*Welcome*
to my site devoted to research on the **physics of baseball**. My particular research interests are two-fold: the physics of the baseball-bat collision and the flight of the baseball. I have done quite a bit of independent research in both areas. I am also heavily involved with several areas of practical interest to the game. One is characterizing, measuring, and regulating the performance of non-wood bats, an area for which I have served on committees advising the NCAA and USA Baseball. Another is exploiting new technologies for tracking the baseball, such as TrackMan, Rapsodo, and now Hawkeye for novel uses in baseball analytics. But this site does much more than catalog my own work. It attempts to provide links to much of the high-quality work done over the past decade or so on various aspects of the physics of baseball. If readers know of a site that I have overlooked, please contact me.

### Demonstrating the sweet spot of the bat in a talk I gave at Wrigley Field for University of Illinois alumni in 1997.

## Recent Research Highlights

Alan Nathan, April 12, 2023.

### Home run probability vs. exit velocity for 2022 fly balls hit with launch angles in the range 27-28 degrees. The data clearly show the temperature dependence, indicated by color.

This article is an update to an ** earlier one ** from 2017. In both articles, I find that a 1-deg F increase in temperature results in a 1% increase in home runs. The analysis was inspired by a recent article by a group from Dartmouth entitled ** Global warming, home runs, and the future of America’s pastime**, who found a similar results.

Jim Albert and Alan Nathan, FanGraphs, May 10, 2022

### A plot showing the relative carry of a home run as a function of the density-corrected drag coefficient. Each point is a monthly average over the period 2018-2022.

We have investigated changes in the in-play home run rates for the 2018-22 seasons, using April ’18 as our reference. We have developed a technique to separate the changes into those due to changes in launch conditions and those due to changes in carry. We have further shown that the latter are directly correlated with changes in the density-corrected drag coefficient. Finally we have shown that the significant reduction in the in-play home run rate for the 2022 season relative to April ’21 is due in part to changes in launch conditions (approximately one-third) and in part to changes in the drag (approximately two-thirds).

Alan Nathan, March 29, 2021.

There has been much discussion about changes to the baseball for the 2021 season and how those changes might have affected various statistical metrics. In this brief article, I discuss how changes to the weight, size, and COR ("bounciness") of the baseball affect exit velocity and fly ball distance.

Alan Nathan, January 4, 2021 and updated January 5 with corrections.

Alan Nathan, March 18, 2021.

Alan Nathan, March 1, 2023.

Alan Nathan, Powerpoint Slides from SABR Analytics Talk, March 13, 2021.

A plot showing the movement on Alex Cobb's sinker. The green, blue, and red points are the total, lift, and side movements, respectively. The black dots are the averages of each. For each point and for the averages, the green points are the vector sum of the corresponding blue and red points. The pseudo-nature of these data are discussed in the text.

As a followup to the "Not Just About Magnus Anymore" article (see link below), the first article sets up a formalism for separating the movement on a pitch into Magnus and non-Magnus components, using Hawkeye data on the spin components. The second article takes a slightly different approach in that it sets up a formalism for separating the movement into lift (i.e., in the direction of the Magnus force) and side (perpendicular to the Magnus force) components. Whereas the side component is necessarily non-Magnus, the lift component, while predominantly Magnus, may have some non-Magnus mixed in. An example of how to use the second formalism is shown in the above graph and is discussed in the text. Click **here** for a spreadsheet template for performing the calculations using both formalisms.

Barton Smith, Alan Nathan, and Harry Pavlidis, Baseball Prospectus, November 5, 2020

This article reports the first use of Hawkeye data to find evidence for pitch movement beyond that expected due to the Magnus force. The key to the analysis is that Hawkeye directly measures the spin axis, which can be compared with the axis inferred from the movement. Any difference between them is direct evidence for **non-Magnus movement **. Such behavior is shown in the graph below. Interestingly, the data show more arm-side movement for sinkers and changeups than would be expected based on Magnus, whereas the opposite is true for fastballs. Once the spin axis in 3D becomes publicly available, one will be able to measure directly the spin efficiency and quantiatively measure the magnitude and direction of both Magnus and non-Magnus movement. One will also be able to determine how drag coefficients depends on both the active and gyro spin rates.

Plot of the optical spin axis, as measured by Hawkeye, vs. the spin axis inferred from the movement, for changeups, fastballs, and sinkers, with the line indicating equality. These data show clear evidence for non-Magnus movement.

Alan Nathan, May 25, 2020 update, Unpublished

Alan Nathan, August 31, 2020, Unpublished

A plot of movement M vs. R, which is the number of rotations of the ball between release and home plate. The curve represents the upper limit under standard atmospheric conditions and is achieved when the spin efficiency is unity.

