MotoGP Data dominance · tyre · race pace
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Jack Miller

Australia

Career

Pace and style

Pace

MetricValueUsual rangeRankn
Race pace+0.81%0.39–1.1425/54206
One-lap pace (qualifying)+0.74%0.35–1.0711/54150
Long run (practice)+0.81%0.51–1.1312/59320
Fast lap (practice)+0.76%0.35–1.189/60445

Percentage above the fastest rider of each session (median over all their sessions): 0% would mean always being the fastest. The usual range goes from their good quartile to their bad one. Rank: among all riders with enough data.

Style

MetricValueUsual rangeRankn
Consistency (pace variation)0.29%0.22–0.3825/54178
Places gained grid → finish0.00-3.00–3.0063/82213
Hides pace for qualifying+0.10-0.67–0.7519/49151
Hides pace for the race+0.27-0.43–0.7314/3864
Qualifying specialist (+) or diesel (−)+0.23—6/43150
Races better than qualifies−0.76-2.82–1.6253/74160
Top speed363.6 km/h—17/6416576

Descriptive: how the rider has done so far, not a prediction. "Hides pace" is not very stable from one year to the next: always look at the usual range.

Race and qualifying

Racecraft by phase

+1.13
Start
−0.05
First laps
−2.48
End of race
−2.53
Total

Positions gained (+) or lost (−) in each phase versus what the rider’s position would suggest, over 35 races. Descriptive.

Races better than qualifies?

Positions finished better (+) or worse (−) than the starting position suggests, per season.

SeasonBeats the gridn
2026−2.2513
2025−2.0215
2024−1.7815
2023−1.0115
2022−0.1716
2021−0.3614
2020+0.4010
2019+0.0815
2018−1.1314
2017+0.3313
2016+1.089
2015−1.4611

Q2 risk

4%
Q2 with best lap cancelled
0%
Disastrous Q2

Over 73 Q2 sessions since 2021. Disaster = best lap more than 1% slower than the others' median.

Tyres

Tyre management

Metrics/lapUsual rangeRankn
Degradation in practice · Hard+0.0920.076–0.2102/68
Degradation in practice · Medium+0.1050.066–0.2402/3413
Degradation in practice · Soft+0.1550.140–0.1993/136
Race degradation (fuel-corrected)+0.0500.017–0.09245/54178
Pace loss in the race+0.029-0.002–0.07245/54178

Seconds lost per lap over a run. Lower is better: better tyre management.

Tyres race by race

GPFront / rearRel. paceDeg. s/lapGrid→Pos.
2026 ARAMedium / Medium+1.00%−0.00413→11
2026 AUTMedium / Medium+1.63%+0.15621→21
2026 BRAHard / Medium—%—18→—
2026 CATMedium / Medium ★+0.87%+0.10511→15
2026 CZEMedium / Medium+1.27%+0.00116→16
2026 FRASoft / Medium ★+1.50%+0.04018→15
2026 GBRHard / Medium+1.18%+0.05411→13
2026 GERHard / Medium+1.16%+0.07511→12
2026 HUNMedium / Soft+0.93%−0.00712→8
2026 ITAMedium / Medium+1.40%+0.07616→15

★ = chose the minority compound. Degradation includes fuel and traffic: only compare within the same race.

Circuits, team and injuries

Strongest and weakest circuits

Over one lap · Strong at: POR (+0.56), VAL (+0.44) · Weak at: AUS (−0.49), ARG (−1.74)

In races · Strong at: VAL (+0.61) · Weak at: NED (−0.51), AUT (−0.65)

Affinity: how much better (+) or worse (−) the rider performs at this circuit versus their own average, in percentage points of gap. Only cases with enough signal are shown.

Against the teammate

SeasonTeamTeammateRaceGridPoints
2026Prima Pramac Yamaha MotoGPToprak Razgatlioglu8–711–427–17
2025Prima Pramac Yamaha MotoGPMiguel Oliveira9–816–379–43
2024Red Bull KTM Factory RacingBrad Binder3–166–1487–217
2023Red Bull KTM Factory RacingBrad Binder5–159–11163–293
2022Ducati Lenovo TeamFrancesco Bagnaia7–127–13189–265
2021Ducati Lenovo TeamFrancesco Bagnaia5–126–12181–252
2020Pramac RacingFrancesco Bagnaia6–29–2132–47
2019Pramac RacingFrancesco Bagnaia13–214–4165–54
2018Alma Pramac RacingDanilo Petrucci5–125–1491–144
2017EG 0,0 Marc VDSTito Rabat12–415–182–35
2016Estrella Galicia 0,0 Marc VDSTito Rabat8–313–357–29
2015CWM LCR HondaCal Crutchlow2–130–1817–125

Who finishes ahead in the races both started; a retirement loses to a classified finish. With fewer than 8 races together it is only indicative.