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In Part 8 of his fantasy baseball strategy series to make sabermetrics accessible to fantasy owners, Rick Lucks breaks down how to use FIP and xFIP to identify pitching sleepers and busts.
The first advanced pitching stat most fantasy owners encounter is FIP. FIP stands for Fielding Independent Pitching, and attempts to measure a pitcher's actual skill instead of the effects of luck or his supporting cast. According to the DIPS theory that the metric is based upon, pitchers control only Ks, BBs (and HBP) and home runs allowed. Therefore, Ks, walks and dingers are the only inputs to determine the number.
Calculating FIP requires way more math than most fantasy owners are comfortable with, so don't worry about the formula. For fantasy purposes, it is sufficient to understand the three primary inputs listed above and the fact that the stat is on the ERA scale. That means that if a FIP would be a good ERA, it is a strong FIP. The math is perfect, meaning that the league average FIP and ERA are identical (4.36 in 2017).
Sometimes xFIP is cited instead of FIP. The x stands for expected, and the stat is rooted in the fact that HR/FB varies for pitchers just as much as hitters. While FIP uses a player's actual homers allowed, xFIP charges him with a league average amount of homers based on his fly balls allowed. Some pitchers are consistently more or less homer-prone than average, but studies show xFIP is a more reliable predictor of future ERA than regular FIP.
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How to Use FIP and xFIP to Draft and Manage Your Team
This predictive nature of FIP and xFIP is the reason fantasy owners should care about them. Both metrics predict future ERA more reliably than ERA itself, making them a good go-to stat to determine if an early breakout may be for real or if a struggling superstar is likely to rebound. All things being equal, it is generally expected that a pitcher's ERA will regress towards his current FIP and xFIP over the long season.
For example, some panicked when Trevor Bauer got off to a bad start in 2017 with ERAs of 6.26 and 5.82 over the first two months. FIP and xFIP could have reassured them, as his FIPs (4.46 and 3.86) and xFIPs (3.49 and 3.03) in the same time frame suggested that Bauer was actually approaching his potential. He went on to post a dominant second half (3.01 ERA) en route to a solid season (4.19 ERA, 3.88 FIP, 3.60 xFIP). Advanced metrics helped prevent Bauer's owners from dropping him too early.
There are certain types of pitchers that may consistently defy FIP. The first is knuckleball guys, who have challenged DIPS theory since its introduction. Atlanta's RA Dickey had a 4.26 ERA last year despite a considerably higher FIP (4.72) and xFIP (4.79). Regression should not be expected however, as he did the same thing in 2016 (4.46 ERA vs. 5.03 FIP and 4.76 xFIP), 2015 (3.91 vs. 4.48 and 4.72), 2014 (3.71 vs. 4.32 and 4.14), etc. For Dickey and other knuckleballers, there is no need to bother with FIP.
The other type is simply called a "FIP-beater" that manages to control the quality of contact against him to the point that he outperforms his peripheral stats. Johnny Cueto has been a poster boy for this group for a while. He posted a sterling 2.25 ERA in 2014 before following it up with a solid 3.44 mark the next year. The 2016 season saw Cueto return to ace status with an ERA of 2.79.
Sabermetricians never saw Cueto that highly, however. His 3.30 FIP and 3.21 xFIP in 2014 made that campaign's 2.25 ERA look like a fluke, while his regression in 2015 (3.44 ERA, but 3.53 FIP and 3.78 xFIP) seemed like a harbinger of things to come. His sterling ERA in 2016 (2.79 ERA) was again undermined by considerably larger FIP (2.96) and xFIP (3.42) marks. Many analysts projected his demise in each of these years only to be proven wrong.
In 2017, they were proven correct. Cueto struggled to a 4.52 ERA, with a FIP (4.49) and xFIP (4.45) to match. Some are blaming health concerns for Cueto's struggles, but I struggle to see the upside in doing so. Strikeouts are a key component of FIP, so pitchers who defy it are still lacking in a common fantasy category. Why risk another poor ERA for two category upside?
Personally, I'm leery of anyone's ability to consistently defy FIP, knuckleballers notwithstanding. Matt Cain's story is very similar to Cueto's, and we know that it did not have a happy ending. There is an ongoing debate in the sabermetric community though, so my word is not gospel on the subject.
To conclude, FIP and xFIP are metrics that try to determine the ERA a given pitcher deserves based only on the outcomes he actually controls: Ks, BBs, and home runs allowed. While FIP uses the pitcher's actual homers allowed, xFIP regresses it to the league average figure. Both metrics are on the ERA scale, and may be used to predict future ERA with more accuracy than ERA alone. Of course, we can also predict how some of the "luck" that separates ERA from FIP will play out. BABIP for pitchers will be discussed in Part 9.
