Time, Date and Timing - Explanation

Working with Formula 1 data (or any motorsport data for that matter) requires working working with lots of different values of time. These values can denote a point in time or a duration. The following is an explanation of the different values of time which are used in this module.

Points in Time

Three different values of time can denote a point in time in this module. These are:
  • Time

  • SessionTime

  • Date

SessionTime is a time which has a more or less arbitrary zero point multiple minutes before a session officially starts. It is directly provided by the F1 live timing api. SessionTime zero marks the beginning of all data streams. The SessionTime references events within the boundaries of one session.

–> SessionTime is relative to the beginning of a session

Date is a UTC timestamp containing date and time. It is therefore an absolute reference to tell when something happened in the context of the time of day.

–> Date is an absolute point in time

Time references data relative in the current data set. The first sample of a given set of data will always mark the zero point for this set of data.

–> Time is relative to the first sample of data in a set of data

For a sample of telemetry data this means, that the sample will always have the same value for its Date and SessionTime. But its value for Time will change when the dataset is modified.

Take the following set of example telemetry. Lets assume the variable tel holds previously loaded telemetry data for one driver an for a full session (only time data is shown for simplification).

>>> tel = session.car_data['33'].loc[:, ['Time', 'SessionTime', 'Date']]
>>> tel
                        Time            SessionTime                    Date
0     0 days 00:00:02.984000 0 days 00:00:02.984000 2020-09-06 12:40:03.180
1     0 days 00:00:03.224000 0 days 00:00:03.224000 2020-09-06 12:40:03.420
2     0 days 00:00:03.464000 0 days 00:00:03.464000 2020-09-06 12:40:03.660
3     0 days 00:00:03.704000 0 days 00:00:03.704000 2020-09-06 12:40:03.900
4     0 days 00:00:03.944000 0 days 00:00:03.944000 2020-09-06 12:40:04.140
...                      ...                    ...                     ...
35533 0 days 02:23:27.764000 0 days 02:23:27.764000 2020-09-06 15:03:27.960
35534 0 days 02:23:28.004000 0 days 02:23:28.004000 2020-09-06 15:03:28.200
35535 0 days 02:23:28.244000 0 days 02:23:28.244000 2020-09-06 15:03:28.440
35536 0 days 02:23:28.484000 0 days 02:23:28.484000 2020-09-06 15:03:28.680
35537 0 days 02:23:28.724000 0 days 02:23:28.724000 2020-09-06 15:03:28.920

[35538 rows x 3 columns]

The telemetry comprises approximately 2 hours and 23 minutes of data. The session (a race in this case) did not last this long, but the data starts before the beginning of the session and ends after the end. SessionTime and Time are exactly the same for this set of data. This is how the data looks as it is created by the api functions. Next, the data is sliced to only include a subset of the full session.

>>> t1 = pandas.Timedelta(hours=1, minutes=20)
>>> t2 = pandas.Timedelta(hours=1, minutes=30)
>>> tel.slice_by_time(t1, t2)
                        Time            SessionTime                    Date
19811 0 days 00:00:00.195000 0 days 01:20:00.195000 2020-09-06 14:00:00.391
19812 0 days 00:00:00.435000 0 days 01:20:00.435000 2020-09-06 14:00:00.631
19813 0 days 00:00:00.676000 0 days 01:20:00.676000 2020-09-06 14:00:00.872
19814 0 days 00:00:00.916000 0 days 01:20:00.916000 2020-09-06 14:00:01.112
19815 0 days 00:00:01.156000 0 days 01:20:01.156000 2020-09-06 14:00:01.352
...                      ...                    ...                     ...
22288 0 days 00:09:59.076000 0 days 01:29:59.076000 2020-09-06 14:09:59.272
22289 0 days 00:09:59.277000 0 days 01:29:59.277000 2020-09-06 14:09:59.473
22290 0 days 00:09:59.517000 0 days 01:29:59.517000 2020-09-06 14:09:59.713
22291 0 days 00:09:59.757000 0 days 01:29:59.757000 2020-09-06 14:09:59.953
22292 0 days 00:09:59.997000 0 days 01:29:59.997000 2020-09-06 14:10:00.193

[2482 rows x 3 columns]

SessionTime and Date have kept there reference point. But the reference point for Time has changed and its new zero is now the first sample of this set of data. If this subset of data was sliced again, Time would change again so as to start at zero on the first sample.

All three of these values have a use for different reasons. Here are some exmaples.

To check which other cars are on track while one driver is on a fast lap SessionTime is useful.

When overlapping multiple laps for comparison the data can be plotted over Time so that different laps have a common zero point.

Date is useful when checking out something in reference to commonly used human time.

The relation between SessionTime and Date is a constant offset. This value is available through the session class.

>>> session.t0_date
Timestamp('2020-09-06 12:40:00.196000')

As already mentioned above, the zero point of the session time is before the actual start of a session. The session itself officially starts at some later point in time. This value is also available through the session class.

>>> session.session_start_time
datetime.timedelta(seconds=2008, microseconds=79000)
>>> str(session.session_start_time)

Lap timing

Lap timing data is available in an instance of Laps in Session.laps. For each lap, the usual official timing information is available. It consists of the lap time and the sector times. The columns for this data in Laps are called ‘LapTime’, ‘Sector1Time’, ‘Sector2Time’ and ‘Sector3Time’. These four values are highly accurate and are considered the absolute truth.

For each lap, additional data is available. Amongst others the following time related data:
  • ‘Time’: This marks the point in time when a lap was set, i.e. finished, as a SessionTime. The name ‘Time’ is confusing here as it should be ‘SessionTime’. It is kept mainly for backwards compatibility.

  • ‘Sector*SessionTime’: For each sector a session time is available. This marks the point in time when a sector time was set. The ‘Sector3Session’ time is mathematically the same as the end of the lap (‘Time’). In most cases this is true, but there can be minor deviations in some edge cases. (In some cases the api function can not calculate the timestamps correctly.)

  • ‘LapStartTime’: This marks the point in time (SessionTime) when a lap was started and is equivalent to the ‘Time’ and ‘Sector3Time’ of the previous lap.

  • ‘LapStartDate’: This is the same as ‘LapStartTime’ just expressed as ‘Date’ instead of ‘SessionTime’

  • ‘PitInTime’/’PitOutTime’: This marks the point in time (SessionTime) when a car entered or left the pits.

These additional timestamps are not provided by the api. They are calculated as accurate as possible from the available data but the accuracy can not be verified to millisecond precision.