The time has come; near the midpoint of the baseball season, and in the midst of a hectic school semester, I could put off the baseball power rankings I’ve been meaning to generate no longer. College baseball statistics are sparse, far less specific than their professional counterparts - as far as I know, we don’t have Statcast, xwOBA, or exit velocity for the college game. Nonetheless, we can use a model to do what our eyes cannot - parse through a smattering of disjointed datum to identify trends and inform analysis. The initial power rankings are far from ideal, of course, but they work to establish a baseline, and they performed pretty well when I tested them last season - I predicted 7 of the 8 Omaha teams, and two of the final four. Below, I’ll briefly touch on the methodology, examine the overall rankings, and paint a picture of the Big 12 (and the region).
1. The Methodology
In my attempt to construct a meaningful model of college baseball team quality, I began with trying to find the most basic things teams do well. What do good teams in baseball do?
First, they limit runs. ERA/FIP/DRA what-have-you all isolate pitcher performance, and if I were to attempt to project individual performance, I’d be interested in those isolationist measures, but essentially, when trying to ascertain a team’s quality over a long fixed period of time and then project their quality over a short period of time, the individual will be less important. I want to know how many runs a team gives up per game. In college, this is important for a few reasons: it captures some of the unobservable characteristics of style - a high amount of runs allowed isn’t necessarily a complete picture of a bad team - what if they mash and win games 5-4, 7-6, etc? It also standardizes the ranking a bit in that instead of adjusting for style of play between conferences (different) and regions (really different), it captures the team’s potential for losses. A level of randomness in runs allowed is acceptable, as there is definitely a more controllable/replicable skill in preventing runs than in driving runs.
Secondly, a good team scores runs. More importantly, a good team puts itself in a position to score runs. Instead of simply doing run differential, I include On Base Percentage. A team that creates more scoring opportunities will score more, and a team that scores more is more likely to win more. (This has some friction, but the expected adjustment below accounts for that).
The third component necessary for college baseball evaluation includes the quality of opponent. There are plenty of ways to address this, but the simplest (in the spirit of the first attempt at a model), is strength of schedule. Due to the variation in conference depth and scheduling patterns, some standardized measure of how good your opponents are is crucial to a useful and reliable measure of quality. (A limitation - SOS statistics punish teams at the top and glorify teams in the middle of the pack, since those second tier teams are playing those first tier teams and getting a schedule bump, but again, it’s what we have.
The fourth and final component in the ranking acknowledges the physical parameters of success in the game. While the nine innings framework does not perfectly encapsulate how a team performed and all the breaks they got/didn’t get, it does provide the bottom line of success: a win or a loss. So, to that end, I include expected winning percentage, acknowledging that the tolerable non-constant variance of this and the strength of schedule will capture the randomness of wins and the ability of a team to win against quality competition.
For those of you skimming, the rankings are a formula of scoring opportunities, run prevention, strength of schedule, and expected wins (a variation on pythagorean).
2. The Rankings
Baseball Power Rank Top 25 (and 10)
|1||Texas Tech (Big 12)||1.835644|
|5||North Carolina (ACC)||1.766759|
|8||Coastal Caro. (Sun Belt)||1.736795|
|9||Ole Miss (SEC)||1.736063|
|14||Tennessee Tech (OVC)||1.714203|
|16||South Fla. (AAC)||1.705978|
|17||East Carolina (AAC)||1.705865|
|18||Wichita St. (AAC)||1.705217|
|20||NC State (ACC)||1.699101|
|22||Southern Miss. (C-USA)||1.695712|
|23||Indiana (Big Ten)||1.690575|
|24||Oregon St. (Pac-12)||1.690516|
|25||Texas A&M (SEC)||1.684262|
|26||Troy (Sun Belt)||1.673087|
|27||Florida St. (ACC)||1.665921|
|33||South Carolina (SEC)||1.647925|
|35||New Mexico St. (WAC)||1.64425|
Key outliers from the consensus:
- The metrics love Missouri. The Tigers (21-7) rank no higher than 22nd in any of the major polls, and sit at 22nd overall in RPI, yet my rankings have them in the top ten. The Tigers are in that sweet spot of playing a pretty decent but not too good schedule, and their underlying performance supports their record.
- North Carolina in the top ten, and as the best team in the ACC is... Something. The Tarheels are in the top forty of ERA, hit a whole bunch of home runs, and score a lot. The Ranking favors balance, and the Tarheels in theory are balanced.
- Tennessee Tech leads the nations in Home Runs - Home Runs are the most efficient way to score runs, and so that factors into the rankings. Still a little weird.
- Alabama? I mean, they’re Alabama. But it is baseball. I’m not sure. There’s definitely some residual weirdness in the rankings - as the season goes on, I expect convergence, but right now, clearly a little wonky.
3. The Big 12 at Large
The Big 12 has no surprises, really, in their rankings. The rankings have the Big 12 sorted as follows:
- Texas Tech
- Kansas State
- Oklahoma St.
That’s just about what you would expect, although that gap between TCU and Baylor is closing fast. The Frogs sit at 67th overall, which might even feel a little bit generous given the lousy play of late. I take it as a confirmation, a proof of concept, that these rankings have TCU so low.
I’ll update these rankings (and of course continue to tweak the methodology) as the season goes on, and once Omaha rolls around, we’ll get into the prediction game. For now, here is an impartial look at performance trends and teams in the NCAA.