Buying in Bulk

We talked at length in our short season pitching strategy and RP positional episodes of the Ottobot podcast about the importance of bulk relievers in this oddball season. We’re starting to get a sense of how teams are utilizing their bullpens, but we also have some actual pitching performance results to analyze. This post is ostensibly a post about one particular bulk reliever target, but it is also a post to discuss one tool in my arsenal I use to identify early season pitching targets.

As a known spreadsheet nerd, it probably won’t surprise you that this tool is a Google Sheet. It’s not as fancy or complicated as the ottoneu Surplus Calculator, but it is something I’ve found to have value to my process. To put it simply, I have a custom FanGraphs pitching leaderboard that I export to a .csv file, and then paste into Google Sheets. The purpose of the Sheet is to combine four different ERA estimators (in this case FIP, xFIP, SIERA, and kwERA) into one combined/consensus ERA estimation (that I call cERA). Why those four estimators? Those are the ones available on FanGraphs. Why average them together and not just choose one? Mostly because I’m a big proponent of aggregated projections, so I figured averaging all four estimators would help eliminate any outliers or biases that any one estimator would have, especially over extremely tiny samples.

After calculating the cERA for each pitcher season to date, the spreadsheet also calculates the league average cERA (in this case it is about 4.12), and then calculates a simple Runs Above Average (RAA) metric (league average cERA less player cERA times player IP divided by nine). I then use those RAA rankings to identify both players who have performed extremely well that might be available in my ottoneu leagues, and also identify players whose results have been worse than they probably should have been (to either trade for at a discount or scoop up as a free agent before the performances catch up and their price goes up). Our mystery target falls in the latter camp, as a player whose ERA isn’t particularly good right now, but whose performance has been among the best in the league so far.

Before I make my big reveal, here are some of the names in the top ten so far by RAA:

#1- Shane Bieber
#2- Trevor Bauer
#4- Kyle Hendricks
#5- Luis Castillo
#6- Carlos Carrasco
#7- Zack Godley
#8- Jacob deGrom
#9- Patrick Corbin
#10- Dinelson Lamet

With the possible exception of Godley, that list doesn’t offer much surprise. So who is our mystery player? None other than Jalen Beeks of the Tampa Bay Rays. Beeks has pitched in two games, with 5 IP 4 H 0 BB and 12 SO, but a home run allowed and a wild pitch has resulted in a 5.40 ERA. His cERA 0.03, however, mostly due to his ridiculous 12/0 strikeout to walk ratio. He has been letting the ball be hit in the air quite a bit (22.2% ground ball rate), so the long ball may continue to be an issue, but so far he’s commanding his pitches well and generating swinging strikes (24.1%). The Rays have been at the forefront of the opener/bulk reliever strategy, and Beeks has twice now come in to pitch multiple innings after a Rays starter has left after four innings. Last season Beeks threw 104.1 innings in 33 games and 3 starts, and I expect him to have a similar adjusted workload this year (so maybe 35-40 IP with one start?). The role has value, especially in ottoneu head to head leagues or in leagues where you find yourself behind pace of the innings cap, but if Beeks continues to pitch anywhere near as well as he has so far he could have standalone value as well. For now, Beeks is owned in just 10.75% of ottoneu leagues, and his average salary is $2.40, so he should be available cheaply (though act fast, he is up for auction in 26 ottoneu leagues as of the time I write this).

I will likely continue to highlight surprising names and targets on my cERA sheet in future articles, so stay tuned!


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