lapbox

FastF1 gives you the data. lapbox tells you what it means — and says so when it can't.

lapbox is a Python library of Formula 1 analysis built on FastF1. It is the analysis engine behind LapBox, extracted so anyone can use it on FastF1 data.

Status: 0.1.0, the first release. In the library: lapbox.telemetry (one lap, two laps matched by track position), lapbox.data (laps, pit stops, stints, gaps), lapbox.practice (long runs), lapbox.pace (lap cleaning, fuel-corrected race pace, clean air vs traffic, consistency, ideal lap) and lapbox.tyres (degradation per compound and stint by stint), with charts as an optional extra (lapbox.plot). The strategy engine moves over from LapBox in 0.2. Until 1.0 the API may still change between minor releases; every change is listed in the changelog.

Install

pip install lapbox

Python 3.11+. Depends on FastF1, pandas, NumPy and SciPy. For the charts, add the plot extra, which brings Plotly:

pip install "lapbox[plot]"

Getting the data (October 2026). lapbox reads what FastF1 downloads. Since mid-October F1's live-timing archive answers FastF1's downloads of past sessions with 401 Unauthorized — for every user (FastF1 issue #978) — and FastF1 3.8.3, the latest release, sends no credentials for them. A FastF1 cache filled before then still works; every example here was run that way.

Example

For the whole library on real sessions, with every output saved, see the notebook examples/lapbox-tour.ipynb: two laps compared, a pair the library refuses, race pace with the fuel taken out, and tyre wear. The short version:

Every function takes FastF1 telemetry exactly as lap.get_telemetry() returns it.

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(result.summary)
    print(f"{delta_time(result.aligned).iloc[-1]:+.3f} s")
    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")

{'minisectors_a': 14, 'minisectors_b': 7, 'more_minisectors': 'VER', 'max_speed_a': 324.0, 'max_speed_b': 319.0}
+0.118 s
VER gains most from 2957 to 4765 m: +0.202 s

Long runs in practice

session.laps goes straight in. A practice session is split into runs between pit visits; a run is a long run when at least five of its laps were at race pace (within 107 % of the run's best), so a qualifying run with its out-lap and cool-down laps is not one.

import fastf1
from lapbox.practice import long_run_pace, session_runs

session = fastf1.get_session(2025, "Monza", "FP2")
session.load(telemetry=False, weather=False, messages=False)

paces = [
    long_run_pace(run)
    for runs in session_runs(session.laps).values()
    for run, kind in runs
    if kind == "long_run"
]
for p in sorted(paces, key=lambda p: p.median_s)[:5]:
    print(f"{p.driver} {p.compound:<6} {p.laps:>2} laps  {p.median_s:.3f} s  deg {p.deg_slope_s_per_lap:+.3f} s/lap")

NOR MEDIUM 11 laps  83.616 s  deg -0.004 s/lap
VER MEDIUM 10 laps  83.688 s  deg +0.008 s/lap
PIA MEDIUM  9 laps  83.956 s  deg -0.032 s/lap
LEC SOFT    7 laps  84.025 s  deg -0.175 s/lap
RUS HARD    9 laps  84.034 s  deg -0.154 s/lap

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 normalises every lap to end-of-race fuel and ranks each driver's median over their representative laps (from lap 2, no pit laps, within 107 % of their best). clean_air_ranking splits those laps by the gap to the car ahead at the line (2 s or less is traffic) and shows what traffic cost.

import fastf1
from lapbox.pace import clean_air_ranking, true_pace_ranking

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

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

traffic = clean_air_ranking(session.laps)
print(traffic[["driver", "clean_pace", "traffic_pace", "delta", "traffic_share"]].head(3).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
  driver  clean_pace  traffic_pace  delta  traffic_share
0    LAW      82.109        82.756  0.647           80.0
1    ALB      81.953        82.496  0.542           52.0
2    PIA      81.643        82.134  0.492           16.0

On raw medians PIA was second and NOR third. Once fuel is taken out they swap, and on this race the corrected top five matches the finishing order. One race proves nothing about the method.

Tyre degradation, two ways

stint_degradation fits a slope through each stint's fuel-corrected laps, and compound_summary takes the median per compound. TyreDegradationModel fits one line per compound over every driver: lap time, with the same assumed fuel effect taken off, against tyre age, on laps within 107 % of the driver's best. Clean the laps first; the 107 % filter catches safety-car laps, not every slow one.

import fastf1
from lapbox.pace import LapCleaningPipeline
from lapbox.tyres import TyreDegradationModel, compound_summary, stint_degradation

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

stints = stint_degradation(session.laps)
print(compound_summary(stints).round(3))

model = TyreDegradationModel().fit(LapCleaningPipeline().run(session.laps))
print(model.summary()[["compound", "degradation_s_per_lap", "r2", "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
  compound  degradation_s_per_lap     r2  laps
0     SOFT                 -0.133  0.173    24
1   MEDIUM                 -0.008  0.021   399
2     HARD                  0.014  0.067   491

Monza barely wears tyres, and here both methods say the soft got quicker with age, on 20–24 laps. Neither is a measurement of the tyre alone: the slopes also carry track evolution, fuel assumptions and who ran which stint. The low r2 is the honest part: tyre age explains little of how the lap times vary here. Read every rate with its laps (and, for the model, its r2).

