Thursday, March 04, 2010

Sports Analytics Evolution: Framework to compare analytics usage across fields

Analytics magazine, published by INFORMS, has an article in the February 2010 edition that talks about the drivers that lead to the maturity of the sports analytics field in the last few decades. I thought it is a great framework for the maturity of statistical analysis in sports. According to James J. Cochran, Louisiana Tech University, the following are 4 profound changes that have happened in the last few decades in sports analytics.
  1. Availability of data: Early on the data that was available on a player could fit in a trading card. Now data is available down to each pitch at each game - who was the pitcher, what type of pitch was it, what was the speed of the pitch, whether the batter was batting right handed or left handed, what was the result etc.
  2. Electronic storage of this data together with incredible computational power: This has allowed for incredible level of analysis. A while back just free throw percentage was available for a player. Now statistics include PER (Player Efficiency Rating), DWS (Defensive Win Shares), USG% (usage percentage) etc.
  3. More people started applying their brain power to these analysis including Bayesian statistics, mathematical programming, and multi-variate statistics to gain insight. You can figure out how much each player has influenced the win-loss ratio, for example.
  4. Management and player decisions started relying on these rather than just on gut feel. This was made popular first by the Oakland Athletics. They utilized it very well in scouting for new players, when to trade a player because even though a player is very productive, his contract or future salary expectation is not worth the production etc. Curt Schilling is famous for his preparation using "data analysis" which turned him from an average pitcher to a great pitcher - thoroughly studying opposing hitters, watching for tendencies he could exploit on the mound, what he had done before and how the hitter had reacted to it.
The same kind of maturity has happened in a lot more fields: credit risk analysis, retailer segmentation of consumers, equity analysis, etc.

I thought this is a very nice framework to compare the maturity of statistical analysis in different fields.

Thoughts?

Karthik

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