After having typically appeared in the hallowed pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have now been released at FanGraphs for eight years. The exercise continues this offseason. Below are the projections for the Arizona Diamondbacks.

Batters

ZiPS projects Ketel Marte to regress a bit towards the mean, but not by enough to prevent him from remaining a star center fielder. The outfield corners remain more troublesome, and I believe it is extremely dodgy to assume that a healthier shoulder will be enough to return David Peralta to his career-best 2018 form. Peralta’s quite old for a player with just five years’ worth of playing time — he’ll turn 33 late in 2020 — and it’s quite likely that some age-related decline will counteract a portion of the benefits of (hopefully) avoiding further injury. I’m a fan of Josh Rojas, but I’m not sure his value will really come as a starter in a corner outfield position, where I think his bat will be stretched a bit.

I really would have liked to see Arizona go after Marcell Ozuna, who may actually be underrated at this point, including by me; I was leery of a team chasing after two seasons after him failing to hit the three-win line, but after running his ZiPS projection and looking at my BABIP model (zBABIP), I may have been too hasty to dismiss him. ZiPS thinks Ozuna should have had a .316 BABIP in 2019 based on his profile (his actual was .259) and gives him a .281/.346/.509, 3.1 WAR projection in 141 projected games in Arizona.

The projections aren’t completely sold on Christian Walker, still seeing him as a league-average first baseman. Problem is, ZiPS also is a fan of Kevin Cron — his emergence was huge even by Pacific Coast League standards — and projects him as the slightly better player right now. If trading Walker at some point can get the Diamondbacks another corner outfielder or a starting pitcher, it’s going to be hard to say no to. I don’t think Arizona is a top offense, but I don’t think there are any serious holes at the moment, an impressive turnaround for a team that had an 86 wRC+ as recently as 2018. Read the rest of this entry »

After having typically appeared in the hallowed pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have now been released at FanGraphs for eight years. The exercise continues this offseason. Below are the projections for the Chicago White Sox.

Batters

Yasmani Grandal was a tremendously important addition for the White Sox, giving the team an instant star behind the plate. He’s a good enough hitter that if the physical rigors of catching start to wear on him as he ages, he will still retain some value at first base or DH. ZiPS has been all over the map with Yoan Moncada, but believes that he’s finally turned the corner for good. I’ve declared this several times and been burned before, but I’m going to again make the claim, hopefully for the last time.

ZiPS sees Luis Robert and Nick Madrigal as instant contributors, though just how much the White Sox wring out of those positions will depend on how quickly they reach the majors in 2020. The computer thinks Danny Mendick is a serviceable stopgap who will have value as a utility guy when he eventually loses his job to Madrigal. The non-tendering of Yolmer Sanchez really makes the way the White Sox managed second base in 2019 seem odd in retrospect. If Sanchez’s performance was such that there was a chance the team wasn’t going to offer him a contract for 2020, why not give Mendick more time at second base? It’s not as if Sanchez’s projection got worse between September and November, so the team had to have been at least on the fence by late in the season. A more extensive audition for Mendick would have given the team more information. Read the rest of this entry »

After having typically appeared in the hallowed pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have now been released at FanGraphs for eight years. The exercise continues this offseason. Below are the projections for the Colorado Rockies.

Batters

The 2019 Colorado Rockies ranked fourth in the National League in runs scored, which is actually a rather bleak ranking for a team that plays at Coors Field. “OMGTEHCOORSHANGOVER” has become a convenient excuse for the club’s struggles — at least when they accidentally suggest they’re aware there are struggles — but it’s become a bit of a crutch when talking about the team. There appears to be an effect, but a minor one, unlikely to be worth more than three-to-five points of OPS for Rockies hitters. ZiPS doesn’t take into consideration any “Coors hangover,” and if this were a big deal, then ZiPS would be systematically too optimistic on players going to Coors and too pessimistic on players leaving. But it is not.

I feel like we’ve been over this story a billion times, but very little has changed in Colorado. The team’s offense is largely reliant on having two-to-three players in any given season being MVP candidates, with Nolan Arenado and Trevor Story likely being those two players again in 2020. Ryan McMahon and David Dahl both receive projections that are kinda disappointing, but it’s hard to forget that Dahl’s injury history is long and the Rockies spent two prime development years jerking McMahon around. Read the rest of this entry »

The 2020 ZiPS projection season starts Friday, and before it does, I wanted to offer a brief refresher of what ZiPS is and is not.

ZiPS is a computer projection system, initially developed by me from 2002-2004, and “officially” released in 2004. As technology and data availability have improved over the last 15 years, ZiPS has continually evolved. The current edition of ZiPS can’t even run on the Pentium 4 3.0 processor I used to develop the original version starting in 2002 (I checked). There are a lot more bells and whistles, but at its core, ZiPS engages in two fundamental tasks when making a projection: establishing a baseline for a player, and estimating what their future looks like using that baseline.

ZiPS uses multi-year statistics, with more recent seasons weighted more heavily; in the beginning, all the statistics received the same yearly weighting, but eventually, this became more varied based on additional research. Research is a big part of ZiPS and every year, I run literally hundreds of studies on various aspects of the system to determine their predictive value and better calibrate the player baselines. What started with the data available in 2002 has expanded considerably; basic hit, velocity, and pitch data began playing a larger role starting in 2013, and data derived from StatCast has been included in recent years as I got a handle on the predictive value and impact of those numbers on existing models. I believe in cautious, conservative design, so data is only included once I have confidence in improved accuracy; there are always builds of ZiPS that are still a couple of years away. Additional internal ZiPS tools like zBABIP, zHR, zBB, and zSO are used to better establish baseline expectations for players. These stats work similarly to the various flavors of “x” stats, with the z standing for something I’d wager you’ve already figured out!

When estimating a player’s future production, ZiPS compares their baseline performance, both in quality and shape, to the baseline of every player in its database at every point in their career. This database consists of every major leaguer since the Deadball era — the game was so different prior to then that I’ve found pre-Deadball comps make projections less accurate — and every minor league translation since what is now the late 1960s. Using cluster analysis techniques (Mahalanobis distance is one of my favorite tools), ZiPS assembles a cohort of fairly similar players across history for player comparisons, something you see in the most similar comps list. Non-statistical factors include age, position, handedness, and, to a lesser extent, height and weight compared to the average height and weight of the era (unfortunately, this data is not very good). ZiPS then generates a probable aging curve — both midpoint projections and range — on the fly for each player. Read the rest of this entry »