OUTs Top Ranked Bats Heading Into 2018

Earlier this year I developed two closely related stats which I called OUTs and bbFIP. I’m reasonably proud of these two stats, as I feel they do a pretty good job capturing the skill of each player. They account for the numbers of weakly and strongly hit balls, balls that have high home run rates, strikeouts and walks.

In other words, it accounts for every aspect of bat generated offense, ignoring base running ability. However running speed is used to judge whether batted balls are weakly or strongly hit for each individual batter. For example, a batted balls by Billy Hamilton may be near automatic singles, whereas they would be almost guaranteed outs if hit by Albert Pujols.

The formula is constructed as follows:

$\mathrm{OUTs}=\frac{\left(.77\times \mathrm{W}\right)+\left(.17\times \mathrm{K}\right)-\left(.98\times BB\right)-\left(.69\times \mathrm{HBP}\right)-\left(1.52\times \mathrm{S}\right)-\left(2.52\times s\mathrm{HR}\right)}{\mathrm{PA}}$

Where W = weak contact (xOBA ≤ .245), S = strong contact (xOBA ≥ .634), and sHR = strong home runs (xHR% ≥ .55).

You can convert this OUTs score to an ERA scalar by multiplying by -11 and adding a constant (~5.4). This will give you what I call bbFIP, a version of FIP that is superior to standard FIP both in season and between seasons. You can also find an offense’s average OUTs score by weighting each batter by their number of plate appearances, and then translate that number to the ERA scalar to figure out how many runs you might expect them to score through the course of a season.

There are a few things to keep in mind:

1. Lower numbers are better. I tried to build this concept into the name, so it is easier to remember. It is called OUTs, outs are bad, whoever has the least of them is the best.
2. The average score is about 0.1. This season it is closer to .09.
3. The standard deviation is about 0.1.

As the 2017 regular season is coming to a close and we begin to gear up towards the 2018 season, I have a few preliminary OUTs and xOBA projections. These projections haven’t yet baked in the aging curve, so maybe ‘projection’ is the wrong word to use here. Either way, I have selected what I refer to as the ‘significantly above average’ projections in terms of OUTs. In other words, anyone who has a score less than 0. I’ve also supplied Z-Scores, xOBA, and xOBA Z-Scores.

There are 45 batters on this list. Most of them are expected names in their expected ranking, give or take. However, a few stand out.

Looking at the top of the list, I doubt there will be much controversy outside of Miguel Cabrera. Goldy, Votto, Freeman, and Trout are likely the four best hitters in the game, although maybe you will argue over the order.

Miguel Cabrera has been one of the greatest hitters for many years, and even after a miserable season his projections are still strong. The aging curve will pull him back a bit, once implemented, but projections assume health. Unfortunately, Cabrera’s health is in question now that we know he has two herniated discs. This is an injury he may never recover from, so this projection may be an optimistic relic from the past.

Jumping down the order a bit, there are several rookies, and a few near rookies that should catch your eye:

Aaron Judge, Rhys Hoskins, Cody Bellinger, Gary Sanchez, Kris Bryant, Michael Conforto, and Carlos Correa.

Of course, Correa, Conforto, and Bryant are no longer rookies, but they are only a year removed. To be honest, I’m not entirely sure when Gary Sanchez lost his rookie status. Either way, it doesn’t particularly matter, these are seven of the most talented players in MLB, and Kris Bryant is the oldest at 25 and 8 months.

Due to sample size, each of these batters have relatively low reliability ratings, which you can see in the chart. As such, keep in mind that these numbers are being tugged back a bit by regression, and with health and continued performance they could very well out perform these numbers. 

Speaking of health, Michael Conforto suffered a potentially devastating shoulder injury, so his position here may be taken into question.

None of the players mentioned so far should raise eyebrows. Perhaps you disagree with the order they have been presented, but obviously each of them should make the list of top projected bats going into 2018. Let’s get into some of the more controversial names.

Joe Mauer, Jose Martinez, Justin Smoak, and Chris Davis.

I am sure, going down this list, that the majority felt uneasy about at least one of these four guys. Chris Davis has been in freefall for years. Justin Smoak had struggled for several years prior to putting up a single good season this year. Jose Martinez is a 29 year old rookie. And, well, Joe Mauer.

First, I want to comment on Joe Mauer. xStats has loved Joe Mauer from day one, granting him high marks in each of the three seasons I have been operating these stats. He’s a good hitter, but he’s not great. Although, he has put up a very solid season in 2017. I am not sure why xStats loves him so much, but I feel he is a consistent outlier and I would toss him out for that reason alone. He’s good, but something funky is going on with him with regards to these stats. Maybe it is a park effect thing, I’m not sure.

Justin Smoak has rated above average in OUTs (and other xStats metrics) for each of the past three seasons, so maybe you could say it predicted his breakout season. In 2015 he rated well above average with a score of -.006. In 2016 it was only a bit above average with a score of 0.069, and this year it is a rock solid -.095. This year Smoak has had a marked increase in plate discipline, swinging at fewer pitches outside of the zone and dealing more damage to those within the zone. While Smoak has produced above average quality plate appearances for three seasons in a row, maybe it is a bit rash to place him among the top 45 batters. However, he has made the top 50 batters prior to 2017, so it isn’t entirely unfounded.

Chris Davis has been an above average bat for each of the past three years, but his reputation, at least in terms of the OUTs stat, appears to be built atop of the 2015 season. He was exceptional in 2015, and he was even better in 2013. However, those seasons appear to be peak years for him, and he’s been steadily declining ever since. I suspect after applying aging curves, which will then reduce the dependence on the 2015 season, his projection will fall dramatically, and he will no longer be on this list.