R03 — GRAND PRIX 2026

Russell sets fastest lap

42%

Fastest lap prediction based on car speed and position.

42%Russell
35%42%49%

42% [35%–49%] (95% CI)

01 / KEY FACTORS

Impact by factor

Team car performance
+8.0%
Ensemble model prediction
+6.0%
Recent race form
+5.0%
Elo historical rating
+3.0%
Circuit characteristics
+2.0%
Impact total+24.0%
02 / MONTE CARLO SIMULATION

Distribution and quantitative analysis

10,000 simulations Monte Carlo

Position Distribution

10,000 simulations

Distribution Statistics

N Simulations10k paths
ExpectedP3
MedianP2
ModeP1
Std Dev3.50
Skewness+0.80 →
VaR 95%P12
CVaR 95%P15
Prob(P1)0.4%
Prob(Podium)0.8%
Prob(Points)1.0%
Prob(DNF)0.1%

Convergence

Scenario Analysis — Russell

ScenarioPositionProb.Conditions
Best caseP10.42%Dominant weekend
Base caseP20.4%Expected result
Worst caseP10+0.05%Difficult race

Sensitivity Analysis

03 / FEATURE ANALYSIS

Variable analysis

Base value5.0%
Output42%
5.0%
42%
Team car+8.0%
Recent form+5.0%
Elo rating+3.0%
Feature importancei
team_season_points
0.150
recent_avg_finish
0.120
elo_combined
0.100
Feature correlationsi
elo
form
team
elo
form
team
-1.0
0
+1.0
04 / MODEL VALIDATION

Model performance

Model performancei
Accuracy0.440+0.390 vs baseline
Brier Score0.041-0.006 vs baseline
Log Loss2.800
AUC-ROC0.820
Calibration curvei
0%0%20%20%40%40%60%60%80%80%100%100%PredictedActual
Confusion matrix
Predicted vs Actual44% accuracy
P1
P2-3
P4-6
P7-10
P11+
Actual
P1
3
1
0
0
0
P2-3
1
2
1
0
0
P4-6
0
1
3
1
0
P7-10
0
0
1
2
1
P11+
0
0
0
1
3
Pred.
Track characteristicsi

Accuracy

44%

across 127 race winner predictions

Average Brier score

0.041

probability calibration

Last correct

R02

R02 — Chinese Grand Prix

05 / EVOLUTION & DATA

Probability over time

Training Data Sample1 rows x 2 cols
#FeatureValue
1Probability42%
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