Part one of Ryan Nelson’s College Baseball Power Rankings statistical dive can be found here.
Part two of Ryan Nelson’s College Baseball Power Rankings statistical dive can be found here.
Through the first two parts of our Power Ranking Series, we covered our new ranking algorithm, Team Rating, and we then went deeper into offensive and defensive strength with Batting Rating and Pitching Rating. Now we are going to explore the capabilities and applications of our newly created metrics, the first of which will be measuring conference strength.
While there may not be a very analytically applicable use for conference rankings, it can certainly act as a good shorthand for strength-of-schedule. While it may not be easy to track the records, players, and stats of a team’s opponents, it can be easy to remember if a team’s conference is generally stronger or weaker than others. It can also be an entertaining exercise, as it can help fuel competition between teams of different conferences, a sort of Conference Cup, if you will. So without further ado, we can get started on the methodology, and then reveal the results and their implications.
Compared to the rather mundane and complex methods of calculating Team Rating, and its sister metrics Batting Rating and Pitching Rating, the Conference Rankings are actually very simple. If you average the Team Ratings of each team in a conference, you get the Conference Rating. It’s that simple! This method calculates the strength of a typical team in the conference. If we look at the rankings based on last year’s schedule, we see some surprises, but overall the results fall into the general preconceptions of most college baseball fans:[table “7” not found /]
We can see that our Top 5 consist of 4 Power Five conferences, as well as the American Athletic Conference. Some potential surprises for the casual fan could include that the Big Ten, which is fairly dominant in other sports like football and basketball, is at 8 in the rankings, and the fact that the Southland Conference, a relative non-factor in most other sports, is actually quite strong in baseball, sitting at 6 in our rankings.
One other fun application of these ratings is measuring the general run-scoring environments of each conference to determine if they are more offense-centric or pitching-centric. The way we do this is take the average Batting Rating of each conference and divide it by the Pitching Rating of each conference, and take the resulting number, scale it and standardize it, and we get a new metric called Run Environment Factor. The interpretation of this metric is this: a positive number means that the offenses of the conference are generally stronger than the pitching staffs/defenses, and a negative number means that the pitching staffs/defenses are stronger than the offenses. Additionally, the further the Run Environment Factor is from 0, the larger the disparity. It is a pretty simple concept, but it can help us determine if a conference is “hitter friendly” or “pitcher friendly.”[table “8” not found /]
Of the strongest conferences in our rankings, most tend to be pitcher friendly: the SEC and Big 12 are among the most pitcher friendly, the AAC and Pac-12 are both relatively pitcher friendly, and the ACC is fairly neutral, but even that conference leans pitcher friendly. On the opposite side, all of the 5 worst One way to look at this is that good pitching beats good hitting, and so conferences with good pitching will in general be the best conferences overall. An alternative way of thinking could be that there are fewer good pitchers to go around, so they go to the top teams in the top programs, where as there is a larger supply of skilled hitters that smaller teams can pull from. This means that the top teams have pitchers that can fend off skilled hitters, while smaller programs do not.
That pretty much covers the conference rankings thoroughly and ends this article, as well as the retrospective portion of our Power Rankings Series. Starting next week, we will post rankings for 2018, and analyze them on a weekly basis. We will also continue to develop new presentations and applications of this data, venturing further into team performance metrics, and breaking into player performance metrics as well.