How does drag timing work in GPS-based apps?

TL;DR

GPS-based drag timing apps generally: (1) stream speed/position samples, (2) detect a run start from a launch threshold, (3) integrate speed over time to estimate distance, and (4) compute times when distance thresholds (like 60 mph or 1/4 mile) are crossed—often using interpolation between samples.

This is an explainer, not a guarantee of accuracy.

The core ingredients

Most GPS-based systems depend on:

• A telemetry stream (speed and/or position at a fixed rate)
• A start boundary (when the run actually begins)
• A distance model (how far you traveled over time)
• Interpolation (estimating the exact crossing moment between samples)

Step 1: Start detection (when does the run begin?)

A common approach:

• The system monitors speed while “armed”
• The run start boundary is triggered when speed crosses a threshold (for example, moving from near-0 into motion)

Why it matters: If you include staging creep or rolling starts, your 0–60 can be artificially inflated or artificially improved.

“Arming” and rolling starts

Many apps have an “armed” state (ready to detect a run). The important practical point:

• If you start rolling before the system considers the run started, your results won’t represent a true 0–60.
• If the system triggers too early (noise at low speed), it can create weird starts.

When comparing runs, keep your start procedure consistent: stage the same way, launch the same way, and don’t change modes mid-session.

Step 2: Distance integration (how far did you go?)

A simple mental model:

1. You receive speed samples over time.
2. You estimate distance by integrating speed:
   • distance ≈ sum(speed × deltaTime)

Better implementations smooth the speed signal and handle irregular sample timing.

Step 3: Interpolation (pinpointing the exact moment)

If you sample at (say) 10–25 Hz, you don’t get an exact moment of “60 mph” or “1/4 mile.” You get two samples around the crossing.

Interpolation estimates the moment between samples when the threshold was crossed. Without interpolation, times can “snap” to sample boundaries and look jittery.

Step 4: Finish thresholds (0–60, 1/8, 1/4, traps)

Two common kinds of thresholds:

• Speed thresholds (0–60 mph, 0–100 km/h)
• Distance thresholds (1/8 mile, 1/4 mile)

Trap speed is usually estimated from the speed near the finish distance window.

A simple diagram (why interpolation matters)

Think of speed samples like dots on a line. Your app has to estimate the exact crossing time between dots:

speed
  ^
  |                 • (65 mph)
  |            •
  |       •
  |  • (55 mph)
  +--------------------------> time
        crossing of 60 mph happens between samples

If the app didn’t interpolate, it would “round” to the nearest sample and your time would jump around.

Why 0–60 and 1/4 mile behave differently

These metrics respond to different parts of the run:

• 0–60: very sensitive to launch, traction, and how start detection is handled.
• 1/4 mile: more sensitive to sustained acceleration, shifting, and power delivery.

That’s why a car can show a great 0–60 but a mediocre 1/4 (or vice versa). They’re measuring different things.

Grade and “free speed”

Road grade is one of the easiest ways to fool yourself:

• A slight downhill can improve times and trap speeds.
• A slight uphill can make “gains” disappear.

If you’re comparing setups, run both directions and average, or use the same road and direction every time.

Common sources of error (and why your run looks weird)

1. Poor sky view / multipath: buildings, trees, and reflective surfaces distort signals.
2. Variable sample rate: inconsistent timing makes integration less stable.
3. Road grade: downhill vs uphill changes results (and can look like “gains”).
4. Wind and traction: environmental changes can dominate small mod differences.
5. Mounting movement: device shifts add noise and can break assumptions.

A practical comparison protocol (so the numbers mean something)

If you’re comparing mods or tuning changes, use this protocol:

1. Same road, same direction, same start procedure.
2. Wait for readiness (clean telemetry + usable fix signals).
3. Do multiple runs and look at the trend, not the single best.
4. Treat small differences as noise until repeated.

This keeps you honest and makes your data useful.

Common mistakes (why timing comparisons lie)

• Comparing runs from different roads/directions (grade matters a lot)
• Mixing rollout and standstill workflows without noting the difference
• Starting before readiness is stable (then blaming the device)
• Treating one run as proof instead of looking for repeatable trends
• Changing multiple variables at once (then you can’t tell what worked)

How Drivurs approaches trust (without hype)

Drivurs includes readiness and diagnostics concepts so you can see when data is usable:

• “GPS Ready” gating
• Telemetry freshness and sample rate
• Run labeling (valid / warnings / invalid / incomplete)

The goal is to help you avoid comparing bad data as if it’s truth.

Next steps (Drivurs)

• Feature page: RaceBox in Drivurs
• Use case: For Track Drivers

Related guides

• Pillar: Performance tracking for cars (GPS timing + validation)
• Same cluster: How accurate is GPS for racing apps?
• Same cluster: Dragy vs RaceBox vs Solo II: which should you use?
• Different cluster: How can you keep a car meet safe?