Charts (optional)

lapbox.plot draws the results with Plotly; nothing else in lapbox imports it. The charts keep the library's caveats: a pair that couldn't be matched is drawn without its time gap and says why, frozen car data is shaded, and minisectors decided by less than 20 ms are grey. Continuing the first example:

from lapbox.plot import channel_traces, speed_delta, track_dominance

result.plot().show()                 # the time gap, then both laps' channels
speed_delta(result).show()
track_dominance(result, ver).show()  # VER's racing line, coloured by who was quicker
channel_traces(ver, driver="VER").show()

Why the laps are matched first

FastF1's Distance is integrated from speed, so two laps of the same circuit disagree on their total length and can start from slightly different points. Comparing them at equal distances lines one car's corner up against the other's straight. Over a season of LapBox comparisons, 14 % of driver pairs compared that way gave corner speeds more than 40 km/h apart — up to 181 km/h, which is impossible between two F1 cars.

So align puts lap B onto lap A's basis first: it stretches B to A's lap length, then slides it by the offset that best lines up the two speed traces.

Some laps still can't be matched: a telemetry dropout stretches one part of a lap, and no global shift can undo that. compare reports how far apart the two speed traces still are after alignment (result.residual, km/h RMS), and result.matched is False above 15 km/h. That threshold was measured on 250 driver pairs: it caught 32 of 33 physically impossible pairs and withheld 8 of 217 sound ones. When matched is False, don't use the time delta, speed delta, corner speeds, minisectors or segments — they describe the mismatch, not the drivers. result.frozen_a and result.frozen_b say whether a lap's car data froze in the timing feed, and where (see Limits): on 2026 qualifying that was behind every refusal measured.

FastF1's own utils.delta_time is deprecated and documented as "not actually very accurate". lapbox's delta_time takes its name on purpose: it compares the laps on the same piece of track and ends exactly at the official lap-time gap.

