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Philosophy of hockey Sabremetrics: Can hockey accurately be measured?

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07-27-2014, 04:39 PM
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Finally had time to read "The Signal and the Noise" by Nate Silver. Few excerpts I liked from the chapter on baseball.

On the "fear" that analysts would be taking jobs from scouts:

Beane told me the A’s scouting budget is now much higher than it has ever been. Moreover, he said it was the A’s fascination with statistical analysis that led them to increase it. As we’ve seen, baseball players do not become free agents until after six full seasons, which is usually not until they’re at least thirty. As Bill James’s analysis of the aging curve revealed, this often leads clubs to overspend on free agents—after all, their best years are usually behind them. But there is a flip side to this:*before*a player is thirty, he can provide tremendous value to his club. Moreover, baseball’s economics are structured such that younger players can often be had for pennies on the dollar

On objective analysis vs. gut-feel decisions:

“From our standpoint in Oakland, we’re sort of forced into making objective decisions versus gut-feel decisions. If we in Oakland happen to be right on a gut-feel decision one time, my guess is it would be random. And we’re not in a position to be making random decisions and hope we get lucky. If we’re playing blackjack, and the dealer’s showing a four and we have a six, hitting on the sixteen just doesn’t make sense for us.”

On the decision-making process:

The key to making a good forecast, as we observed in chapter 2, is not in limiting yourself to quantitative information. Rather, it’s having a good process for weighing the information appropriately. This is the essence of Beane’s philosophy: collect as much information as possible, but then be as rigorous and disciplined as possible when analyzing it.

On categorization:

When we can’t fit a square peg into a round hole, we’ll usually blame the peg—when sometimes it’s the rigidity of our thinking that accounts for our failure to accommodate it. Our first instinct is to place information into categories—usually a relatively small number of categories since they’ll be easier to keep track of.

This might work well enough most of the time. But when we have trouble categorizing something, we’ll often overlook it or misjudge it. This is one of the reasons that Beane avoids what he calls “gut-feel” decisions. If he relies too heavily on his first impressions, he’ll let potentially valuable prospects slip through the cracks—and he can’t afford that with a payroll like Oakland’s.

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07-28-2014, 04:58 PM
Doctor No
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Originally Posted by Mathletic View Post
When we can’t fit a square peg into a round hole, we’ll usually blame the peg—when sometimes it’s the rigidity of our thinking that accounts for our failure to accommodate it.
When I give modeling presentations, there's a joke that I've probably used to death at this point - we take great care to build these elaborate models that predict human behavior perfectly with great accuracy.

And then imperfect humans have to come along and ruin our perfect model by acting irrationally.

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10-23-2014, 07:47 PM
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I feel like it would take an absolutely immense amount of work to get statistical analysis of hockey to the point where the insight it provides will approach the insight that a knowledgeable and experienced scout would get just from watching and reflecting on a player or team. Of course the benefit is that even if it doesn't approach that level of insight, a collection of stats would be widely available, whether publicly or in-house, you wouldn't be relying on the views and opinions of an individual - you'd be relying on something that's objective and that can be easily disseminated.

Now, if I'm an executive and I'm going to be drafting a kid, I for sure want to have people I trust and whose opinions I value who have seen him play a lot of games in person and got a feel for his game and his tendencies. It's not going to be stats that are ruling the day there, not ever. There are just too many variables in hockey, you can't account for them all in a statistical model, not to the level that you'd have to be able to reach in order to form a real strong opinion.

But for sure those statistical models have a very important place, and it's getting much more significant. It's having an effect already, teams are changing their playstyles and changing their points of emphasis based on - just one thing - the idea that possession correlates to winning over the long haul, it doesn't matter whether you think the possession stats are accurate or useful or not, you still can see that that has had a clear effect on the style of play of a lot of teams.

It's had a huge impact, even when you hear Doughty saying "Corsi's crap," well, a big part of them winning those two Cups was Sutter putting major emphasis on a possession style of play - compare it to when he took the Flames to the Final, or even the first year he was with the Kings, they played a much different style than they do now. Maybe Doughty's right. Maybe he knows it's crap because they have something way better, something that's in Sutter's head - and there's no statistical model that is ever gonna approach the knowledge of a guy that's been in and around the game as long as Sutter. Those models would be for everyone else, who haven't been around that long and don't have that knowledge, or to approximate that knowledge for all the games you just aren't able to watch because you had a game that night or because you want to do a quick overview of some euro leagues you don't get to see or something. That's where the value really lies, to supplement the traditional ways of doing things, not to replace them.

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10-23-2014, 08:03 PM
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I think the biggest problem I have with a lot of these statistical models that are being thrown around now - there's too little attempt to account for the roles different players play and how they are used. Things like qualcomp and zone starts are just scratching the surface and it's almost lip-service, it doesn't go nearly as far in depth as it needs to to provide an accurate measure and give context to some of the other stats. Without that context they can end up being very misleading.

There are a TON of very solid defensive players with very lousy possession stats because they get put out on the ice against opponents' best possession guys and most dangerous scorers. And their role is not to regain and then retain possession; it's to limit the damage those top guys can do and hem them in and then get off the ice.

It's no different than saying (bear with me), why is the checking line's +/- so lousy, they are supposed to be the checking line, right? Well, they are out there against the biggest scoring threats, of course it is going to look lousy. The only way to know whether it is REALLY lousy, though, is to have some theoretical baseline to compare it to, to estimate what the typical line would do against that level of competition, and that's what you base your assessment off of. You can't just say, well Scuderi or Bolland or Callahan, this guy's supposed to be a good defensive guy but his numbers really suck, maybe we were wrong in that assessment. No, the numbers probably suck for that sort of player because the coach knows they are the best defensive guys, the best guys at playing without the puck, so they go out there when the coach figures the other team's top guys are gonna have the puck and he tries to stem the tide, and it's because he's used that way and that's his role that his numbers look bad next to other guys who have other roles that end up giving them pretty numbers.

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