Your verification ID is: guDlT7MCuIOFFHSbB3jPFN5QLaQ Big Computing: Would we have found out San Diego Basketball through Analytics

Friday, April 15, 2011

Would we have found out San Diego Basketball through Analytics

This week the San Diego State Basketball program had a number of its players arrested for shaving points in a game in February of 2010 and trying to do the same thing in a game against UCR in February of 2011 (News Story). It appears that these guys were caught by human intelligence. This type of cheating is bad for both the sports organizations (NCAA, NFL, NBA, MLB) and the major betting community. It is bad for the sports organizations because who, except WWE fans, are going to watch fixed match. It is bad for the betting community because they really make their money on the Juice they charge gamblers. The betting community wants fair games that split the money evenly over the spread. Anything that shifts that is a problem for their business model. People believing that the games are fixed could reduce the amount of money bet on games. Also bad for the Bookies.

In the book Freakomonics by Levitt and Dubner, they expose match fixing in sumo matches using statistical analysis. It was a fun read and showed that analytics have the ability to expose cheating in sports without human intelligence. It was also a safe sport to look at because Americans do not really care about sumo nor do they bet on it.

It is interesting to me that there exists so much data and analysis with a goal of prediction on sport, but I have found nothing on using predictive analytics to discover point shaving or game fixing. I realize that doing this kind of work in team sports would be more completed than something like sumo. I just feel that it might be another tool to add to the effort to deter this kind of problem. At first pass it seems the most likely times there is potential cheating is when a players statistics in a game are an outlyer, and the team did not beat the spread. The problem that I see is the sparsity of known point shaving in games. For example I only know of one alleged fixed game in the NCAA basketball season in 2010 out of something like 5,000 games. I do not think that is enough to be useful. Sad to say that if there were more fixed games we might be able to build a better model. Someone suggest to me that I would get better data on game fixing if I looked at Italian football. However, I call it soccer and care Italian football about as much as I do about sumo.

So I have no data to present or model to put forth. I just hate cheaters, and this story has bothered me since it came out on April 11th.

3 comments:

  1. I wonder if Salford Systems' data mining could have helped... I went to this awesome presentation at the MIT Sports Analytics Conference in March.

    http://techtv.mit.edu/videos/11707-a-step-by-step-introduction-to-data-mining-for-sports-analysis---mikhail-golovnya-salford-systems

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  2. I have met with the Salford Systems guys a number of times at JSM. Random Forests and CART are great. I do not think they could predict an event like this on their own rather, but maybe as some part of an emsemble method like those the won the Netflix prize. Even then I have my doubts. Can you really train a model on such a rare event? In Sumo the advantage the modelers had was that these fixed matches happen in virtually every tournament and more than once a tournament.

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  3. Interesting news article about the San Diego point shaving that claims statistics support point shaving. I do not believe analysis in the article is very good. Looking how a team performed against the spread can be deceiving because how the money is spent across the line is an important factor in where the line is set. If the basis was used in isolation the Dallas Cowboys would always pop up as shaving points.

    http://www.fox5sandiego.com/news/kswb-explaining-usd-point-shaving,0,6197569.story

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