F1 RACE RESULT VISUALIZATION USING PYTHON, POLARS, AND ‘GREAT TABLES’: 2024 AUSTRALIAN GP

Programmatically create an F1 race analysis table using the FastF1 API, the Great Tables library, and Polars
Published

February 15, 2025

f1_2024_aus.webp
Screenshot of 2024 F1 Australian Grand Prix Data Table

_

Background

As an F1 fan, I always like to prepare for an upcoming race by looking up results and stats about the circuit from the previous year or earlier. I’ve always found this process to be a bit cumbersome as I would usually only find the final results but no other meaningful data, at least not all in one place. I decided to tackle this in a way that could be repeatable for future races. I had recently stumbled upon the Great Tables project which I thought would be a nice compact way to summarize the results & stats that I care about. I also know most of the raw data is available via the awesome FastF1 project. Finally this would be an opportunity for me to test out the pandas alternative, Polars, which many have been praising for its ease of use and speed. Let’s go.

For the impatient who want the TLDR: click here

High Level Approach

  • Fetch the race data using the FastF1 API
  • Extract the results, lap, and speed info
  • Assemble all of the above into a Polars Dataframe
  • Add in graphics metadata for each team and driver
  • Add desired summary data
  • Create Great Table object from the dataframe, customize, and publish!

Tools Used

  • Python (Primary Scripting Language)
  • FastF1 API (Python Library providing historical F1 race and telemetry data)
  • Polars (Pandas alternative Dataframe library built in Rust designed for speeeeed)
  • Great Tables (Python based library to make great looking display tables)

Importing modules and basic setup

import polars as pl
import fastf1

# set max rows to display to 50
pl.Config.set_tbl_rows(50)

# Find the specific race
sesh = fastf1.get_session(2024, 'Melbourne', 'R')
# Load the race data into local cache
sesh.load()
req         WARNING     DEFAULT CACHE ENABLED! (275.4 MB) /Users/foo/Library/Caches/fastf1
core           INFO     Loading data for Australian Grand Prix - Race [v3.4.4]
req            INFO     Using cached data for session_info
req            INFO     Using cached data for driver_info
req            INFO     Using cached data for session_status_data
req            INFO     Using cached data for lap_count
req            INFO     Using cached data for track_status_data
req            INFO     Using cached data for _extended_timing_data
req            INFO     Using cached data for timing_app_data
core           INFO     Processing timing data...
req            INFO     Using cached data for car_data
req            INFO     Using cached data for position_data
req            INFO     Using cached data for weather_data
req            INFO     Using cached data for race_control_messages
core           INFO     Finished loading data for 19 drivers: ['55', '16', '4', '81', '11', '18', '22', '14', '27', '20', '23', '3', '10', '77', '24', '31', '63', '44', '1']

For reference, a slice of the session results table

sesh.results[['DriverNumber','Abbreviation','TeamName','FullName','CountryCode','Position','Time','Status','Points']]
DriverNumber Abbreviation TeamName FullName CountryCode Position Time Status Points
55 55 SAI Ferrari Carlos Sainz ESP 1.0 0 days 01:20:26.843000 Finished 25.0
16 16 LEC Ferrari Charles Leclerc MON 2.0 0 days 00:00:02.366000 Finished 19.0
4 4 NOR McLaren Lando Norris GBR 3.0 0 days 00:00:05.904000 Finished 15.0
81 81 PIA McLaren Oscar Piastri AUS 4.0 0 days 00:00:35.770000 Finished 12.0
11 11 PER Red Bull Racing Sergio Perez MEX 5.0 0 days 00:00:56.309000 Finished 10.0
18 18 STR Aston Martin Lance Stroll CAN 6.0 0 days 00:01:33.222000 Finished 8.0
22 22 TSU RB Yuki Tsunoda JPN 7.0 0 days 00:01:35.601000 Finished 6.0
14 14 ALO Aston Martin Fernando Alonso ESP 8.0 0 days 00:01:40.992000 Finished 4.0
27 27 HUL Haas F1 Team Nico Hulkenberg GER 9.0 0 days 00:01:44.553000 Finished 2.0
20 20 MAG Haas F1 Team Kevin Magnussen DEN 10.0 NaT +1 Lap 1.0
23 23 ALB Williams Alexander Albon THA 11.0 NaT +1 Lap 0.0
3 3 RIC RB Daniel Ricciardo AUS 12.0 NaT +1 Lap 0.0
10 10 GAS Alpine Pierre Gasly FRA 13.0 NaT +1 Lap 0.0
77 77 BOT Kick Sauber Valtteri Bottas FIN 14.0 NaT +1 Lap 0.0
24 24 ZHO Kick Sauber Guanyu Zhou CHN 15.0 NaT +1 Lap 0.0
31 31 OCO Alpine Esteban Ocon FRA 16.0 NaT +1 Lap 0.0
63 63 RUS Mercedes George Russell GBR 17.0 NaT Accident 0.0
44 44 HAM Mercedes Lewis Hamilton GBR 18.0 NaT Engine 0.0
1 1 VER Red Bull Racing Max Verstappen NED 19.0 NaT Brakes 0.0

