types today. In Russia, meanwhile, hockey developed from soccer. Possession there was also critical, and simply speaking the best players played. There wasn’t much need for pests, or rats, or whatever you want to call them. It would have been out of character considering the sport’s origins.

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On whether descriptive, non-predictive stats really have use

As many of you know, I love reading about baseball and soccer analytics. I think that in baseball’s case, having the “first mover” element means that later-adapting sports like hockey can look to copy many of their concepts and ideas, even if the sports are quite different. Soccer, meanwhile, is quite a similar game to hockey – just a slower version – and therefore many of the more specific practices translate quite well.

Soccer is at an interesting point though because they, unlike baseball, are uncovering new statistics (courtesy of companies like Opta) while also just now figuring out which of those statistics are meaningful. You can get an idea of how far behind analytics in soccer is by the fact that its main predictive statistic: Total Shots Ratio – which is essentially the soccer version of corsi – is actually based on corsi.

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On the Green Bay Packers and public sports ownership

Packers ownershipJack Han, formerly of the Montreal Canadiens, now of Habs EOTP, wrote an interesting article yesterday on whether the Canadiens should allow their fans to vote on the team’s captaincy, or, on a larger scale, whether sports are trending towards a non-profit model.

It was only this summer that I found out that such a model was present anywhere, let alone  in the NFL – for a Bears fan, the idea that the Packers are innovative frustrates me to no end – but I spent several hours researching the idea, and was fascinated by what I found. For those that don’t know, allow me to summarize.

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On the Chicago Cubs, Draft Inefficiencies, and the Last Mover’s Advantage

061312_epsteinThere’s a scene in Moneyball where Red Sox owner John Henry offers his vacant GM position to Billy Beane. “Anybody who’s not tearing their team down right now and rebuilding it using your model,” he says. “They’re dinosaurs.” The Oakland A’s were the first movers of modern sports analytics. They took a risk, and while there were stumbles along the way, they benefitted as a result. In hockey, it took a decade longer for any kind of true analytic implementation, and we’re still not quite in “tear down and rebuild using your model” territory. So why has it taken so long? I think the answer lies in the hockey world’s view of baseball. NHL executives are drawn to the differences between the two sports rather than their similarities. Yes, baseball is a stop-start game whereas hockey is fluid. And yes, baseball involves more one-on-one matchups and less team play. But beyond that, the games – and the strategies that result in building the best possible teams – are actually quite similar.

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