Lap Telemetry
This Tool Works By Going Through All The Laps Of A Specifed Race To Get A Drivers Lap Time, Which Is Then Compared To The Fastest Time Of That Lap To Produce A Delta Value That Roughly Shows Their Tyre Performance. When Used With The Vervose Parameter It Also Outputs A Graph Showing The Delta.
This Tool Is Run By Using The Mode LT
Requires:
driver- Drivers Three Letter Identifier (E.G. VER)-y--year- Year The Session Took Place (E.G. 2021)-r--race- The Race Weekends Number (E.G. 22 (Abu Dhabi))-s--session- The Session Name (E.G. R, SQ, Q, FP3, FP2, FP1)-l--lap- The Lap Number (E.G. 58)
Optional:
-sd--secondDriver- Another Drivers Three Letter Identifier (E.G. HAM)
Example Run Code: python main.py LT VER -y 2021 -r 22 -s R -l 58 -sd HAM
Which Outputs:
Drivers Tyre Performance
This Tool Works By Going Through All The Laps Of A Specifed Race To Get A Drivers Lap Time, Which Is Then Compared To The Fastest Time Of That Lap To Produce A Delta Value That Roughly Shows Their Tyre Performance. When Used With The Vervose Parameter It Also Outputs A Graph Showing The Delta.
This Tool Is Run By Using The Mode DTP
Requires:
driver- Drivers Three Letter Identifier (E.G. PER)-y--year- Year The Session Took Place (E.G. 2021)-r--race- The Race Weekends Number (E.G. 18 (Mexico))-s--session- The Session Name (E.G. R, SQ, Q, FP3, FP2, FP1)
Optional:
-v--verbose- Adds More Output Data
Example Run Code: python main.py DTP PER -y 2021 -r 18 -s R -v
Which Outputs:
core INFO Loading laps for Mexico City Grand Prix - Race [v2.1.10]
api INFO Using cached data for timing_data
api INFO Using cached data for timing_app_data
core INFO Processing timing data...
api INFO Using cached data for driver_info
api INFO Using cached data for session_status_data
api INFO Using cached data for track_status_data
api INFO Using cached data for car_data
api INFO Using cached data for position_data
api INFO Using cached data for weather_data
core INFO Loaded data for 20 drivers: ['63', '77', '33', '7', '47', '31', '4', '3', '9', '16', '22', '5', '14', '44', '18', '11', '10', '99', '6', '55']
Tyre Lap Info
Lap Stint Compound Life Fresh Lap Time Delta Fast Lap Fast Lap
Time Driver
─────────────────────────────────────────────────────────────────────────────────────────────────
2 1 MEDIUM 5.0 False 0:02:11.575… 0:00:00.17… 0:02:11.403… HAM
3 1 MEDIUM 6.0 False 0:02:06.355… 0:00:01.82… 0:02:04.534… GIO
4 1 MEDIUM 7.0 False 0:02:06.308… 0:00:20.45… 0:01:45.850… RIC
5 1 MEDIUM 8.0 False 0:01:23.185… 0:00:01.57… 0:01:21.615… VER
6 1 MEDIUM 9.0 False 0:01:22.356… 0:00:00.72… 0:01:21.635… VER
7 1 MEDIUM 10.0 False 0:01:22.330… 0:00:00.70… 0:01:21.622… VER