Get each driver’s median and top speed from car_data telemetry

# Fetch the data using a list comprehension
speed = [ [i[0], int(i[1]['Speed'].median()), int(i[1]['Speed'].max())] for i in sesh.car_data.items() ]

# Just FYI how to get the flat list of all speeds from telemetry
# pl.DataFrame([list(i[1]['Speed']) for i in sesh.car_data.items()]).unpivot()

# Load it into a Polars Dataframe reorienting it so each driver get his own row
speed_df = pl.DataFrame(speed, schema=['DriverNumber','Median Speed','Top Speed'], orient='row')
speed_df
shape: (19, 3)
DriverNumber Median Speed Top Speed
str i64 i64
"55" 149 325
"16" 150 332
"4" 148 324
"81" 148 330
"11" 151 339
"18" 150 332
"22" 151 334
"14" 147 331
"27" 149 336
"20" 148 338
"23" 144 332
"3" 147 334
"10" 147 337
"77" 149 327
"24" 144 332
"31" 146 335
"63" 147 331
"44" 0 331
"1" 0 320

Load all Team/Driver lap times into a dataframe

laps = pl.DataFrame(
    [{
        'Team':i[1]['Team'],
        'Driver':i[1]['Driver'],
        'LapNumber':int(i[1]['LapNumber']),
        'LapTime':i[1]['LapTime'].seconds*1.0 + i[1]['LapTime'].microseconds/1000000
    } for i in sesh.laps.iterlaps() ], strict=False)

# Drop rows with any NaNs
laps = laps.fill_nan(None).drop_nulls()

laps
shape: (995, 4)
Team Driver LapNumber LapTime
str str i64 f64
"Red Bull Racing" "VER" 1 87.458
"Red Bull Racing" "VER" 2 84.099
"Red Bull Racing" "VER" 3 83.115
"Alpine" "GAS" 1 97.304
"Alpine" "GAS" 2 84.649
"Alpine" "GAS" 3 83.851
"Alpine" "GAS" 4 83.7
"Alpine" "GAS" 5 84.526
"Alpine" "GAS" 6 83.912
"Alpine" "GAS" 7 83.445
"Alpine" "GAS" 8 83.879
"Alpine" "GAS" 9 83.829
"Alpine" "GAS" 10 83.856
"Alpine" "GAS" 11 85.397
"Alpine" "GAS" 12 84.625
"Alpine" "GAS" 13 84.049
"Alpine" "GAS" 14 84.104
"Alpine" "GAS" 15 84.1
"Alpine" "GAS" 16 85.367
"Alpine" "GAS" 17 117.788
"Alpine" "GAS" 18 89.888
"Alpine" "GAS" 19 83.922
"Alpine" "GAS" 20 83.662
"Alpine" "GAS" 21 83.241
"Alpine" "GAS" 22 83.045
… … … …
"McLaren" "PIA" 34 82.238
"McLaren" "PIA" 35 82.059
"McLaren" "PIA" 36 82.047
"McLaren" "PIA" 37 81.89
"McLaren" "PIA" 38 85.203
"McLaren" "PIA" 39 96.781
"McLaren" "PIA" 40 86.93
"McLaren" "PIA" 41 81.424
"McLaren" "PIA" 42 81.08
"McLaren" "PIA" 43 80.485
"McLaren" "PIA" 44 80.395
"McLaren" "PIA" 45 80.223
"McLaren" "PIA" 46 80.615
"McLaren" "PIA" 47 80.682
"McLaren" "PIA" 48 80.588
"McLaren" "PIA" 49 80.352
"McLaren" "PIA" 50 80.496
"McLaren" "PIA" 51 80.718
"McLaren" "PIA" 52 80.295
"McLaren" "PIA" 53 80.308
"McLaren" "PIA" 54 80.199
"McLaren" "PIA" 55 80.754
"McLaren" "PIA" 56 80.357
"McLaren" "PIA" 57 85.255
"McLaren" "PIA" 58 124.363

