Subscribe!
CourtIntelligence powered by kenpom.com

The good stuff


At other venues...
  • ESPN.com ($)
  • Deadspin
  • Slate

  • Strategy
  • Whether to foul up 3 late
  • The value of 2-for-1’s

  • Philosophy
  • Brady Heslip’s non-slump
  • The magic of negative motivation
  • A treatise on plus-minus
  • The preseason AP poll is great
  • The magic of negative motivation
  • The lack of information in close-game performance
  • Why I don’t believe in clutchness*

  • Fun stuff
  • The missing 1-point games
  • Which two teams last lost longest ago?
  • How many first-round picks will Kentucky have?
  • Prepare for the Kobe invasion
  • Predicting John Henson's free throw percentage
  • Can Derrick Williams set the three-point accuracy record?
  • Play-by-play Theater: earliest disqualification
  • Monthly Archives

  • September 2014
  • July 2014
  • May 2014
  • April 2014
  • March 2014
  • February 2014
  • January 2014
  • December 2013
  • November 2013
  • October 2013
  • September 2013
  • August 2013
  • July 2013
  • June 2013
  • May 2013
  • April 2013
  • March 2013
  • February 2013
  • January 2013
  • December 2012
  • November 2012
  • October 2012
  • September 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • March 2010
  • February 2010
  • January 2010
  • December 2009
  • November 2009
  • October 2009
  • July 2009
  • February 2009
  • January 2009
  • December 2008
  • November 2008
  • October 2007
  • September 2007
  • July 2007
  • June 2007
  • May 2007
  • April 2007
  • March 2007
  • February 2007
  • January 2007
  • December 2006
  • November 2006
  • October 2006
  • September 2006
  • August 2006
  • July 2006
  • June 2006
  • May 2006
  • April 2006
  • March 2006
  • February 2006
  • January 2006
  • December 2005
  • November 2005
  • October 2005
  • September 2005
  • August 2005
  • July 2005
  • June 2005
  • May 2005
  • April 2005
  • March 2005
  • February 2005
  • January 2005
  • December 2004
  • November 2004
  • October 2004
  • September 2004
  • August 2004
  • July 2004
  • June 2004
  • May 2004
  • April 2004
  • March 2004
  • February 2004
  • January 2004
  • December 2003
  • November 2003

  • RSS feed

    The predictive value of overtime margin

    by Ken Pomeroy on Wednesday, January 29, 2014


    Overtime games can produce strange results. On January 15, Mississippi beat LSU by 14 after going to an extra period when the Tigers’ Anthony Hickey missed a 15-foot shot at the end of regulation. If Hickey makes the shot, one might view LSU as two points better than the Rebels on that night. Instead, we got five more minutes of data and it turns out that LSU was 14 points worse. In a way, it defies logic.

    If you had to pick one, would it be that the Rebels were 14 points better than the Tigers that night or were they zero points better? Some would say you don’t really have to choose because the Rebels won the game, so end of discussion. Those people probably aren’t interested in my work. But hypothetically, if you care about using that game for your own evaluation purposes, you have to wonder at some point if that 14-point win should be treated differently than a game where the teams were tied with five minutes to go and Mississippi won by 14 in regulation.

    In order to examine this, I used the same technique I’ve used in the past to examine the meaning of scoring margin and one-point home wins. I took all cases since 2003 where two teams played each other twice. If one of those games went to overtime, I recorded the result in the other contest. For instance, last season Towson beat William & Mary by 13 in double overtime and then lost to the Tribe by seven in the rematch. That is viewed as some sort of truth of what a 13-point overtime win means in real life. Of course a 13-point overtime win at home doesn’t really mean that team is seven points worse on the road. That’s why I’m looking at 11 years worth of data.

    Even over 11 years, one is left wanting for more data. There were just four cases of a home team winning an overtime game by 13 points and then playing that opponent again. And Towson’s subsequent seven-point loss was actually the best performance by the overtime winner in the other game. But there are many more examples of overtime margins in the single digits. Enough that we can compare the findings from this exercise to a similar approach using non-overtime games to shed some light on the importance of overtime scoring margin.

    The results of this approach are summarized in the plot below.

    The plotted points represent the average road rematch margin for the home margin given. For instance, in the 89 cases of a team losing an overtime game at home by one point, the rematch resulted in an average margin of -3.6 points. (Don’t take “rematch” literally here. The order of the games doesn’t matter. If two teams played an overtime game in the second of the pair of games, I treated the first game as the rematch.)

    The lines represent the linear trend of those points, weighted by the number of observations, so the goofy points in the double-digits of the overtime cases don’t affect the regression much.

    Because the slope of the OT regression is flatter than the regulation regression, the result is that overtime margin of victory clearly has meaning, but it’s not quite as useful as regulation margin from a predictive standpoint. Each additional point of home regulation margin increases the expectation on road margin by 0.36 points. In overtime, each additional point of home margin increases the expectation on road margin by just 0.27 points. It takes about a 14-point home regulation win for us to expect that the rematch would more often than not go to the road team. In the overtime cases, it takes about 19-point win to reach that point.

    It should go without saying that in the real world, you wouldn’t want to simply use the last time two teams met to make your best guess on what the outcome will be the next time those teams play. It was done here because this is about the simplest approach I can think of to investigate the use of overtime performance from a predictive standpoint.

    The results are consistent with what I think most people would expect. In some cases, overtime margin is just as useful as regulation margin, but in other cases, particularly where key players may have fouled out, it figures to be less useful. When all of those cases get averaged together, you get the outcome shown here. Overtime margin isn’t entirely indicative of the difference between two teams, but it’s a lot better than assuming those teams were completely even on that night.