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The Importance of Using Existing Movement Data to Better Understand Player Performance

When making determinations based on new research or data collection, there are few (if any) fields of scientific or empirical study that don’t make use of a control group or existing data. For instance, how can an economist detect the signals of a looming financial downturn or a climatologist learn the telltale patterns of a dangerous weather event without the benefit of established context from which to draw their conclusions?



It’s no different in the field of athletics. In fact, setting baselines for player performance may be more critical to the process of accurately interpreting new data than in most other endeavors. The variables that factor into athletic performance (fueling, hydration, equipment, weather, psychology and more) all but demand that individual baselines are not only established, but also that they are aggregated – at the highest level of consistency and accuracy – over multiple test runs.


Yet there are inherent difficulties in the collection and analysis of this data. Legacy performance-tracking technology has lacked accuracy and flexibility, and has sometimes required the use of wearables that have the potential to influence performance outcomes. Without the ability to consistently measure performance – in training and at games, both at home and on the road – any aggregation of results could be skewed by missing (and/or inaccurate) data.


At the heart of this discussion remains a key question: Why, exactly, is existing movement data so important in understanding player performance? Here are a few guiding principles that shed some light on the topic:


More is better. The more data collected as part of any measurement project, the greater the likelihood of removing noise (outlier variables) and improving the reliability of overall results. Because athlete capacities and outputs are constantly changing, it’s impossible to verify with 100 percent accuracy a single performance as a marker of baseline ability. But a plurality of data and an aggregation of trials ensure the most reliable rendering of current established individual athletic performance.


Gauging player quality. Quantitative movement tracking isn’t the only form of athlete evaluation, but it provides – again – an excellent baseline from which to analyze player quality. Intangible elements such as instinct and chemistry can’t be captured by a performance tracking system, but at the same time, speed, power, efficiency and any number of measurable actions shouldn’t be left to the “eyeball test.” With modern tracking tech, such as Sportlight’s LiDAR-based system, teams can use these accurate readings to make better informed decisions based on empirical player-to-peer and player-to-player comparisons.


Multiple timelines and contexts. Baseline data gives performance practitioners an invaluable starting point from which to analyze player data in numerous ways. Let’s say a club your national team has tracked the performance of a 26-year-old player dating back to his academy career. That data can be analyzed on a day-to-day, game-to-game and even year-to-year basis. Game performances can be compared to training results, home-venue performances can be compared to results from travel dates, and so on. Although data isn’t predictive, the collection, aggregation and analysis of player movement measurements can reveal patterns and markers that help inform a coach’s on-field decisions, an executive’s front office moves and a practitioner’s approach to load management and injury prevention.


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