Calculate aggregate lap stats per driver into a separate dataframe using group_by

stats = laps.group_by(
    ['Team', 'Driver'], maintain_order=True).agg(
        [
            pl.col('LapTime').median().alias('Median'),
            pl.col('LapTime').std().alias('Std Dev'),
            pl.col('LapNumber').max().alias('Laps')
        ]
    )

stats
shape: (19, 5)
Team Driver Median Std Dev Laps
str str f64 f64 i64
"Red Bull Racing" "VER" 84.099 2.277161 3
"Alpine" "GAS" 83.245 7.519535 57
"Red Bull Racing" "PER" 82.139 6.824686 58
"Aston Martin" "ALO" 82.7415 7.554687 58
"Ferrari" "LEC" 81.9085 5.240059 58
"Aston Martin" "STR" 83.061 6.743054 58
"Haas F1 Team" "MAG" 83.315 5.773301 57
"RB" "TSU" 83.04 6.360524 58
"Williams" "ALB" 83.066 5.535442 57
"Kick Sauber" "ZHO" 83.196 7.547661 57
"Haas F1 Team" "HUL" 83.0565 6.98707 58
"RB" "RIC" 83.105 6.085147 57
"Alpine" "OCO" 82.989 7.817079 57
"McLaren" "NOR" 81.803 5.37894 58
"Mercedes" "HAM" 83.436 4.340289 15
"Ferrari" "SAI" 81.578 5.638844 58
"Mercedes" "RUS" 82.7065 4.021696 56
"Kick Sauber" "BOT" 82.976 8.316051 57
"McLaren" "PIA" 82.072 6.62356 58

Pull race results data into a dataframe

results = pl.DataFrame(sesh.results).select(['DriverNumber','Abbreviation','Status','Position']).rename({'Abbreviation':'Driver'})
# Convert position to Int
results = results.cast({'Position': pl.Int8})
results
shape: (19, 4)
DriverNumber Driver Status Position
str str str i8
"55" "SAI" "Finished" 1
"16" "LEC" "Finished" 2
"4" "NOR" "Finished" 3
"81" "PIA" "Finished" 4
"11" "PER" "Finished" 5
"18" "STR" "Finished" 6
"22" "TSU" "Finished" 7
"14" "ALO" "Finished" 8
"27" "HUL" "Finished" 9
"20" "MAG" "+1 Lap" 10
"23" "ALB" "+1 Lap" 11
"3" "RIC" "+1 Lap" 12
"10" "GAS" "+1 Lap" 13
"77" "BOT" "+1 Lap" 14
"24" "ZHO" "+1 Lap" 15
"31" "OCO" "+1 Lap" 16
"63" "RUS" "Accident" 17
"44" "HAM" "Engine" 18
"1" "VER" "Brakes" 19

Join the above Speed, Lap, and results into a single table

final = speed_df.join(results, on='DriverNumber')
final = stats.join(final, on='Driver')

