The bat tracking period is here, and absolutely nothing will ever be the exact same once again. Wait, no, that’s wrong. Baseball is going to continue basically precisely as it was. Pitchers will toss the ball, players will swing at it, and after that individuals will run around the field either attempting to capture it or touch bases. But baseball analysis is going to begin looking various, since we experts have brand-new glossy toys and a wide variety of originalities to evaluate out. That’s really interesting, and likewise potentially a little frustrating. So today, I believed I’d take you on a trip of what the top-level summary numbers do and don’t state about striking, in addition to stump for more granular analysis. I’m sure I’m not alone on either of those points, however still, it’s excellent to state it aloud. So let’s speak about typical swing speed, typical swing length, squared-up rate and blast rate, shall we?

Swing harder, do much better, right? Well, perhaps. That makes good sense broadly, and it especially makes good sense when you take a look at a few of the names dotting the top of the swing speed leaderboard. Juan Soto, Aaron Judge, Yordan Alvarez, William Contreras, Mike Trout, Shohei Ohtani, Gunnar Henderson — there are a lot of players at the top of the swing speed leaderboard who are undoubtedly outstanding.

Pop down to the bottom, however, you might make a quite fantastic offending group out of the soft swingers too: Luis Arraez, Steven Kwan, Justin Turner, Marcus Semien, Isaac Paredes, Will Smith, Jose Altuve. In aggregate, there just isn’t much connection in between typical swing speed and offending production, as determined by wRC+. More particularly, there’s a 0.11 connection coefficient in between swing speed and wRC+. That suggests, broadly speaking, that variation in swing speed discusses just 1% of variation in wRC+ (0.012 r-squared). A fast note: I utilized an 80 PA cutoff for this and all subsequent computations in the short article, so we’re comparing apples to apples.

That’s apparent if you stop and think of it. Baseball isn’t a quick swing competitors. Swinging quick assists, clearly. But Giancarlo Stanton isn’t the very best player in baseball history in spite of likely being the hardest swinger, and Luis Arraez isn’t the worst player in baseball, or perhaps near to it. Swing speed is simply one information point that explains a bit of what a player does at the plate.

Swing length is another intriguing brand-new information point. Just like swing speed, you can draw some apparent conclusions without even browsing the information. A longer swing suggests more strikeouts, right? Well…

Swing length and strikeout rate have actually a .277 connection coefficient and a 0.077 r-squared. That’s okay. You can do much better by taking a look at swing length versus whiffs per swing, which removes out strike zone judgment (not what we’re searching for here). That gets us an r-squared of .152, which is much better on a relative basis, however still not substantial – it’s about the like the year-over-year connection of BABIP, which we understand is rather loud. Both typical swing speed and squared-up rate (in the opposite instructions) have more powerful connections to whiffs per swing, in truth. But it’s usually real that slower, much shorter swings that focus on getting the head of the bat on the ball lead to less strikeouts.

Swing length is likewise greatly associated to pull rate. If you’re attempting to strike the ball in front of the plate to pull the ball, your bat will naturally take a trip further, even with the precise very same swing mechanics, than if you satisfy the ball previously in your swing. I don’t have a terrific method of managing for this yet, however it’s practical that by managing for batted ball propensity, you might discover even more powerful relationships in between swing length and contact rate. It’s not to state that a long swing is bad, simply that it features tradeoffs.

Let’s return to bat speed. It’s clear from a preliminary examination that you can’t state a load about a player’s general efficiency simply by taking a look at how quick they swing. There are still some things to discover here, though. For example: The more difficult your typical swing, the more damage you do on contact in basic. I’ve noted enough connection coefficients to put even the most stat-obsessed readers to sleep, so at this moment I’m simply going to reveal a grid of all of them and be finished with it:

Why is swing speed more carefully associated to xwOBACON than wOBACON? Two factors. First, we’re handling little samples throughout the board and production on contact is loud, which suggests that a player with a lots of seeing-eye songs on mishits can screw up the information. Second, wOBA cares a lot about the horizontal angle of your hits, however neither xwOBA nor swing speed does. Isaac Paredes doesn’t swing hard, and doesn’t strike the ball especially hard as an outcome. His xwOBACON and swing speed concur. But he’s discarding those batted balls over the left field fence for crowning achievement, and wOBA understands that.

I believe that raw bat speed information is going to wind up a lot like other raw pitch and exit speed information: intriguing however insufficient. I didn’t require a leaderboard to inform me that Giancarlo Stanton swings more difficult than any other gamer in baseball, since I have actually seen Giancarlo Stanton swing before. It’s actually cool that we’re now determining this, and I’m sure you’ll hear it on broadcasts all the time moving forward, however the link to production is rare enough that I don’t believe it’s a terrific figure all by itself.

Now onto the a little more complex stats: squared-up rate and blast rate, which determine players’ capability to strike the ball right on the nose, and do so with high swing speed when it comes to blasts. Squared-up rate appears like an undoubtedly fantastic metric right off the bat. When you struck the ball right on the sweet area, you’d anticipate a lot more line drives. After all, Luis Arraez is the king of soft line drives and likewise the king of squared-up rate. Just one issue: there’s no connection in between squared-up rate and line drive rate, a minimum of in 2024 information.

