MotoGP Data dominance · tyre · race pace
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Brad Binder

South Africa

Career

Pace and style

Pace

MetricValueUsual rangeRankn
Race pace+0.54%0.32–0.8810/54176
One-lap pace (qualifying)+0.95%0.62–1.2523/54119
Long run (practice)+0.99%0.58–1.4922/59271
Fast lap (practice)+0.83%0.55–1.3119/60314

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)0.27%0.19–0.3413/54146
Places gained grid → finish+5.001.00–7.002/82129
Hides pace for qualifying−0.16-0.83–0.5837/49117
Hides pace for the race+0.520.00–1.027/3864
Qualifying specialist (+) or diesel (−)−0.22—40/43119
Races better than qualifies+2.440.97–4.542/74111
Top speed366.1 km/h—9/6413174

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

+2.55
Start
+0.34
First laps
+0.32
End of race
+0.67
Total

Positions gained (+) or lost (−) in each phase versus what the rider’s position would suggest, over 46 races. Descriptive.

Races better than qualifies?

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

SeasonBeats the gridn
2026+1.9913
2025+3.2218
2024+1.8518
2023+1.2716
2022+2.8619
2021+3.6317
2020+1.7610

Q2 risk

5%
Q2 with best lap cancelled
5%
Disastrous Q2

Over 57 Q2 sessions since 2021. Disaster = best lap more than 1% slower than the others' median.

Tyres

Tyre management

Metrics/lapUsual rangeRankn
Degradation in practice · Medium+0.3270.163–0.47219/3414
Degradation in practice · Soft+0.1250.122–0.4401/135
Degradation in practice · Wet-Soft+0.6360.610–0.6637/84
Race degradation (fuel-corrected)+0.0390.013–0.07430/54146
Pace loss in the race+0.019-0.006–0.05529/54146

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+0.87%−0.02014→10
2026 AUTMedium / Medium+0.46%+0.06411→6
2026 BRAHard / Medium+1.76%—21→—
2026 CATMedium / Soft+0.43%+0.0028→7
2026 CZEMedium / Medium+0.98%−0.03318→12
2026 FRAHard / Soft+0.56%+0.02420→—
2026 GBRHard / Medium+0.96%+0.03718→8
2026 GERHard / Medium+1.23%+0.04814→10
2026 HUNMedium / Soft+0.76%−0.03317→10
2026 ITAMedium / Medium+1.08%+0.07414→11

★ = 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: CAT (+0.52), VAL (+0.52), SPA (+0.31) · Weak at: ITA (−0.36), AUS (−0.38), POR (−0.38), EMI (−0.47), FRA (−0.50)

In races · Strong at: VAL (+0.42), QAT (+0.28) · Weak at: AME (−0.66)

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
2026Red Bull KTM Factory RacingPedro Acosta3–120–1596–234
2025Red Bull KTM Factory RacingPedro Acosta6–150–22155–307
2024Red Bull KTM Factory RacingJack Miller16–314–6217–87
2023Red Bull KTM Factory RacingJack Miller15–511–9293–163
2022Red Bull KTM Factory RacingMiguel Oliveira16–412–8188–149
2021Red Bull KTM Factory RacingMiguel Oliveira12–67–11151–94
2020Red Bull KTM Factory RacingPol Espargaro4–101–1387–135

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.