final
shape: (19, 10)
Team Driver Median Std Dev Laps DriverNumber Median Speed Top Speed Status Position
str str f64 f64 i64 str i64 i64 str i8
"Ferrari" "SAI" 81.578 5.638844 58 "55" 149 325 "Finished" 1
"Ferrari" "LEC" 81.9085 5.240059 58 "16" 150 332 "Finished" 2
"McLaren" "NOR" 81.803 5.37894 58 "4" 148 324 "Finished" 3
"McLaren" "PIA" 82.072 6.62356 58 "81" 148 330 "Finished" 4
"Red Bull Racing" "PER" 82.139 6.824686 58 "11" 151 339 "Finished" 5
"Aston Martin" "STR" 83.061 6.743054 58 "18" 150 332 "Finished" 6
"RB" "TSU" 83.04 6.360524 58 "22" 151 334 "Finished" 7
"Aston Martin" "ALO" 82.7415 7.554687 58 "14" 147 331 "Finished" 8
"Haas F1 Team" "HUL" 83.0565 6.98707 58 "27" 149 336 "Finished" 9
"Haas F1 Team" "MAG" 83.315 5.773301 57 "20" 148 338 "+1 Lap" 10
"Williams" "ALB" 83.066 5.535442 57 "23" 144 332 "+1 Lap" 11
"RB" "RIC" 83.105 6.085147 57 "3" 147 334 "+1 Lap" 12
"Alpine" "GAS" 83.245 7.519535 57 "10" 147 337 "+1 Lap" 13
"Kick Sauber" "BOT" 82.976 8.316051 57 "77" 149 327 "+1 Lap" 14
"Kick Sauber" "ZHO" 83.196 7.547661 57 "24" 144 332 "+1 Lap" 15
"Alpine" "OCO" 82.989 7.817079 57 "31" 146 335 "+1 Lap" 16
"Mercedes" "RUS" 82.7065 4.021696 56 "63" 147 331 "Accident" 17
"Mercedes" "HAM" 83.436 4.340289 15 "44" 0 331 "Engine" 18
"Red Bull Racing" "VER" 84.099 2.277161 3 "1" 0 320 "Brakes" 19

Populate graphics metadata to reference later

import glob

def get_insig(team):
    if team.lower() == 'rb':
        return 'racing-bulls-logo'
    else:
        return team.lower().replace(' ','-').replace('-f1-team','')

mugs =   [ glob.glob(F'img/f1-drivers/F1-{name[0] + name[1:].lower()}*')[0] for name in final['Driver'] ]
insigs = [ glob.glob(F'img/f1-teams/{get_insig(team)}*')[0] for team in final['Team'] ]

final.insert_column(0, pl.Series('insigs', insigs))
final.insert_column(2, pl.Series('mugs', mugs))
shape: (19, 12)
insigs Team mugs Driver Median Std Dev Laps DriverNumber Median Speed Top Speed Status Position
str str str str f64 f64 i64 str i64 i64 str i8
"img/f1-teams/ferrari-logo.png" "Ferrari" "img/f1-drivers/F1-Sainz.png" "SAI" 81.578 5.638844 58 "55" 149 325 "Finished" 1
"img/f1-teams/ferrari-logo.png" "Ferrari" "img/f1-drivers/F1-Leclerc.png" "LEC" 81.9085 5.240059 58 "16" 150 332 "Finished" 2