Now, perhaps that’s simply a sample size problem. Line drive rate is loud even at a seasonal level, never ever mind after a month and a half of play. If you focus more detailed, the result appears genuine. Batted balls that Statcast classifies as squared up bring a 28% line drive rate up until now this year; balls that aren’t squared up have a 19.1% mark. The problem here is that for a player who increases their squared-up rate by 5 portion points, we’re speaking about a boost of 0.4 portion points of line drive rate. Half the gamers in baseball have a squared-up rate in between 22% and 29.4%. The distinctions here are little. Beyond that, squared-up rate is almost uncorrelated to BABIP, wOBACON, and xwOBACON. Should we simply quit on squared-up rate?

I don’t believe so, in spite of those uninspiring numbers. Squared-up rate and typical swing speed are rather associated themselves, and in the rational method. The more difficult you swing, usually speaking, the less regularly you struck the ball square. That’s why Juan Soto’s mix of terrifying swings and fantastic contact is so remarkable. If you run both swing speed and squared-up rate through a multivariate direct regression versus steps of production on contact, they’re both considerable. In other words, swinging more difficult and squaring the ball up more regularly both boost production.

Without digging too deep into the analytical minutiae, these 2 stats are so associated that I don’t have a great deal of self-confidence because regression. But even after you remedy for that multicollinearity, there’s a clear relationship: swing the bat harder or make ideal contact more regularly, and you’ll tend to do much better on contact. But even then, those 2 things don’t discuss all or perhaps the majority of a player’s production on contact. There’s plenty more than simply swinging difficult and capturing the ball on the barrel of the bat. That’s a milquetoast conclusion, sure, however it’s still a helpful one to me; it’s excellent to make certain that 2 plus 2 is 4 before you begin on differential formulas.

The exact same is usually real of blast rate, the rate at which a player squares the ball up while swinging hard. It does a bit much better since it’s recording what I was speaking about up above; more difficult swings square the ball up less regularly in basic, so you desire both when you’re searching for production. But once again, a lot of other variables enter into this also. As you may anticipate, swinging your bat difficult and squaring the ball up are at their finest when it pertains to producing strong contact at favorable launch angles.

That sounds a lot like launch angle and exit speed, and we understand that those inputs do an excellent task, however not an ideal task, of describing production. As best as I can inform, that’s simply a basic constraint of stats like this that separate a little part of what’s associated with striking a baseball. There are a lot of other things you can do to create worth, and a few of them may even reduce your swing speed or squared-up rate, which will permanently irritate analysis.

Okay, so we understand that neither raw swing speed nor squared-up rate do a terrific task of anticipating general production. What can we gain from this brand-new bat tracking information, then? First of all, it’s simply exceptionally cool that it exists. Hitters can and ought to act in a different way based upon their swing speed, and now we can measure that more than ever in the past.

More notably, the cool part of this information is mostly in granular analysis. I discover it interesting that in-zone swings at secondary pitches are meaningfully much faster, usually, than in-zone swings at fastballs. Hitters swing much faster at in-zone fastballs when they’re ahead in the count than when they’re behind in the count; that makes user-friendly sense, however we have the real proof now. Before, you might state something like, “Hitters can sit on a fastball thrown to a particular area when they’re ahead in the count and unload if they get it, but they have to react when they’re defending the zone,” today you can show it. They do far better on those early-count swings, which we currently understood; now we feel in one’s bones why with more certainty.

Here’s an enjoyable one: Early-count secondary pitches get squared up less regularly than two-strike ones, however at greater bat speeds. There’s a strong user-friendly pattern here. Hitters slow their swing and focus on contact when they support in the count. But although they’re squaring the ball up a little more regularly, that squared-up contact is less efficient – they’re swinging more gradually, after all.

When we get more months and years of bat tracking information, the applications will just increase. Is a player cold since that’s how players get in some cases, or is he physically jeopardized? Is that brand-new much shorter swing offseting its lower bat speed with much better contact numbers? Has that aging veteran remade his swing to focus on contact now that his bat speed is flagging? Mike Petriello is currently speaking about new applications, too: attack angle and miss out on range will put swing speed information in far better context, and I’m delighted to get them in the fold.

There are undoubtedly some cool applications on the pitching side, too; clearly pitchers don’t do a load to impact bat speed, however “avoiding the fat part of the bat” has actually long been the holy grail of contact supervisors. Only 11.1% of swings at Hunter Harvey’s fastball square the ball up, while 34.4% of swings at Adrian Houser’s fastball do. That seems like a remarkable discovery, however we’ll require to see how steady these stats are to actually understand for sure.

When we discover methods to determine something formerly unmeasurable, it’s appealing to ascribe fantastic untapped analytical power to those things. And to be clear, I believe that there are going to be some cool advances in public-side analysis that wouldn’t have actually been possible without this information. One thing that likely won’t advance public-side analysis, however, is asking, “Oh hey, who swings the hardest?” and leaving it at that.

So head out and have a good time taking a look at this brand-new details, and checking out the analyses and musings of individuals like me who are looking for some brand-new stories to inform with it. But understand the intrinsic constraints. Bat tracking isn’t adequate to inform you who’s excellent and who isn’t, and it doesn’t need to be. We currently have a lot of methods of determining that. Now, we’re simply broadening our horizons a bit more.