Can physics invent new pitches?

1. Framing the question

Each recent wave of pitch design filled a visible hole. Travis Sawchik's 2025 Driveline essay traces the sweeper (around 2022), the splitter boom (around 2024) and the kick change (2025) as shapes that occupied empty parts of the movement plot, and concludes that probably "not that many dramatically different shapes" remain [1]. Driveline's own projection tools build a pitcher's feasible shapes from what similar pitchers already throw, and its researchers acknowledge that such methods interpolate rather than extrapolate [2]. Meanwhile crowded shapes lose value; Chris Langin reports that sweeper whiff rate for right-handed pitchers against right-handed hitters fell from 39.3% in 2021 to 33.1% in 2025 as the shape spread [3].

A data-driven search can only find what already exists somewhere. This series asks a different question. If we start from the forces on a pitched baseball and from what the hand and forearm can do at release, are there shapes that the body can plausibly produce but that MLB pitchers do not throw? We treat every answer as a hypothesis. None of the six pitches below has been thrown under measurement, and the numbers are model outputs with stated uncertainty.

2. The two-dial framework

2.1 The flow regime

A pitched baseball flies at a Reynolds number of about 2,180 times its speed in mph (Nathan 2008), roughly 1.5 to 2.1 x 10⁵ for pitches from 70 to 95 mph [4]. This is the regime in which surface roughness controls where the boundary layer separates. White places the critical Reynolds number of typical sports balls near 1.4 x 10⁵, Smits and Ogg show roughness lowering the critical value on Achenbach's spheres, and Mehta and Pallis describe how a single raised ridge on a tennis ball first helps and then hurts as the Reynolds number rises [5][6][7]. Anderson and Cadou show a trip-wire hoop forcing turbulent separation far aft on a sphere, and Bertin and Cummings name baseball seams as transition-promoting roughness [8][9]. Seams are trips, and where they sit decides where the air leaves the ball.

2.2 Dial one, Magnus force from the hand plane

The Magnus force comes from the transverse component of spin, which makes separation asymmetric and deflects the wake; Blazevich gives this separation-based account [10]. In our model a 95 mph, 2,400 rpm, 95% efficient fastball carries about 1.0 N of Magnus force, roughly 70% of the ball's weight [11]. The spin axis follows the hand. Jinji, Sakurai and Hirano (2011) found spin-axis direction tracking hand orientation about 6 ms before release, with the palm facing home plate for maximal backspin [12], and Sakurai et al. (1993) showed that the curveball's spin comes from a hand orientation pre-set before release rather than from twisting at release [13]. The component of spin along the flight path, gyro, makes essentially no Magnus force; Lyu, Kensrud and Smith (2020) measured lift coefficients near zero for gyro-spinning balls [14].

2.3 Dial two, the seam-shifted wake from the grip

The seam-shifted wake (SSW) is a non-Magnus force. A seam near the hemisphere plane, the plane through the ball's center perpendicular to its velocity, makes the boundary layer on its side separate earlier; the wake leaves the ball off-center toward that side, and the ball is pushed the other way [15][16]. Smith, Garrett, Dufour and Francis (2024) measured the separation points directly and found the effect real on average but non-deterministic from throw to throw [17]. Its size can rival spin-based movement. Alan Nathan's Hawk-Eye analysis attributed 9.4 inches of side movement on Kyle Hendricks's sinker to non-Magnus force, against about 2 inches on his four-seamer [18], and Utah State's laboratory pitches, the "Looper" and the "Scuffball", produced about 4 inches each at 90 mph and 1,100 to 1,200 rpm [19][20].

The two dials are separately controllable. The grip sets which seams face the flow; the hand plane sets the axis. Because the arm action and the axis can stay fixed while the seams rotate, SSW is the most load-neutral design lever we know of, an inference from the biomechanics reviewed below. The league uses it unevenly. 24% of sinker, 19% of changeup, 31% of splitter and 28% of cutter pitcher-seasons showed axis deviations of 30 degrees or more, against 0.2% of four-seamers, 3% of sweepers and 2% of curveballs [21]. Utah State concluded that sweepers may in fact be the most seam-shifted pitch, with two-seam and four-seam gripped versions pushing in opposite directions and cancelling in averages [22].

