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

Batters

The White Sox offense projects similarly to how it did prior to the 2021 season, which is good news for the team considering how easily they made the playoffs. But that also means the fundamental problems in the offense remain, with an additional issue compared to last year. ZiPS may be underestimating Eloy Jiménez — it’s always hard to evaluate players coming back from injury — but we can’t pretend that there isn’t some risk involved there. The remaining outfield position and DH are thornier concerns, and ZiPS is more pessimistic here than Steamer is. This is not a popular opinion, but if the White Sox plan to use Andrew Vaughn like our depth charts indicate they will, I really hope they just let him crush Triple-A pitching for a few months rather than juggling him with Gavin Sheets and Adam Engel. The basic problem is that though Vaughn was deservedly a terrific prospect, and 2020’s lost season of course did him no favors, he still doesn’t have a professional season in line with what you would expect from a top first base prospect. Read the rest of this entry »

The first ZiPS team projections will hit the site this coming Monday, and as I typically do, I’m going to use this space to talk a little bit about my philosophy behind ZiPS, my goals, and the new things I’ve worked on before they go live.

ZiPS is a computer projection system I initially developed in 2002–04 and which officially went live for the ’04 season. The origin of ZiPS is similar to Tom Tango’s Marcel the Monkey, coming from discussions I had with Chris Dial, one of my best friends (my first interaction with Chris involved me being called an expletive!) and a fellow stat nerd, in the late 1990s. ZiPS moved quickly from its original inception as a reasonably simple projection system, and now does a lot more and uses a lot more data than I ever envisioned 20 years ago. At its core, however, it’s still doing two primary tasks: estimating what the baseline expectation for a player is at the moment I hit the button, and then estimating where that player may be going using large cohorts of relatively similar players.

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. And research is a big part of ZiPS. Every year, I run 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 ’13, while data derived from StatCast has been included in recent years as I’ve gotten 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. Read the rest of this entry »