Limits

• Winning more minisectors is not being quicker. Each minisector goes to the driver who spent less time in it, and summary["more_minisectors"] counts them — but a driver can lose most minisectors and gain the lap in a few. In 2023 Bahrain qualifying LEC won 12 of 21 and VER took pole by 0.292 s.
• Many minisectors are too close to call. 22–45 % of minisectors on four qualifying sessions were decided by less than 20 ms. time_a and time_b are in the table: read a few hundredths as a tie.
• The time delta takes its total from the laps' own clocks. The shape of each lap comes from integrating its speed; with FastF1's telemetry (which carries Time) each lap is then scaled to its measured lap time, so the gap ends exactly at the official one, as in the example above. In between it is as good as the alignment: at the official sector lines it was a median of 29–42 ms off (at most 143 ms), and on the wrong side in 2–7 of 90 checks (four qualifying sessions, 45 pairs each), each time where the official gap was 59 ms or less; the median gap at those lines was 181 ms. Without a clock the integral stands alone: it ended a median of 92–113 ms from the official gap and on the wrong side in 2–9 of 45 pairs. Over 19 qualifying sessions (2023–2026, 500-point grid) the sessions' medians were 12–65 ms (2026 Suzuka, with 3 clean pairs, 40 / 75 ms), except 2026 China: 114 / 77 ms at the two lines on laps with no frozen data, 6–9 m of misplacement. One lap's speed reading stalled for under a second at a time while RPM and gear moved on (stalls like it occur in every season without raising the error); without it China is still 88 / 39 ms. The cause is not known.
• Where time changes hands is less certain than how much. A few metres of misplacement between two laps cost the most where the car is slowest: the same lap compared with itself, with 5 m of distance error at its slowest corner, shows the gap curve gaining 0.10 s and losing 0.17 s around that corner. So read gains and losses from segment_gaps, which splits the lap at its speed peaks, where misplacement costs almost nothing: in that case no segment moved by more than 49 ms. On six qualifying sessions the curve's biggest rise and fall were a median 113 ms from a gap rebuilt from the cars' positions, the biggest gaining and losing segments 65 ms, about that check's own accuracy. A segment often holds several corners.
• Some 2026 laps have frozen car data. The timing feed sometimes repeats one car reading — speed, RPM, gear and throttle all unchanged, often with a throttle of 104 — for one to 25 seconds while the car's position moves on. Distance is integrated from that speed, so the lap is misplaced (the five worst laps came out 380 m short to 85 m long), and it can't be matched with another. detect_frozen_stretches(tel) finds these stretches. On 2026 China, Suzuka and Monza qualifying it was behind all 57 refused driver pairs (14, 16 and 5 of 22 fastest laps had one); on 183 laps from 2023, 2025 and three other 2026 sessions it flagged none. Rebuilding the speed from the positions was tried twice and isn't reliable: with a smoothed position speed the repaired pairs were a median 132 ms off at the sector lines at Monza (clean pairs 19 ms), 69 ms at Suzuka and 172 ms at China.
• Don't read the peak lateral g. gforces takes lateral g from the curvature of the racing line, and the position feed bends it: on 1,008 clean qualifying laps (2023–2026) the single highest lateral reading passed 6 g on 554 (up to 105 g), usually at the same place on every lap of a session. The 95th percentile of |a_lat| stayed at 2.3–5.3 g and ranked drivers consistently from lap to lap (median Spearman 0.66, against 0.24 for the peak). Longitudinal g comes from the car's speed and is physical.
• Corners are found where the car slows down, so the count won't match the circuit's official corner numbering: flat-out kinks produce no speed minimum. What you get is the same corners on every lap of a circuit, which is what comparisons need.
• Corner classes are rough bands of apex speed: slow < 130 km/h, medium 130–210, fast > 210.
• Long-run pace is indicative between teams. Fuel loads are never published, so two cars' absolute long-run times may reflect different fuel, not different pace.
• The long-run degradation slope adds back an assumed fuel effect of 0.048 s/lap (FUEL_EFFECT_S_PER_LAP), the same for every car, not measured. The slope also contains track evolution and the driver building up, so it can be negative: the car got quicker through the run, not the tyres younger.
• Gaps to the car ahead only count cars on the same lap. A lapped car physically in front is not counted as traffic.
• Race-pace fuel correction uses the same assumed 0.048 s/lap for every car and race.
• The 2 s dirty-air threshold is a convention, not a measurement. Pass threshold= to change it; a per-circuit curve fitted from data is on the roadmap.
• An ideal lap is refused, not faked. ideal_lap returns None when a sector was never timed on a clean lap, or when the sum of best sectors comes out slower than the fastest lap (FastF1 leaves lap 1's S1 blank after a standing start).
• Per-compound degradation is weakly determined on low-wear circuits. TyreDegradationModel pools every driver on a compound, and the slope is small next to the noise: over 65 races of 2024–2026, 23 % of whole-race compound rates still come out negative. The library reports the fit as it comes out, with r2 and laps beside it; clamp it before planning with it.
• The model's fuel effect is assumed, not fitted: the same 0.048 s per lap of fuel as everywhere else. Fitting it (up to 0.1.0.dev11) could not tell fuel from tyre age, which both move with the lap number. Fitting each race's first half and predicting the second half's representative laps, the fixed effect with the 107 % filter cut the median error by 19 % (0.79 → 0.64 s) on 65 races and was better in 71 % of them; how lap times change inside a stint was predicted no better (−2 %, better in half the races). fuel_coef reports the correction in your fuel's unit: s/kg with LapBox's fuel_load_est (which needs a lap-number column to know how much a lap burns), s per lap of fuel from FastF1's laps, 0 when the load never falls.
• Rain tyres are mostly left out in a mixed-weather race. The 107 % filter keeps laps against the driver's best of the race, usually on slicks: of eight rain-tyre fits in those 65 races, five went and two kept under a third of their laps. Fit rain tyres on their own laps.
• Stint degradation uses the same assumed 0.048 s/lap fuel effect, and a stint needs three laps within 107 % of its best; a three-lap stint is a weak trend.

What's in lapbox.telemetry

One lap
resample(tel)	Channels on a uniform distance grid
detect_corners(tel)	Apexes found by the size of each speed drop, so fast corners count too
corner_analysis(distance, speed, apexes)	Entry, minimum and exit speed per corner, classed slow / medium / fast
detect_braking_zones(tel), detect_full_throttle_zones(tel)	Zone tables
gforces(distance, speed, x, y)	Longitudinal and lateral g from speed and the racing line
channel_summary(tel)	Top speed, full-throttle %, braking %, …
detect_frozen_stretches(tel)	Where the timing feed's car data froze (one reading repeated), which misplaces the lap
TelemetryAnalyzer().analyze(tel)	All of the above for one lap