"img/f1-teams/mclaren-logo.png" "McLaren" "img/f1-drivers/F1-Norris.png" "NOR" 81.803 5.37894 58 "4" 148 324 "Finished" 3
"img/f1-teams/mclaren-logo.png" "McLaren" "img/f1-drivers/F1-Piastri.png" "PIA" 82.072 6.62356 58 "81" 148 330 "Finished" 4
"img/f1-teams/red-bull-racing-l… "Red Bull Racing" "img/f1-drivers/F1-Perez.png" "PER" 82.139 6.824686 58 "11" 151 339 "Finished" 5
"img/f1-teams/aston-martin-logo… "Aston Martin" "img/f1-drivers/F1-Stroll.png" "STR" 83.061 6.743054 58 "18" 150 332 "Finished" 6
"img/f1-teams/racing-bulls-logo… "RB" "img/f1-drivers/F1-Tsunoda.png" "TSU" 83.04 6.360524 58 "22" 151 334 "Finished" 7
"img/f1-teams/aston-martin-logo… "Aston Martin" "img/f1-drivers/F1-Alonso.png" "ALO" 82.7415 7.554687 58 "14" 147 331 "Finished" 8
"img/f1-teams/haas-logo.png" "Haas F1 Team" "img/f1-drivers/F1-Hulkenberg.p… "HUL" 83.0565 6.98707 58 "27" 149 336 "Finished" 9
"img/f1-teams/haas-logo.png" "Haas F1 Team" "img/f1-drivers/F1-Magnussen.pn… "MAG" 83.315 5.773301 57 "20" 148 338 "+1 Lap" 10
"img/f1-teams/williams-logo.png" "Williams" "img/f1-drivers/F1-Albon.png" "ALB" 83.066 5.535442 57 "23" 144 332 "+1 Lap" 11
"img/f1-teams/racing-bulls-logo… "RB" "img/f1-drivers/F1-Ricciardo.pn… "RIC" 83.105 6.085147 57 "3" 147 334 "+1 Lap" 12
"img/f1-teams/alpine-logo.png" "Alpine" "img/f1-drivers/F1-Gasly.png" "GAS" 83.245 7.519535 57 "10" 147 337 "+1 Lap" 13
"img/f1-teams/kick-sauber-logo.… "Kick Sauber" "img/f1-drivers/F1-Bottas.png" "BOT" 82.976 8.316051 57 "77" 149 327 "+1 Lap" 14
"img/f1-teams/kick-sauber-logo.… "Kick Sauber" "img/f1-drivers/F1-Zhou.png" "ZHO" 83.196 7.547661 57 "24" 144 332 "+1 Lap" 15
"img/f1-teams/alpine-logo.png" "Alpine" "img/f1-drivers/F1-Ocon.png" "OCO" 82.989 7.817079 57 "31" 146 335 "+1 Lap" 16
"img/f1-teams/mercedes-logo.png" "Mercedes" "img/f1-drivers/F1-Russell.png" "RUS" 82.7065 4.021696 56 "63" 147 331 "Accident" 17
"img/f1-teams/mercedes-logo.png" "Mercedes" "img/f1-drivers/F1-Hamilton.png" "HAM" 83.436 4.340289 15 "44" 0 331 "Engine" 18
"img/f1-teams/red-bull-racing-l… "Red Bull Racing" "img/f1-drivers/F1-Verstappen.p… "VER" 84.099 2.277161 3 "1" 0 320 "Brakes" 19

