How accurate are cycling race predictions?

Physics-based models are typically accurate to within 2–5% for time trials and individual efforts on known courses. Group races are harder to predict due to drafting, tactics, and variable pacing. This tool assumes a solo, steady-power effort.

What is the fatigue discount?

For rides over 2 hours, the prediction adds 2% extra time. For rides over 4 hours, 5% is added. This accounts for the natural decline in sustainable power over long durations due to fatigue, fueling challenges, and environmental stress.

Does wind affect the prediction?

Yes. Headwind is added to ground speed in the aerodynamic drag equation, substantially increasing drag. A 15 km/h headwind at 35 km/h can add 40–60% to aerodynamic resistance. Tailwinds are entered as negative values.

What values should I use for CdA and Crr?

Default values (CdA 0.30, Crr 0.004) represent a typical road cyclist on hoods with good road tires. Use our CdA Calculator and Rolling Resistance Calculator for more personalized values based on your position and equipment.

Cycling Race Predictor

Name: Cycling Race Predictor
Author: Manish Kumar

Key Takeaways

Race time predictions are based on a physics model that balances aerodynamic drag, rolling resistance, and gravity against rider power output.^[1]
On flat time trials, CdA is the dominant variable — reducing CdA by 0.02 can save 30–60 seconds over 40 km. On hilly courses, W/kg becomes more important.^[2]
A fatigue discount of 2–5% is applied for rides over 2 hours to account for power decline, dehydration, and fueling challenges.
The what-if scenarios help identify which variable (power, weight, CdA) provides the biggest return for your specific course profile.

The Physics Model

The calculator uses the standard cycling power equation to solve for speed at a given power output:^[1]

P = P_aero + P_rolling + P_gravity
P = 0.5 × ρ × CdA × (v+vw)² × v + Crr × m × g × cos(θ) × v + m × g × sin(θ) × v

The equation is solved iteratively (Newton-Raphson method) for each course segment: flat, climbing, and descending. Descent power is modeled at 50% of target power, reflecting typical real-world behavior.

Course Segmentation

The course is divided into climbing, flat, and descending segments based on total elevation gain. The average grade is calculated from the ratio of elevation gain to climbing distance. This simplified model provides a good approximation for most courses.

For complex courses with variable gradients, actual segment-by-segment analysis (using GPX data) would provide higher accuracy. This tool is designed for quick estimation and planning.

Frequently Asked Questions

Sources & References

Validation of a Mathematical Model for Road Cycling Power. Martin JC, Milliken DL, Cobb JE et al.. Journal of Applied Biomechanics (1998)
Predicting Cycling Time-Trial Performance. Olds TS, Norton KI, Craig NP. Journal of Applied Physiology (1993)
Modeling the Power Requirements for Cycling Performance. Multiple authors. Journal of Science and Cycling (2020)

Manish Kumar

Certified Personal Trainer & Sports Nutritionist

NASM-CPTCertified Sports Nutritionist10+ Years Experience500+ Clients Coached

NASM-certified fitness and nutrition coach with over 10 years of hands-on experience helping people build strength, lose fat, and live healthier lives. Specializing in gym-based workouts with a strong focus on lifting technique, biomechanics, and practical exercise science. Through FitLifeRegime, sharing the tools, tips, and insights that have worked for hundreds of clients — helping you start your own fitness journey with confidence and clarity.

Learn more about Manish

NASM Certified

Certified Personal Trainer & Sports Nutritionist from the National Academy of Sports Medicine

This tool is for informational and journaling purposes only.

I am NOT a doctor.