“” A Deeper Dive into Fastball Spin Rate - Driveline Baseball

A Deeper Dive into Fastball Spin Rate

| Pitch Design, Pitching Mechanics, Research
Reading Time: 13 minutes

In a previous article, we discussed the difference between cues and measurements as well as how technology is changing baseball. We now know that instead of simply repeating cues verbatim to players, learning more background knowledge of certain topics is needed. One of those topics is spin rate. As you’ll see today there are lots of layers that go into understanding how a pitch is effective.

We’ve previously covered some basics of fastball spin rate and spin rate of off-speed pitches, but it’s time for another look.

In this article, we first look at high-spin fastballs and ball axis, and then we move deeper into the complex nature of spin, axis, and movement. We also explain some of the nuance with high spin and its relationship to movement. This article intends to show how a concept, like spin rate, can scale from relatively simple to more complex when you really dive into the metrics.

While the available information and what’s relevant to each player scales per level, equipment, and knowledge, today we aim to show what you can learn from even relatively simple explanations, which will open the possibility to learn more down the road.



Basics of Spin Rate: A Recap

When a fastball is thrown with backspin, we can explain its vertical movement with Magnus force. The ball spins, pushing the air downward behind it and creating and equal, opposite force upward. So a good part of the movement of a fastball depends on how fast or how slow, called the rate, it is spinning.

This can have effects on hitters, which we’ve observed through research using a pitching machine with different spin rates.

From: The Effect of Fastball Backspin Rate on Baseball Hitting Accuracy: Higuchi, et al., 2013

Picture from: Baseball Spin and Pitchers’ Performance: Kanosue, et al. (Open Access)

Now the fastest spin rate used in the study above is faster than what we’ve seen pitchers throw, but the point still stands: hitters are more likely to swing under a high-spin fastball. We also see that hitters tend to hit more fly balls and have more swings and misses on high-spin fastballs, which is why they’re more likely to hit low-spin fastballs on the ground.

We can also look at the relationship of fastball spin rate (while holding velocity constant) and its relationship to swinging strike percentage and average launch angle.

Now, based on reading the above chart, it would be easy to conclude that high-spin fastballs should be thrown high in the zone and low-spin fastballs should be thrown low in the zone. However, this conclusion isn’t so simple, and we need to look deeper to find better context.

First, you’d need to control for velocity. We’ve seen that spin rate increases with fastball velocity, but it’s also easy to misapply basic lessons—especially if you are comparing pitchers with different velocities. This is largely why we like to use Bauer Units, to better compare fastball spin rates, because you can have pitchers that throw the same spin rates, and whether they are high or low depends on velocity.

Below is a simple example, showing how pitchers with the same spin rate but different velocities have different Bauer Units.

Understanding Bauer Units and how to judge spin with them gives coaches further context of the spin rate of fastballs. But, especially with fastballs, ball axis also needs to be taken into account—and it’s often forgotten.

Fastballs and Spin Axis

There is a distinction between a high-spin fastball that has a high degree of vertical movement and one that does not. This is because of the ball’s axis.

So, it’s not just the spin that we want to focus on. We want to focus on the spin, what the axis is, and how that is related to the movement of the pitch. Even more specific, you can have two pitches with the same velocity and spin rate but have different movement profiles based on the axis of the ball.

Below is a pitch with a “1:00” spin axis and a 95% spin efficiency, based on Rapsodo metrics.

Below is a pitch with the same 1:00 axis and a 50% spin efficiency, based on Rapsodo metrics, giving it more cut relative to the pitch with 95% spin efficiency.

Now, video and some published research can tell us that the spin axis of each pitch significantly correlates with the orientation of the hands and fingers just before and at ball release. Which makes having a camera especially important when trying to piece together what ball tracking technology is saying.

When looking closer to see if a pitcher cuts his fastball we need to understand that some pitchers have essentially grown up cutting the ball when they throw a fastball, which is incredibly difficult to change when they’ve thrown thousands if not millions of pitches with that pattern. Other pitchers may have slight fluctuations in their release point, or fingers at release, that can change their spin axis, which can be adjusted given proper video and spin-rate feedback. This is important because one of the differences between amateurs and professional pitchers is the consistency of their spin axis.

The movement of a fastball will also be affected by a pitcher’s arm slot, or release point. As arm slot changes, so does the orientation of the hands and fingers at release, thus affecting the axis of the ball.