8 1 MEDIUM 11.0 False 0:01:21.954… 0:00:00.43… 0:01:21.520… VER
9 1 MEDIUM 12.0 False 0:01:21.766… 0:00:00.71… 0:01:21.051… VER
10 1 MEDIUM 13.0 False 0:01:21.687… 0:00:00.58… 0:01:21.105… HAM
11 1 MEDIUM 14.0 False 0:01:21.389… 0:00:00.43… 0:01:20.958… VER
12 1 MEDIUM 15.0 False 0:01:21.464… 0:00:00.45… 0:01:21.014… VER
13 1 MEDIUM 16.0 False 0:01:21.520… 0:00:00.31… 0:01:21.209… VER
14 1 MEDIUM 17.0 False 0:01:21.457… 0:00:00.29… 0:01:21.158… VER
15 1 MEDIUM 18.0 False 0:01:21.173… 0:00:00.07… 0:01:21.100… VER
16 1 MEDIUM 19.0 False 0:01:21.393… 0:00:00.36… 0:01:21.026… VER
17 1 MEDIUM 20.0 False 0:01:21.652… 0:00:00.37… 0:01:21.274… HAM
18 1 MEDIUM 21.0 False 0:01:21.260… 0:00:00.05… 0:01:21.203… HAM
19 1 MEDIUM 22.0 False 0:01:21.380… 0:00:00.69… 0:01:20.685… VER
20 1 MEDIUM 23.0 False 0:01:21.765… 0:00:00.63… 0:01:21.126… VER
21 1 MEDIUM 24.0 False 0:01:21.758… 0:00:00.52… 0:01:21.231… VER
22 1 MEDIUM 25.0 False 0:01:22.017… 0:00:00.70… 0:01:21.316… VER
23 1 MEDIUM 26.0 False 0:01:21.482… 0:00:00.06… 0:01:21.417… VER
24 1 MEDIUM 27.0 False 0:01:21.783… 0:00:00.38… 0:01:21.398… HAM
25 1 MEDIUM 28.0 False 0:01:21.359… 0:00:00.07… 0:01:21.430… HAM
26 1 MEDIUM 29.0 False 0:01:21.409… 0:00:00.08… 0:01:21.491… VER
27 1 MEDIUM 30.0 False 0:01:21.170… 0:00:00.09… 0:01:21.267… VER
28 1 MEDIUM 31.0 False 0:01:21.420… 0:00:00.21… 0:01:21.203… VER
29 1 MEDIUM 32.0 False 0:01:22.118… 0:00:01.08… 0:01:21.029… VER
30 1 MEDIUM 33.0 False 0:01:21.841… 0:00:00.51… 0:01:21.328… VER
31 1 MEDIUM 34.0 False 0:01:22.092… 0:00:02.13… 0:01:19.953… HAM
32 1 MEDIUM 35.0 False 0:01:21.858… 0:00:01.64… 0:01:20.218… HAM
33 1 MEDIUM 36.0 False 0:01:21.196… 0:00:00.74… 0:01:20.456… HAM
34 1 MEDIUM 37.0 False 0:01:21.152… 0:00:00.62… 0:01:20.526… HAM
35 1 MEDIUM 38.0 False 0:01:20.890… 0:00:00.36… 0:01:20.527… HAM
36 1 MEDIUM 39.0 False 0:01:21.068… 0:00:00.80… 0:01:20.266… VER
37 1 MEDIUM 40.0 False 0:01:20.761… 0:00:00.50… 0:01:20.261… VER
38 1 MEDIUM 41.0 False 0:01:21.541… 0:00:01.45… 0:01:20.083… VER
39 1 MEDIUM 42.0 False 0:01:21.786… 0:00:01.44… 0:01:20.340… VER
42 2 HARD 2.0 True 0:01:19.659… 0:00:00.36… 0:01:20.025… VER
43 2 HARD 3.0 True 0:01:20.152… 0:00:00.16… 0:01:19.989… VER
44 2 HARD 4.0 True 0:01:19.712… 0:00:00.33… 0:01:20.049… BOT
45 2 HARD 5.0 True 0:01:19.840… 0:00:00.00… 0:01:19.834… HAM
46 2 HARD 6.0 True 0:01:19.949… 0:00:00.18… 0:01:19.761… BOT
47 2 HARD 7.0 True 0:01:21.324… 0:00:01.11… 0:01:20.211… VER
48 2 HARD 8.0 True 0:01:20.552… 0:00:00.13… 0:01:20.419… HAM
49 2 HARD 9.0 True 0:01:19.468… 0:00:00.45… 0:01:19.921… VER
50 2 HARD 10.0 True 0:01:20.188… 0:00:00.27… 0:01:20.461… HAM
51 2 HARD 11.0 True 0:01:19.675… 0:00:00.43… 0:01:20.111… HAM
52 2 HARD 12.0 True 0:01:19.890… 0:00:00.89… 0:01:18.999… VER
