I just put together some statistical leaderboards for the 2011 campaign so far. You can find these easily under the 2011 Leaderboard tab on the top right and they will be updated at my discretion (read: when I have time). In an ideal world, I’ll have these updated every Monday or Tuesday, but I make no promises on that.
As of right now, I’ve compiled the team Defensive Efficiency Ratio and data for individual hitters, starting pitchers and relief pitchers. If you’re looking for regular earned run average or a hitters batting average, or on-base percentage, then these leaderboards are not for you.
What I’ve presented, I’ll quickly run through.
What I’ve included is the percentage of plate appearances the hitter either strikes out, walks/gets hit by a pitch. Along with that, I’ve included a hitter’s isolated power which I have adjusted using these park factors from Boyd’s World. The data, unfortunately, is not adjusted for competition. As of right now, I’d have to calculate the strength of schedule adjustment on my own and, honestly, I’m not willing to really mess with that for the time being. I also have a Runs Above Average total for each hitter listed that is calculated using the Base Runs method.
I’ve broken pitchers into two leaderboards and three separate roles. For pitchers who start a game in 70% or more of their appearances, they are labeled as starting pitchers. For those who get a start in 40-69% of their appearances, I’ve dubbed them swingmen. Finally, those who start games in 39% of their appearances or fewer, they are relievers.
The stats I have for all pitchers are K% and BB%, like I do for hitters. These are the most important numbers to look at. Sure, I’ve got advanced data with an ERA replicate born out of the Base Runs method and then adjusted for park — from which I get the runs above average totals — but the meat of the data for pitchers is in the strikeout and walk/HBP totals.
I currently have Defensive Efficiency Ratio which is calculated just like it is at Baseball Prospectus. This is the best measure for defense at the major league level. I feel that this holds true — perhaps even more so — at the college level, as well.
For team hitting, I’ve included the teams K%, BB%, BABIP and park adjusted isolated power. Pitching, I have included K%, BB% and BABIP, but also give the teams’ traditional ERA as well as a base runs derived runs allowed metric.
I have a standings page as well. Here, you’ll find actual runs scored and allowed, estimated runs scored and allowed — derived, again, from the Base Runs method — and the team’s actual winning percentage, predicted win percentage and estimated win percentage. The difference between the three are explained on that page itself.
I’m stealing the format of this first post from a fellow Michigan State athletics blogger, Kyle, when he ran Spartans Weblog. Since that post, Kyle’s moved onto authoring The Only Colors which is largest Michigan State sports blog on the web, with a major focus on basketball and football — the “money making” sports, so-to-speak.
What does that have to do with me? I’ve been a fan of Minor League Baseball and Sabermetrics for a few years. I was a forum-ite at Project Prospect and through that, fell into adjusting college baseball statistics.
To keep a long story short, I was engrossed by the 2010 College World Series and continued to update my college baseball statistics, and figured the best way to learn more about college baseball was combine it with my love of Michigan State athletics.
What you should absolutely expect from this blog: statistical analysis for Michigan State and the Big Ten as a whole. Both on the mound and offensively, as well as at the team level.
What you might see on the blog: Timely news updates, links, and anything else pertaining to the Spartans baseball team. Like Kyle, I have a vision of updating this as much as possible with unique analysis not found for any other college baseball program in the country.
Now, there are major caveats with this type of analysis. I am limited to certain data sources to help in my adjustments, and that’s Boyd’s World. Also, keep in mind that the sample sizes for a full Major League season are small in the grand scheme of things. College baseball are roughly 1/3rd of a major league season and, at most, when you include the postseason, come out to around half of a Major League season. So the analysis won’t be perfect, but I’m going to do my best to show, with the data and limitations who the best teams, pitchers, and hitters in the Big Ten really are.