Skill-Interactive ERA (SIERA) is the newest in a long line of ERA estimators. Like it’s predecessors FIP and xFIP, SIERA attempts to answer the question: what is the underlying skill level of this pitcher? How well did they actually pitch over the past year? Should their ERA have been higher, lower, or was it about right?
But while FIP and xFIP largely ignore balls in play — they focus on strikeouts, walks, and homeruns instead — SIERA adds in complexity in an attempt to more accurately model what makes a pitcher successful. SIERA doesn’t ignore balls in play, but attempts to explain why certain pitchers are more successful at limiting hits and preventing runs. This is the strength of SIERA; while it is only slightly more predictive than xFIP, SIERA tells us more about the how and why of pitching.
Here’s what SIERA tells us:
Strikeouts are good…even better than FIP suggests. High strikeout pitchers generate weaker contact, which means they allow fewer hits (AKA have lower BABIPs) and have lower homerun rates. The same can be said of relievers, as they enter the game for a short period of time and pitch with more intensity.
Also, high strikeout pitchers can increase their groundball rate in double play situations. Situational pitching is a skill for pitchers with dominant stuff.
Walks are bad…but not that bad if you don’t allow many of them. Walks don’t hurt low-walk pitcher nearly as much as they hurt other pitchers, since low-walk pitchers can limit further baserunners. Similarly, if a pitcher allows a large amount of baserunners, they are more likely to allow a high percentage of those baserunners to score.
Balls in play are complicated. In general, groundballs go for hits more often than flyballs (although they don’t result in extra base hits as often). But the higher a pitcher’s groundball rate, the easier it is for their defense to turn those ground balls into outs. In other words, a pitcher with a 55% groundball rate will have a lower BABIP on grounders than a pitcher with a 45% groundball rate. And if a pitcher walks a large number of batters and also has a high groundball rate, their double-play rate will be higher as well.
As for flyballs, pitchers with a high flyball rate will have a lower Homerun Per Flyball rate than other pitchers.
Finally we have a stat that A) is accurate and predictive, and B) accounts for some of the complexity of pitching.
SIERA is on a similar scale to ERA, so any score that is a good ERA is also a good SIERA. Please note that the following chart is meant as an estimate, and that league-average SIERA varies on a year-by-year basis. To see the league-average ERA for every year from 2002 to the present, check the FanGraphs leaderboards.
In general, relief pitchers have lower SIERA scores than starting pitchers. As a handy shortcut, a pitcher that switches from starting to the bullpen will on average see their SIERA drop by 0.37 points (and vice versa).
Things To Remember:
● Interested in calculating SIERA yourself? Good luck. But if you want to try, here’s Matt Swartz’s formula and explanation.
● As always, when evaluating pitchers, it’s best to use multiple statistics instead of relying on one alone. While SIERA is the most accurate of the ERA-estimators, it’s only slightly more accurate than xFIP. Both xFIP and FIP still have their uses, so I wouldn’t recommend ditching them entirely and using only SIERA — a balanced approach is always best. You can learn a lot about a pitcher by looking at which metrics like and dislike them, and for what reasons.
● In and of itself, SIERA works as well as many projection systems in terms of predicting a player’s future ERA. But be careful of this distinction: SIERA is technically a backward-looking ERA estimator and not a forward-looking projection system. If you want to turn SIERA from an estimator and into a projection, you can follow the general formula laid out by Matt Swartz in this piece.
● SIERA is park-adjusted, meaning it adjusts for the fact that some pitchers play in PETCO Park and others in Yankee Stadium.
●SIERA is updated for the new (low-scoring) run environment around the majors.
Links for Further Reading: