Insights

Model report: Hungarian Grand Prix — ρ 0.839

The model delivered a ρ of 0.839 at Hungarian Grand Prix — in line with recent form (recent avg: 0.839). The predicted finishing order matched 64% of actual positions within two places.

Model accuracy in Hungarian Grand Prix: rho = 0.839

Called 92% of the field correctly.

RUS was the data's Driver of the Day in Hungarian Grand Prix

Composite score 0.757.

40 on-track moves in Hungarian Grand Prix — power deficit dominated

First move: RUS on SAI lap 3. 0 DRS, 40 power-deficit overtakes.

ERS pressure: ALO led with 422 depletion events in Hungarian Grand Prix

ALO, STR, HUL pushed ERS hardest. High depletion with sustained pace signals maximum deployment.

LAW leads LIN by 0.807s in the intra-team battle

Based on multiple comparable sessions in Hungarian Grand Prix.

NOR vs LEC — the closest battle at Hungarian Grand Prix

NOR vs LEC: predicted within 0.352s. The Hungarian Grand Prix data suggests these two will decide qualifying.

Model report: Belgian Grand Prix — ρ 0.900

The model delivered a ρ of 0.900 at Belgian Grand Prix — in line with recent form (recent avg: 0.900). The predicted finishing order matched 59% of actual positions within two places.

Model accuracy in Belgian Grand Prix: rho = 0.900

Called 95% of the field correctly.

HAD was the data's Driver of the Day in Belgian Grand Prix

Composite score 0.779.

52 on-track moves in Belgian Grand Prix — power deficit dominated

First move: STR on ALO lap 5. 0 DRS, 52 power-deficit overtakes.

ERS pressure: ALO led with 130 depletion events in Belgian Grand Prix

ALO, LAW, GAS pushed ERS hardest. High depletion with sustained pace signals maximum deployment.

VER leads HAD by 0.958s in the intra-team battle

Based on multiple comparable sessions in Belgian Grand Prix.

NOR vs VER — the closest battle at Belgian Grand Prix

NOR vs VER: predicted within 0.009s. The Belgian Grand Prix data suggests these two will decide qualifying.

Model report: British Grand Prix — ρ 0.272

The model delivered a ρ of 0.272 at British Grand Prix — in line with recent form (recent avg: 0.272). The predicted finishing order matched 36% of actual positions within two places.

Model accuracy in British Grand Prix: rho = 0.272

Called 64% of the field correctly.

PIA was the data's Driver of the Day in British Grand Prix

Composite score 0.708.

32 on-track moves in British Grand Prix — power deficit dominated

First move: GAS on OCO lap 3. 0 DRS, 32 power-deficit overtakes.

ERS pressure: STR led with 146 depletion events in British Grand Prix

STR, OCO, RUS pushed ERS hardest. High depletion with sustained pace signals maximum deployment.

Model report: Austrian Grand Prix — ρ 0.834

The model delivered a ρ of 0.834 at Austrian Grand Prix — in line with recent form (recent avg: 0.834). The predicted finishing order matched 41% of actual positions within two places.

Model accuracy in Austrian Grand Prix: rho = 0.834

Called 92% of the field correctly.

ANT was the data's Driver of the Day in Austrian Grand Prix

Composite score 0.718.

59 on-track moves in Austrian Grand Prix — power deficit dominated

First move: STR on BOT lap 2. 0 DRS, 57 power-deficit overtakes.

ERS pressure: ANT led with 247 depletion events in Austrian Grand Prix

ANT, HAM, LEC pushed ERS hardest. High depletion with sustained pace signals maximum deployment.

VER leads HAD by 0.543s in the intra-team battle

Based on multiple comparable sessions in Austrian Grand Prix.

RUS vs ANT — the closest battle at Austrian Grand Prix

RUS vs ANT: predicted within 0.038s. The Austrian Grand Prix data suggests these two will decide qualifying.

Model report: Barcelona Grand Prix — ρ 0.648

The model delivered a ρ of 0.648 at Barcelona Grand Prix — in line with recent form (recent avg: 0.648). The predicted finishing order matched 45% of actual positions within two places.

Model accuracy in Barcelona Grand Prix: rho = 0.648

Called 82% of the field correctly.

HAM was the data's Driver of the Day in Barcelona Grand Prix

Composite score 0.664.

44 on-track moves in Barcelona Grand Prix — power deficit dominated

First move: HAD on SAI lap 3. 0 DRS, 42 power-deficit overtakes.