Generate Lap summary row

# Calculate univeral median and standard deviation for all lap times
race_median = laps['LapTime'].median()
race_stddev = laps['LapTime'].std()

# Add above summary data to the main dataframe
# Need to look into a more efficient way to do this using *align* or similar
summary = pl.DataFrame({
                    'insigs':[None,],
                    'Team': ['SUMMARY',],
                    'mugs':[None,],
                    'Driver': [None,],
                    'Median': [race_median,],
                    'Std Dev': [race_stddev,],
                    'Laps': [None,],
                    'DriverNumber': [None,],
                    'Median Speed': [None,],
                    'Top Speed': [None,],
                    'Status': ['Finished',],
                    'Position': [None,]
})

final = pl.concat([final, summary])
final
shape: (20, 12)
insigs Team mugs Driver Median Std Dev Laps DriverNumber Median Speed Top Speed Status Position
str str str str f64 f64 i64 str i64 i64 str i8
"img/f1-teams/ferrari-logo.png" "Ferrari" "img/f1-drivers/F1-Sainz.png" "SAI" 81.578 5.638844 58 "55" 149 325 "Finished" 1
"img/f1-teams/ferrari-logo.png" "Ferrari" "img/f1-drivers/F1-Leclerc.png" "LEC" 81.9085 5.240059 58 "16" 150 332 "Finished" 2
"img/f1-teams/mclaren-logo.png" "McLaren" "img/f1-drivers/F1-Norris.png" "NOR" 81.803 5.37894 58 "4" 148 324 "Finished" 3
"img/f1-teams/mclaren-logo.png" "McLaren" "img/f1-drivers/F1-Piastri.png" "PIA" 82.072 6.62356 58 "81" 148 330 "Finished" 4
"img/f1-teams/red-bull-racing-l… "Red Bull Racing" "img/f1-drivers/F1-Perez.png" "PER" 82.139 6.824686 58 "11" 151 339 "Finished" 5
"img/f1-teams/aston-martin-logo… "Aston Martin" "img/f1-drivers/F1-Stroll.png" "STR" 83.061 6.743054 58 "18" 150 332 "Finished" 6
"img/f1-teams/racing-bulls-logo… "RB" "img/f1-drivers/F1-Tsunoda.png" "TSU" 83.04 6.360524 58 "22" 151 334 "Finished" 7
"img/f1-teams/aston-martin-logo… "Aston Martin" "img/f1-drivers/F1-Alonso.png" "ALO" 82.7415 7.554687 58 "14" 147 331 "Finished" 8
"img/f1-teams/haas-logo.png" "Haas F1 Team" "img/f1-drivers/F1-Hulkenberg.p… "HUL" 83.0565 6.98707 58 "27" 149 336 "Finished" 9
"img/f1-teams/haas-logo.png" "Haas F1 Team" "img/f1-drivers/F1-Magnussen.pn… "MAG" 83.315 5.773301 57 "20" 148 338 "+1 Lap" 10
"img/f1-teams/williams-logo.png" "Williams" "img/f1-drivers/F1-Albon.png" "ALB" 83.066 5.535442 57 "23" 144 332 "+1 Lap" 11
"img/f1-teams/racing-bulls-logo… "RB" "img/f1-drivers/F1-Ricciardo.pn… "RIC" 83.105 6.085147 57 "3" 147 334 "+1 Lap" 12
"img/f1-teams/alpine-logo.png" "Alpine" "img/f1-drivers/F1-Gasly.png" "GAS" 83.245 7.519535 57 "10" 147 337 "+1 Lap" 13
"img/f1-teams/kick-sauber-logo.… "Kick Sauber" "img/f1-drivers/F1-Bottas.png" "BOT" 82.976 8.316051 57 "77" 149 327 "+1 Lap" 14
"img/f1-teams/kick-sauber-logo.… "Kick Sauber" "img/f1-drivers/F1-Zhou.png" "ZHO" 83.196 7.547661 57 "24" 144 332 "+1 Lap" 15
"img/f1-teams/alpine-logo.png" "Alpine" "img/f1-drivers/F1-Ocon.png" "OCO" 82.989 7.817079 57 "31" 146 335 "+1 Lap" 16
"img/f1-teams/mercedes-logo.png" "Mercedes" "img/f1-drivers/F1-Russell.png" "RUS" 82.7065 4.021696 56 "63" 147 331 "Accident" 17
"img/f1-teams/mercedes-logo.png" "Mercedes" "img/f1-drivers/F1-Hamilton.png" "HAM" 83.436 4.340289 15 "44" 0 331 "Engine" 18
"img/f1-teams/red-bull-racing-l… "Red Bull Racing" "img/f1-drivers/F1-Verstappen.p… "VER" 84.099 2.277161 3 "1" 0 320 "Brakes" 19
null "SUMMARY" null null 82.852 6.514701 null null null null "Finished" null

Generate the final display table

There’s a lot to unpack here so I commented each layer of the display table object inline as I saw fit, which was facilitated by Polars and GT’s chaining style syntax. Honestly though most of this is very self-explanatory.

from great_tables import GT, md, html, loc, style

# Polars expression function to determine if a driver did not DNF
status_color = (
    pl.when(pl.col("Status").str.contains('Finished|1 Lap'))
    .then(pl.lit("black"))
    .otherwise(pl.lit("grey"))
)

