With Valkyrie’s machine learning team, we built a Flutter analytics app that turns raw telemetry and lap timing into visuals drivers and race engineers can act on, lap by lap and against their rivals.
- comparisons across races and rivals
- Lap by lap
- built for use in a racing environment
- Trackside
- one app on mobile and trackside systems
- Flutter
Valkyrie’s race analysis app for the 2022 6 Hours of Monza: two LMP2 cars, Hanson’s 22 and Kubica’s 41, on a map of Monza split into three sectors, showing where the 22 gained or lost time in each sector, lap by lap. The times are an illustration.
01The ask
Winning margins hide in fractions of a second.
Valkyrie is a data science company founded in 2017 in Austin, Texas. Its scientists and strategists build custom machine learning capabilities for complex problems.
Timing systems gave racing teams lap times but hid the patterns underneath, like consistency, cornering efficiency, and speed across sectors. Every race produced mountains of data, with no simple way to compare a driver across tracks or against rivals on the same course.
02The build
Data science in, driver language out.
VGV’s product and engineering teams worked as one team with Valkyrie’s machine learning experts. The models process raw telemetry and lap timing data to find patterns people miss, and the app turns them into visuals a driver can read at the track.
We built it in Flutter for mobile devices and trackside systems alike, and iterated side by side with drivers and engineers until it fit the demands of a racing environment.
03In the app
Where the time went, lap by lap.
Lap-by-lap comparisons show where a driver gained or lost time, across laps, races, and competitors. Dashboards surface trends in lap performance, speed variation, and consistency.
Side-by-side views compare circuits, weather conditions, and rivals, which gives coaches the material for in-race adjustments and more targeted training off the track.
04What changed
From mountains of data to the next lap.
-
Drivers
Clear feedback on every lap.
Visual feedback shows where time was lost or gained on track.
-
Coaches
Better calls in the race and in training.
Adjustments in real time during the race, and more targeted training plans away from it.
-
Valkyrie
A platform other teams can adopt.
Built to take on new data integrations and to extend beyond a single racing series.
05If you’re next
What we’d tell a team in Valkyrie’s seat.
- 01
Put the data scientists and the product team together from day one. A model helps only when the driver can read its output at the track.
- 02
Lead with comparisons. Drivers improve against their own last lap and against their rivals.
- 03
Test where it gets used. Iterating next to drivers and engineers shaped the app for a racing environment.
Roll credits.
Who did what, and with which tools.
- Client
- Valkyrie
- Our part
- Product designFlutter engineeringData visualization
- Platforms
- Mobile devices and trackside systems
- Built with
- FlutterValkyrie’s machine learning models
Built by Very Good Ventures
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