Ranking Small Sample Pitchers

A couple of weeks ago, I examined four starting pitchers using a small sample of predictive data. It was a half-hearted comparison. I’m correcting that today with a simple FanGraphs download to spreadsheet comparison.

I decided on the stats to use based on work I did for The Process. Here is a look at what early-season stats are most predictive of rest-of-season stats (R-squared values).

I used the two overall STUFH values (BotOverall, Pitching+) and the top two ERA estimators (SIERA, xFIP). Additionally, I wanted to weigh the stats that immediately stabilize (Ball%, SwStr%, Fastball velocity).

One major change, I put all the values on an ERA scale for easier comparison. Three values came in an ERA format, but the others need to be calculated. Here are the formulas I used (also in the provided spreadsheet).

• Pitching+ ERA: (Pitching+) * (-0.067) + 10.815
• SwStr%-Ball% ERA: (-19.4472)* SwStr% + 9.985324 * (Balls/Pitches) + 2.672178
• Fastball Velo (FBv) ERA: FBv * (-0.1416) + 17.339

Here is a link to the values along with the pitchers featured in this week’s waiver wire column. I limited the time frame to just the past two weeks. I use a method that makes the best decision with a small sample.

Not all the values are in the download, so I created a copyable spreadsheet (File, Create a Copy) to paste the pitchers’ stats (cell L4), and then the various ERA values are calculated along with an overall value.  The average ERA can be ordered by using the promote option in cell J4.

In this instance, Brandon Walter stands out with everything looking great, except his 92 mph (4.32 ERA) fastball. He could bomb, but the goal is to make the best decision with little information.

One possible change is to adjust the weightings. I equally weighed the values. Someone can always give more weight to certain values (Pitching+) or just remove others (FBv). For me, the table allows for easy comparison of pitchers. If I were to evaluate each pitcher, I’d look at the above stats. This makes the analysis easier.

Two additions could be included: the first is to add a projection based on the preseason prospect rank (overall and team), the other one is to include the pitcher’s ERA projection (via xlookup).

Let me know if you have any questions on setting up the spreadsheet to help evaluate pitchers with a small MLB data sample.