A technique is developed to determine the direction of the spin axis for pitched
baseballs. The method utilizes the Trackman measurement of the trajectory, specifically the spin-induced movement, to determine
the active spin. An important physics input to this method is the relationship between active spin and movement, which is separately determined from laboratory experiments under controlled conditions. The combination of active spin and the Trackman measurement of the total spin allow a determination of the spin axis in 3 dimensions and the spin efficiency, the latter being the ratio of active to total spin. A useful formula is developed relating the movement to the number of rotations of the ball between release and home plate. The role of experimental noise on the measurement of the trajectory is discussed. An important caveat is that the technique implicitly assumes that the movement is due to the Magnus effect and not to other possible forces on the ball. Click **here** for the spreadsheet template for performing the calculations.

The addendum points out an unresolved ambiguity in the relationship between spin and movement whenever the spin efficiency is less than one.

Alan Nathan, Unpublished

### Statcast data showing the dependence of distance on spray angle (adjusted so that negative corresponds to pull, positive to opposite), color coded by the magnitude of sidespin (rounded to the nearest 1000).
These data show that the distance depends on spray angle and that sidespin reduces the carry of the ball.

This article is an analysis of the reasons why fly balls with otherwise identical launch conditions carry farther to centerfield than to left or right field.
I gave a talk on this topic at the 2022 SABR Analytics Conference. You can watch the video ** here** and download the slides **here**.

Alan Nathan, Fangraphs, July 6, 2020.

Alan Nathan, July 20, 2020.

Plot of fitted vs. actual distance, controlling for the parameters shown at the top. The remaining rms variation of ~5 ft is due to measurement noise.

It is a known fact that for given launch conditions, there is a variation in the distance a fly ball carries. Various aspects of this were studied an earlier **article** and again in a **followup article**. The currently research takes full advantage of the Statcast capabilities for tracking the batted ball and determining its spin rate and spin axis to answer this question. After controlling for exit velocity, spray angle, and launch angle, the remaining rms spread of 11 ft come from four different sources in roughly equal contributions: variation of backspin from mean value; variation of sidespin from mean value; ball-to-ball variation in drag coefficient; measurement noise. The followup article shows, among other things, that the total spin rate provides far less information in determining the distance than does the individual backspin and sidespin components. It also shows that the measurement noise is dominated by the noise in the actual distance measurement rather than in the drag coefficient measurement.

Alan Nathan, Presentation at 2019 SABR Analytics Conference

### Color contour plot relating exit velocity and launch angle to attack angle and centerline angle. The dashed red line indicates where the attack and centerline angles are equal. The blue curves and colors are exit velocity contours and the black dashed lines are launch angle contours.

In this presentation (**audio link**), I present the results of various experiments on oblique ball-bat collisions and show how they are used to predict batted ball parameters from the swing parameters, as shown in the figure. Then I start to address the "reverse engineering" problem, whereby one tries to determine the swing parameters, especially the attack angle, from the batted ball parameters. The issue of timing enters if the attack angle of the bat differs from the descent angle of the ball, and this issue is investigated quantiatively. Finally, the question is addressed whether it is advantageous to alter the swing to sacrifice exit velocity to gain some extra spin on the batted ball. Further links related to this topic can be found by clicking **here**.

Alan M. Nathan, The Hardball Times, August 27, 2018

This article takes a critical look at how movement is determined from measurements of the trajectory. Two techniques are investigated. Technique 1 is that used currently by Statcast/Trackman. Technique 2 is based on one that I ** investigated** over 10 years ago. I show that Technique 1 results in systematic deviations of the movement from the exact values whereas Technique 2 does much better. The underlying physics behind Technique 2 is discussed **here**; click **here** for the spreadsheet template described there.

This is a link to a page describing the latest version of my Trajectory Calculator, which is now fully 3-dimensional and utilizes drag and lift coefficients that have been optimized using Statcast data.

Alan Nathan, The Hardball Times, April 6, 2016

### Sunset over Coors Field in Denver, where the ball really flies.

In this **article**, I use Statcast fly ball data from the 2015 season to investigate how fly ball distance depends on exit speed, vertical launch angle, and elevation. The Coors Field effect is quantified. Indirectly, this analysis is used to determine the effect on fly ball distance of temperature, relative humidity, and wind. A perhaps surprising result is the weak dependent of distance on the rate of backspin, in agreement with earlier findings reported in **this article**.

Alan Nathan, Baseball Prospectus, March 31, 2015

### The forces on a spinning baseball.

This article describes how to use **Trackman** data to separate the spin of a pitched baseball into a part that leads to movement (the "useful" spin) and a part that doesn't (the "gyrospin"). It is shown that fastballs and changeups are consistent with all their spin being useful, whereas breaking pitches (including cutters) have varying but significant degrees of gyrospin. The ratio of useful to total spin might be a helpful diagnostic for pitchers, especially those who throw breaking balls. Random measurement error in the movement means the type of analysis discussed in the article should only be used for averages of collections of pitches rather than for individual pitches. For those of you interested in technical details, you can read all about them in my unpublished **companion article**.

Jeff Long has written several articles for Baseball Prospectus,
**Spin That Curveball**,
**The Next Collin McHugh?**,
**Mother May I?**,
and especially **What We Know About Spin Rate**, in which he has done some analysis using the concept of useful spin.