2.4 Second-order seam effects

Seam orientation also changes drag and lift. With gyro spin at 80 mph and 1,500 rpm, Lyu, Kensrud and Smith (2020) measured a drag coefficient of 0.29 in a two-seam orientation against 0.37 in a four-seam orientation [14]. At low spin factor, Lyu, Smith, Elliott and Kensrud (2022) found the four-seam axis receiving two to three times the lift of the two-seam axis (0.15 versus 0.05 at a spin factor of 0.05, 0.16 versus 0.07 at 0.10), with the two converging at 0.15 and near a Reynolds number of 1.75 x 10⁵, about 80 mph [23]. These are the mechanisms behind two of the six designs.

3. Methods

3.1 Gap map

We assembled 5,102 pitcher-pitch-type-seasons with at least 50 pitches from Baseball Savant for 2025 and 2026 (2026 season to date), covering 915 pitchers and 1,371,201 pitches, with velocity, IVB, horizontal break (HB, arm side positive), Statcast arm angle, Hawk-Eye active spin and axis deviation [21]. We searched for empty or thin regions in velocity, movement and arm angle, by movement direction and by slot, and tested gaps named by analysts, including Sawchik's 2025 list of missing pitches, Langin's 2025 and 2026 Unfiltered Labs analyses and Lance Brozdowski's 2026 "Corbin Burnes territory" [1][3][24]. Statcast pitch types are classifier outputs, not grips, so we report counts by movement as well as label.

3.2 Trajectory model and calibration

Trajectories are integrated with a fourth-order Runge-Kutta scheme using Nathan's pitched-ball model [25][4].

a = −K C_D v v + K C_L v² m̂ + K C_S v² ŝ − g ẑ,   K = ρA / (2m)

C_D = 0.28 + 0.0292 × (transverse spin, krpm)    C_L = [1.12 S / (0.583 + 2.333 S)] × efficiency    S = Rω / v

Here m̂ is the Magnus direction, ŝ the seam-force direction and C_S our added side-force coefficient. Nathan (2020) describes two ways of applying the lift curve to partly gyro pitches and notes that no experiment has settled which is right [26]; we chose the one that fit Statcast better. The spin vector is held fixed in space, spin decays with a 25 s time constant (Nathan's 2026 review finds decay negligible for pitch shapes) [27], and air density is 1.194 kg/m³. Movement is computed from release to the plate with gravity removed, as on Baseball Savant.

We calibrated against 2025 Statcast pitcher averages (438 four-seamers, 307 sinkers, 268 changeups and 224 curveballs), feeding in speed, spin and active spin and comparing movement out. Median observed-to-modelled movement was 1.01 for four-seamers, 0.99 for changeups, 1.08 for sinkers (the excess is consistent with sinker SSW) and 0.87 for curveballs, so we scale curveball lift by 0.87. The alternative lift prescription fit worse (0.97 and 0.76) [11].

3.3 Seam-force uncertainty

SSW magnitudes at full game spin have not been measured for the orientations we propose, so every SSW design is run at LOW, MID and HIGH values of C_S. Over a 52.6 ft flight, one inch of seam-driven movement corresponds to C_S of about 0.0112, nearly independent of speed [11]. The laboratory anchors are the Scuffball (about 4 inches at 90 mph and 1,100 to 1,200 rpm) [20][28], the Looper (about 4 inches vertical and 4 horizontal at 90 mph and 1,200 rpm) [19], MLB sinkers (6 to 9 inches of non-Magnus movement) and MLB four-seamers (about 2 inches) [18].

