FanPost

Baseball Thesis

Greetings denizens of Purple Row. I've been a constant reader, occasional poster on this fine blog for some years now, and this is my first fanpost. I'm heading into the last semester of my undergrad career, and I need an idea for my senior thesis. I was hoping the intelligent and imaginative folks here could help me, since I want my idea to center around Major League Baseball.



I'm an economics major, and I want my thesis to explore some statistical or econometric aspect of MLB. For example, Ross Ohlendorf, formerly of the Pirates (no idea where he is now; didn't Tulo conk him over the head with a line drive once?) did a study on draft pick compensation and how much value a team could expect to receive from their picks. I want to do something in that vein, in other words, formulate a question, gather some data, and test for correlation and statistical significance. Unfortunately, I don't have the foggiest idea what my question should be.

I've kicked around a few ideas in my head. For instance: what determines free agent salary and do teams receive fair value for their signs? Another: what factor does weather play on offense, defense, and pitching league-wide? Can we find statistical proof of the use of steroids affecting performance?

These are just a few ideas that I think are somewhat interesting, but perhaps not meaty enough to fill out a thirty page thesis. So I'm throwing this out to the very knowledgeable Purple Row community: what questions do you have about Major League Baseball, but don't really know the answers to? What study do you want to have done, but don't have the time to tackle? I'm open to any and all ideas, and would greatly appreciate some feedback. I'll let you know which topic I pick, and if you all are interested, I'll fanpost my thesis when I finish it next spring.

Eat. Drink. Be Merry. But the above FanPost does not necessarily reflect the attitudes, opinions, or views of Purple Row's staff (unless, of course, it's written by the staff [and even then, it still might not]).

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