# A Different Take on NL Outfield Prospects: June 2015 Update

A month before the season started, I introduced a model that predicts a Minor League hitter’s chances of Major League success based on his statistics during his most recent AAA stint. I will use that same model now to update the list of the National League’s top MLB-ready outfield prospects, the key term here being MLB-ready, not prospect — the goal is to identify players who scouts may not love but stats do.

The model looks at how a hitter once performed in AAA and compares it to his known career outcome, ultimately calculating probabilities that a certain career outcome will occur. These probabilities can then be applied to current Minor League players in order to project their currently unknown career outcomes. I discuss the model’s nitty-gritty, as well as its similarities with and potential shortcomings to Chris Mitchell’s KATOH, in the link provided in this post’s first sentence. Both models share the same goal and their methodologies are almost identical, so their results can be considered comparable; mine simply takes a more subjective approach, as I will explain shortly. This is not a turf war.

The model employs an ordered probit regression, which is a type of linear regression that uses a coded, rather than nominal, dependent variable. In other words, I assign numbers to a series of outcomes that I rank in order, and the model calculates probabilities that a certain outcome will occur based on the explanatory variables. In this case, the explanatory variables are a hitter’s statistics and specifications such as age, batting average, isolated power (ISO), plate discipline (K% and BB%) and stolen base rate (SB/PA). I code the dependent variable as follows:

Outcome Description of outcome
0 Bust: Did not reach majors
1 Liability: Reached MLB + accumulated negative career WAR
2 Bench piece: Reached MLB + positive WAR, but never qualified for batting title (502 PA)
3 Starter: Reached MLB + positive WAR + at least one qualified season
4 All-Star: Appeared on at least one All-Star team
5 Award-winner: Rookie of the Year, Silver Slugger or batting champ at least once
6 MVP: Most Valuable Player at least once

I received a lot of great feedback when I originally introduced the model. I will be completely transparent and admit that I, regretfully, have not had time to implement a lot of the changes as recommended by commenters. Also, keep in mind that the dependent variable loses some significance when you consider that fans vote for All-Stars, meaning the fate of every AAA player’s career is in the hands of Royals fans. Again, the model isn’t meant to be overly sophisticated, and it’s very far from perfect. As the title indicates, it’s simply a different take on projecting prospects.

The projection for Houston Astros’ Domingo Santana tops — topped, before his promotion — all AAA outfielders. The model predicts that the 22-year-old, who posted a .320/.444/.584 triple-slash line through 241 plate appearances, has a 70-percent chance of sticking as a full-time outfielder with a 42-percent chance of making at least one All-Star team. KATOH lauds the outfielder as well, but KATOH’s overseer expresses his reservations. I, too, share them: while the walk rate is healthy, the strikeout rate is disconcerting, and the .444 batting average on balls in play (BABIP) certainly confound his projections.

Unfortunately, I talk NL outfielders, and the field of NL outfielders with more than 100 PAs (an arbitrary cutoff) in AAA this season is depressingly thin. Still, I think it’s worth it to discuss some new names and revisit old ones. Besides, I’ve come too far. Here are your top five.

1. Elian Herrera, MIL
At least a “Starter” / All-Star: 41.4% / 17.1%

You know it’s a bad list when your “top outfield prospect” is 30 years old and not really playing that much outfield. Herrera’s projection is confounded by a .402 batting average fueled by a sky-high .443 BABIP in the hitter-friendly Pacific Coast League (PCL). Regardless, he held his own, walking exactly as often as he struck out and stealing the occasional base. Herrera more or less replicated his AAA line during his brief call-up but with half the BABIP, resulting in a mid-June demotion. Once a speed demon, he hardly runs anymore, and the four home runs he hit for the Brewers earlier this season may very likely be the only four he hits for them all year. He’s possibly one more Aramis Ramirez injury away from more playing time, but he would be still be mostly a Hail Mary play even in NL-only leagues.

2. Darrell Ceciliani, NYM
Starter / All-Star: 34% / 13%

Confession: I did not know who Ceciliani was until yesterday. (Forgive me for not watching many Mets games.) Ceciliani has been up since late-May, primarily functioning as a pinch-hitter and performing miserably in the process. Before his promotion, however, he hit the ball well for a guy whose calling card is speed, not power. That power, however, was nowhere to be found prior to his outburst, and it has all but vanished since the Mets called for his services. He continues to flounder, and it would be unwise to expect more production than what we’ve already seen from him. For now, consider his projection a mirage.

3. Jaff Decker, PIT
Starter / All-Star: 33% / 12%

Decker cracked the inaugural list at No. 9. His probabilities have since improved, although it’s worth acknowledging the obligatory small sample size caveat. The .305/.375/.449 line is impressive sans the PCL asterisk. However, it’s his baserunning that has bolstered his value, as he has already stolen more bases than last year in one-third the time. He flashed more power in his youth, so if he could sustain his proficiency on the base paths, he could become a modest five-category contributor should he ever see the playing time.

4. Eury Perez, ATL
Starter / All-Star: 31% / 11%

Perez ranked fourth last time and he ranks fourth now. His probabilities have declined slightly, but that’s strictly an age-related development: he’s walking more and stealing way more, the latter being a great sign from a guy who’s already 25. The Braves very recently recalled Perez and he has since started every game in left field in favor of Kelly Johnson. If you’re hurting for stolen bases in a NL-only or deep mixed league, he’s a borderline must-add.

5. Arismendy Alcantara, CHC
Starter / All-Star: 30% / 10%

It’s unfortunate to see Alcantara, one of many Cubs top prospects, not projecting for a brighter future, especially in light of his youth relative to the names preceding his. He looks like the same hitter he was last year, albeit with fewer stolen bases, and I consider that a bad thing. His lack of plate discipline undermines his other tools and will likely prevent him from ever seeing consistent playing time amid a Cubs lineup (and farm system) packed with young talent.

6. Yorman Rodriguez, CIN
Starter / All-Star: 27% / 9%
7. Jose Tabata, PIT
Starter / All-Star: 26% / 9%
8. Stephen Piscotty, STL
Starter / All-Star: 24% / 8%
9. Matt Szczur, CHC
Starter / All-Star: 24% / 7%
10. John Andreoli, CHC
Starter / All-Star: 24% / 7%

Currently investigating the relationship between pitcher effectiveness and beard density. Two-time FSWA award winner, including 2018 Baseball Writer of the Year, and 8-time award finalist. Previously featured in Lindy's Sports' Fantasy Baseball magazine (2018, 2019) and Rotowire's Fantasy Baseball magazine (2021). Tout Wars competitor. Biased toward a nicely rolled baseball pant.

Guest
Mike s.

This is a beautiful statistical amount and the results are always interesting. Out of curiosity have you done something like estimate the model through say 2011 and use it for 2012 forward predictions? It would be interesting to see if there are overlooked players who performed well.