MotoGP Data dominance · tyre · race pace
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Francesco Bagnaia

Italy

Career

Pace and style

Pace

MetricValueUsual rangeRankn
Race pace+0.23%0.06–0.552/54186
One-lap pace (qualifying)+0.38%0.07–0.764/54128
Long run (practice)+0.68%0.31–1.308/59260
Fast lap (practice)+0.62%0.22–1.257/60394

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)0.26%0.20–0.339/54152
Places gained grid → finish0.00-5.00–2.0063/82144
Hides pace for qualifying+0.43-0.18–1.173/49126
Hides pace for the race+0.03-0.84–0.7024/3853
Qualifying specialist (+) or diesel (−)−0.03—23/43128
Races better than qualifies+1.29-0.16–2.769/74104
Top speed364.8 km/h—9/6414068

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

+0.66
Start
+0.52
First laps
−0.07
End of race
+0.45
Total

Positions gained (+) or lost (−) in each phase versus what the rider’s position would suggest, over 39 races. Descriptive.

Races better than qualifies?

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

SeasonBeats the gridn
2026+1.438
2025+1.3115
2024+2.2817
2023+1.7216
2022+2.2015
2021+0.8116
2020−0.605
2019−0.5412

Q2 risk

8%
Q2 with best lap cancelled
1%
Disastrous Q2

Over 103 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+0.2210.170–0.3667/349
Degradation in practice · Wet-Soft+0.5400.253–0.8266/84
Race degradation (fuel-corrected)+0.0390.017–0.07728/54152
Pace loss in the race+0.019-0.003–0.05729/54152

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+1.07%—8→—
2026 AUTMedium / Medium+0.31%+0.0527→7
2026 BRAHard / Medium+0.59%−0.08511→—
2026 CATMedium / Medium ★+0.56%+0.11013→3
2026 CZEMedium / Medium+0.05%+0.0103→3
2026 FRAHard / Soft+0.07%+0.0091→—
2026 GBRHard / Medium+1.33%—16→—
2026 GERHard / Medium+0.98%+0.0469→6
2026 HUNMedium / Medium ★+0.52%+0.0125→3
2026 ITAMedium / Medium+0.36%+0.1196→3

★ = 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: EMI (+0.49), AME (+0.27), SPA (+0.25), ITA (+0.22) · Weak at: AUS (−0.48), ARG (−0.64)

In races · Strong at: QAT (+0.36), EMI (+0.35), ITA (+0.27) · Weak at: AME (−0.46)

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
2026Ducati Lenovo TeamMarc Marquez2–104–10156–294
2025Ducati Lenovo TeamMarc Marquez3–145–13288–545
2024Ducati Lenovo TeamEnea Bastianini15–517–3498–386
2023Ducati Lenovo TeamEnea Bastianini10–113–1467–84
2022Ducati Lenovo TeamJack Miller12–713–7265–189
2021Ducati Lenovo TeamJack Miller12–512–6252–181
2020Pramac RacingJack Miller2–62–947–132
2019Pramac RacingJack Miller2–134–1454–165

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.