Lap Track: A New Way to Review Every Lap of a Cyclocross Race

Lap Track Gets a New Race Analysis Workspace

Lap Track began as a focused way to compare cyclocross lap times. The legacy version answered an important question, but only part of the question: which lap was faster?

The new version is built around the analysis that follows. It connects lap time to course position, power, heart rate, cadence, speed, grade, elevation, and race execution. The result is a more complete view of how a race was ridden and where the next gains are available.

The legacy app remains available at laptrack.racing/legacy, while the current application is available at laptrack.racing/app

A clearer race model

The new app treats a recording as more than a list of laps. It identifies the race effort inside the recording, builds a course model, maps comparable locations across laps, and exposes the measurements needed to explain the time differences.

This matters for real activity files, which often contain warm-up, staging, cooldown, or a ride home after the finish. Race-window detection looks for the longest sustained effort instead of relying only on the first or last active record. The detected start and finish can still be refined manually when necessary.

The race tuning controls have also been reorganized as Race & Lap Tuning. They include race boundary adjustment, lap overlap radius, and smoothing, and can be hidden when the analysis is ready for review.

Maps that explain the course

The Course Overview is now an interactive analysis surface rather than a route preview. The same course can be viewed over a standard basemap or satellite imagery and colored by the data that matters for the question being asked.

Available map layers include power zones, heart-rate zones, cadence, speed, speed changes, grade, elevation, corners and straights, fastest lap by section, section review, and quadrant analysis.

The map legend reports both percentage distribution and time distribution. Selecting a lap updates both values to describe that lap, making it possible to compare not only where a rider spent time, but how the distribution changed between laps.

Elevation is calculated relative to the selected course range, with bands from Lowest through Highest. The optional grayscale elevation relief underlay varies with the recorded elevation and provides a visual sense of the terrain.

Grade and elevation context

Elevation data is now used throughout the analysis instead of being limited to a single chart. The map can show relative elevation bands and grade coloring, while section and sector data can carry entry, exit, minimum, and maximum elevation values.

This gives an analysis report useful terrain context: a time loss can be considered alongside the elevation change through that part of the course, rather than treating every section as flat and interchangeable.

The parser also handles enhanced FIT altitude records and fills short gaps in recorded altitude. That is important for files where the device records position but omits altitude on some samples.

Six consistent course sectors

The new Section Analysis identifies physical course sections from route geometry, such as corners and straights. Their boundaries are based on course position and are mapped to equivalent distances on every lap.

Those physical sections are also grouped into six virtual sectors for a higher-level comparison. The sectors are not time percentages. Sector 1 represents the same portion of the course on every lap, even when one lap takes longer than another.

Sectors are available above the physical sections in the selector and in Section Analysis. They can be selected directly from the Lap Review strip, which highlights all of the grouped ranges on the map. Sector rows include aggregated per-lap times, speed, power, heart rate, cadence, and elevation context.

This creates two useful levels of detail: sectors for quickly finding where a lap changed, and individual corners or straights for deciding what to work on.

More useful power metrics

Lap tables now group related metrics under shared headers and include normalized power, variability index, efficiency factor, and intensity factor when the required data is available.

Variability index, calculated as normalized power divided by average power, shows how evenly the effort was paced. Efficiency factor relates power to heart rate, while intensity factor places normalized power in relation to FTP.

Quadrant Analysis adds another view of power production. It plots power against cadence using FTP as the power boundary and 90 rpm as the cadence boundary, separating:

• High power and high cadence
• High power and low cadence
• Low power and low cadence
• Low power and high cadence

The quadrants make it possible to see whether similar power came from high force and low cadence, or from lighter and faster spinning.

An AI-ready handoff

The new Summary view includes a Copy AI prompt action. It produces a guided coaching prompt together with structured analysis JSON, allowing the data to be taken into a preferred AI assistant without manually assembling a report.

The exported data includes whole-race and per-lap measurements, lap comparisons, zone distributions, physical section metrics, virtual sector metrics, and rankings. Section and sector records include entry, exit, minimum, and maximum elevation. Whole-race and per-lap temperature statistics are included when the source file contains temperature records.

The prompt asks for evidence-based highlights, opportunities, and recommendations. It instructs the AI to distinguish observations from hypotheses, cite the relevant lap or course location, and avoid inventing weather, terrain, tactics, or causes that are not present in the data.

The action gives brief visual feedback and then returns to Copy AI prompt, making it reusable for revised settings, lap selections, or follow-up analysis.

Designed for repeated race review

The new app keeps the workflow local-first and practical. FIT, GPX, and TCX files are parsed in the browser. Athlete settings cover FTP, threshold heart rate, units, and map style, while race-specific tuning stays with the race boundary and lap controls.

The interface also adapts to smaller screens, groups repeated table headers, keeps map controls together, and lets the viewer move between lap, sector, section, map, and chart views without losing the current analysis context.

Lap Track is now less about recording a result and more about understanding it: what happened, where it happened, how the effort was produced, and what to change before the next start.

Open Lap Track or read the app tutorial.