In last week’s Pitching by the Numbers, we looked at the best pitchers (who qualified for the ERA title) according to a pitching model I have used for projection purposes.
We listed the top 20 and noted others, which inevitably led to readers asking me in the comments and on Twitter (@michaelsalfino) about where their specific pitcher of interest ranked. But by then, the rankings had changed. So I hesitated to quote the old rankings.
To address these inquiries, I’ve updated the stats through April 29 and listed all qualifying pitchers below. These pitchers all had at least one inning pitched per team game played. Also, note that this is a bonus Pitching by the Numbers. Regularly scheduled PBTN programming will resume on May 3.
The reason why I like this model is that it correlates so well with actual fantasy performance. Why not just use fantasy stats then? Because then we have no outliers. Ideally, we want to use a model like this to find a handful of cheap buys and another handful of guys we can sell at a profit (because they are likely overachieving and/or are viewed as being better than they’ve actually pitched). Yes, I could use other stats. But ISO allowed is something I uncovered last year for this purpose and I like it a lot. Guys who are good in it allow cheaper hits that are more BABIP variable. And they also force teams to compile more hits to score runs. I’ve been using K/9 and K/BB for all my baseball life and thus am very comfortable with reading them.
The test of a model is whether you would draft based only on it if you only knew where the pitchers would end up ranking in only the model. I definitely would. Of course, it wouldn’t work every time. Some guys would just be unlucky in things like runners left on base or the sequencing of hits or bullpen support or a number of other factors. But the vast majority of times, it would work out very nicely.
The other caveat of course is that pitchers given the relatively small sample of innings thus far in 2013 may not continue to perform as well or as poorly in these key stats. I will stipulate to that, especially with ISO, which is most sample-size influenced. But the current stats are all we have and this makes the best use of them. I believe the rankings speak for themselves. But feel free to ask me questions, especially on Twitter where I’m sure to see them quickly. “Power” is simply the total number of ranking points in each category, with lowest number being best.