3.4 Feasibility screen

Each design was checked against release mechanics and anatomy. The axis follows the hand plane [12]. Breaking-ball orientation is pre-set before release [13]. Distal segments ride on interaction torques from the trunk and shoulder, so grip and hand orientation can change without changing the engine (Hirashima et al. 2007) [29]. Wrist radial deviation is limited to about 20 degrees and the fastball wrist moves toward about 20 degrees of ulnar deviation during acceleration (Barrentine et al. 1998, reported by Zheng et al. 2004), which limits backspin from low slots [30][31]; the wrist and forearm anatomy is summarized in Gray's Basic Anatomy and Hall's Basic Biomechanics [32][33]. Contralateral trunk tilt adds about 3.7 N·m of elbow varus moment per 10 degrees (Solomito et al. 2015) [34]. Curveballs carry no more elbow load than fastballs in collegiate or youth pitchers (Fleisig et al. 2006; Dun et al. 2008) [35][36], but a larger forearm supination moment goes with a larger varus moment (Solomito et al. 2018) [37]. The OpenBiomechanics Project, an open dataset of 411 fastball trials from 100 mostly collegiate pitchers with motion capture and force plates, has no ball-spin or finger data and was consulted only for scope; its licence excludes commercial reuse of its values [38].

3.5 Descriptive outcome model

We fitted a gradient-boosting model of whiff rate and expected wOBA (xwOBA) on shape, using velocity, IVB, HB, arm angle, extension and differences from the pitcher's primary fastball in velocity, IVB and HB, weighted by pitches, on 5,036 pitcher-pitch-seasons with complete data [11]. In five-fold cross-validation grouped by pitcher, it explained 56% of whiff variance (RMSE 6.6 percentage points) and 24% of xwOBA variance (RMSE .058). Pitch-weighted league baselines were 21.6% whiffs and .339 xwOBA for four-seamers, 13.7% and .347 for sinkers, 22.6% and .348 for cutters, 33.3% and .289 for sliders, 31.5% and .269 for sweepers, 30.8% and .279 for curveballs, 31.2% and .280 for changeups and 34.4% and .255 for splitters.

The model has two hard limits. It is descriptive, so it says how outcomes vary with shape today, not what a new shape would cause, and it ignores location, command, deception and sequencing. And tree ensembles cannot extrapolate, so in an empty region they return the value of the nearest populated region. For every design we therefore report the number of real pitcher-seasons within a scaled distance of 1 and 2 (3 mph, 3 inches, 3 inches and 10 degrees of arm angle per unit) and the nearest real comparables. Where that count is zero, the prediction is extrapolation.

3.6 Tunnels

We use Baseball Prospectus's 2017 definition of the tunnel point, 23.8 ft from the plate and about 175 ms before contact. Their league averages were 10.0 inches of separation at the tunnel point, 18.7 inches at the plate and a 26 ms difference in flight time [39].

4. The six proposed pitches

Shapes are for a right-handed pitcher, release to plate, IVB / HB in inches with arm side positive. Gap counts from Baseball Savant 2025 to 2026 [21]; shapes, predictions and distances from the Pitch AI models [11]. Each linked article covers one design in detail.

Three patterns run through the table. The designs with the strongest physics (mirror sweepers, bullet fastball) target familiar shapes, so their value depends on unpredictability or velocity rather than on new movement. The designs that open genuinely empty space (reverse hammer, float cutter) have the least nearby data, so their predicted value is the least trustworthy. And four of the six are seam-orientation designs whose magnitude at game spin is the central unknown. In every case the predicted whiff gain is smaller than the outcome model's cross-validated error for a single pitcher.

Video overviews of the six proposals (vertical format). Flight paths are played back from our simulation and the movement plots show the real Statcast cloud; every new pitch is simulated, not measured.

Two interactive examples

The first figure shows the seam dial on its own. The two simulated sweepers share speed, spin rate, tilt and efficiency; only the seam orientation differs, so the seam force points up for one and down for the other. The slider sets the strength of that force, and its shaded band is the LOW to HIGH range we consider plausible (C_S 0.03 to 0.06); at zero the two collapse into one ordinary sweeper. The catcher's view plays the pitches in real time (tick slow motion for a 4x replay). With the tunnel box ticked, every pitch is aimed through the first fastball's point 23.8 ft from the plate, so you see only the separation that develops after the hitter's decision window; untick it to aim at the middle of the zone.

