Weekly Roundup

I was blown away last week when within hours of posting the win probability calculator, reader Zach wrote up an analysis of when to go for a 2-point conversion. Very cool.

Jim Schwartz is the new head coach of the Lions. Besides being a fellow native of Baltimore, I like him because he's known to have a solid grasp of statistics. Like Bill Belichick, Schwartz has an economics degree. The New York Times has a good write up on him from last fall.

The new issue of the Journal of Quantitative Analysis in Sports is out. There's an article on ranking teams and predicting games, including in the NFL. I've only skimmed it. There are a couple of other articles that look interesting too. There is an article on determining the evenness of sports competitions in rugby, essentially doing the same thing--ranking and forecasting. There is also an article on using neural networks to predict NBA games. (I've experimented with neural network software. I can't say I completely understand it, but I was able to get close to the same prediction accuracy from my usual regression model.)

Sometimes the articles in JQAS are crackpot nonsense. So be warned--just because something has a fancy academic title, comes wrapped in a pretty .pdf, and is loaded with references, doesn't guarantee it has any value. These particular articles don't immediately jump out as kooky, thankfully.

Math and stats pay. Check out the top 3 jobs. Funny, I don't see Navy carrier pilot on the list. When I used to fly, I often wondered how much you'd have to pay someone to do that in an open and competitive market. Take away the "serving your country" aspect, and how much money would someone with those skills make? Throw in the danger and the fact that they have to live at sea for extended periods, and you might have to pay them like these guys.

The PFR blog has the usual installments of best-ever, worst-ever trivia. This time, it's best-ever Super Bowl losers (part 2). I'd like to see worst-ever Super Bowl winners too. [Edit: Here it is.] What kills me is that the two biggest championship upsets in American sports history feature an upstart second-fiddle team from New York beating an overwhelming favorite from Baltimore. The Mets upset the O's in '69, and the winter before, the Jets shocked Baltimore in Super Bowl III. I wasn't even born yet, and it still hurts. One thing forgotten about the Super Bowl back then is that it was more of an actual bowl game--a post-season exhibition. Baltimore had already won the NFL Championship. Back then, as I understand it, the Super Bowl was a cross between a meaningless Pro Bowl-type game and the modern championship as we now know it. Not totally meaningless, but not yet considered the championship either. The Jets certainly changed that.

PFR also has a new Super Bowl history page.

Smart Football teaches us about zone blitzes.

Dave Berri has his final rankings of the year, plus he looks at the Lions.

Over at the community site, Denis O'Regan compares scoring frequency in soccer and football using Poisson distributions. Also, Oberon Faelord (real name?) reminds us that not all 10-point leads are the same.

Since the Steelers beat my Ravens last Sunday to reach the Super Bowl, I'm allowed one outburst of sour grapes. When I was in the Navy, I noticed every part of the country seemed to have a sizable stable of Steeler fans. I remember going to watch a Steelers-Browns playoff game at a sports bar in Pensacola and couldn't believe how many fans of each team were there. And here in Northern Virginia, they're everywhere. Now I understand why. I think a lot of it just bandwagon types from the 70s, but the economic dispersion of the rust-belt is also obviously part of the reason.

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5 Responses to “Weekly Roundup”

  1. Unknown says:

    "So be warned--just because something has a fancy academic title, comes wrapped in a pretty .pdf, and is loaded with references, doesn't guarantee it has any value."

    It doesn't even guarantee that the research is done in a rigorous fashion. I have stumbled across several papers from recent years claiming that neural networks can predict NBA winners more often than the experts (i.e., the Vegas line). I am always eager to read these papers because I will freely admit that I don't understand how to replicate/construct a neural network so I'm naturally curious to determine if I should be learning more about them.

    The paper that you reference falls into the same lazy, statistical trap as previous neural-net studies that I have found. They slice-and-dice a single data set many different ways in an effort to create multiple test results. Unfortunately, this approach has absolutely NO bearing on reality.

    It floors me that so-called academics think that you can test the PREDICTIVE nature of an algorithm by lazily slicing PAST data into small chunks and then using the neural-net rankings that you have compiled from the ENTIRE data set to pick winners. If you want to test the predictive efficacy of a statistical method, there is only one way to do this with any credibility:

    1. Write yourself a program (or put in the time to do it by hand) so that your predictive model is continually restricted to only those results that would have been available up to that point in the given season.
    2. Use that restricted data to predict the next day's results.
    3. Repeat

    Let me give you an example of this:

    I learned that if you use a simple Elo rating system and feed the system the ENTIRE season's worth of data, you can then go back and consistently beat the Vegas experts over any reasonable data set drawn from the same season. When I write it in this way, it sounds very obvious that this is a flawed method, but if you go back and read that referenced paper, that is exactly what they are doing with their neural networks.

  2. Brian Burke says:

    The results they cite do seem too good to be true predictions.

  3. Anonymous says:

    I must comment on Bytebodger's view. As a retired academic I agree with what you say that too much of what is published is spurious. The problem is that academics are under pressure to publish so they generate papers which are often not rigorous nor contribute to the general store of knowledge. There are hundreds of journals that exist to fill this need for publication. Occasionally they publish a reliable article but more often than not it is pap. That said, these journals are still worth perusing in search of the occasional study which holds water.

    And it goes without saying that if I develop a model based on a set of data and then run those data through the model, I will predict results better than anyone else. My problem with the article under discussion is that they don't clearly detail the model. (at least it wasn't clear to me.) Their model appears to be predictive over a span of several years but all they give us are the final percentages of success. We have no idea how they did their picking.


  4. Anonymous says:



    That's the post on the worst SB winners ever.

  5. Brian Burke says:

    Cool. Thanks.

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