# Create the Great Tables object
melbourne_2024 = (
    # Pull out the Position column to use as a Row names (1,2,3 ..)
    GT(final, rowname_col = "Position")
    .cols_move_to_end(
            columns="Laps"
    )
    .tab_header(
        title="F1 2024 Australian Grand Prix",
        subtitle="Results, Lap Time, and Speed Statistics",
    )
    .tab_options(
        table_background_color="white"
    )
    # I'm not sure that this actually works
    .opt_row_striping(row_striping=False)
    # Create a table border
    .opt_table_outline()
    # Set decimal place limits for certain columns
    .fmt_number(
        columns=["Median", "Std Dev"], decimals=3
    )
    # Handle image alignments
    .fmt_image("insigs").cols_align(align="left")
    .fmt_image("mugs").cols_align(align="left")
    # Suppress image column labels
    .cols_label(
        insigs="",
        mugs=""
    ).
    # Redo some column labels
    cols_label({
        'Median Speed':'Median',
        'Top Speed':'Top',
        'Laps':html('Laps<br>done')
    })
    # Create groupings of related columns
    .tab_spanner(
        label="Lap Times",
        columns=["Median","Std Dev"]
    )
    .tab_spanner(
        label="Speed",
        columns=["Median Speed","Top Speed"]
    )
    # Suppress columns that were only used for internal reference
    .cols_hide(
        columns=["Status","DriverNumber"]
    )
    # Fix Summary Row Name
    .sub_missing(
        columns=["Position"],
        missing_text="-"
    )
    # Replace any missing data with empty string
    .sub_missing(missing_text="")
    # Display driver's row text in grey if they did not finish the race (from function above)
    .tab_style(
        style=style.text(color=status_color),
        locations=loc.body()
    )
    # Highlight lowest median lap time
    .tab_style(
        style.text(weight = "bolder", color="purple", size="large"),
        loc.body(
            columns = ["Median"],
            rows = pl.col("Median") == pl.col("Median").filter(pl.col("Status").str.contains('Finished|1 Lap')).min()
        )
    )
    # Highlight top speed
    .tab_style(
        style.text(weight = "bolder", color="purple", size="large"),
        loc.body(
            columns = ["Top Speed"],
            rows = pl.col("Top Speed") == pl.col("Top Speed").filter(pl.col("Status").str.contains('Finished|1 Lap')).max()
        )
    )
    # Highlight lowest standard deviation lap time (most consistent)
    .tab_style(
        style.text(weight = "bolder", color="purple", size="large"),
        loc.body(
            columns = ["Std Dev"],
            rows = pl.col("Std Dev") == pl.col("Std Dev").filter(pl.col("Status").str.contains('Finished|1 Lap')).min()
        )
    )
    .tab_source_note(source_note="Source data from FastF1")
)

melbourne_2024.show(target='notebook')
F1 2024 Australian Grand Prix
Results, Lap Time, and Speed Statistics
Team Driver Lap Times Speed Laps
done
Median Std Dev Median Top
1 Ferrari SAI 81.578 5.639 149 325 58
2 Ferrari LEC 81.909 5.240 150 332 58
3 McLaren NOR 81.803 5.379 148 324 58
4 McLaren PIA 82.072 6.624 148 330 58
5 Red Bull Racing PER 82.139 6.825 151 339 58
6 Aston Martin STR 83.061 6.743 150 332 58
7 RB TSU 83.040 6.361 151 334 58
8 Aston Martin ALO 82.742 7.555 147 331 58
9 Haas F1 Team HUL 83.056 6.987 149 336 58
10 Haas F1 Team MAG 83.315 5.773 148 338 57
11 Williams ALB 83.066 5.535 144 332 57
12 RB RIC 83.105 6.085 147 334 57
13 Alpine GAS 83.245 7.520 147 337 57
14 Kick Sauber BOT 82.976 8.316 149 327 57
15 Kick Sauber ZHO 83.196 7.548 144 332 57
16 Alpine OCO 82.989 7.817 146 335 57
17 Mercedes RUS 82.707 4.022 147 331 56
18 Mercedes HAM 83.436 4.340 0 331 15
19 Red Bull Racing VER 84.099 2.277 0 320 3
- SUMMARY 82.852 6.515
Source data from FastF1

Note: I notice that the graphics do not appear on mobile devices in portrait mode which might a way that Great Tables managees responsive sizing. Turning to landscape should remedy this.

There you have it!

I am really impressed by Great Tables ability to create easily reproducible quick n’ dirty stats that are dense but also aesthetically pleasing. Based on the above I get a basic overview of how a race went down, which drivers had good consistent pace, and perhaps which ones were running with less downforce (higher top speeds). I also like how the graphics help to quickly identify the team/driver which saves me a few mental cycles.

There’s so much more data available from FastF1 that I’ve barely scratched the surface with this. Other data points we could consider adding:

  • Historical number of DNFs (useful for fantasy)
  • Sector info
  • Historical weather data
  • Throttle data
  • Race result position compared to qualifying outcome

Great Tables also has support for inline graphics similar to sparklines which I didn’t think would be too useful for this exercise because the number of laps would be way too dense. Rather a heatmap style graphic would work better to illustrate distributions of data but there is no native support for this so it’d probably have to be some kind of manual solution.

Hope you enjoyed this. Happy upcoming F1 2025 season! Stay tuned for updates.

Thank you for taking the time to read this post. If you'd like to collaborate on a project please contact me via LinkedIn or info@jimmieyoo.com.