“If I had a world of my own, everything would be nonsense. Nothing would be what it is, because everything would be what it isn’t. And contrary wise, what is, it wouldn’t be. And what it wouldn’t be, it would.” Yes, I am trying to quantify a largely subjective MLB element. Why do you ask?
Back in 2021, I wrote a thing for Beyond the Box Score in which I attempted to quantify the aesthetics of an MLB swing. I created some nonsense metrics based on performance vs. visual appeal before determining who was the best at swinging a bat as far as my eyes were concerned. Of course, it was a ridiculous premise completely undermined by the subjectivity of what makes an appealing swing. It’s one of my favorite things I’ve ever written.
Since that point, I’ve wanted to do something similar in the quantification realm. I’ve spent the last two years, specifically, in pursuit of quantifying “vibes.” Whether it’s my classroom, a function, etc., I’m a big vibes guy. When they’re good, I thrive. As an MLB outsider, though, it’s a virtually impossible task to determine. You might be able to glean something from body language or post-game pressers. But you’re never going to get a genuine sense of what’s happening in the fortress of a Major League clubhouse.
Nevertheless, I decided it’s finally time to put this aspiration into some kind of action. I’m doing this without a plan. Without a basis. And without any insider knowledge of what’s actually happening off the field. There’s absolutely nothing that can go wrong.
Key Vibes Components
The first thing we need to establish in being able to quantify vibes is exactly what it is we’re looking for. Accounting for body language is one. Visible fun and rapport is another. But there’s also something to be said about performance. There’s even more to be said about performance versus expectations. An over-performing team is more likely to have positive vibes. An under-performing one would be just the opposite.
I think it’s also important to take in roster construction, in a sense. Are we looking at a team with big turnover? Or are we looking at a more stable situation wherein teammates have an opportunity to mesh? Where turnover exists, is it in pursuit of competition or to lower payroll? Taking that last one a bit further, is it a team built to compete or is it a rebuilding situation where everyone’s expecting to be traded? These things matter in the grand context of vibes.
So let’s boil it down to this:
- Legible body language & rapport
- General performance
- Productive turnover
- Perceived ambition
Each of these represents a fairly objective element in determining vibes. Happy teams with productive turnover that are performing at or above expectations with an end goal of a title. It’s hard to dispute that as the mark of a team with excellent vibes. Of course, actually blending that all together into something tangible will be challenging.
Working the Vibes Metric
With that in mind, we need a metric. This is where things get tricky. Because in order to have a metric, we need to be able to make each of those four components as concrete as possible. Performance is easy. We’ll deploy PECOTA or some other projection against actual record. But you can’t do that with body language. Perhaps we can…rate it? Derive a number that way? Turnover is similarly difficult. There’s nothing that says a high turnover team can’t have good vibes if the intentions are good. Perception of their goals, too, reveals that same quandary. Even if we kind of know.
Let’s do this, then. We’re going to eliminate the body language portion of it. It’s too much of an unknown. But we’re also going to hold onto it to revisit in future iterations of this metric. We’ll take wins above or below projected, using an aggregated version of various projection systems. We’ll use FanGraphs’ projection system for rest-of-season wins. Adding above, subtracting below.
Then, we’ll add those wins to the total WAR added by the team to this point in the year, using FanGraphs’ fWAR because of my own comfort level with it. To keep things simple, we’ll take the three highest figures from players added, with their last full big league season. Not everybody adds enough notable players to go above that.
For the ambition, we’ll add up total postseason appearances in the last…seven years. Ten seems like too many. Five too few. I like seven.
With all that, we arrive at a formula:
Wins Above/Below Projected + WAR Added (Top Three) + Postseason Appearances
We also need a name for our metric. Vibes+ seems like an obvious one. Everything’s got a plus these days. wRC+. Stuff+. OPS+. ERA+. But given that the plus is indicating above or below average, we’re not at that point yet. We don’t know what vibes look like, let alone average ones. We’re going to keep it super simple for now: Vibe Score.
Vibes Case Studies
Now we’ve got our formula. We have our metric. Let’s test it out on a few samples. I’ve chosen a sample of five teams at varying stages:
There are a few teams I considered, but am leaving off for now. The Philadelphia Phillies have had outstanding vibes the last few years into 2024. We don’t need to make theirs tangible. Kansas City seems to have excellent ones, too. While they’re over-performing, so does Milwaukee. I kept Kansas City off in favor of the Cubs, who spent all of May under-performing. In sum, we have a good team that was expected to be good. We have teams over-performing (Brewers) and under-performing (Cubs). Throw in a team that tries really hard to win but doesn’t (San Diego) and an objectively awful one (White Sox), and it’s a sample with which we can work for now.
- Projected Wins vs. Initial Projections: 8 (89 preseason proj. win average)
- WAR Added: 10.2 (Juan Soto, Alex Verdugo, Marcus Stroman)
- Postseason Apps.: 6
Vibe Score: 24.2
- Projected Wins vs. Initial Projections: 8 (79)
- WAR Added: 5.4 (Rhys Hoskins, Gary Sánchez, Joe Ross)
- Postseason Apps.: 5
Vibe Score: 18.4
- Projected Wins vs. Initial Projections: 2 (82)
- WAR Added: 9.4 (Dylan Cease, Michael King, Luis Arráez)
- Postseason Apps.: 2
Vibe Score: 13.4
- Projected Wins vs. Projections: -8 (65)
- WAR Added: 3.3 (Erick Fedde, Nicky Lopez, Paul DeJong)
- Postseason Apps.: 2
Vibe Score: -2.7
- Projected Wins vs. Projections: -1 (81)
- WAR Added: 0.8 (Héctor Neris)
- Postseason Apps.: 4
Vibe Score: 3.8
Work Still To Be Done
Interestingly, I’m not unhappy with the results. These certainly seem as they should be. Outstanding vibes for the Yankees. Very good vibes for the Brewers. Better than expected for the Padres, but they also get to play roughly 81 games in San Diego. The White Sox falling on the negative end certainly tracks, as does the Cubs’ minuscule vibe measurement.
At the same time, there is still plenty to do in order to make this viable (if that’s even the goal at this point). The projected wins against the preseason projection likely needs work. If I’m utilizing three systems for the initial ones, it should probably feature three systems on the backend. WAR added also needs a reevaluation. Three was probably an arbitrary number. There’s also no accounting for teams, like the Brewers, relying on the graduation of youth rather than outside help. Or the Cubs who added Shota Imanaga, who has no fWAR measurement. Do we take into account their WAR at the time of review? Or is there a different measurement to indicate productive turnover?
The body language piece missing from this is problematic, too. Teams who are visibly having fun should be rewarded in the vibes department. Miserable teams should see that reflected in the Vibe Score, too.
This is merely the first part of the process, of course. We’re far from a finished product. And that’s where I expected to end up. The aim here was to create a very public hashing out of my thought process in trying to do something that probably ends up being complete nonsense anyway. In that respect, I’ve had success. There is potential here, though. I think that we can absolutely quantify a team’s vibes. Maybe just not in a way that we might expect.