53 2 HARD 13.0 True 0:01:19.686… 0:00:00.44… 0:01:20.130… HAM
54 2 HARD 14.0 True 0:01:19.882… 0:00:00.16… 0:01:20.045… VER
55 2 HARD 15.0 True 0:01:19.643… 0:00:00.43… 0:01:20.078… VER
56 2 HARD 16.0 True 0:01:19.960… 0:00:00.02… 0:01:19.988… VER
57 2 HARD 17.0 True 0:01:20.309… 0:00:00.27… 0:01:20.039… VER
58 2 HARD 18.0 True 0:01:20.151… 0:00:00.31… 0:01:19.839… VER
59 2 HARD 19.0 True 0:01:20.398… 0:00:00.14… 0:01:20.249… VER
60 2 HARD 20.0 True 0:01:20.406… 0:00:00.00… 0:01:20.413… VER
61 2 HARD 21.0 True 0:01:20.878… 0:00:00.59… 0:01:20.287… VER
62 2 HARD 22.0 True 0:01:20.536… 0:00:00.31… 0:01:20.220… HAM
63 2 HARD 23.0 True 0:01:20.091… 0:00:00.26… 0:01:19.825… VER
64 2 HARD 24.0 True 0:01:20.395… 0:00:00.69… 0:01:19.704… VER
65 2 HARD 25.0 True 0:01:21.119… 0:00:01.90… 0:01:19.210… VER
66 2 HARD 26.0 True 0:01:19.936… 0:00:00.11… 0:01:19.820… HAM
67 2 HARD 27.0 True 0:01:19.890… 0:00:00.23… 0:01:20.127… HAM
68 2 HARD 28.0 True 0:01:19.889… 0:00:00.54… 0:01:20.430… HAM
69 2 HARD 29.0 True 0:01:19.846… 0:00:02.07… 0:01:17.774… BOT
70 2 HARD 30.0 True 0:01:20.183… 0:00:00.13… 0:01:20.319… HAM
71 2 HARD 31.0 True 0:01:21.150… 0:00:00.64… 0:01:20.502… VERPredicted Race Position
This Tool Works By Getting Every Race Start & End Positions From An Inputted Driver Works Out The Most Common Result, And It's % Of Happening. Drivers With Larger Data Sets E.G. Räikkönen or Alonso Have The Highest Chance Of Being Close To The Actual Result Compared To A Rookie Like Mick Schumacher.
This Tool Is Run By Using The Mode PRP
Requires:
driver- Drivers Three Letter Identifier (E.G. HAM)-sp--startingpos- The Drivers Starting Pos (E.G. 1)
Optional:
-v--verbose- Adds More Output Data
Example Run Code: python main.py PRP HAM -sp 1 -v
Which Outputs:
Race Start To Race End
Positions
Start Pos End Pos
─────────────────────
1 1
1 1
1 3
1 1
1 1
1 1
1 1
1 5
1 3
1 12
1 1
1 2
1 1
1 1
1 2
1 3
1 3
1 1
1 1
1 3
1 4
1 5
1 1
1 3
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 2
1 1
1 1
1 3
1 1
1 2
1 1
1 6
1 1
1 1
1 2
1 3
1 1
1 1
1 1
1 2
1 1
1 1
1 1
1 1
1 2
1 1
1 1
1 1
1 5
1 1
1 1
1 1
1 2
1 1
1 1
1 2
1 1
1 1
1 2
1 1
1 2
1 1
1 1
1 3
1 1
1 1
1 2
1 1
1 1
1 9
1 1
1 1
1 1
1 1
1 1
1 1
1 7
1 1
1 3
1 1
1 1
1 2
1 1
1 2
Most Likely Finishing Place Is 1.
With A 65% Chance. Fastest Technically Possible Lap Time
This Tool Works By Going Through A Drivers Mini Sectors Of Every Lap And Finds The Fastest Ones. These Are Then Added Together To Produce The Fastest Technically Possible Lap Time. This Obviously Completely Ignores Tyre Wear, Tyre Temp, Fuel Load, Slipstream And Dirty Air. This Time Is Near Impossible To Actually Achieve But Is A Fun Metric To Know.
