I will be posting hitting, pitching, and defensive updates for each team in the Big Ten to recap their 2012 seasons with the proper scrutiny. Those are linked at the 2012 Season Recaps tab at the top right. I have no planned schedule on when I will have them posted, but the order is going to be alphabetical. Their timing will likely be sporadic, however.
The Fighting Illini finished the 2012 campaign three games over .500 at 28-25. Boyd Nation currently has Illinois ranked 96th overall. Despite this, the Illini finished just 11-13 in conference play, in a tie for sixth place which left them on the outside of the Big Ten Tournament invitees. This is a recap of their hitting as both a team and individuals.
As a Team
On the whole, the Illini were middle-of-the-road offensively. Their 309 runs on the season were the sixth most in the conference. Illinois posted a 0.326 Weighted On-Base Average (wOBA; refer to this post for more links on what wOBA is) which was a shade over the 0.320 conference average. That gives Illinois seven runs above average offensively which was actually the fourth-best mark in the Big Ten.
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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.