Assuming pure transverse spin, a pitcher who throws from “over the top” will have a nearly horizontal spin axis. A high percentage of backspin with a horizontal axis causes most of the spin deflection to be in the positive vertical direction. (That is to say, basically up.) With a more traditional three-fourths slot, we start to see more similar horizontal and vertical break values. This can be attributed to the axis shifting to a more diagonal position—meaning more sidespin. Lastly, a “low slot” or sidearm pitcher, will likely have a fastball spin axis that’s almost purely vertical. As a result, nearly all the movement will be lateral to the arm side.

As you can see, high spin rate by itself does not mean more positive vertical break. In this case, the pitch movement largely depends on the axis which the ball revolves around.

This was examined in 2015 in the Baseball Prospectus annual report. They used Brooks Baseball data along with arm angle measures of 25 pitchers (provided by ASMI). They ended up finding a correlation of .75 between four-seam fastball angle and arm angle and a correlation of .79 between two-seam fastball (sinker) movement and arm angle.

Lastly, how spin axis is measured depends on the technology used to track ball flight.

Both Trackman and Rapsodo measure spin axis but in different ways. Trackman follows the entirety of ball flight and infers spin axis from the trajectory. Rapsodo calculates spin axis directly, but infers trajectory based on the axis itself. Each method has its strengths and shortcomings, for example laminar flow isn’t accurately accounted for, but the main advantage of Rapsodo is that it can accurately measure spin efficiency. This is very helpful for designing pitches.

In short, knowing a pitcher’s spin rate on his fastball is important, but knowing his spin axis is also important because that gives you more context for how the pitch moves and how he can use it, or if it may be beneficial to try and change it.

Tying the Influence of Spin Rate and Spin Axis Together: An Even Deeper Look

Beyond the relationships mentioned above, we also know that spin axis influences both spin rate and spin efficiency. Essentially, the more you cut the ball, the more total spin you’re able to generate, often at the expense of transverse spin.

This means that a high-velocity pitcher who naturally cuts the baseball may have a significantly higher raw spin rate than a low-velocity pitcher with natural arm-side run, despite not actually being better at generating spin. In other words, a high-velocity, over-the-top pitcher who is actually less able to use a combination of finger forces and friction to add spin on the ball at release could produce a higher spin rate than a low velocity, sidearm pitcher who can generate spin well.

To control for this, we created Spin+, which attempts to predict a player’s spin rate, using MLB Statcast data, based on both spin axis and velocity. This allows us to isolate only the rotations per minute generated from factors outside of velocity and axis.

Spin+ is useful because we can compare the spin rates of pitchers like Andrew Triggs—who had an average spin rate in 2018 of 2,414 rpm with just an 89 mph fastball and a completely vertical spin axis (in x-z direction) of 269 degrees—with Carl Edwards Jr.—who throws a 94.5 mph fastball that averages 2,658 rpm and an almost completely horizontal axis of 173 degrees.

Although Edwards Jr. boasted the second highest fastball spin rate of any pitcher in the big leagues in 2018 with at least 75 fastballs thrown, Spin+ believes Triggs’ ability to spin the baseball is actually ~90 rpm higher than Edwards Jr.’s, once you control for the advantage that Edwards Jr. has with regards to velocity and axis.

The main takeaway is that raw spin rate can be deceiving without additional context—particularly in physically maturing pitchers where velocity may jump periodically, and spin axis may be more volatile. In designing a pitch, it is important consider how an athlete’s spin rate might change based on the adjustments you’re trying to make. Therefore, it’s important to not only monitor the raw spin rate but the axis as well. Video can also be helpful in this case to get a better idea of how the ball is coming off the fingers.

Applying Spin Rate and Spin Efficiency to Pitch Design

Additionally, we can also apply the Spin+ methodology to spin efficiency, which like raw spin also shares a relationship with spin axis and velocity. In combining both internal measurements of Spin+ and spin efficiency, we’re able to gain a much better understanding of where a pitcher has the potential to improve based on his underlying data.

To highlight an example using Statcast data in the illustration below, we see the top ten fastballs in 2018 that scored highest in our “pitch potential” metric. This metric is designed to identify fastballs in the big leagues with the greatest potential to increase total movement based on a pitcher’s inherent spin characteristics. By having a pitcher’s predicted and actual spin rate alongside his predicted and actual spin efficiency percentage, we’re able to compare what we’d expect his transverse spin rate should be compared to what actually occurs.

