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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I had participated in a too-early industry mock draft that ended a couple of weeks ago. Then I attended First Pitch Arizona (FPAZ), which I reviewed and at which I participated in another arguably too-early draft. This post is about that latter draft. Sorry for all the reviews lately.

The draft, which took place live at FPAZ, is hosted by the National Fantasy Baseball Championship (NFBC). It’s a 15-team league, but instead of being a standard 30-round draft (seven bench players) with in-season transactions, it’s a 50-round draft-and-hold with no transactions (don’t worry, this only covers the first 23 rounds). I won’t say it completely changes the dynamic, but it does, or at least should, dramatically alter the plans of owners who take a lot of risks and plan to hedge them using their free agent acquisition budgets (FAABs).

What’s worse is I had, and still have, done virtually zero planning for the 2018 season. Accordingly, I relied on a strategy I outlined in the previously hyperlinked too-early mock draft review. I’m a risk-averse guy, so I typically draft player who are boring — boring because they’re old and/or you know what you’re getting. Low variance, things of that nature. This strategy inadvertently exacerbated this tendency of mine in these drafts, and the draft-and-hold nature of this league further compounded it. Without looking, the average age of my team must be, like, 33 years old.

But I like it. It’s so unsexy, so unapologetically (actually, very apologetically) boring that it’s almost not worth review. Except when I talk about boring, unsexy teams, I hear from guys like Jeff Zimmerman, Mike Gianella and so on — guys in the industry expert leagues such as Tout Wars and LABR — who say this is their jam, and that’s reassuring.

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No need to fret. The unofficial conductor, who is also the author of this post, does not expect the cabin to reach capacity. Not prior to April, at least.

Brett Lawrie’s time with any organization, not just the White Sox, was ticking away; more writing was scribbled on more walls with each passing year since his debut. So when the White Sox released Lawrie, the first thought on most fantasy owners’ minds was not Where will Lawrie end up? but, rather, When will Yoan Moncada get the call? Moncada, a consensus top-5 prospect, changed socks and is now the marquee name of the South Side’s now-promising future.

Yet one could argue Moncada’s not quite ready for the big show. After raking and running absolutely wild in High-A in 2016, Moncada graduated to Double-A and, well, his performance is open to interpretation. On one hand, his 11 home runs, nine stolen bases and .277/.379/.531 triple-slash in 207 plate appearances amounted to a batting line that was more than 50% better than the league. On the other hand, he struck out more than 30% of the time — and that lack of contact carried over into his Major League debut, during which he struck out in 12 of 20 PAs. The tools are immense, but, at 21, he could definitely use some polish, and the White Sox have no incentive to rush him along.

Allow me to (re)introduce you to Tyler Saladino.

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Playing time can make or break a baseball player’s fantasy value. An elite player may not finish above replacement level if he suffers an injury and plays only half the season, and a lackluster player could finish above replacement level simply by playing every single day. This is all intuitive, and the fantasy community generally approaches these kinds of things rationally. In other words, most players are appropriately valued, outside of the market inefficiencies that inevitably warp player values.

One-dimensional speedsters — dudes who steal a bunch of bases and do little else — are much harder to peg. Their value is tied up primarily in one category, as stolen bases (SBs) do not directly correlate with other categories the way home runs would with runs and RBI, for example. The issue becomes all the more confounding when one considers the contemporaneous scarcity of SBs relative to home runs. There’s more to value than just SBs and plate appearances (PAs), but the fact of the matter is the two statistics by themselves correlate very strongly with a player’s end-of-season (EOS) value (which, here, are informed by Razzball’s Player Rater).

In the last five years, baseball has seen 75 player-seasons of 30-plus SBs — 15 steals a year on average, a trend that didn’t fundamentally change in 2016 (although that doesn’t mean SBs aren’t scarce). A simple linear regression of SBs and PAs, the latter of which serves as a proxy for other counting stats such as runs and RBI, against EOS value produces a remarkable 0.71 adjusted R²:

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The fantasy community is down on Justin Upton. I get it, but it’s a little strange to me given our collective penchant for recency bias. Upton had a monster second half and finished the season an almost-perfect replica of his usual self. (The operative qualifier being “almost.” We’ll get to that in a second.) Sure, it was a rocky year, but hey, Joey Votto had one, too. Dude was batting .213 with a 27 percent strikeout rate (K%) through May…

Right, so Upton was an almost-perfect replica of himself. In a vacuum, his production looks nearly identical to his typical annual accomplishment, down to nearly every statistic except for his batting average on balls in play (BABIP). In my investigation of his woes, I noticed his uncharacteristically low infield hit rate (IFH%). Here’s a list of hitters with higher infield hit rates than Justin Upton in 2016:

• Salvador Perez
• Kendrys Morales
• Albert Pujols
• One hundred and thirty five other players not surnamed Ortiz, Mauer, Seager, Granderson, Cabrera, Votto or Kemp

Yes, Upton ranked among the bottom 6 percent of hitters in terms of infield hits. If there’s a single bone to pick about Upton’s season — well, aside from the insane volatility — it’s that his BABIP failed to get back on track, continuing to linger at a league-average mark. It seems a trend has emerged; accordingly, it’s easy to accept said trend as a new normal, as a resignation of Upton’s fifth tool.

I’m here to make the classic* Infield Hit Rate Defense, or IHRD, as it’s known in the infield hit community.**

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Robbie Ray is already shaping up to be one of 2017’s most contentious starting pitchers headed into draft day. (This isn’t even my first time writing about him in the last half-year.) His 28-percent strikeout rate (K%) and 3.45 xFIP scream of an elite starter, but his 4.90 ERA and 1.47 WHIP, sustained during more than 170 innings pitched, seem to say otherwise.

Analysts and laymen who have expressed optimism about Ray have done so in regard to his alleged hittability. That 1.47 WHIP didn’t come from nowhere: his .352 batting average on balls in play (BABIP) got him there. You’ll hear a variety of arguments: he struggles on his third time through the zone; he lacks a quality third, or maybe even second, pitch; and so on. I’m not here to argue the validity of those sentiments.

I want to talk exclusively about Ray’s BABIP. Well, his sinker, too. And maybe even his strand rate (LOB%)… But mostly his BABIP. Please, have a seat. I don’t want to fluster you.

Ray’s .352 BABIP in 2016 was the second-worst of the last 15 years. That’s out of 1,281 individual player-seasons posted by qualified starting pitchers. His BABIP was historically bad — strange, you’d think, for a pitcher who has quickly demonstrated a lot of promise. So, I want to approach this whole BABIP thing in a vacuum. Let’s just look at the facts — not even alternative facts, but real facts!

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