No matter your sport, goals, starting level of fitness, tolerance for training or training approach, as an athlete or a coach when creating a training plan the aim is to try and improve performance through a process of applying and adapting to work load, as a series of discrete and usually progressive sessions. Inherent in this process is a prediction of performance – ie we are predicting that this series of sessions will improve / produce a desired performance.

So far this all sounds straight-forward; but how do we know what effects each session has on the progression and contribution to the ultimate performance? How frequently should the session be repeated or the load increased for maximum fitness benefits? This is where the skill and experience of a coach together with the coaching relationship comes in. When working closely with an athlete a good coach can fine tune a schedule to maximise the athletes improvements whilst avoiding overtraining. With remote coaching this relies on honest feedback on levels or fatigue and performance. Where the coaching relationship is largely face to face the coach can also observe the athlete and assess how the current approach is working. There is a more scientific approach which is used by many. It is an approach I use and have started to trial with some of my athletes.

What can we do to try and model the effects of our training on performance?

When we do a session the immediate effect is a drop in performance. Just think about it. Having just run a fast 10k you are unlikely to be able to immediately run as fast a 10k. This isn’t because you’ve suddenly got worse at running  or less fit as a result of that training session! It’s because you’re fatigued. Give it a few days recovery and perhaps you will run faster.

What is happening ? So we see that for a given training session the immediate effect is to increase your fatigue but also to increase your fitness by exposing your body to certain physiological demands during the training, it will adapt to those new demands placed on it. Initially the increase in fatigue far outweighs the increase in fitness but, since the fatigue effect decays quicker than the fitness effect, after a certain amount of time you will see performance improvements. The key to a training plan is to manage the training load so that your fitness improves over time whilst not letting fatigue increase so much that you are over trained.

The graph shows the effect of a single training session over time. Fatigue starts much higher than fitness but after about 15 time periods the residual fitness is higher than the fatigue.  The graph uses the typical factors for increase in fitness / fatigue and speed of decay but every athlete is different and though this generalised approach deciding these factors for a specific athlete can optimise the increase in training loads and help develop the ideal taper.

How is this modelled ?

There are four parameters we are interested in:

The fitness impact for a given training load

The fatigue impact for a given training load

The rate of decay of fitness

The rate of decay of fatigue

The key is to establish what your parameters are. ie how much fatigue and fitness does a session create together with how quickly you lose this fatigue and fitness. I am using a tool called Raceday Apollo and testing it out with some athletes which helped model this whole process. The use of Power Meters, Heart Rate Monitors and recording swim sessions together with calculating critical speeds and powers for the individual allow a precise measurement of the stress of any given session. Regular testing allows the calibration of the model to define the above four parameters. From this it becomes easy to monitor fitness and fatigue gains together with planning towards a target performance.

I will continue this series of articles to go in to the details of assessing training stress, calculating critical speeds and ongoing testing of performance.