For example, in looking at the 2018 leaderboard above, we see that Tyson Ross threw his two-seamer (FT) with an additional 347 RPMs of raw spin compared to what we would predict given the velo and axis on the pitch. With this information, we can then calculate a Spin Efficiency percentage, which expects that Ross would generate ~2,190 rpm of transverse spin on his two-seamer during the 2018 season.

However, in looking at his output for the 2018 season, we see that Ross came up about 291 rpm short of that figure and only 14 rpm higher than what we would expect an average pitcher with an average spin rate would produce at his axis and velo. This means that Ross is essentially throwing his two-seamer (and four-seamer [FF]) with just league-average movement, despite being amongst the league leaders in generating spin.

Now, in spite of leaving movement on the table, this does not necessarily mean that Ross, or any other pitcher on the list above, has a “poor” design on their fastball. Rather, as you may have noticed, most of these pitchers are sinkerballers by trade, and any increase in transverse spin would impart more “carry” onto their fastballs. By killing movement via gyro spin, they are essentially making their sinkers or two-seamers “heavier,” which almost certainly better plays to their approach of how to get batters out.

This highlights a more parsimonious relationship between spin rate, spin efficiency, and the “right” approach to designing a fastball. There really is no a + b = c magic formula where player x needs to throw a fastball with a specific axis and efficiency percentage to have more success. Rather, we need to consider the constraints of the individual alongside a proper game plan to formulate something that will work best for an athlete’s specific skill set.


As you can see, there is a lot of depth to the discussion around spin rate. The rate that a pitch spins matters, as does the axis. But the axis can change depending on arm slot and the orientation of the hand and fingers at ball release. Beyond even that, you can get even more granular when looking into MLB spin-rate data.

Your head might be spinning from the more complicated sections at the end, but there are a few key points for coaches and players.

  • Spin rate is important to understand, because it can affect the movement of the pitch, which helps determines the amount of whiffs and types batted ball outcomes a pitcher gives up.
  • If you’re looking to judge a pitchers fastball, you’ll need to look past the raw spin and control for velocity. Bauer Units are a good way to do that, this is especially important for pitchers with outlier fastballs velocities, either lower or higher.
  • Next you’ll need to look at the pitches spin axis. The spin axis, along with a movement profile, will tell you more about a pitch than just spin rate alone.
  • There is a large amount of MLB data available that can tell you even more about different pitchers and their repertoires. We’re just starting to scratch the surface of what it means and how it’s most useful.

Lastly, we need to take a data-driven approach when asking a player to make a specific adjustment on how to throw a pitch. This assures both the coach and athlete that the time and effort spent making said adjustments do not go to waste.

This takes some getting use to, but recognizing what pitches have the potential to be good or bad is the first step in using technology. Ultimately, knowing whether a pitch is ‘good’ depends not only on the spin, axis, and movement profile of the pitch, but also how it performs in game.

As a result, being able to utilize technology to objectively assess a given pitch within the context of relevant metrics is vital to get the most out of a player’s given abilities.

This article was co-written by Dan Aucoin, Michael O’Connell, and Eric Jagers

Comment section

  1. Karl Robbins -

    Great article… love the conclusion and those four bullet points, but wanted to ask how strong the relationship was between Spin Rate and Velo/Spin Axis? in my limited experience, those would not have much predictive power to Spin.

  2. Karl Robbins -

    did you really delete my earlier comment? I think it’s a reasonable question to ask what kind of relationship you found from Spin Rate to Spin Axis and Velo. you lose credibility when you’re not willing to share the data and models that go into your assumptions and conclusions. Disappointing to see the deleted comment b/c generally you guys consider yourself very “data driven”

    • Karl Robbins -

      whoops… did not see that my previous comment was still awaiting moderation. it did not appear when I revisited the blog post. apologies.

  3. Patrick -

    hi, this is an awesome article. kind of a dumb question, but Is there a way i can access this data myself and make my own spreadsheet, similar to the ones showed in here? or is some of it proprietary data not publically available? thanks

  4. danisongray.robinson -

    This is fascinating data-based insight. I have been using pitchLogic for a while now and have found it very helpful for understanding what is happening with my spin axis and spin rates. What surprised me the most was how much of my spin action was around the riflespin axis.

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