Can we help you train for your next marathon?
Maybe we've built something to get you started.
@TerraAPI , we analysed six months of real training data from 101 recreational to sub-elite marathon runners and created a predictive model that explains 79.3% of the variance in actual finish times (R² = 0.7933), with an average prediction error of about 18 minutes. I must admit, the accuracy is a long way off being useful. I think I could be closer to most people's time, without a fancy model! But we decided to publish it anyway to demonstrate the inherent difficulties of modelling human performance.
The model uses five key inputs you can plug in yourself:
• Your baseline running pace
• Total training volume over 6 months
• Intensity distribution
• Training frequency
It incorporates non-linear effects and interactions we observed in the data, such as:
• Diminishing returns on extra volume (gains are bigger when you're at lower totals)
• Accelerating benefits from easy miles — the higher the % of easy training, the bigger the payoff
• High-intensity work best kept under ~20% of total time (beyond that, it often hurts more than helps)
• Faster natural runners get disproportionately larger gains from volume and easy work
This isn't magic or a guarantee; marathon performance is messy and multifactorial. The model misses race-day chaos (weather, nutrition, psychology, course quirks), incomplete GPS logs (not every session gets recorded), individual genetic/response differences (20–50% non-responders in some studies), and more. It's correlational, based on a modest sample, and probabilistic at best.
But the patterns align with broader running science: easy-heavy pyramidal distributions dominate among faster runners, volume matters hugely, but plateaus, and personalisation beats one-size-fits-all.
That's why we're sharing an interactive version for you to experiment with: input your own numbers, adjust sliders for different scenarios, see probabilistic predictions, and use it as a thought experiment for your training.
It's a rough prototype, insightful for sparking ideas, but treat it lightly. Far more advanced versions (larger datasets, better handling of missing data, race-day factors, ML personalisation) are in development.
Ready to play?
Link in the comments below.
Whether you're aiming for sub-4, sub-3, or just to finish strong, what's the one training change you're considering right now? Drop it in the comments; let's discuss!
#MarathonTraining #RunningScience #EnduranceSports #DataDrivenFitness #PersonalisedTraining