Archive for 2019

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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