2018 Pod Projections: Luis Castillo — A Review
Let’s return to reviewing my preseason Pod Projections, this time with Luis Castillo, a popular sleeper choice and breakout candidate, and for good reason. My original writeup is here.
Let’s return to reviewing my preseason Pod Projections, this time with Luis Castillo, a popular sleeper choice and breakout candidate, and for good reason. My original writeup is here.
Yesterday, I reviewed my stolen base upside guys, after comparing my Pod Projections to Steamer projections in the preseason. Today, I’ll review my downside guys. Let’s see how they performed.
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).
Mitch Haniger surprised about everyone this season with top-25 production when his average draft position was over 200 in NFBC leagues. In all fairness, he should have made several sleeper lists but the industry failed to pick up on his productive but ignored 2017 season. He’s the type of hitter owners need to focus on rostering, late-round injured hitters.
His stats speak volumes. Here are some of Haniger’s projections and results over the past two seasons.
| Stat Source | PA | AVG | OBP | SLG | HR | SB |
|---|---|---|---|---|---|---|
| 2017 Steamer | 470 | .249 | .315 | .413 | 15 | 6 |
| 2017 April | 95 | .342 | .447 | .608 | 4 | 2 |
| 2017 April – June | Strained | Oblique | ||||
| 2017 June | 65 | .231 | .367 | .354 | 2 | 1 |
| 2017 July | 68 | .176 | .233 | .279 | 1 | 0 |
| 2017 August | 38 | .211 | .250 | .474 | 2 | 0 |
| 2017 Sept/Oct | 119 | .353 | .374 | .613 | 7 | 2 |
| 2017 Full Season | 410 | .282 | .352 | .491 | 16 | 5 |
| 2018 Steamer | 536 | .253 | .324 | .433 | 19 | 7 |
| 2018 Full Season | 683 | .285 | .366 | .493 | 25 | 8 |
His projection coming into 2017 was decent with a 20 HR and 8 SB profile when prorating to 600 PA. The season started out great until he went on the DL with an oblique injury which lasted for over a month. He came back from the DL, struggled, got hurt a coup of times (finger and face), and finally turned it on over the last month. This profile screams sleeper and everyone slept on him.
By just prorating his 2017 season to 600 PA, he would be at 23 dingers and 7 bags with an acceptable .282 AVG. His results were similar to another pre-season unknown, Marwin Gonzalez (23 HR, 8 SB, .303 AVG). Gonzalez’s average ADP was 123, about 100 picks before Haniger went off the board.
As for Haniger’s 2018 season, he showed the value of a well-rounded player. A near .300 batting average, over 20 homers, and about 10 stolen bases placed him as a top-25 overall batter. Unexciting stats can still be good.
Going forward, owners can take several lessons from this failure. First, dig into hitters after pick 100 who struggled with injuries but showed positive production when healthy. Most owners are going to hope the top names like Kris Bryant and Jose Altuve will rebound. They aren’t going to surprise anyone. Instead, players like Kyle Seager (toe), Steven Souza Jr.(pec), and Jorge Soler (rib) might be acquired for nothing and end up being a top-50 player.
Another item is to prorate each player’s previous season to 600 plate appearances to see if anyone pops up if given more playing time. With Haniger’s nearly identical pro-rated 2017 and 2018 seasons, his 2017 season would have stood out and owners could have taken notice. Instead, he was relegated to the reserve rounds.
Missing on Haniger’s points to some obvious projects for me later in the offseason. Until then, let me know of any players who the industry missed on and there was no obvious cause.
Yesterday, I reviewed the first preseason Pod vs Steamer projections article, beginning with the hitters I projected for home run upside. Today, we’ll flip to the other side, as I review the home run downside guys. Hopefully I perform better than my upside guys!
When I hear or read about a hitter playing through an injury my interest perks up and he becomes an immediate draft target. Standard projections have no idea these players played hurt and the lower production keeps down future estimations. Savvy owners can give these players a small talent bump and reap some nice rewards. My current request to create a detailed list of hitters playing through injuries for 2019 preseason research and to test after next season.
A fun activity for me each preseason is comparing my Pod Projections to the Steamer projections. While the forecasts in various categories for most players are negligible, of course there are some that are wildly different. One comparison I made was looking at which hitters I projected for more homers than Steamer. However, rather than compare the raw home run total, which is greatly influenced by the at-bat projection, I computed each projection’s AB/HR ratio. So let’s find out how the guys I identified as having AB/HR upside performed.
As has now been an annual tradition, I published a series of Pod Projections before the season begin, and then compared my projection to the rest of the systems available on the player pages. We’ll start the reviews with Whit Merrifield, who came out of nowhere in 2017 to become a fantasy stud, swatting 19 homers and stealing 34 bases. Let’s see how he performed compared to my projection and the computer systems.
Looking back, Jesus Aguilar had so many forces working to hold down his pre-season value, I’m amazed some teams rostered him (566th in NFBC ADP). While he had the tools for a breakout, it’s tough to find actionable pre-season moves to prevent similar players from slipping through the cracks. Once he got the opportunity to play, owners should have jumped in to roster him.
The first item to consider in the miss is that Aguilar’s projections weren’t glowing. Of all projected hitters in our pre-season depth charts, he came in at 245th by OPS. Not the best ranking for a 1B, especially compared to his teammate Eric Thames.
Here are the pair’s various OPS projections coming into the season.
| ZiPS | Steamer | ATC | The BAT | Average | Actual | |
|---|---|---|---|---|---|---|
| Eric Thames | 0.855 | 0.834 | 0.865 | 84% | 85% | 78% |
| Jesus Aguilar | 0.734 | 0.728 | 0.818 | 77% | 76% | 89% |
In the #2earlymock drafts run by our own Justin Mason, Javier Baez is going 17th and Trevor Story is going 20th among all hitters. The picks are quite high considering Baez was the 58th hitter taken, and Story was 65th in NFBC drafts last year. The pair didn’t have must-draft preseason hype and their suspect plate discipline limited their perceived value. Both exceeded all expectations as they came in at 6th and 7th overall this year. This was a huge miss by the industry and I’m going to see if some traits point to why some low plate discipline players break out and others don’t.
For every Baez and Story, other bad plate discipline hitters failed like Byron Buxton (.383 OPS), Chris Davis (.539 OPS), Miguel Sano (.679 OPS) and Jonathan Schoop (.682 OPS). No obvious difference stood out. While Chris Davis is old, Buxton, Sano, and Schoop should be in their primes. To find out who may break out, I decided to start with the 2018 Bad Plate Discipline Class.