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    The 18th-most improbable win

    by Ken Pomeroy on Sunday, September 30, 2012


    One of the more famous comebacks last season was Western Kentucky’s triumph over Mississippi Valley State in the NCAA tournament. Sadly, it just missed the list published here last week. But since it was probably viewed in real-time by a few of you, I thought it would be a way to show why the win probability model seems like it may be a bit too certain of itself in some cases.

    After Cor-J Cox scored with 5:03 left, the Delta Devils led 53-37. At this point the Hilltoppers had just a smidge more than a 1% chance of winning*. Western Kentucky eventually won 59-58, becoming the only team last season to overcome a deficit of at least 15 points at the five-minute mark.

    Read more...

    The 1%-ers: 2012’s craziest comebacks

    by Ken Pomeroy on Tuesday, September 25, 2012


    The new win-probability algorithm has been activated and it’s time to look at the most improbable wins of last season. Going forward, I plan to have better tools around here so you can identify these games without having to wait five months after the season ends for me to write something on them. What follows are the 17 games last season where the winning team had less than a one-percent chance of being victorious at some point in the game.

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    Predicting individual 3-point production

    by Drew Cannon on Monday, September 17, 2012


    There’s been quite a bit of recent discussion on this blog on the topic of three-point shooting. (Here, here, here, here, and here.) 

    There are two key takeaways from these articles: First, that it’s really hard to predict a team’s three-point percentage for a game, even knowing how well they tend to shoot three-pointers, and second, that to rate shooters against each other we need to keep a close eye on frequency of attempts as well as percentage. What I’m about to show you demonstrates that the difficulty of projecting three-point percentage also exists on the year-to-year player level and that we really need, as a basketball society, to move away from pure three-point percentage as the measure of a shooter.

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    The game nobody remembers

    by Ken Pomeroy on Monday, September 10, 2012


    Forty years ago yesterday, the USSR beat the USA 51-50 in the 1972 Olympic gold medal game, handing the Americans their first loss in Olympic competition. From a global perspective, it’s the most famous basketball game ever played. The contest is notorious not only for the outcome, but for the series of game management errors that caused the controversial finish.

    (For those unfamiliar with the game, it was re-aired during a 2002 special on ESPN Classic and can be viewed here. If you don’t have an hour to spend on this, see this 15-minute piece narrated by Jim McKay 20 years ago. It has a couple of minor factual errors, but is the most accurate portrayal of events that I’ve seen.)

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    Win probability for grown-ups

    by Ken Pomeroy on Monday, September 3, 2012


    About three seasons ago I tried to develop some sort of algorithm to assess a team’s chance of winning at various points in the game. It was the middle of the NCAA tournament, and as favored teams were finding themselves in a deficit at some point during the game, it seemed like it would be a good thing to know exactly where their chance of winning stood. On a larger scale there would be other uses, like measuring the magnitude of a comeback in any situation, or more advanced analysis like measuring how a team performs when the game is truly on the line.

    The method I came up with to accomplish this was rather amateurish, but it worked well in most cases, so there wasn’t a big incentive to go changing it. It wasn’t until I was preparing a list of the most improbable wins from this past season that I noticed the system had a small glitch, mainly in cases where win chances would be small.

    So I’ve spent the past few days taking a more adult approach to this and applying regression to the problem.  Every possession of D-I on D-I action from last season was included in the analysis, and the variables used in the regression were initial win probability estimate, the team with possession, and the current margin. Since the effect of time remaining is non-linear, separate equations were derived for each minute of play, and also for the following times in the final minute: 0:30, 0:15, 0:05, and 0:03.

    All in all, the results aren’t going to be that much different than the old system, but at least this one is grounded in reality and not some theory that was cobbled together in a couple days. The main difference is that the new system is a little more sure of itself - there are more cases of high win probability. I was a little surprised by this, but a check of my work indicates that there is support for this artifact.  Here’s how the model forecasted possessions for various ranges of probabilities last season…

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