Much to the chagrin of geriatric baseball analysts and Alex Rodriguez, analytics are an enormous part of the modern game in Major League Baseball, whatever Brian Snitker does or doesn’t do.
This is my favorite thing to happen on this app in a long time pic.twitter.com/5q1PbzU5Eb
— Brett Anderson (@_BAnderson30_) October 24, 2021
We have repositories of information on sites like Fangraphs, Rotographs, Baseball-Reference, and Baseball Savant. We have heat maps and hot zones, rolling graphs and sliders, and more rows of data than you can shake 1,000 sticks at. IT’S SO AWESOME!
The only problem is that in a sea of available data, it can be really easy to overlook some of the newer or more obscure stats that can be useful for context or shedding some light on otherwise confusing scenarios. Let’s take a look at some that you may never have heard of and definitely won’t find on sites like Fangraphs or Savant.
Pitchers do amazing things to and with baseballs. Sure, you can go look and see that Dylan Cease’s curveball may have just had more than 12 inches of drop or that Lance McCullers had more than 10 inches on his changeup. You can see that Jacob deGrom had an average four-seam velocity of 99.2 mph and that Garrett Richards led the league in spin rate on his curveball with 3142 rpm.
But the real proof in the pudding comes from ooMPH. There is no better measure of a pitcher’s effort, combining not only elements of velocity and ride, but factors in the intensity of important things like grunt dB and glove pop. It is a true measure of a pitcher’s effort on his most laborious pitches. Nothing says, “I put everything into that one,” like ooMPH or expected ooMPH, xooMPH.
Even on full-effort pitches, sometimes the pitcher simply cannot miss bats. This is a fairly obvious statement because if it was as easy as choosing to miss bats, then every MLB hurler would have a 100% swinging-strike rate and no game would ever end, stuck in perpetual 0-0 ties for eternity. When this happens, there is nothing worse than defensive errors that lead to runs. When these errors come on routine plays it is especially frustrating.
To calculate these in a way that is fair to pitchers we could use “earned errors” to describe balls that were scorched versus “weighted errors” that should’ve been fielded. In other words, if the pitcher gives up hard contact and it eats up the defender, that’s really on him. But if a player just boots it, that’s a true error.
The first attempt at this was EWE like the female sheep and pronounced the same way (as in you). But really, earned plus weighted errors is better described as forced earned plus weighted errors, or fEWE. It goes without saying that the lower the fEWE the better because you really don’t want to have to hear your coach say it over and over and over again.
And while we are speaking of ferocity, there is a trio of stats that work together to capture a more holistic view of the raw, animal nature of a pitching performance. It combines elements like velocity, run averages, and pitch torrent (a combination of speed and ride). If you want something that gets at the true intensity, the vicious, razor-sharp, claw-like truth of a gutsy pitching performance, you want to look at this:
Velo / cIRA / pTORs.
Just be careful, you may get outmaneuvered and just when you think you have it figured out, you’ll be blindsided by a sneak attack out of nowhere. If you get the teeth, it’s already too late.
The only thing harder in sports than throwing baseballs is hitting baseballs. Things like Barrels and Barrel% are all well and good. HardHit% is fine and max EV is fun, especially when you look at Giancarlo Stanton’s production (spoiler alert: he hit a ball 122.2mph in 2021).
But if you really want to look at the swing you want to look at a batter’s SwIM / SwUM numbers. These are basically the same number with slightly different names and calculations because they come from two different sites, but nothing is better at measuring stroke than SwIM (or SwUM). It’ll be amazing when the park-adjusted version comes out in early 2022 and we can work with SwIM+.
A nice companion stat for stroke looks at how a player drives the ball, mostly focused on the gaps, with a focus on “runs-scored” as a result of the hit. There’s a huge difference in the repeatability of a ball that finds the gap on fairly soft contact and those that get there in a hurry, especially in terms of expected runs as a result. So when we want to see how well a ball is driven, we want to look at CaRS. Nothing measures drives like CaRS.
While we are talking about how well a ball is struck, we hear a ton about launch angle (LA) which, for the uninitiated, is simply a measure of the angle at which the round ball comes off of the round bat. It is used as part of the formula for Barrels and is the only thing that matters in LD%, or line-drive rate. I would suggest, however, that if you want to get a handle on the best versions of a Barrel, you take a look, not at LA, but RA, which tallies balls hit so hard they are launched directly into the sun (to give credit where credit is due, this comes to us from the Egyptian leagues).
Playing with Baseballs
Some measures are not specific to just one side of the ball. For example, according to this article from 7upsports, players like Noah Syndergaard, Kevin Kiermaier, Mookie Betts, and George Springer have high SWooN% numbers.
One of the most oft-cited numbers is a player’s WAR. There’s fWAR and bWAR, depending on where you get your information. The idea here is to get a sense of a player’s value above a replacement-level player. It stands for “Wins Above Replacement” and is designed to tell you how much better or worse a player is above a fairly average guy.
What you might now know about are talWAR and seWAR which are, admittedly, real stretches here in this piece. Don’t feel bad if you aren’t sharp enough to get the point, but these are cutting-edge ideas in the field of analytics. If you’re holding onto one of these, you may find yourself in a duel, but stand your ground because giving up would be like bringing a club to a swordfight: pointless.
This next set of metrics are more imperial than they are empirical, but a couple of WAR alternatives that are up and coming are tSAR and cZAR. Once again, they get at largely the same things but come from different stat farms, but they look at a brand new concept in “above replacement” with the strength of a good vodka and a heartiness that can survive a long, cold winter. Ultimately they may give way to a revolution, so enjoy them while they last.
I sincerely hope that you have been in on the joke throughout this piece and are not on a frustrating deep dive into Baseball-Reference looking for any of these non-existent stats and metrics. Everything in bold is not real and if some of the references went over your head, go back through it, ask a friend, Google it… do whatever you need to do.
Until we meet again.
Photo by David J. Griffin/Icon Sportswire | Design by Michael Packard (@designsbypack on Twitter @ IG)