Neural Networks: Possibly the Most Important Training Tech You’ve Never Heard of—with Alan Couzens

We live in an era of data overload, so knowing how to interpret that data is key. Alan Couzens talks with us about how neural networks might be the answer.

Neural networks and artificial intelligence.
Photo: Shutterstock

Long gone are the days of going out for a workout and having nothing to report to your coach except how it felt. We now live in an era of information overload: we track heart rate, power, speed, distance, body temperature, HRV, sleep, blood glucose, TSS, and more. It feels like every month there’s a new metric to monitor—and coaches can sometimes struggle to keep up with them all.

In this week’s show, we’re joined by coach and exercise physiologist Alan Couzens as we talk about data and how to interpret it in a meaningful way. Couzens is way ahead of the curve on this one, pioneering the use of neural networks for training. For the uninitiated, neural networks are a sophisticated form of artificial intelligence that learns the way the human brain does. By taking in large amounts of data, they can learn what that data means and provide interpretations. Those interpretations, based on thousands of data points, can be simple, accurate, and highly useful—especially, for Couzens, when it comes to coaching athletes to their peak performance.

RELATED: What Are Neural Networks and How Can They Help Your Training?

Joining Couzens on this episode, we have two highly respected coaches: Ryan Bolton, the owner of Bolton Endurance Sports Training, and Lauren Vallee, the owner of Valiant Endurance. We also have two-time cyclocross U.S. national champion Stephen Hyde.

So, dump your data into a neural network and let’s see if it recommends we make you fast!

References

Couzens, A. (2018). Why Neural Networks are better than the old Banister/TSS model at predicting athletic performance. Retrieved October 25, 2022, from https://alancouzens.com/blog/Banister_v_Neural_Network.html#:~:text=The%20Banister%20model%20has%20a,the%20reality%20of%20diminishing%20returns.

Jobson, S. A., Passfield, L., Atkinson, G., Barton, G., & Scarf, P. (2009). The Analysis and Utilization of Cycling Training Data. Sports Medicine, 39(10), 833–844. Retrieved from https://doi.org/10.2165/11317840-000000000-00000

Kumyaito, N., Yupapin, P., & Tamee, K. (2018). Planning a sports training program using Adaptive Particle Swarm Optimization with emphasis on physiological constraints. BMC Research Notes, 11(1), 9. Retrieved from https://doi.org/10.1186/s13104-017-3120-9

Taha, T., & Thomas, S. G. (2003). Systems Modelling of the Relationship Between Training and Performance. Sports Medicine, 33(14), 1061–1073. Retrieved from https://doi.org/10.2165/00007256-200333140-00003

Wallace, L. K., Slattery, K. M., & Coutts, A. J. (2014). A comparison of methods for quantifying training load: relationships between modelled and actual training responses. European Journal of Applied Physiology, 114(1), 11–20. Retrieved from https://doi.org/10.1007/s00421-013-2745-1

Wallace, Lee K, Slattery, K. M., Impellizzeri, F. M., & Coutts, A. J. (2014). Establishing the Criterion Validity and Reliability of Common Methods for Quantifying Training Load. Journal of Strength and Conditioning Research, 28(8), 2330–2337. Retrieved from https://doi.org/10.1519/jsc.0000000000000416

Episode Transcript

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