Dec 14

Race Time Prediction

Categories: Glossary, Performance

Race time prediction involves estimating the time it will take to complete a particular race distance based on various factors such as previous performances, training data, and individual characteristics. While race time prediction models can provide useful insights, it’s important to remember that they are only estimates and individual performance can still vary significantly.

There are several methods and models used for race time prediction, ranging from simple calculations to complex algorithms. Here are some commonly used traditional approaches:

Personal Experience: One of the simplest methods is to rely on your personal experience from previous races. If you have run a similar distance in the past, you can estimate your race time based on your performance in that race. However, this method assumes that your fitness level and training conditions have remained relatively constant.

Rule of Thumb: The rule of thumb method uses a general formula to estimate race times. For example, one commonly used formula is the “doubling time” rule, which states that if you double the distance of a race, you should expect your time to be roughly 2.1 times longer. While this method can provide a rough estimate, it may not account for individual variations in fitness and training.

Race Time Calculators: Online race time calculators are popular tools that use algorithms to predict race times. These calculators typically ask for input such as your recent race times, distance, and terrain. They use statistical models and algorithms to generate a predicted race time based on the input data. It’s important to note that these calculators are based on averages and may not capture individual characteristics accurately. The most popular example is Riegel’s Formula.

Regression Models: More advanced race time prediction models use regression analysis to estimate race times based on multiple variables. These models take into account factors such as age, gender, training volume, recent performances, and environmental conditions. They use historical data to create a mathematical equation that predicts race times based on these variables.


Utilising cutting-edge Artificial Intelligence and Machine Learning technologies TrainAsONE has created a unique race time prediction methodology. Through extensive testing and analysis, these innovative algorithms have proven to outperform existing published approaches in terms of accuracy. Users can now access these enhanced predictions as part of the Artemis algorithm update.

About The Author

Dr. Sean Radford, the Founder & CEO of TrainAsONE, is a medical doctor, IT expert, coach and podium finisher in international endurance events. He has dedicated more than 20 years to the research of health, fitness and social well-being of the general population. He has been developing Artificially Intelligent (AI) and Machine Learning (ML) tools to help tackle some of the world’s leading health issues. Dr Radford is a Tech Ambassador for the UK, considered a leading expert in his field, and is a regular speaker at key events, as well as an author of numerous research publications.