Fantasy owners have been chasing, to no avail, Chris Archer’s 2015 season, during which he recorded a 29% strikeout rate with a 3.23 ERA. After finishing just outside the top-50 overall by National Fantasy Baseball Championship (NFBC) average draft position (ADP), Archer averaged the No. 50 pick from 2016 through 2018. Unfortunately, the outcomes annually and in aggregate have been awful…

… even though his peripherals before and after 2015 have been nearly identical. That’s the persistent problem with Archer: he has given us perpetual reason to chase results he may never again achieve.

I’m here to argue Archer’s woes started not in 2016, when his ERA ballooned to 4.02, but in 2015 — yes, his career year. That’s because he stopped throwing his sinker in 2015, opting instead to rely on a pitifully bad four-seam fastball as his primary offering. His 2015 success can be attributed primarily to his slider, which he began to feature much more prominently, but the remaining success was thanks to good luck.

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About a month ago, I published a post that predicted 2019 ADP (“xADP”) values using eight years’ worth of average draft position (ADP) data from the National Fantasy Baseball Championship (NFBC) and end-of-season (EOS) values from Razzball. The model was pretty good — it explained nearly 60 percent of the data’s variance (adjusted r² = 0.59), which is pretty dang good. It felt unfulfilled, though; it accounted for some players but not others — namely, breakout rookies who were completely off the radar the previous season and top prospects who had yet to debut.

I took some time (really, a lot of time) to clean up my data to see how much it would improve my model, if at all:

1. Originally, my data set did not account for players who were not drafted (aka had no ADP value) but made an impact in 2018 (think Juan Soto). Conversely, my data did account for players who were drafted but made no impact in 2018 (think, uh, Troy Tulowitzki, I guess). It was kind of like addressing a Type I error but ignoring a Type II error (or the other way around? I don’t know). I took painstaking care to fill in these holes.
2. I took equally painstaking care to ensure all player names were consistent — no “Nick Castellanos”/”Nicholas Castellanos” mismatches that might pollute the analysis. Odds are, there are a couple of players I missed, but having spent hours poring over the data, I feel confident that the issue is no longer pervasive.
3. I added ages! They make a small impact, most meaningful to players at the extremes, such as the very young (think Ronald Acuna) and the very old (think Nelson Cruz).
4. Lastly, a theoretical and methodological adjustment: I forced negative ADP values to $0. I wanted the model to reflect an actual draft, in which players are never bought at auction for negative dollars — rather, their values converge on zero. It’s important to note here that a player can still end the season with negative value based on the concept of replacement level. Accordingly, only negative ADP values, and not negative EOS values, were forced zero.

Fortunately, the extra work was worth it: the model boasts an adjusted r² of 0.75 (with ages; 0.73 without). That’s a massive improvement, and it can be attributed almost entirely to the slight (but profound) change in the model specification.

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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 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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The third in an ongoing series (Pt. 1) (Pt. 2), I’ll continue to compare hitters using blind résumés to highlight “generic-brand” players who could be drafted as substitutes for “name-brand” players in later rounds of your draft. This entry will differ slightly in substance; I’ll focus on 2018 projections to identify similarly skilled players rather than use hindsight to rationalize (or poke holes in) how we’ve valued past performance.

I will use National Fantasy Baseball Championship average draft position (NFBC ADP). Reminder: we host them here on FanGraphs, too. It’s worth noting I pulled these projections this past weekend, so the numbers will likely differ slightly but not unconscionably. The ADPs are current as of yesterday.

Comparison #1: Power-Speed Outfielders, Mostly

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In case you missed the announcement in Paul Sporer’s latest post:

FanGraphs now has NFBC ADP data!

NFBC ADP data used to be hosted at Stats, Inc. Prior to last week, 2018 data had only been available to NFBC contestants.

Anticipated FAQs:

Where can I find the data?

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A month ago, I compared “name-brand” and “generic-brand” players using blind résumés, an exercise we’ve reiterated across several authors and years at RotoGraphs. A few years ago, you’d never find me in a mock draft, let alone several in one preseason. (More a matter of blocking out three hours on a whim, but also I probably (very foolishly) thought I didn’t need to partake in such trivialities.)

I’m super, super into the whole slow draft mock thing, though; I’ve completed a few this preseason, and I think they have already helped me understand the kinds of market inefficiencies (i.e., differences between perceived and actual values) that will arise on draft day. One of the easiest ways to identify said inefficiencies is to compare similarly skilled players, then assess their other pros and cons — the intangibles, if you will, such as health and age — to determine who’s the better value.

