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Why Contextual Athlete Effort Matters When Measuring Movement

In the same way a powerlifter follows a different training regimen than a long-distance runner, athletes everywhere are assessed and trained based on the demands of their sport and role. A world-class cyclist who has trained much of their life to develop powerful legs, a lean upper body and a stratospheric VO2 max would have trouble holding up on a rugby front line. Meanwhile, a professional rugby prop – thickly muscled and supremely powerful in short bursts – might struggle just to finish a Tour de France stage.

Context matters in most settings, but perhaps nowhere more than in athletics. An offensive lineman and a cornerback in American football, for instance, could not have more different on-field duties – and thus physical requirements. Even in a sport such as world football, in which players 2 through 11 on a side are subject to relatively similar physical demands, there is enough differentiation between player roles and skills to make contextualizing their movements necessary for the sake of evaluation and competitive comparison.

By this rationale, then, we intuitively understand that the same movement performed by the same athlete in different contexts can yield different outputs. A player may give a slightly lesser effort in training, for instance, than they do in a match. Categorizing those varied contexts, measuring outputs within categories and comparing the aggregated measurements isn’t an easy task, but it can lead to new and powerful insights that can be passed on to managers and coaches. Here are a few reasons why measuring athlete movement within context is worth the hassle:

Establishing baselines. Comparing a striker’s speed breaking into the box at the end of a scoreless Premier League relegation match to a similar sprint at the end of a long day of training is likely to yield different results. Evaluation of athlete performance – which can vary based on fitness, health and effort, among other factors – demands context in these situations, and even comparison within context requires consistently precise measurement.

Once those categorical baselines have been set, club personnel can compare apples to apples, so to speak. If that striker’s PL match sprint and end-of-day training sprint are both significantly off the player’s established baselines, a trainer may intervene to check for injury or a developing imbalance, or prescribe more rest. The implications for load management and performance optimization are obvious.

Developing training. As practices and training at the highest levels of athletics continue to become more sport- and player-specific, the prevailing approach figures to grow even more granular. Movement-specific training can help address certain physical or skill weaknesses, in some cases exponentially elevating a player’s contextual value to a club or overall effectiveness.

“In Premier League soccer, a player may sprint around 20-40 times during a match,” Dr. Paul Caldbeck, Sportlight account director, told SimpliFaster. “If we can even slightly enhance their effectiveness in one of these efforts, then we can potentially have a direct influence on match outcome.”

Deploying players. With a full season of contextual movement data, a gaffer can know whether to trust his intuitions about which players are performing at their peaks. He can confidently put together a lineup in which the pieces optimally complement one another, based on the fit of the players’ contextual talents. Analyzing the totality of a match, the manager may choose a player whose rounded runs are slower than another player’s in order to leverage their specific abilities in set pieces.

Now just imagine how a club could benefit from several seasons of that data, further strengthening the contextual understanding of player strengths and weaknesses in the moment – but also identifying long-term improvements and drop-offs, which, for example, can help an executive team determine appropriate player contract values. The more a club knows about segmented, contextually specific player data, the more likely it is to make adjustments that translate to incremental improvements on the pitch.

Assessing Talent. The transfer window is a time when granular data and a profound understanding of your personnel is only half the equation. A club also needs similar insight into players around the league who may be available for acquisition. Obvious needs (a stronger goalkeeper, more team speed, etc.) are better understood – and can sometimes be more acutely and affordably addressed – with more granular contextual insights.

A qualitative observation (your Player A is widely considered “better” than another club’s Player B) may overlook key nuances that quantitative analysis can reveal. What if Player A’s skills are duplicative of a younger, more promising player on the roster? And what if Player B, despite being a “lesser” player on balance than Player A, has one of the best corner kicks in the sport? If your club has struggled to accurately place the ball on corner kicks, it might find more value in Player B than Player A based almost solely on a single contextual skill. It stands to reason that your club would want comprehensive, accurate measurements proving that skill beyond a shadow of a doubt.

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