"It's a lot easier being a drug dealer than an AAU coach" - this guy. Tell me something I don't know. I mean, don't think but have never tried either.
It's home-opener week, which seems like a great time to start looking at cumulative stats. This will be one of a few of these I do over the rest of the season.
Baseball has a long lived on the forefront of statistics in sports. From the heavy emphasis on batting over .300 or advanced sabermetrics, baseball's history is forever linked with teaching kids that not all math is useless. In honor of that longstanding tradition, today we look at some stats from our baseball team and then wonder what the hell they might mean. College baseball stats are not just loosely kept, but they fluctuate wildly over periods of time.
First, because of the nature of college baseball's shortened season (compared to major leagues), pitching statistics don't really offer enough data until very late in the season, if at all. There's just not enough to say about 17 innings of work for a starter or 8 innings, if that, for a reliever. So we're going to focus on just batting statistics in this and most future posts of this type.
Second, college baseball stats are very basic. There is no way to track pitches accurately without either a dedicated sports information director or someone at games. It's painstakingly, eye-gouging-ly monotonous to calculate batting averages with runners in scoring position. You have to hope your team has play-by-play on the bottom of their box score, and then you have to read through each at-bat, and all surrounding at-bats in order to come up with the raw data. Just to come up with the data that I have, I had to go through each box score and type in each statistic to have a game log for each player.
This is just the way things are.
The first thing I always like to post is a track on how our team batting average, on base percentage, and slugging percentage have progressed over the season.
This year I've tried to add a fourth line to represent the quality of competition Michigan has faced. The purple line represents RPI, with a team registering a 1.000 as the #1 team in the nation, a team at .500 being the 151th team in the nation, and a team scoring zero as the 302nd team in the nation, with the RPI coming from Boyd's World (in this case, the data was taken on Sunday 3/21). I felt this would help identify certain peaks and valleys as a reference.
Other than the realization that we've played a tough schedule this year, what jumps out to me is the lower slugging percentage. Last year, Michigan regularly slugged around .475. The last graphic I made last year was this one, 37 games through the season:
We're slugging just over .440 this season, where last season was spent hovering around .475. Sure, the competition has gotten a bit tougher, but something else seems spotty here. We'll look at the slugging percentage and other non-Excel visualizations after the jump…