This Tool Is Run By Using The Mode FTPLT
Requires:
driver- Drivers Three Letter Identifier (E.G. NOR)-y--year- Year The Session Took Place (E.G. 2021)-r--race- The Race Weekends Number (E.G. 10 (Austria))-s--session- The Session Name (E.G. R, SQ, Q, FP3, FP2, FP1)
Optional:
-m--minisectors- The Amount Of Mini Sectors To Analyse, Defaults To 25-v--verbose- Adds More Output Data
Example Run Code: python main.py FTPLT NOR -y 2021 -r 9 -s R -v
Which Outputs:
core INFO Loading laps for Austrian Grand Prix - Race [v2.1.8]
api INFO Using cached data for timing_data
api INFO Using cached data for timing_app_data
core INFO Processing timing data...
api INFO Using cached data for session_status_data
api INFO Using cached data for track_status_data
api INFO Using cached data for car_data
api INFO Using cached data for position_data
api INFO Using cached data for weather_data
core INFO Loaded data for 19 drivers: ['14', '3', '16', '5', '77', '44', '4', '47', '9', '22', '63', '18', '33', '6', '99', '11', '7', '55', '10']
Fastest Technically Possible MiniSectors
MiniSector Speed Gear ~Time Lap
─────────────────────────────────────────────────────────────────────────────────
1 298.09090909090907 7.818181818181818 2.0780290210430015 53
2 315.5882352941176 8.0 1.9628157539608582 53
3 247.52173913043478 6.6521739130434785 2.502574368522748 42
4 221.2173913043478 5.043478260869565 2.8001485613207557 1
5 270.0 6.7368421052631575 2.2942280000000004 1
6 295.8421052631579 7.631578947368421 2.0938248781355635 53
7 316.47058823529414 8.0 1.9573432193308553 53
8 300.8333333333333 8.0 2.0590855180055407 21
9 138.57894736842104 3.0 4.469954287884544 1
10 249.95 6.0 2.4782618923784763 1
11 285.6111111111111 7.277777777777778 2.168828648122934 53
12 317.1764705882353 8.0 1.952987114243324 53
13 254.52380952380952 6.714285714285714 2.433727363891488 4
14 194.69230769230768 4.230769230769231 3.1816437297510873 1
15 249.8695652173913 6.0 2.479059662432574 1
16 247.95454545454547 6.0 2.498206108157654 67
17 215.95833333333334 5.208333333333333 2.8683383060003864 1
18 233.0 5.695652173913044 2.6585474678111596 62
19 244.13636363636363 5.7727272727272725 2.537276916775275 1
20 265.35 6.65 2.3344321085358963 55
21 288.6842105263158 7.0 2.145741046490429 55
22 293.57894736842104 7.0 2.1099658730727864 64
23 245.63636363636363 6.136363636363637 2.521782812731311 57
24 199.15384615384616 4.423076923076923 3.1103670451911944 30
25 255.09677419354838 6.225806451612903 2.4282610470409716 56
Fastest Technically Possible Lap Time ~0:01:02.125431
Actual Fastest Lap 0:01:08.471000 On Lap 62.0
Fastest Technically Possible Time Made Up From MiniSectors On Laps: [53.0, 42.0, 1.0, 21.0, 4.0,
67.0, 62.0, 55.0, 64.0, 57.0, 30.0, 56.0]
Differance In Lap Times: 0:00:06.345569
core INFO Loading laps for Austrian Grand Prix - Race [v2.1.8]
api INFO Using cached data for timing_data
api INFO Using cached data for timing_app_data
core INFO Processing timing data...
