While sitting at my desk today, I started developing a somewhat satirical statistic to measure the overall value of a Homerun Derby participant (Rewarding strikeouts and flyballs, penalizing inside-the-park homeruns, and infield hits). But this got me thinking; What if we used advanced stats to try to predict the Homerun Derby champion? Derby pitchers have historically suffered from inflated FIPs, a trend that’s unlikely to change (Ha). However there are some statistical tendencies of the eight participants that I will be examining.
I frantically put together a spreadsheet when I got home from work of the 2012 Homerun Derby Standings, and the corresponding players’ statistics. The stats I used relate to the percentages of types of batted balls, and other rates of production, or lack thereof. On another tab, I listed the same statistics for the 2013 participants. I then used the 2012 data to find the correlation between the stats I selected, and the participant’s position in the HR Derby. I multiplied this correlation by the z-score for the player’s value for the statistic compared to the rest of the field, and gave them a score for each category. I then summed up their scores for each category to give them their total Home Run Derby prediction score (I’ll come up with a cool name for it later). I’m pretty sure that this is similar to linear weights by my impression of it. I didn’t have enough time to look up the correct way to create a score based on correlation, I just did what made sense to me, and I want to get this post out before the derby starts. Now, here are my results.
I’m going to record statistics for this year’s Homerun Derby for fun. I’ll be calculating “BABIP” and “Pitcher’s FIP” among other things. I’ll make a post with my “groundbreaking” results.