The second figure shows the orientation-dependent lift behind the two-face splitter. There is no seam-force slider here, because the difference comes from the lift coefficients Lyu et al. (2022) measured for the two axis orientations at low spin, about 0.16 and 0.07 at a spin factor near 0.1 [23]. The same grip, arm speed and spin rate produce two shapes about 8 inches apart in our model.

5. Frontier ideas we did not promote

Four further ideas came out of the physics review. We describe them because they are interesting and because the reasons we set them aside are informative.

• Late-turn gyro. Because the spin axis stays fixed in space while the path bends, part of a gyro component becomes transverse spin late in flight, a point David Kagan made qualitatively in 2017 [40]. Our calibrated model gives only about 1.4 inches for a 70 mph, 2,500 rpm pure gyro (about 2.3 inches with the alternative lift prescription) [11]. The effect is real but small, and Statcast's constant-acceleration movement fit absorbs a late component of this kind [41].
• Reverse seam-shifted wake. Cricket balls reverse their swing as conditions shift the boundary layer from laminar to turbulent on both sides (Mehta and Koga 2000; Scobie et al. 2013) [42][43]. A 95 mph pitch slows to about 86 mph, a Reynolds number falling from about 2.07 to 1.88 x 10⁵ [4], and Smith's group found laminar separation modes on baseballs that disappear near 92 mph sea-level equivalent [17][44]. A seam mode might therefore change mid-flight. Its magnitude and even its sign are unknown, which makes this the most speculative idea we considered.
• Corkscrew knuckleball. A non-spinning ball held at an asymmetric orientation feels a steady side force (Borg and Morrissey 2014 measured lift coefficients up to about 0.25) [45]. A slow gyro would rotate that force, and our simulation gives smoothly curving paths of roughly 6 to 20 inches. But the aerodynamic torque Borg and Morrissey measured is large relative to the ball's angular momentum at such low spin, so the axis would probably wander.
• Drag twins. Two gyro pitches with identical release and opposite seam faces would differ only through drag. With the drag coefficients of Lyu, Kensrud and Smith (2020), an 86 mph gyro slider arrives about 1.8 mph slower and 5 ms later in the high-drag face [14][11]. That is small next to the 26 ms league-average flight-time difference between consecutive pitches [39].

6. A note on the Navier-Stokes equations

The boundary layer, the separation and the seam-shifted wake around a pitched ball are all governed by the incompressible Navier-Stokes equations. Nobody solves those equations in closed form for a spinning, seamed sphere at a Reynolds number near 2 x 10⁵, which is why pitch aerodynamics rests on wind tunnels, particle image velocimetry, launch cannons and Hawk-Eye tracking. In September 2026 OpenAI claimed that an internal model had produced a proof, accompanied by a Lean formalization, that a smooth three-dimensional incompressible flow driven by a smooth external force can develop a finite-time singularity, which it presents as settling the breakdown statements (C and D) of the Clay Mathematics Institute's official problem [46][47]. As of this writing the claim had not been through peer review. Whatever its fate, it concerns a specially constructed, forced flow whose singularity forms at arbitrarily small scales. It says nothing about the flight of a baseball, whose flow is smooth and well behaved for every practical purpose, and it does not change any coefficient used here. If anything, it illustrates how far exact mathematics remains from the engineering questions in this series.

7. Learning a new pitch

A new grip is a motor-learning problem as much as a physics one. Rob Gray's How We Learn to Move summarizes evidence that skilled athletes implement prescribed technique changes with errors larger than the change requested, and that changes made from explicit rules tend to revert under pressure [48]. The constraints-led approach he describes instead defines the task by its outcome, shapes the practice environment and gives feedback on whether the search is heading the right way, while differential learning adds small random variations, fewer for novices and youth [48]. For these designs the outcome is a target on the movement plot at a given velocity and efficiency, and the feedback is the measured shape. Practitioners report that cues often matter more than grips for breaking balls; Chris Langin showed one slider grip moving from sweeper to gyro by cue alone [49]. Pitch AI's Pitch Lab supports this loop with movement profiles from imported TrackMan or Rapsodo CSV files, estimated arm angle, pitch design recommendations and grip library videos.

