After reading chapter three of The Signal and the Noise and without much mention of it on here yet I felt like this was a good opportunity to dive in. The relationship between statistics and sports let alone statistics and baseball is by no means nothing new. Statistics have been meticulously recorded in baseball for well over 100 years. As Silver mentions in chapter 3, the highly ordered format of mostly independent events has made baseball a haven for large data sets. However, the immense amount of unforeseen variables whether it be day to day events like a player nursing a hangover or long term injuries, a lot of challenges present themselves in predicting both future player and team success. I find it interesting how much baseball and biology can have in common when it comes to data.
In both biology and sports the way we analyze data can alter the conclusions we make. The interesting dynamic with statistics in baseball is the full circle effect that has become more prevalent over recent years. With increased popularity in fantasy sports, interest in baseball statistics has only grown and changed the way people watch baseball. As a billion dollar industry and with pressure to win, many organizations have turned to the statistics and advanced “sabermetrics” as not only a predictor of future prospect success but as a key role in game strategy.
A huge change in defensive strategy has been adopted by most of the MLB as recently as the last couple of years due to a suggestion from a sabermetrics study. Almost all teams have implemented the defensive shift as a regular approach, a strategy that was historically only used on rare occasions. There were 2,357 total shifts in 2012 and 8,134 in 2013 according to the MLB. I am interested to see further data on what effect the strategy has had on hitting.
As foolish as it sounds I think there are striking similarities to the cycle of events of some biological processes and baseball when it comes to stats. The statistics of baseball, especially when we talk about the defensive shift almost sounds like it can serve as a useful statistical model in science. For example in a biological setting like reversing climate change or preventing the detrimental effects of environmental contaminants on an amphibian river population.