Here are a couple more pairings:

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Fantasy owners participate in mock drafts to practice for their actual league drafts. Without the internet, neither mock drafts nor average draft position (ADP) data would be possible or available to us. The union of the two allow owners to understand approximately when they can expect players they plan to target and devise an appropriate plan of action.

What ensues when you draft for the next baseball season before the current season has ended and without ADP data and having not refined/run your 2018 projections yet? Chaos, namely. For the second year, the Honourable Justin Mason organized the Too-Early (#2early) mock drafts for me and a few dozen other analysts, the final ADP “data” (in air quotes, because a sample size of four drafts is not particularly strong data, and, as aforementioned, it is way too early to be mock-drafting) for which can be found here.

I drafted out of the 11th spot (of 15) in draft #4. I made a concerted effort to draft a good team, but I also deliberately avoided certain players just to see where other owners would draft them. (Had I drafted these players for my team, I would have a weaker understanding of how other owners valued them.) The lack of ADP data made it incredibly difficult to implement this plan. In a nutshell, my plan consisted of drafting the best player with a track record still available on the draft board (mind-blowing, I know), with a strong preference for hitting. There’s an objective basis to this that, rather than trying to explain, I’ll let reveal and define itself as I go. This plan served me admirably in all rounds of the draft.

Here’s my team, by round. “Rk” indicates a player’s draft rank within his assigned position. Columns labeled “All” and “Pos” depict players’ end-of-season values overall and by position, respectively, each of the last four years. These ranks come courtesy of Rudy Gamble, who concocts them for Razzball. If you’re concerned that these might somehow be inferior to ESPN’s Player Rater, know that Rudy won this year’s Tout Wars Mixed by a landslide, so I thoroughly trust his process.

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Masahiro Tanaka and Jeff Samardzija had perplexing, enigmatic, and ultimately bad seasons. Many attempts were made to ascribe reasons or causes for their struggles. I think both will bounce back for very simple reasons; accordingly, I think both will be undervalued in 2018 for equally simple reasons.

Masahiro Tanaka

Travis Sawchik and Eno Sarris discussed his various ailments, so to speak, long after I gave up trying to diagnose him. The heat maps are interesting, and the splits are interesting, albeit a bit of an archaism.

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I’m already thinking about 2018. It’s not that my teams are doing poorly; they’re fine, for the most part. It’s that the economist nerd in me, when thinking abut fantasy baseball, most often evaluates the disparities between perceived and actual values, and how long, if ever, it takes for the market (aka fantasy owners) to come to equilibrium, to use economic parlance.

For example: you may or may not be aware that Kevin Gausman, despite his atrocious start to the season, has been magnificent the last five weeks. In seven starts from July 2 onward, he’s posted a 3.24 ERA (supported peripherally by a 2.81 xFIP and 3.40 FIP) with 11.4 strikeouts and 2.6 walks per nine innings. The strikeout rate is fueled by a 15% swinging strike rate (SwStr%), which have come consistently, ascending into double-digit percentages in all seven starts (and in eight of his last nine). His strikeout-to-walk differential (K-BB%) by month: 2.0%, 8.8%, 9.2%, 23.4%, and, in one August start, 28.0%.

Meanwhile, he’s inducing ground balls almost half the time (49.5% GB). You could say he’s due for batting average on balls in play (BABIP) regression, and he probably still is. His BABIP constantly hovering above .349 does not inspire confidence, but few pitchers have ever been BABIP’d so hard in a single season — I discussed this phenomenon in regard to Robbie Ray. All said, while there’s no guarantee his BABIP regresses before October, Gausman still shows the promise we once expected of him — perhaps more — and it’s going largely unnoticed because of his downright repulsive first half. (He’s baseball’s #12 starter the last month.)

Such is the gist of this post, in which I’ll briefly touch upon players I anticipate to have average draft positions (ADPs) in 2018 that will lend themselves to relatively low-risk, high-reward opportunities in standard mixed leagues. Whether such expectations become reality is another story; that’s why I’m relying on ownership levels as a proxy for perceived value. All ownership levels likely retain some amount of draft day inertia, for better or for worse — in other words, leftover ownership (or lack thereof) in abandoned leagues — so take it all with a grain of salt.

Please note this is, by no means, an exhaustive list — just the first few players who come to mind, mostly because I’ve paid close attention to them all season.

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