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Driver Intelligence — 2026 Season

DNA profiles computed from FP and race data. More rounds = higher confidence. Dimensions are normalised 0–10 across the field.

Compare vs…vs
ALB17 rounds

Teammate Edge

car · -0.8 kph vs field

pts/race trend 1.0 avg
ALO17 rounds

Tyre Management

car · -12.0 kph vs field

pts/race trend 0.2 avg
ANT17 rounds

Overtake

car · +1.9 kph vs field

pts/race trend 25.0 avg
BEA17 rounds

Teammate Edge

car · +0.9 kph vs field

pts/race trend 0.0 avg
BOR17 rounds

Overtake

car · -4.0 kph vs field

pts/race trend 0.0 avg
BOT17 rounds

Consistency

car · -3.9 kph vs field

pts/race trend 0.0 avg
COL17 rounds

Quali Ceiling

car · +1.2 kph vs field

pts/race trend 2.4 avg
GAS17 rounds

Teammate Edge

car · +1.2 kph vs field

pts/race trend 5.4 avg
HAD17 rounds

Quali Ceiling

car · +0.7 kph vs field

pts/race trend 4.0 avg
HAM17 rounds

Teammate Edge

car · +0.0 kph vs field

pts/race trend 8.4 avg
HUL17 rounds

Overtake

car · -4.0 kph vs field

pts/race trend 0.2 avg
LAW17 rounds

Deg Resistance

car · -0.9 kph vs field

pts/race trend 4.0 avg
LEC17 rounds

Race Start

car · +0.0 kph vs field

pts/race trend 9.2 avg
LIN17 rounds

Quali Ceiling

car · -0.9 kph vs field

pts/race trend 1.6 avg
NOR17 rounds

Race Start

car · +0.0 kph vs field

pts/race trend 10.6 avg
OCO17 rounds

Deg Resistance

car · +0.9 kph vs field

pts/race trend 0.8 avg
PER17 rounds

Deg Resistance

car · -3.9 kph vs field

pts/race trend 0.0 avg
PIA17 rounds

Race Start

car · +0.0 kph vs field

pts/race trend 13.0 avg
RUS17 rounds

Deg Resistance

car · +1.9 kph vs field

pts/race trend 9.2 avg
SAI17 rounds

Tyre Management

car · -0.8 kph vs field

pts/race trend 0.4 avg
STR17 rounds

Deg Resistance

car · -12.0 kph vs field

pts/race trend 0.0 avg
TSU17 rounds

Quali Ceiling

3 rounds

VER17 rounds

Overtake

car · +0.7 kph vs field

pts/race trend 7.2 avg

Season Driver Rankings

Composite score across DNA dimensions weighted by confidence. Uncertainty bands reflect data confidence per dimension.

Rankings
#DriverScoreRounds
1VER
6.86.67.0
17 rounds
2HAD
6.76.56.9
17 rounds
3LIN
6.56.36.7
17 rounds
4NOR
6.26.06.4
17 rounds
5ALB
5.85.66.0
17 rounds
6GAS
5.75.55.9
17 rounds
7HAM
5.65.35.8
17 rounds
8LAW
5.45.25.7
17 rounds
9COL
5.35.05.5
17 rounds
10PIA
5.25.05.4
17 rounds
11BEA
5.14.95.3
17 rounds
12SAI
5.04.85.3
17 rounds
13ANT
5.04.85.2
17 rounds
14HUL
5.04.85.2
17 rounds
15RUS
4.34.14.5
17 rounds
16STR
4.34.14.5
17 rounds
17LEC
4.24.04.5
17 rounds
18PER
4.24.04.4
17 rounds
19BOR
4.03.84.2
17 rounds
20ALO
3.83.64.1
17 rounds
21BOT
3.83.64.0
17 rounds
22OCO
3.23.03.4
17 rounds
23TSU
2.72.43.0
17 rounds

Rankings measure driver style and consistency. Toggle Car-Adjusted to remove car speed from qualifying scores.

Model Accuracy

How accurately the model predicted each driver's finishing position across 17 scored rounds. Green = model over-predicted (driver finished better than expected), red = under-predicted.

DriverCountWithin 2Avg ΔBiasSeason Δ
NOR1384.6%-0.08neutral
ALB1275.0%+0.33neutral
STR771.4%-2.43over
HUL1266.7%+0.50neutral
TSU366.7%-1.33over
HAD1163.6%-1.18over
COL1656.2%-0.62over
OCO1656.2%-1.81over
ANT1553.3%-1.47over
LEC1553.3%+0.20neutral
HAM1752.9%-0.29neutral
PIA1241.7%-0.75over
VER1241.7%-1.25over
BOT1040.0%-2.00over
RUS1338.5%-4.77over
GAS1637.5%-0.50neutral
BEA1233.3%-1.92over
SAI1533.3%+0.20neutral
ALO1030.0%-3.70over
LAW1526.7%-1.33over
LIN1625.0%-0.06neutral
BOR1323.1%-1.15over
PER1216.7%-4.33over