The principles at a glance. Detailed rules, formulas and special cases are in the FAQ.
01Timing
iPhone sensor fusion
GPS provides reference points; motion sensors add information between them. The app uses both to reconstruct launch and sprint time.
Sensor fusion
Developed with AI assistance.
GPS reference pointsSensor fusion
Speed
Time
GPS provides individual speed readings. The orange dots show these reference points.
Between GPS samples, more frequently recorded motion data helps trace changes in speed.
The app checks whether both sources agree. For suitable recordings, it reconstructs the sprint and calculates the sprint time. The GPS comparison time remains visible.
Schematic illustration, not recorded data.
Only for eligible 0–100 km/h and 0–60 mph sprints using internal iPhone GPS. Developed with AI assistance; the GPS comparison time remains available.
GPS anchors the speed. More frequent motion samples help reconstruct what happens between these reference points. The app checks whether both sources agree; only eligible recordings receive a regular fusion result.
Sprint: start to target speed
The target speed usually falls between two GPS samples. The app calculates the crossing time between them instead of waiting for the next sample.
Below targetTarget crossedAbove target
Crossing time between two GPS samples
Schematic illustration, not recorded data.
GPS 0–X timing starts at the 3 km/h crossing after confirmed standstill. Fusion uses a modelled launch; rolling sprints start at their lower speed threshold.
For example, one sample is below the target speed and the next is above it. GPS timing assumes a linear change in speed between them. This gives a calculated finish time, not an additional measured GPS sample.
Distance: speed over time
The app calculates distance travelled from speed and time, then determines when each split or finish distance is reached.
Illustrative example, not recorded data.
Starts at 3 km/h after confirmed standstill. Distance timing does not use sprint sensor fusion.
Distance is the area under the speed curve: higher speed held for longer adds more distance. Within each sample interval, the app accounts for the change between the two speed values.
Laps: crossing the line
Crossing the start/finish line in the configured direction starts or ends a lap. The crossing time is calculated between GPS positions. Sector lines divide the lap.
GPS beforeStart / finishGPS after
The line crossing determines the time
Schematic illustration, not recorded data.
Data gaps or implausible crossings can prevent a valid result. Live delta compares progress along the track.
The endpoints show when each run reaches the target speed. Horizontal separation at the same speed shows the difference in time to reach it; vertical separation shows the speed difference at the same time.
Ghost Lap & ideal lap
Replay compares a lap with the best lap in the same recording. The ideal lap combines the best usable sectors.
A single lap does not have to be fastest everywhere. In this example, sectors 1 and 3 come from lap A and sector 2 from lap B. They add up to 31 seconds: a combination, not another recorded lap.
Compare laps & corners
Time differences are compared along track distance to show where time is gained or lost.
The change in the time gap shows where time is gained or lost. Schematic illustration.
Theoretical best time and consistency describe session potential and variation.
The curve shows how speed falls through a section. Lap comparison also considers how the time gap changes across the whole section. Stronger deceleration alone therefore does not imply a time gain.
g-forces & friction circle
The friction circle plots longitudinal and lateral acceleration together. GPS-derived values and suitable sensor data are identified separately.
Illustrative example, not recorded data.
The point cloud does not measure remaining grip. The iPhone must be firmly mounted.
Each point combines longitudinal and lateral acceleration at one moment. Points near one axis mainly show one component. A point between the axes means both occur together.
Corner dynamics
Motion sensors capture rotation of the mounted iPhone. Suitable data enables yaw, roll and pitch analysis.
Three rotations, inferred from the mounted iPhone’s motion. Schematic illustration.
Phone motion approximates vehicle motion; this does not detect oversteer or understeer.
The analysis shows vertical motion at the mounted iPhone: intensity, peaks and changes throughout the drive.
Such peaks can come from kerbs, bumps, potholes or other vertical inputs. Schematic illustration.
The analysis shows location, strength and progression. Sensor data alone cannot determine the exact cause or depth; mounting and vehicle response affect the values.
GPS quality, sensor fusion, labelled estimates and missing regular results are different classifications. Read the colour, label, source and status together.
Good GPSQuality criteria met
Limited GPSValid result with higher uncertainty
Sensor fusionGPS + motion sensors
Estimate / quality unknownRead the label beside the result
No regular resultThe saved status explains why
Read colour, label, source and status together. Colours do not guarantee an error bound.
Track4Facts is not a calibrated measuring instrument. Display precision is not a guaranteed error bound.
