Buying Low on Hitters Using xwOBA by Alex Chamberlain June 4, 2018 There are, like, a dozen articles of this nature written daily — that is, “buy-low” candidates using some kind of xMetric, likely derived from Statcast. That’s fine. I’m not hating on it. This was my modus operandi when I first started writing at RotoGraphs, and it’s how I really started to understand the cyclicality of player performance and the differences between descriptive and predictive metrics. Speaking of which, I have no desire to rehash the “what xwOBA should really represent” discussion that consumed the sabermetric sphere a week or two ago. (Although, for reference, I’ll link you to Baseball Prospectus, MLBAM’s Tom Tango, and FanGraphs’ Craig Edwards.) Primarily, I want to provide some facts about xwOBA followed by some non-facts about how I use xwOBA to keep my biases in check. There are two important tenets to xwOBAism. At the player level, wOBA does not always converge on xwOBA… in a given season. over the course of a career. Distribution of wOBA Minus xwOBA Since the start of the Statcast EraTM in 2015, 431 hitters have recorded player-seasons of at least 500 plate appearances. The following graph depicts the distribution of those seasons in terms of wOBA minus xwOBA (wOBA-xwOBA), which is featured as a filter in Baseball Savant’s Statcast search, binning each wOBA-xwOBA differential in +0.005 wOBA bins: The distribution is pretty normal. Slightly more than half of full-time players outperform their xwOBAs. The slight skew is likely attributable to selection bias: elite hitters who outperform their xwOBAs as well as those can perform unexpectedly well for an extended duration will naturally see the lion’s share of plate appearances. The ones who slump get benched, this risking not meeting the 500-PA threshold to begin with. A standard deviation is about +0.020 wOBA, making two standard deviations — accounting for roughly 95% of all player-seasons — about +0.040 to +0.045 wOBA. Most players will fall within these bounds, and the ones who don’t are likely to be skills-based outliers. For example, the players who, on average, outperform their xwOBAs by the greatest margins: Dee Gordon, Jonathan Villar, Ender Inciarte, Charlie Blackmon, Xander Bogaerts, Jose Altuve, etc. The simplest characterization of these hitters is they’re speedsters capable of legging out infield hits and taking extra bases when able. Even Derek Norris appears on this list, which seems strange, but it’s valid: he’s 12th among qualified hitters in infield hit rate (IFH%) from 2015 through 2017. At the other end of the spectrum, baseball’s biggest xwOBA underperformers include Miguel Cabrera, Ryan Howard, Kendrys Morales, Albert Pujols, Mitch Moreland, David Ortiz, etc. — players with, uh, less-than-ideal body types (coinciding with woeful speed scores). To be clear, I’m not formally declaring that speed explains all wOBA-xwOBA outliers, but it does fit the narrative snugly. This is kind of a roundabout way of saying that using wOBA-xwOBA should be a two-pronged approach: looking at the largest differentials, but also understanding which players naturally over- and underperform their xwOBAs (and adjusting any regression expectations accordingly). Hitter Arbitrage My preseason list of mid-round value bats is littered with xwOBA underperformers: Kole Calhoun, Jason Kipnis, Adam Duvall, Carlos Santana, and Jay Bruce have five of the 16 worst differentials (among 213 hitters with 150+ PAs this year). That’s not to say there’s a light at the end of the tunnel for all of them; Calhoun, for example, and his xwOBA of 0.276 is legitimately miserable. But he has generally been a breakeven wOBA-xwOBA guy for three years, and no one hits with a .185 batting average on balls in play (BABIP) forever. He just hit the disabled list, which is unfortunate timing, but I scooped him up for free in my Great Fantasy Baseball Invitational (TGFBI) league in anticipation of his inevitable batting average regression. It’s not super sexy, but if I can capture Calhoun batting .280 for a couple of months, even if it barely brings his season average up to the Mendoza Line, then I’ll have wrung some value out of a free asset. I’ll outline the remaining “buy-low” candidates more formally here, with their wOBA-xwOBA differentials in parentheses. I’m relying on the differential distribution