For those of you who have read Michael Lewis’ “Moneyball” or have seen the theatrical version, you are at least vaguely familiar with how quantitative analysis can be used in baseball. The concept is quite intriguing once you actually get passed the basics and take a bite of some real data. While quantitative analysis gets me about as excited as Kate Upton in a Hardee’s commercial, I’ll try to keep it simple for those of you who don’t have half a chubby right now (talk nerdy to me). In layman’s terms it all boils down to putting a large amount of data into a spreadsheet and analyzing which bits of data matter and which do not. Those that do matter are called statistically significant and their significance can be measured accordingly depending on how much data you have and how much error you are willing to allow. No, you don’t have to pay Professor Nick for this free statistics lesson, but I do hope you see the value in this concept.
Bear with me as I explain first how I arrived at my findings, then precisely what they mean. I used this method to attempt to map out what exactly matters to a baseball team trying to make the playoffs. I listed all 30 Major League teams and attached a “1” to those teams that made the playoffs last season and a “0” to those that did not. This allows me to use it as a dependant variable and determine which statistics give teams a better chance at participating in the postseason. For independent variables I recorded each team’s 2012 payroll, runs scored, batting average, OPS (On Base % + Slugging %), ERA, Pitcher’s K/9, and errors. While I understand that there are a multitude of other factors that could be thrown in the mix, I wanted to keep it fairly simple and avoid causing myself to have to do an excruciating amount of data mining. Keep in mind that a much more exhaustive list of factors would have modified the results somewhat. What I discovered upon running the regression analysis was rather interesting.
Some, if not most, would argue that all of the aforementioned factors contribute to a team’s chances of making the playoffs. The fact of the matter is that they could not be more incorrect and the data proves that. My research (albeit not extremely in-depth and on a college student’s budget) led me to find that only 3 of these factors were statistically significant. While it may be counterintuitive, payroll did not matter in the slightest, nor did runs scored. The only factors I examined that legitimately contributed to a team’s odds to make the playoffs were OPS, ERA, and pitcher’s K/9. Let me lay it out for you. If a team were to increase their team OPS by .100, they would have been 12% more likely to advance to the postseason. One more K per 9 innings over the course of the season would have made a team 9% more likely to advance, and an ERA decrease (like in golf, lower is better) of 1 run per game would have up’d a team’s chances by.. wait for it.. 30 Martha Focking percent! The lesson to be learned here is that pitching, more than hitting, gets teams into the playoffs. If you would like to speak with me about my findings, ask questions, or sign up to be my Rachel Dawes then illuminate the Bat Signal immediately! You’re welcome for rocking your world to anyone who was able to read through all this. (Notes: 2012 MLB season ONLY, Multiple R value of .724, no animals were harmed in the construction of this article)
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