Two laps
compare(tel_a, tel_b, ...)	Alignment, minisectors, segments, summary, residual and matched in one call
align(tel_a, tel_b)	Both laps matched by track position
alignment_residual(aligned), MAX_ALIGNMENT_RESIDUAL	How well they matched, and the cut-off
delta_time(aligned)	Time gap along the lap (positive: A ahead), from the laps' own clocks when they have them
channel_delta(aligned, channel)	Speed / throttle / brake difference
corner_speeds(aligned, apexes)	Both drivers' minimum speed at the same corners
minisector_dominance(tel_a, tel_b, ...)	Who took less time through each minisector (with both times and mean speeds)
segment_gaps(aligned)	Each driver's time between the lap's speed peaks: where time changes hands, read where misplacement costs least

What's in lapbox.data

normalize_laps(laps)	Adds float-seconds columns (LapTimeSeconds, sector and pit times)
normalize_weather(weather)	Adds TimeSeconds
derive_pit_stops(laps)	One row per stop, in-lap paired with the next out-lap, with the pit-lane time
derive_tyre_stints(laps)	One row per stint: compound, first and last lap, length, tyre life
gaps_to_car_ahead(laps)	Seconds to the car ahead at the line, per (driver, lap)
timedelta_to_seconds, seconds_to_timedelta, format_laptime	Time conversion, NaT-safe; format_laptime(83.245) → '1:23.245'

What's in lapbox.practice

detect_runs(laps) → [Run]	Runs of consecutive timed laps, broken by pit visits, untimed laps and tyre changes
classify_run(run, session_best)	long_run, quali_sim or other
session_runs(laps)	Every driver's runs, classified
long_run_pace(run) → LongRunPace	Median pace, degradation slope (fuel-corrected), consistency, laps dropped
longest_run_length(laps)	The most laps at race pace in one run: at least MIN_LONG_RUN (5) exactly when the session contains a long run

What's in lapbox.pace

LapCleaningPipeline(config).clean(laps) → CleaningResult	Drops duplicate, untimed, pit, inaccurate, deleted, implausible (and optionally outlier) laps, and reports how many each rule removed
representative_base_time(lap_times)	A circuit's normal pace: the median of laps within 107 % of the best, falling back to the plain median when fewer than 20 survive
fuel_correct(laps)	Every timed, non-pit lap normalised to end-of-race fuel
true_pace_ranking(laps)	Drivers ranked on fuel-corrected median pace over representative laps (from lap 2, no pit laps, within 107 % of the best), raw rank alongside
clean_air_split(laps, driver), clean_air_ranking(laps)	Fuel-corrected pace in clean air vs within 2 s of the car ahead
driver_consistency(laps, driver), consistency_ranking(laps)	Spread (std, CV %) of representative laps (from lap 2, no pit laps, within 107 % of the best), lap by lap
ideal_lap(laps, driver), biggest_loss(ideal)	Sum of best sectors vs the fastest lap, and where that lap lost most

These are the same calculations the LapBox Race Analysis page runs in the browser. On two real races (Monza 2025, Silverstone 2026) the Python and the site's TypeScript agree on all 7,226 values compared.

What's in lapbox.tyres

TyreDegradationModel().fit(laps)	One line per compound: fuel-corrected lap time on tyre age, laps within 107 % of each driver's best. .compounds (rate, intercept, fuel correction, r2, laps), .degradation_rate(c), .predict_time_loss(c, age), .summary()
stint_degradation(laps)	Every driver's every stint: laps counted, fuel-corrected median and slope (s/lap)
compound_summary(stints)	Per compound: stints, laps, median / best / worst slope, kindest first

TyreDegradationModel started as LapBox's model, with scikit-learn replaced by a NumPy least-squares fit (identical coefficients and R² on nine real compound fits). Since 0.1.0.dev12 it fixes the fuel effect and fits only representative laps (see the limits above). The stint functions are the LapBox tyre-life panel's TypeScript; on three races they agree on every count, compound and median, and on the slopes to within 6e-17 s/lap.

What's in lapbox.plot (the plot extra)

channel_traces(tel)	One lap's channels against distance, frozen car data shaded
comparison(result), result.plot()	The time gap from compare, then both laps' channels; an unmatched pair without the gap, and the reason in the title
speed_delta(result)	Driver A's speed minus B's; for an unmatched pair, the reason instead
track_dominance(result, tel_a)	A's racing line coloured by who took less time through each minisector; within tie_margin (20 ms) grey

Moved from LapBox's Plotly figures. Without Plotly, import lapbox.plot raises an ImportError that says what to install.

Development

pip install -e ".[dev]"
ruff check . && black --check . && pytest

The unit tests are synthetic and run offline. CI runs them on Python 3.11 and 3.12.

License

MIT