First look at the data¶
Pull F1 timing + telemetry via FastF1. The first run downloads the data to ../cache/.
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import fastf1
import pandas as pd
import numpy as np
# enable local cache (gitignored)
fastf1.Cache.enable_cache('../cache')
import fastf1
import pandas as pd
import numpy as np
# enable local cache (gitignored)
fastf1.Cache.enable_cache('../cache')
Load one session¶
Session types: FP1 FP2 FP3 Q R (sprint: S, SQ).
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session = fastf1.get_session(2024, 'Monza', 'R')
session.load() # downloads timing, laps, telemetry, weather
session = fastf1.get_session(2024, 'Monza', 'R')
session.load() # downloads timing, laps, telemetry, weather
core INFO Loading data for Italian Grand Prix - Race [v3.8.3] req INFO No cached data found for session_info. Loading data... _api INFO Fetching session info data... req INFO Data has been written to cache! req INFO No cached data found for driver_info. Loading data... _api INFO Fetching driver list... req INFO Data has been written to cache! req INFO No cached data found for session_status_data. Loading data... _api INFO Fetching session status data... req INFO Data has been written to cache! req INFO No cached data found for lap_count. Loading data... _api INFO Fetching lap count data... req INFO Data has been written to cache! req INFO No cached data found for track_status_data. Loading data... _api INFO Fetching track status data... req INFO Data has been written to cache! req INFO No cached data found for _extended_timing_data. Loading data... _api INFO Fetching timing data... _api INFO Parsing timing data... req INFO Data has been written to cache! req INFO No cached data found for timing_app_data. Loading data... _api INFO Fetching timing app data... req INFO Data has been written to cache! core INFO Processing timing data... req INFO No cached data found for car_data. Loading data... _api INFO Fetching car data... _api INFO Parsing car data... req INFO Data has been written to cache! req INFO No cached data found for position_data. Loading data... _api INFO Fetching position data... _api INFO Parsing position data... req INFO Data has been written to cache! req INFO No cached data found for weather_data. Loading data... _api INFO Fetching weather data... req INFO Data has been written to cache! req INFO No cached data found for race_control_messages. Loading data... _api INFO Fetching race control messages... req INFO Data has been written to cache! core INFO Finished loading data for 20 drivers: ['16', '81', '4', '55', '44', '1', '63', '11', '23', '20', '14', '43', '3', '31', '10', '77', '27', '24', '18', '22']
Laps¶
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laps = session.laps
print(laps.shape)
laps.head()
laps = session.laps
print(laps.shape)
laps.head()
(1008, 31)
Out[3]:
| Time | Driver | DriverNumber | LapTime | LapNumber | Stint | PitOutTime | PitInTime | Sector1Time | Sector2Time | ... | FreshTyre | Team | LapStartTime | LapStartDate | TrackStatus | Position | Deleted | DeletedReason | FastF1Generated | IsAccurate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 days 00:57:18.931000 | LEC | 16 | 0 days 00:01:28.179000 | 1.0 | 1.0 | NaT | NaT | NaT | 0 days 00:00:29.989000 | ... | True | Ferrari | 0 days 00:55:50.494000 | 2024-09-01 13:03:34.413 | 1 | 2.0 | False | False | False | |
| 1 | 0 days 00:58:44.327000 | LEC | 16 | 0 days 00:01:25.396000 | 2.0 | 1.0 | NaT | NaT | 0 days 00:00:27.707000 | 0 days 00:00:29.265000 | ... | True | Ferrari | 0 days 00:57:18.931000 | 2024-09-01 13:05:02.850 | 1 | 2.0 | False | False | True | |
| 2 | 0 days 01:00:09.506000 | LEC | 16 | 0 days 00:01:25.179000 | 3.0 | 1.0 | NaT | NaT | 0 days 00:00:27.679000 | 0 days 00:00:29.001000 | ... | True | Ferrari | 0 days 00:58:44.327000 | 2024-09-01 13:06:28.246 | 1 | 2.0 | False | False | True | |
| 3 | 0 days 01:01:34.316000 | LEC | 16 | 0 days 00:01:24.810000 | 4.0 | 1.0 | NaT | NaT | 0 days 00:00:27.653000 | 0 days 00:00:28.883000 | ... | True | Ferrari | 0 days 01:00:09.506000 | 2024-09-01 13:07:53.425 | 1 | 2.0 | False | False | True | |
