Statistical Updates

I’ve neglected this blog over over a full calendar year. With the NCAA Super Regional’s currently on television, now is as good a time as any to do my first Big  Ten Conference data dump/season recaps in over a year.

I’ve tweaked my data (again) to what I feel comfortable with. If you’d like to get a bit nerdy, continue reading down after this paragraph. If you don’t, just know that I’m using the following measures for offense, defense and pitching.

Offense – I’m using Weighted On-Base Average, or wOBA. It uses linear weights to give each type of outcome from a plate appearance a run value. You multiply these values by the number of times each outcome occurred and divide by plate appearances. This will give you a team’s (or player’s) wOBA. The nice thing about wOBA is the ability to easily convert it into a runs above average metric dubbed Weighted Runs Above Average or wRAA. Additionally, you can take it a step further and calculate Weighted Runs Created which is adjusted for league average — or wRC+. This number is the same scale as OPS+ where 100 is league average and each point above/below 100 is equivalent to one percentage point. Why do this? Because wRAA is a counting stat, wRC+ is not. This allows us to compare how well a player performed offensively in his 100 plate appearances to a player who had 180. While the wRAA may favor the latter player, wRC+ will help us decide who actually was better. It takes playing time essentially out of the equation.

Defense – I haven’t changed my calculations on defense at all since my last postings on this space. I am using Defensive Efficiency Ratio because it’s the best team-defense metric at the Major League level. Given how little data we have for the Big Ten compared to the MLB, Defensive Efficiency Ratio is almost undoubtedly the best defensive metric available for the Big Ten. I calculate it just as Baseball Prospectus does and put it on the “plus” scale, where 100 is league average. This allows us to compare across various seasons.

Pitching – I changed my metric over to Fielding Independent Pitching, or FIP.  From here, we can calculate a runs above average number for the individual pitcher or team in question quite easily. It attempts to remove defense and focus on things a pitcher can control. However, I haven’t studied whether FIP is a reliable number in college baseball. I’m pretty certain that the theories behind defensive-independent run estimators like FIP hold true at the college level, it’s just not entirely certain. From here, I’ve calculated an Expected FIP (xFIP) of sorts. Instead of normalizing home-run-to-fly-ball-ratio like Fangraphs does, I’ve substituted a league-average home-run-per-contacted-ball percentage for each pitcher. I don’t know how much I’ll present an xFIP data because the lower you move in the baseball ranks, the more a pitcher’s abilities to prevent home runs and induce weak contact increases. It’s a little cross-checker of mine to help me contextualize the data a bit more.

The nerdy part: I’ve calculated custom linear weights by using Tom Tango’s Markov Calculator. For FIP, I calculated custom FIP weightings based on this blog post by Tango called Deconstructing FIP.

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