heavily here. Ninety-five of hitters fall within 45 points of their xwOBA, and only three hitters (0.7%) have finished 60+ points worse than their xwOBA (Cabrera twice and Morales once). Thus, it’d be near impossible for any of the following hitters to continue performing so poorly. They’re much likelier to finish their respective seasons with smaller differentials. Even if they suffer some bad luck in regard to closing the gap between their wOBAs and xwOBAs, they should still rebound to some extent. Jason Kipnis, CLE 2B (+0.001 wOBA-xwOBA): He’s a “true-talent” breakeven wOBA-xwOBA guy whose 0.344 xwOBA comes within swinging distance of his peak production. He’s never going to hit 20 homers again, but he’s also probably better than a .238 BABIP. His -0.086 differential suggests there’s a 40- to 80-point wOBA surge looming. Adam Duvall, CIN OF (+0.011): This is going to get boring quickly. Duvall’s not a .191 BABIP guy, period. He’s hitting for his usual 30-homer power while exhibiting his best contact skills to date (not to mention a double-digit walk rate!). His -0.078 differential and history of breaking even on his differential suggests, like Kipnis, there’s an equally large correction awaiting us. Carlos Santana, PHI 1B (-0.016): Honestly, Santana’s peripherals are about as good as they’ve ever looked. This is another low BABIP situation, and it’s possible it doesn’t fully correct. Frankly, that’s possible for any of these hitters. But with annual differentials of -0.018, -0.018, and -0.013, at least Santana is consistent in his underperformance. He could hit 30 home runs this season and pick up another 30 points of batting average along the way. Jay Bruce, NYM OF (-0.010): Whoa! Something different! Not a BABIP problem, but a home run problem. His HR/FB rate is less than one-third of his career rate and barely one-fourth of the rate he sustained in his two recent 30-homer campaigns. Now, Bruce has dealt with plantar fasciitis this year, and we’ve seen how the ailment has made Pujols looks so painfully human in the twilight of his career. Bruce is not nearly as old, although sometimes it seems like it — 31 years old and already in his 11th season — so he could probably recover a little more gracefully. But it’s worth keeping in mind that Bruce isn’t a surefire rebound candidate. Still, he’s worth speculating on a crazy home run streak if you can afford it. The boring part about this kind of analysis is, at the most fundamental level, a lot of these guys are simply underperforming by BABIP. I feel like I’m picking the lowest-hanging fruit: look at all these hitters with huge xwOBA disparities! Yet this endeavor must be worthwhile; all of these guys have seen their ownership tank relative to Opening Day. It seems fantasy owners are still quick to ascribe talent to a small-sample BABIP without realizing it (that’s what cognitive biases do — obscure our better judgment). So, maybe it’s not so boring. Maybe it’s a friendly reminder to not quit on the guys who are slumping profoundly. I mean, some of them are worth abandoning. But I think many of them, including most of the names I mentioned here, will be among the second half’s hottest hitters. (I acknowledge that this approach assumes player peripherals remain constant, such that current xwOBA levels will not fluctuate moving forward. They will, of course, and it’s possible all of these players’ xwOBAs converge on their bad wOBAs rather than the other way around. I’m betting against it, though, given the xwOBAs for most of them — Kipnis, Duvall, Santana — more closely resemble their wOBAs from previous seasons and their current peripherals suggest nothing is even slightly wrong, let alone egregiously so. Calhoun, obviously, is a trickier, more desperate bet.) All said, the most critical point of this exercise was to understand (1) the distribution of wOBA-xwOBA differentials and (2) individual true-talent xwOBA marks, and how the two of them in tandem might explain a player’s probability or capacity to rebound midseason. I’m buying tons of shares of Kipnis, Duvall, Santana, et al., some of whom are literally free (or a cursory $1 FAAB bid) on the waiver wire, even in 15-team leagues. (And these are far from the only hitters primed to bust slumps this summer.) You might consider doing your own fair share of speculating if your roster allows it.