| 4 | 0 days 01:02:58.919000 | LEC | 16 | 0 days 00:01:24.603000 | 5.0 | 1.0 | NaT | NaT | 0 days 00:00:27.630000 | 0 days 00:00:28.790000 | ... | True | Ferrari | 0 days 01:01:34.316000 | 2024-09-01 13:09:18.235 | 1 | 2.0 | False | False | True |
5 rows × 31 columns
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# columns available
list(laps.columns)
# columns available
list(laps.columns)
Out[4]:
['Time', 'Driver', 'DriverNumber', 'LapTime', 'LapNumber', 'Stint', 'PitOutTime', 'PitInTime', 'Sector1Time', 'Sector2Time', 'Sector3Time', 'Sector1SessionTime', 'Sector2SessionTime', 'Sector3SessionTime', 'SpeedI1', 'SpeedI2', 'SpeedFL', 'SpeedST', 'IsPersonalBest', 'Compound', 'TyreLife', 'FreshTyre', 'Team', 'LapStartTime', 'LapStartDate', 'TrackStatus', 'Position', 'Deleted', 'DeletedReason', 'FastF1Generated', 'IsAccurate']
Weather¶
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session.weather_data.head()
session.weather_data.head()
Out[5]:
| Time | AirTemp | Humidity | Pressure | Rainfall | TrackTemp | WindDirection | WindSpeed | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0 days 00:00:26.141000 | 33.2 | 38.0 | 993.8 | False | 52.1 | 318 | 0.7 |
| 1 | 0 days 00:01:26.139000 | 33.2 | 37.0 | 993.9 | False | 52.1 | 207 | 1.0 |
| 2 | 0 days 00:02:26.141000 | 33.2 | 37.0 | 993.9 | False | 52.8 | 230 | 1.3 |
| 3 | 0 days 00:03:26.146000 | 33.2 | 36.0 | 993.8 | False | 52.8 | 200 | 0.7 |
| 4 | 0 days 00:04:26.151000 | 33.3 | 36.0 | 993.8 | False | 52.8 | 182 | 0.8 |
Telemetry for one driver¶
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ver = laps.pick_drivers('VER').pick_fastest()
tel = ver.get_telemetry()
print(tel.shape)
tel[['Distance', 'Speed', 'Throttle', 'Brake', 'nGear', 'X', 'Y']].head()
ver = laps.pick_drivers('VER').pick_fastest()
tel = ver.get_telemetry()
print(tel.shape)
tel[['Distance', 'Speed', 'Throttle', 'Brake', 'nGear', 'X', 'Y']].head()
(632, 18)
Out[6]:
| Distance | Speed | Throttle | Brake | nGear | X | Y | |
|---|---|---|---|---|---|---|---|
| 2 | 0.266665 | 316.542500 | 100.0 | False | 8 | -1376.705009 | -710.765951 |
| 3 | 5.653889 | 317.000000 | 100.0 | False | 8 | -1372.727497 | -664.290235 |
| 4 | 17.685200 | 317.755556 | 100.0 | False | 8 | -1363.000000 | -553.000000 |
| 5 | 30.097292 | 318.533333 | 100.0 | False | 8 | -1352.000000 | -429.000000 |
| 6 | 37.553889 | 319.000000 | 100.0 | False | 8 | -1345.615497 | -354.485258 |
Lap time vs tire age¶
Is the data usable for modeling?
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import plotly.express as px
df = laps[['Driver', 'LapNumber', 'LapTime', 'Compound', 'TyreLife', 'Stint']].copy()
df = df.dropna(subset=['LapTime'])
df['LapTime_s'] = df['LapTime'].dt.total_seconds()
ver_laps = df[df['Driver'] == 'VER']
fig = px.scatter(ver_laps, x='TyreLife', y='LapTime_s', color='Compound',
title='VER lap time vs tyre life - Monza 2024')
fig.show()
import plotly.express as px
df = laps[['Driver', 'LapNumber', 'LapTime', 'Compound', 'TyreLife', 'Stint']].copy()
df = df.dropna(subset=['LapTime'])
df['LapTime_s'] = df['LapTime'].dt.total_seconds()
ver_laps = df[df['Driver'] == 'VER']
fig = px.scatter(ver_laps, x='TyreLife', y='LapTime_s', color='Compound',
title='VER lap time vs tyre life - Monza 2024')
fig.show()
The chart shows structure, but by itself it doesn't answer the question. Above all, tire age does not explain lap time: the two HARD stints sit about 1.5 s apart at the same tire age (the later one, on a lighter car, is faster), and the MEDIUM stint is faster still, even though the car was carrying the most fuel at the time. Compound and fuel load therefore matter at least as much as wear, and within a single stint the tire-age trend is weak. On top of that come the outliers - the first lap after the start and the in- and out-laps around pit stops (88-108 s) - which have to be filtered out.
So the answer is: yes, but only after cleaning and with more features than tire age alone. We do that in notebook 01 for every race of the 2023-2024 seasons, and in notebook 02 we return to the tire-degradation question on the cleaned data.