Quickstart

1. Install

pip install "lapbox[plot]"

The plot extra adds Plotly for step 4; the rest needs only pip install lapbox. FastF1 comes with either. If FastF1 can’t download a session, read Getting session data first.

2. Compare two laps

Load a session with FastF1, pick each driver’s fastest lap, and pass both laps’ telemetry to compare:

import fastf1
from lapbox.telemetry import compare, delta_time

session = fastf1.get_session(2025, "Silverstone", "Q")
session.load()
ver = session.laps.pick_drivers("VER").pick_fastest().get_telemetry()
nor = session.laps.pick_drivers("NOR").pick_fastest().get_telemetry()

result = compare(ver, nor, driver_a="VER", driver_b="NOR")
if result.matched:
    print(f"VER ahead by {delta_time(result.aligned).iloc[-1]:+.3f} s at the line")
    gain = result.segments.loc[result.segments["time_delta"].idxmax()]
    print(f"VER gains most from {gain.start_distance:.0f} to {gain.end_distance:.0f} m: {gain.time_delta:+.3f} s")
else:
    print(f"Can't compare: laps differ by {result.residual:.0f} km/h RMS after alignment")

VER ahead by +0.118 s at the line
VER gains most from 2957 to 4765 m: +0.202 s

compare first puts Norris’s lap on the same points of track as Verstappen’s. Comparison.matched says whether that worked; only then do the time gap and the segments describe the drivers. delta_time ends exactly at the official lap-time gap, and Comparison.segments splits the lap at its speed peaks to show where the time changed hands. Comparing two laps has the rest.

3. Race pace, with the fuel taken out

A car gets roughly two seconds a lap quicker over a Grand Prix just by burning fuel, so raw lap times compare fuel loads as much as pace. true_pace_ranking corrects every lap to end-of-race fuel and ranks each driver’s median:

from lapbox.pace import true_pace_ranking

race = fastf1.get_session(2025, "Monza", "R")
race.load(telemetry=False, weather=False, messages=False)

pace = true_pace_ranking(race.laps)
print(pace[["driver", "corrected_median", "gap", "raw_rank", "corrected_rank"]].head(5).round(3))

  driver  corrected_median    gap  raw_rank  corrected_rank
0    VER            81.215  0.000         1               1
1    NOR            81.560  0.345         3               2
2    PIA            81.651  0.436         2               3
3    LEC            81.803  0.588         4               4
4    RUS            81.892  0.677         5               5

On raw medians Piastri was second and Norris third; with the fuel taken out they swap. The race needs no telemetry, so load(telemetry=False, …) is much quicker. One race proves nothing about the method — see Race pace.

4. Tyre wear

stint_degradation fits a slope through each stint’s fuel-corrected laps, and compound_summary takes the median per compound:

from lapbox.tyres import compound_summary, stint_degradation

print(compound_summary(stint_degradation(race.laps)).round(3))

  compound  stints  laps  median_deg  best_deg  worst_deg
0     SOFT       3    20      -0.039    -0.149      0.043
1   MEDIUM      15   404      -0.001    -0.091      0.024
2     HARD      16   489       0.005    -0.026      0.061

5. Draw it

With the plot extra, a comparison draws itself: the time gap on top, then both drivers’ channels on the shared distance axis.

result.plot(channels=["Speed", "Throttle", "Brake", "nGear"]).show()

Charts shows the other figures. From here, read Core concepts for the conventions every module shares, or go straight to a guide.