Disclaimer: This is just for fun. I am, by no means, claiming that the predicted average draft positions (ADPs) described below will happen. Obviously! I’m no prophet. Also, I am not claiming these predictions are merely educated guesses. In fact, these aren’t even my predictions — they’re yours. Or, well, they’re not your predictions — they’re my computer’s predictions, but fitting your behavior to observed events.

That’s a complicated way of saying: by using historical ADP data and end-of-season (EOS) values, we can model future ADP values. (xADP, if you will.) Namely, with 2018 EOS, 2018 ADP, and 2017 EOS, we can predict 2019 ADP — and explain almost 60 percent of its variance (adjusted r² = 0.59).

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I use xwOBA as a leading indicator of good or bad things to come mid-season, for better or for worse. It’d be good to know if such reliance is truly warranted. I further talked myself into the idea when I wrote about several underperforming hitters in early June. Many of the names therein went on some serious heaters afterward, too. It wasn’t as prescient as it was playing the odds: the hitters underperforming xwOBA most extremely through two months always, always (in the Statcast Era^TM) bounce back to some degree.

It’s “predictive,” but not universally so, and only by virtue of common sense, in the same way a pitcher who allows a sub-.200 batting average on balls in play (BABIP) through two months could not reasonably sustain this high level of contact management. (There’s a discussion to be had here about the gambler’s fallacy, but I don’t think it necessarily applies to baseball. For another day.)

In terms of prior work, it’s all Baseball Prospectus‘ Jonathan Judge (only a slight exaggeration): he compared xwOBA to BP’s DRA metric as well as FIP (fielding independent pitching), a much simpler ERA estimator, and showed xwOBA is hardly superior to the field, at least for pitching. However, the article only covered year-to-year, not in-season, correlations.

After our dear and departed (but not dead) Eno Sarris asked Judge if he had looked at in-season correlations specifically, and after our dear and departed (and also not dead) Mike Petriello reinforced the notion that xwOBA could serve as an in-season predictor of regression under certain circumstances, I figured it’s high time I just tackle the question.

So: How predictive is xwOBA of wOBA in-season? For hitters and for pitchers?

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I think there are not one, but many, questions because there are not one, but many, ways Adalberto Mondesi and Byron Buxton are similar.

Here’s one answer to one possible question:

who has the best fantasy season in 2019?

— Alex "Oxlade" Chamberlain (@DolphHauldhagen) August 29, 2018

I can’t say I’m surprised, but I’m kind of surprised. I asked this question very deliberately, its design not remotely accidental, the response options dripping with subtext. Mondesi, with his elite speed, decent power for a speedster, and very questionable contact skills, in 2018 is almost a dead ringer for Buxton in 2017. Mondesi doesn’t quite have Buxton’s baggage — he doesn’t carry the weight of expectations of a No. 1 prospect — but he has his own, continuing a familial legacy. But they do have a lot in common, as aforementioned, which can be summarily boiled down to this great quip from our Eric Longenhagen: “wholly untamed physical abilities.”

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(Not to be confused with Jeff Zimmerman’s delightful Quick Looks.)

In terms of fantasy topics to discuss, I’ve been pretty unmotivated for the last month. I took to Twitter to solicit some ideas. Rather than letting myself procrastinate and become unmotivated about these interesting topics, I figured I’d knock a few out at once with some quick takes.

The re-emergence of Ben Zobrist

Or, conversely, the caving-in of the rest of the Cubs’ offense.

Sure, there have been bright spots: Javier Baez makes for a nice down-ballot MVP candidate, Kyle Schwarber is not a liability, and Jason Heyward is a non-zero with the bat for the first time since moving to Chicago. But for everyone else? Not so much.

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If something about Madison Bumgarner’s first eight starts of 2018 have seemed odd to you, it’s because they have been. No matter the fielding independent pitching statistic to which you subscribe — FIP, xFIP, SIERA (although, frankly, it should be SIERA) — Bumgarner’s 2018 has not inspired confidence. Despite a dazzling (and quintessentially Bumgarnerian) 2.90 ERA, his baserunner suppression skills (i.e. strikeouts and walks) have lagged this year, and the various FIPs all portend severe bumps in the road. Granted, Bumgarner has outperformed his FIPs the last three years and throughout his career. I’m here to argue not that we should dismiss our concerns because of this but, instead, that such overperformance has insulated us from what should be potentially serious concerns about MadBum’s long-term health and success.