8. Limitations

• Seam-force magnitudes. No SSW value for our proposed orientations has been measured at full game spin; the laboratory anchors are near 1,200 rpm [20][19]. Seydel's 2024 CFD study suggests seam effects on lift shrink as spin approaches the wake's shedding frequency [50].
• Shot-to-shot variability. Separation on a seamed ball is non-deterministic [17], so even a working design will produce a wide cluster.
• Model structure. A constant side-force coefficient, a fixed spin axis, an unresolved lift prescription and a curveball calibration factor of 0.87 are simplifications [26][11].
• Outcome model. It is descriptive, explains about half the whiff variance and a quarter of the xwOBA variance, cannot extrapolate, and ignores location, command and sequencing [11].
• Data. Statcast labels are classifier outputs, 2026 is a partial season, and arm angle and active spin are measured quantities with their own error [21].
• Humans. No athlete has thrown these designs under measurement. No kinetic data exist for the new grips, and splitter and screwball kinetics are themselves unstudied. Load statements are inferences from the parent pitch.

9. How to test the proposals

1. Instruments. A pitching machine or athletes; a high-speed camera at 1,000 frames per second or more behind the release to confirm seam orientation; Rapsodo for the spin-based axis and TrackMan or Hawk-Eye for observed movement. The gap between spin-based and observed movement, the axis deviation, isolates non-Magnus force (Nathan 2023; Driveline 2020) [18][51], and Smith's single-camera method is a low-cost alternative [52].
2. Paired orientations. Every seam design has a built-in control, the same throw with the seams moved, so compare orientations within a pitcher and session.
3. Sample size. At least 30 throws per orientation, because SSW is non-deterministic [17].
4. Speed dependence. Where the physics is speed sensitive (two-face splitter, reverse SSW), repeat at several speeds, since altitude and speed both shift the Reynolds number [44].
5. Game value. Shape stabilizes within dozens of repetitions, while outcomes need hundreds of pitches (Premier Pitching Performance 2026) [53].

Can physics invent new pitches? Our answer is a qualified yes. It can identify shapes the body can plausibly produce and the league does not throw, and it can specify the measurement that would confirm each one. It cannot yet say how large most seam effects are at game spin, and no model can say how hitters will respond. Those answers will come from high-speed cameras and tracking data, not from equations.

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Import your TrackMan or Rapsodo sessions into Pitch AI's Pitch Lab to see your movement profile and estimated arm angle, get pitch design recommendations, and study grips in the grip library videos.

References

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44. Smith BL (2022). Effect of velocity or altitude on seam-shifted wake, part 3 of 4 (Post 74). Baseball Aerodynamics (baseballaero.com), Utah State University, April 28, 2022. https://www.baseballaero.com/2022/04/28/effect-of-velocity-or-altitude-on-seam-shifted-wake-part-3-of-4-post-74/
45. Borg JP, Morrissey MP (2014). Aerodynamics of the knuckleball pitch: Experimental measurements on slowly rotating baseballs. American Journal of Physics 82(10), 921 to 927. doi:10.1119/1.4885341. https://doi.org/10.1119/1.4885341
46. OpenAI (2026). On the Navier‑Stokes Millennium Prize Problem. OpenAI, September 8, 2026 (a claim accompanied by a written proof and a Lean formalization; not peer reviewed at the time of writing). https://openai.com/index/navier-stokes-solution/
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48. Gray R (2021). How We Learn to Move: A Revolution in the Way We Coach and Practice Sports Skills. Perception Action Consulting and Education. Ch. 7, The Constraints-Led Approach (pitching example of forearm flyout and the connection ball); ch. 8, Differential Learning; ch. 14, injury prevention through variability.
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53. Premier Pitching Performance (2026). The Pitch Design Phase in Baseball: Developmental Fit, Workload-Informed Timing, Biomechanical Determinants of Effectiveness, and Evidence-Based Evaluation Thresholds. February 2, 2026. https://premierpitching.com/blogs/premier-pitching-chronicles/the-pitch-design-phase-in-baseball-developmental-fit-workload-informed-timing-biomechanical-determinants-of-effectiveness-and-evidence-based-evaluation-thresholds

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