How to Start Improving Performance, The Right Way

  • October 28, 2019

So, Unlimi-Troop, you’ve decided it’s time to up your game. As the ambitious sportspeople you are, you know that taking performance improvement more seriously is critical in order to keep growing as an athlete. Yet, a fundamental question arises immediately: where to start?

As the saying goes, a journey of a thousand miles begins with a single step. However, amidst an online sea full of buzzwords and fads, it’s easy to start with a wrong step that can end up wasting a lot of your time (and money).

In this post we’re going to talk about the first step you should take to improve performance: the output-input model.

The Output-Input Model

The output-input model is a framework to approach performance improvement where you clearly define a set of performance objectives before making changes to your training, and then measuring your results while training until you achieve said objectives.

So, before you buy new gear, change your nutrition habits, or push yourself harder in a certain way, there is one thing that you need to understand as best as possible: your targets.

Sounds easy? Well, if your current targets look or sound something like:

  • I need to go faster!
  • I need to be stronger!
  • I need to endure longer!

Then we’re sorry to tell you: you don’t really have a target.

Goals Versus Targets

People sometimes mix the idea of goals with the idea of targets. If someone says, “I need to go faster”, that person is talking about a goal. Goals are necessary in order to provide direction, but they’re insufficient in the sense that a direction without a destination is not very useful. Targets set the destination.

A well-defined target, therefore, will be expressed in terms of a metric, or a set of metrics, and will have a value associated to it. So, in a simple example, a target for the goal of “going faster” could be “achieve an average speed of 20 kilometres per hour”.

This is an improvement from just having a goal, but it is still missing a crucial ingredient…


Following our previous example: Why would you need to increase your average speed by a certain amount? Why not by another? Why is average speed the best metric for the job at hand? All these questions can be better answered if your targets are chosen and specified within a specific context.

A good way to define the targets’ context is in terms of a specific time and a specific location. This could be achieved if you set a competition as your reference context, for example, where you must consider how the geography, weather, altitude and distances of the competition would affect your performance.

These “context-demands” could later be used to determine what are the best metrics to choose for improving performance, and the values that you would need to achieve in those metrics in order to perform well.

Example: Cycling Uphill

In this simple example, we’ll see how to practically apply the model using the case of a cyclist preparing for an uphill segment of a race.

The cyclist’s first step will be to identify their goal(s) for this segment. In this case, the goal will be to reduce the time taken in the segment.

The next step will be to analyse the context-demands. Let’s say that the length of the segment is of 3 kilometres, and the average slope is of 10 degrees. Most cyclists seem to finish this segment in around 6 minutes, which means the average speed for the segment is of 30 kilometres per hour.

With this information, the cyclist determines the target of achieving an average speed of 35 kilometres per hour on the segment. Due to the average slope, and based on previous measurements, the cyclist further determines that this will require achieving a certain average power output target, and without exceeding a certain oxygen consumption target (i.e. to avoid fatigue).

Now, the cyclist is ready to train and precisely measures speed, power and oxygen consumption during training until the target metrics are achieved.

Output-Input Model Wrap-Up

Today we’ve learned that the key ingredients behind the output-input model are context, targets, and measurement. Putting all these together, the model looks like:

Figure 1: The Output-Input Model

Remember: following the output-input model will save you training time, help you stay focused on improving the aspects of your training that matter the most, and provide you with a clear way of measuring how you improve over time.

Was this post helpful? Would you like to learn more about understanding context-demands, choosing target metrics, or choosing metric values? Be sure to let us know in the comment section below! Until next time!

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