The problems with Bumgarner’s 2018 season — or at least the peripherals that underpin his 2018 season — thus far stem back not to his broken finger but, rather, something both farther back and much more dire. You may or may not recall Bumgarner fell off a dirt bike last year and injured his throwing shoulder. He returned from that injury almost exactly a year ago and promptly underwhelmed us. Sure, he posted a 3.43 ERA through September and has a 3.23 ERA in the calendar year since his return. It’s not vintage Bumgarner, but it’s not awful. But the peripherals, oh, the peripherals: his strikeout rate (K%) has caved dramatically, falling more than 6 percentage points (27.1% from April 2015 through April 2017; 20.9% from July 2017 onward).

It’s his fastball. Bumgarner’s fastball, once elite (relative to other four-seamers), is broken, and it has been broken for a year.

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You don’t care about this part! You care about the predictions.

I originally wrote my bold predictions for 2018 here; they arrived late and incomplete, but they arrived in some capacity, and that’s all we can ask for at this point.

I make bold predictions not for the sake of being bold but, rather, (1) using earnest judgments of player abilities and market inefficiencies and (2) to create teachable moments. My better bold predictions include prescient forecasts for Jose Ramirez and Austin Barnes; my worse ones have typically revolved around Giancarlo Stanton and Chris Davis in some capacity.

Let’s see how everything’s going so far.

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The league-wide hard-hit rate (Hard%) is up. Like, way up, at its highest level by far in the 17 years Baseball Info Solutions has measured and tracked the statistic.

Yet league-wide home runs are down, and way down, too, not in the whole history of the game but at least in the context of the recent Juiced Ball Era^TM. Hard-hit rates and power, as measured by home runs or isolated power (ISO), increased steadily and in tandem from 2015 through 2017. You’d expect, then, that if the ball were still juiced in 2018, the league’s highest hard-hit rate ever might produce the highest league ISO ever.

No such luck, though; 2018’s .161 ISO falls a full 10 points short of last year and a tick short of 2016. Which is odd, see, because batters are hitting the ball harder than ever. Since 2015, when sabermetricians first noticed the ball was juiced…

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In a fairly surprising turn of events, the Rockies demoted Jon Gray Saturday. Gray has arguably been baseball’s most enigmatic pitcher this year, posting a career-worst 5.77 ERA supported by career-best peripherals — e.g., a 13.4% swinging strike rate (SwStr%) underpinning a 28.9% strikeout rate (K%), and fielding independent metrics of 2.78 xFIP, 3.08 FIP, and 3.15 SIERA. Given our most basic sabermetric understandings of baseball, Gray should be a very good pitcher, even if he pitches half his starts at hitters’ paradise Coors Field.

I have written about how a common-breed Rockies pitcher’s peripherals might be penalized for calling Coors Field home (Gray inspired this bit of research as well). FIP metrics generally underestimate ERA by anywhere from 0.8 to 1.3 runs for home starts (compared to 0.0 to 0.2 runs for road starts), suggesting that Rockies pitchers may underperform (a) their FIPs by 0.35 runs or (b) their SIERAs by 0.65 runs — given error bars, maybe more.

Still, that doesn’t explain why Gray’s ERA is nearly 6 right now. I shed light on the ridiculousness of the move; his strand rate (LOB%) is suppressed and his batting average on balls in play (BABIP) is elevated, even compared to his uniquely bad baselines. I’m not sure there’s much more to it.

Nick Mariano of RotoBaller noted here that Gray’s fastball has been incredibly hittable since his debut and especially this year. Despite my thoughts on the inevitability of regression in Gray’s favor, I wanted to pursue Mariano’s train of thought a little further. Gray’s fastball is bad, but how bad? And why?

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This year, I’ve been caught up on pitching. I investigated the nuance inherent to swinging strikes, indirectly made a case for completely abandoning the sinker with this piece comparing pitch type outcomes, and (maybe) identified the keys to unlocking pitcher BABIP and HR/FB.

Here, I’ve modeled swinging strike and ground ball rates using only pitch velocity movement. Surely, this work can be improved; my quantitative tool set, while fairly robust compared to the layman, is meager compared to the professional or even hobbyist statistician. Regardless, I think it’s pretty cool, and I hope it adds to the conversation constructively.

Mostly, this serves to satiate my own curiosity. Unfortunately, it may be denser than I expected — few answers are ever quite as simple as you hope them to be, I guess.

Existing Research

I linked to several of my own pieces above. Dan Lependorf wrote about estimating ground ball rates in 2013 at the Hardball Times, although its conclusions have an anecdotal slant. (It thinks about velocity and movement but doesn’t take the requisite steps to bridge the logic.)
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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…

1. in a given season.
2. over the course of a career.

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