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Quant Library · Sports physics · Football, tennis, basketball

Ball Flight Solver

Drag, gravity and Magnus integrated properly, with a drag coefficient that changes through the crisis.

Fourth-order Runge-Kutta at two-millisecond steps, a speed-dependent drag coefficient per ball model, spin decay, wind and air density, with helpers for free kicks, serves and shots.

Inputs

What it takes

4
NameTypeUnitsWhere it comes from
speed, elevation, azimuthfloatsm/s and degreesyour launch conditions
spinfloatrpmside and top components
ballobjectmass kg, radius m, C_d curvethe catalogue, or your own measurements
air optionalfloatskg/m³, m/sdensity from altitude and temperature; wind vector
Outputs

What it gives back

3
NameTypeUnitsNotes
patharray of (t, x, y, z)s, m
crossingposition at a planemthe goal line, the service line, the rim
flight_time, peakfloatss, m
Method

How it works

  1. Integrate m dv/dt = mg − ½ρC_d A |v| v + ½ρC_l A |v|² (ω̂ × v̂) with RK4.
  2. 3 further steps in the licensed specification
Assumptions
  • Smooth flow apart from the modelled crisis, a rigid ball, and no lateral wind shear along the flight.
Limits
  • Near the crisis the real wake is unsteady, which is the knuckleball; a deterministic solver gives you the mean path and not the wobble.
  • 1 further limits in the licensed specification
Validation

What it was measured at

ok

Computed now, with the solver the films run on.

IntegratorRK4 at 2 msthe step at which the path stops changing
Bend from 800 rpm over 22 m3.00 msame launch, spin the only difference
Drag crisis lawexponent -0.33, R² 0.56fitted on 6 wind-tunnel balls
Unmeasured ballspredicted with a ±20% bandrather than asserted
In the package
  • The full specification: inputs, method, assumptions and limits
  • The validation panel as measured on the day of purchase
  • A reference implementation extracted from the running source
  • Perpetual commercial use for one entity

Reference modules:

  • 1 module(s), named in the licensed specification and shipped in the package
Read this before you buy. The platform's own walk-forward tests say the closing line forecasts football better than these models do. What they give you is a coherent price for every market and match, before the market opens, with the band and the working attached. Any model here that has been beaten by something simpler says so in its limits, and the validation panel above is generated from the platform's result files rather than typed in.