Stats Made Easy

Practical Tools for Effective Experimentation

Sunday, December 16, 2007

Sports ‘randomination’?

In a recent guest post to the Freakonomics blog, Yale economist Ian Ayres suggests that sports teams run randomized experiments to improve their winning ways. He solicited feedback from anyone who has done such an experiment, but so far no one has come forward with an affirmative post to this blog.

It turns out that I performed an experiment back in my days as a slow-pitch softball player. At that time my position was ‘rover’ – a tenth man who augmented the normal three who play the outfield in baseball. Depending on my whim, I would play ‘long’ – I line with the other outfielders – or ‘short’ – nearer the infield in a gap where I guessed a batter might want to drop a hit. It occurred to me that by randomly positioning myself inning-by-inning from one game to the next and measuring the oppositions success I might develop statistics that would show favor to either short or long as a general practice. Our team was originally sponsored by General Mills Chemical Technical Center, so my mates met this proposal with surprising enthusiasm. After all, our prospects for winning in our Class D (lowest level) league were never very good.

As Yogi Berra said, “We were overwhelming underdogs.”

One thing we could count on is that during any given inning, the opposition would be sure to achieve some hits, if not runs. However, it seemed likely that while playing short might cut off singles, it would lead to more doubles and triples from players knocking the softball over my head. Therefore my teammates and I agreed that total bases would be a good measure of success. Thus we counted a single as one, a double as two and so forth. (If you are not familiar with the game of baseball, see these simplified baseball rules from Wikipedia). Since opponents varied in their quality of play, I laid out a randomized block experiment game-by-game (results in first graph -- the points labeled "2" represent two innings with the same total bases).

As the experiment proceeded I assessed the results after each game to see if the cumulative data produced a significant outcome. Patience proved to be the key. During one particularly bad inning – 16 total bases by the opposition – my fellow outfielder screamed at me to abandon my proscribed position and go to the opposite choice. “We are sure to lose,” he yelled – ready to knock sense into my statistically-addled brain. However, my teammates stepped up to protect me and my experiment. “Yes, we may lose this particular game,” said they, “but from what we learn our team will win more games over the course of this season and ones to come.” Indeed we did: From the knowledge gained from this experiment and other strategic moves, our team of techies went on to win our Class D league the following season. True, we did get decimated in the first round of the State Tourney, but at least we got there!

PS. As shown on the second graph, positioning myself short in the outfield proved to be significantly better.


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