api INFO Using cached data for session_status_data
api INFO Using cached data for track_status_data
api INFO Using cached data for car_data
api INFO Using cached data for position_data
api INFO Using cached data for weather_data
core INFO Loaded data for 19 drivers: ['14', '3', '16', '5', '77', '44', '4', '47', '9', '22', '63', '18', '33', '6', '99', '11', '7', '55', '10']
Actual Fastest Lap Vs Fastest Technically
Possible Lap
MiniSector Gain/Loss Time Differance
──────────────────────────────────────────
1 Lost 0:00:00.093926
2 Lost 0:00:00.120999
3 Lost 0:00:00.154982
4 Lost 0:00:00.979029
5 Lost 0:00:00.152316
6 Lost 0:00:00.074049
7 Lost 0:00:00.132227
8 Lost 0:00:00.101624
9 Lost 0:00:01.311655
10 Lost 0:00:00.527613
11 Lost 0:00:00.120045
12 Lost 0:00:00.153580
13 Gained 0:00:00.011781
14 Lost 0:00:01.571408
15 Lost 0:00:00.193952
16 Lost 0:00:00.010538
17 Lost 0:00:00.368759
18 Gained 0:00:00.010377
19 Lost 0:00:00.300802
20 Lost 0:00:00.034154
21 Lost 0:00:00.023732
22 Lost 0:00:00.005689
23 Lost 0:00:00.028359
24 Gained 0:00:00.017741
25 Lost 0:00:00.072156Fastest Lap Mini Sectors
This Tool Works By Getting A Drivers Mini Sectors From Their Fastest Lap. This Then Outputs The Average Speed And Gear Of The Mini Sector, And The Rough Time It Took To Pass Through It.
This Tool Is Run By Using The Mode FLMS
Requires:
driver- Drivers Three Letter Identifier (E.G. NOR)-y--year- Year The Session Took Place (E.G. 2021)-r--race- The Race Weekends Number (E.G. 10 (Austria))-s--session- The Session Name (E.G. R, SQ, Q, FP3, FP2, FP1)
Optional:
-m--minisectors- The Amount Of Mini Sectors To Analyse, Defaults To 25returnMode- Only Used Internally By Other Scripts But Allows Them To Take The Data As Pandas DataFrame
Example Run Code: python main.py FLMS NOR -y 2021 -r 9 -s R -v
Which Outputs:
core INFO Loading laps for Austrian Grand Prix - Race [v2.1.8]
api INFO Using cached data for timing_data
api INFO Using cached data for timing_app_data
core INFO Processing timing data...
api INFO Using cached data for session_status_data
api INFO Using cached data for track_status_data
api INFO Using cached data for car_data
api INFO Using cached data for position_data
api INFO Using cached data for weather_data
core INFO Loaded data for 19 drivers: ['3', '11', '7', '77', '22', '33', '5', '18', '16', '63', '6', '4', '55', '10', '44', '99', '14', '9', '47']
Fastest Lap MiniSectors
MiniSector Speed Gear ~Time
───────────────────────────────────────────────────────────────────────────
1 285.2 7.0 2.166059887798037
2 297.2631578947368 7.0 2.078159582152975
3 233.08695652173913 6.565217391304348 2.6503425554933786
4 163.9090909090909 4.181818181818182 3.7689201774819754
5 253.1904761904762 6.142857142857143 2.439903306375776
6 285.7368421052632 7.0 2.161990296555535
7 296.44444444444446 7.0 2.083898995502249
8 286.6842105263158 7.0 2.154845845419498
9 107.14 3.06 5.765916371103231
10 206.07692307692307 4.653846153846154 2.997716924225458
11 270.63157894736844 6.7368421052631575 2.2826614780241155
12 294.05263157894734 7.0 2.1008493502774304
13 255.76190476190476 6.904761904761905 2.4153725339787755
14 130.325 3.375 4.74015177441013
15 231.7391304347826 5.521739130434782 2.665757305816136
16 246.91304347826087 5.739130434782608 2.501934572988203
17 191.35714285714286 5.0 3.2283105337812623
18 233.91304347826087 5.739130434782608 2.6409826096654276
19 218.2608695652174 5.3478260869565215 2.830375784860558
20 261.5238095238095 6.571428571428571 2.362156933721778
21 285.5263157894737 7.0 2.1635843907834102
22 292.7894736842105 7.0 2.1099128743483737
23 242.9047619047619 6.095238095238095 2.543220129386395
24 200.2962962962963 4.777777777777778 3.084232167159764
25 247.73529411764707 6.029411764705882 2.493630478451858