Below is a simple chart illustrating a random statistic with its independent and dependent variables marked respectively on the x and y-axis. The particular statistic is unknown and irrelevant for our purposes. What a simple chart like this (and many others like it) can show, is the need to individualise.

It’s typical for data to be grouped and represented by its mean value. This is true within many fields of expertise, including the pharmaceutical industry – drugs are accepted on the basis of the average effects they present during trials. Though it is often practical and easier, you can imagine the possible adverse effects of treating a collection of data as one. For instance, looking at the sample graph above only 4 out of the 30 data plots are positioned on or near to the trend line. If we had to use this trend line to represent a ‘rule’ about of the data, it’s clear that this ‘rule’ would fail to accurately represent ~86% of the graph’s data. Though this example may be a bit extreme, what you’re seeing above occurs in sport all the time – athletes are following norm-based models and patterns in their training and development.

Apart from having unique genetics, we’re also subject to unique frequencies and magnitudes of volatility and other external variables. It is also common knowledge that we all respond differently to training. If we were all equal and exposed to a neutral environment, there would probably be less of a call for individualization. This, however, is not the case. If in training we’re not working with an outlier (data extreme) of sorts, of the very least we’re working with someone who has some difference to be individually considered.

Therefore, to truly maximize your training, your approach should be needs-based and reflect your personal characteristics and requirements in terms of how you train, why you train, and the course of your training over time. As said by Daniel Coyle in The Talent Code, “a coach’s true skill consists not in some universally applicable wisdom that he can communicate to all, but rather in the supple ability to locate the sweet spot on the edge of each individual student’s ability, and to send the right signals to help the student reach toward the right goal, over and over.”  

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