We’ve made it to the New Year! It is the dead of winter, but at least with fantasy football being done, we’re flipping the fantasy calendar back toward baseball. Those who really dive into both sports will now begin their baseball deep dive and that’ll move the ADP markets for sure, so keep that in mind when I reference these draft spots. Today I want to look at five players I’m buying back after disappointing 2018 seasons. The hype is gone, their price has tanked, and now is the time to buy!

Ian Happ | Chicago Cubs | 261 ADP

Happ and teammate Javier Baez were going in the same ADP range last year just around the Top 100 and I chose Happ… whoops. Although, Ozzie Albies was going around them, too, and I favored him over the two Cubs so I didn’t get too crushed by my awful Happ over Baez decision. Not that Albies over Baez was some great win. When Happ hit a home run on the first pitch of the season, my confirmation bias went into overdrive. I’m not proud of it, but I definitely got way too hyped over that whopping ONE PITCH SAMPLE! He went 0-for-4 with 3 Ks the rest of that game and wound up with a .233/.353/.408 line, 15 HR, and 8 SB in 462 PA.

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When Yusei Kikuchi was posted in early December, he had only a month to sign with a team. For this reason, his signing decision was going to fall over the holidays and Seattle broke the news right before the end of the year. Because of the unique timing, he didn’t get a ton of press, but owners should be aware that he’s a decent starter and not to be ignored on draft day.

First off, the other Jeff beat me to writing about the 27-year-old lefty. Twice to be exact. Once before the signing and once after.
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The Indians, Rays and to everyone’s surprise, Mariners made a trade last week with the following final results:

• Rays get Yandy Diaz and Cole Sulser
• Mariners get Edwin Encarnacion, 77th pick, and $5M
• Indians get Jake Bauers, Carlos Santana, and $6M

For my analysis, I’m going to focus on just Diaz and Bauers. Both have shown great potential but their minor league results have not yet translated to the majors.

Yandy Diaz (NFBC ADP Rank: 475)

With Jason Kipnis and his $17M contract likely to play second and Jose Ramirez at third, Diaz wasn’t guaranteed to play in Clevland. He is now in Tampa.

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A few days ago, I examined some later round NFBC picks trying to find players I prefer over others. I did rank 201 to 270. Today, I’ll continue the search from rank 271 to 350 with a later surprise.

Note: I don’t like the starters after Urias (#281) but many hitters are still interesting. If I’m in a deeper league, I may try to stock up on pitching before I get into this section of the draft.

271: Isiah Kiner-Falefa
272: Michael Wacha
273: Teoscar Hernandez
274: A.J. Minter
275: Jed Lowrie
276: Marco Gonzales
277: Trevor May
278: Drew Steckenrider
279: Adam Jones
280: Daniel Palka

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The current fantasy focus is evaluating the top-100 or so players, especially the first couple rounds. I’m going to deep dive and investigate some later round values.  I will group 10 players together and pick which one I would take earlier than the others.

201: Arodys Vizcaino
202: Jonathan Schoop
203: Kevin Gausman
204: Luke Voit
205: Chris Taylor
206: Jake Arrieta
207: Forrest Whitley
208: Ross Stripling
209: Odubel Herrera
210: Yusei Kikuchi

There are several players I really like in this group like Taylor, Stripling, and Herrera but easily the best bargain is Kikuchi.

Kikuchi isn’t on par with Asian imports Ohtani or Darvish but he’s still extremely talented. He’s considered to be a better talent than Miles Mikolas and Mikolas is going 86th overall. I believe once Kikuchi signs and everyone gets to know him, his ADP will be cut in half and around a top-100 pick, if not double digits.
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Introduction:

Today, I will introduce a game theory approach for comparing baseball projection systems. The day’s venture will not be a typical statistical analysis. I won’t be using any Chi-squared tests, nor will I calculate Type I or Type II errors. I won’t be evaluating MSEs or the like.

Instead, I will look to determine the profitability potential of each projection system by simulating what would have happened in a fantasy auction draft. Instead, I’ll play a game.

What do I mean by this?

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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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It’s rankings time so the comments are open for everyone to go batshit crazy about how a computer program and I are wrong.  We are both ready for everyone’s best shot as neither one of us gives a flying  f***.

As part of my soon to be released e-book, The Process, the Standing Gain Points formulas will be included for several league types. I took the one for The Great Fantasy Baseball Invitational, merge it with Steamer projections, and created some overall rankings.

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With steals becoming more scarce, owners were forced to reach for the few stolen base sources which could carry a team. In 2018, Dee Gordon and Billy Hamilton were supposed to be two such sources. Both disappointed their owners but the risk of a decline was evident even though their owners, which includes the author, ignored them. The owners were hoping for a stolen base panacea but ended up with burnt pancakes.

Going into the season, our Depth Charts projected Hamilton to have the most stolen bases at 52 and Gordon was third at 46. Both missed badly with Hamilton stealing 34 and Gordon with 30. In my Tout Wars league, 16 steals were the difference between 7th and 2nd in the category.

The reason for their decline didn’t involve their ability to steal a base. Both couldn’t hit enough to get on base and continue leading off. Both had on-base rates under .300 and OPS’s in the low .600’s. By the season’s end, both were deservingly hitting at the bottom of the lineup (Hamilton 106 times, Gordon 34 times). In all fairness, their projected OPS values (.674 and .648) were below the average catcher (.676).

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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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