If you’re as online as a normal Razzball reader would be, you’ve seen a lot of talk about MLB Statcast’s new bat tracking metrics. Bat Tracking data was officially introduced to the public last year on May 13, starting with data for the 2024 season, and since then they’ve backfilled data up to the All Star Break of 2023.
https://x.com/MLBStats/status/1788253809979834387?s=19
The initial dataset has included three primary items:
- Bat Speed – This is the speed the bat is moving *at the point of contact* (this is important and will come up later)
- Swing Length – The distance the bat traveled from the beginning of the swing to the point of contact
- Squared Up Rate – What percentage of balls you hit achieved 80% of attainable exit velocity based on the speed of the bat and ball
In addition to the above, we’re going to see “attack angle” introduced to baseball savant this season. This will be the angle that the bat is moving through the zone when it contacts the ball (think how much of an “uppercut” swing someone has). The data has provided countless insights into player performance and abilities, and here are some truths and myths I’ve seen from the past year of looking into swing metrics.
Truth #1 – This Is A Game Changer (or at least it will be)
Remember when you were first hearing about “pitch modeling”, or some new metric called “stuff+”, and the idea that you were only evaluating your pitcher draft targets based on result based metrics (even good ones like K-BB% and FIP) wasn’t feeling like enough? We’re still in the early days of swing metrics but don’t be surprised if you’re hearing similar swing modeling terms working their way into your lexicon in the next few years. There’s a lot more to hitting than swinging the bat hard, but have no doubt we’re only seeing the tip of the iceberg with bat speed and swing length, and with the addition of attack angle, we’ll soon have a whole new way to measure a player’s approach at the plate, all of which will be critical to use in your fantasy performance moving forward.
Myth #1 – Swing Speeds Are Like Pitch Speeds
Although individual swing metrics seem like pitch metrics, someone swinging the bat at eye popping speeds doesn’t work the same way as someone throwing their fastball at eye popping speeds. This is because the distribution of your swing speeds vary much more, and while Miles Mikolas will never randomly pop 100mph on a fastball, someone like Maikel Garcia who’s considered a “slow swinger” could swing the bat 80mph, which is faster than Oneil Cruz’s average swing speed.
More over, *where* you hit the ball in your swing plays a big part in determining your swing speed. Remember when I said *at the point of contact* earlier in the bat speed definition? As you’re swinging down on the ball, the bat is moving faster and faster, and if you’re getting “jammed” and making contact early in your swing, the bat won’t be moving as fast as catching the ball out front:
This is why hitters typically swing “Slower” on higher pitches, not because they’re physically swinging slower, but because those are catching the bat before it reaches maximum speed.
A pitcher can’t decide, “I’m going to throw low in the zone because I can throw faster there”, but when it comes to hitters, there is a bit of a selection bias based on which pitches they are swinging at.
Truth #2 Swing Metrics Are A Leading Indicator
Just like pitch modeling has given us instant insight into a player’s performance and future ability, swing metrics will let us spot physical abilities of a position player like never before. The same way we rely on modeling numbers to determine if that waiver wire SP having two good starts in a row is for real, swing metrics will help us determine if the 29 year old Triple A player the Marlins just called up and had a 2 HR game is someone you’d want to risk your FAAB money on.
Despite the variations in swing speeds mentioned above, the average swing speed stabilizes incredibly quickly, with different research suggesting somewhere under five tracked swings to determine a player’s ability to move his wood. This is not only going to give us data on random Marlins call up guy, but to evaluate prospects who play in statcast equipped parks both in college and the minor leagues.
Myth #2 Swing Speeds Are All About Damage
Swing fast, do damage, hit ball far might be the idea behind our big focus on swing speeds initially, but just like there is more to pitching than throwing the ball fast, we’ll see swing metrics used to evaluate a player’s overall approach at the plate. For example, the addition of swing length and squared up at the launch of bat tracking gave us a couple of important insights beyond “wow Giancarlo Stanton swings fast”:
Players like Luis Arraez And Steven Kwan intentionally swing slower because they are trying to “square up” the ball
Players like Isaac Paredes and Nolan Arenado have very “long” swings based on swing length, because they are trying to loop around the ball and pull it in the air
It’s important to keep in mind that there is an intentionality to what a player is doing, and it’s not going to be as simple to say “this player doesn’t swing fast so they could not hit for power.” Which brings me to the final point:
Truth #3 There Is Just A Lot We Don’t Know Yet
We barely have year-over-year data, and it’s only one half of 2023 vs 2024. We’re still getting a grounding on how much a player’s swing metrics change over time, and more importantly how those changes affect their performance at the plate. The introduction of attack angles will unlock a whole new tier of data and get us closer to a holistic swing and approach scoring system that I’m certain someone (smarter than me) will introduce in the coming year.
Another level of discovery will be unlocked when we begin merging our pitching modeling against hitting modeling. Think about the LHP who’s said to put hitters “in the rocking chair”. Theoretically we should be able to measure uncertainty or hesitation in a hitter’s swing, since we know how fast they normally swing vs how fast they swing at rocking chair guy’s pitches.
The fact is we’re still at the infant stage of bat metrics. This is the early days of statcast 10 years ago where we were saying “Well.. this guy’s curveball spins a lot… so that sounds good?” And think of how far pitcher evaluations have come since. We’ll simply have to wait and see what the smart people (not me) discover next.
