Every time we open a weather app, it doesn't show us certainties, but probabilities. "40% rain," it tells us. Does this mean it will rain? Not necessarily. It means that, given current conditions, there is a 40% chance of rain. But what do we do with that information? Some will carry umbrellas, some will not. But we have no absolute certainty whether it will rain or not.
This same principle, so standardized in meteorology, is exactly what we need to better understand when we talk about injury risk in the context of sports performance. Injury risk is not a binary prediction, but a probability.
For years people have tried to explain injuries as a direct consequence of a specific cause: "He got injured because the load was too high", "it was the fault of playing three games in one week". But today's sports science has shown that this is rarely true. Injuries are not explained by a single factor, but by the dynamic interaction between many factors within a complex system [1]. In particular, it is a non-linear interaction, in which the effect (the occurrence of injury) is not proportional to the observed changes, much less directly attributable to a single stimulus, as we often do when we point to increased load. The same load increase may be well tolerated in one context and trigger injury in another, depending on the state of the system as a whole: previous fatigue, sleep quality, stress, injury history, etc. [2]
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☔ The presence of clouds does not guarantee rain.
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The human body is a complex adaptive system: multiple subsystems (physical, neuromuscular, psychological) interact with each other and change over time. In these environments, predicting specific events such as injury is extremely difficult. But we can identify conditions that increase the probability of their occurrence. This has given rise to approaches such as load monitoring, risk factor detection or the evaluation of the player's condition over time.
To this end, increasingly sophisticated methodologies have been developed, ranging from the analysis of time series to detect deviations or anomalous trends, tomachine learning models capable of processing large volumes of data and finding individualized risk patterns. All of this is framed within a vision inspired by complex systems, which recognizes that the appearance of an injury arises from the dynamic interaction of multiple variables, and not from linear cause-effect relationships. We can therefore conceptualize injury as an "emergent phenomenon" [2].
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🧠 Understand injury risk as a complex and probabilistic system [3].
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Another fundamental aspect of complex systems is their historical dependence. That is, the current state of the system cannot be understood without considering its previous evolution. What happens to an athlete today is the result of multiple interactions accumulated over time: previous training, matches, rests, past injuries, stress, recovery, etc. Do we take this into account when analyzing performance or injury risk? Are we including these accumulated trajectories in our models or are we still relying on descriptive, cross-sectional and disconnected analyses of the past? Thus, a point assessment does not accurately describe the functional status of the player. An isolated strength test, for example, does not tell us how his neuromuscular system behaves in the days or weeks prior to an injury. Similarly, pre-season, inter-cycle or end-of-season assessments provide only a snapshot (a still photograph) that does not allow us to understand the real dynamics of the system.
It is key to understand that, if we want to better understand and anticipate changes in a player's condition, we need data collected continuously or frequently, over time. Only then can we identify relevant patterns, trajectories and inflection points. In this sense, approaches such as longitudinal monitoring, time series analysis and dynamic models make it possible to capture the evolution of the system beyond isolated observations.
However, this does not mean that one-off assessments are of no value. On the contrary: within these complex systems, detecting parameters that we know influence injury risk can help us to better understand how the various components of the system interact and therefore to intervene more effectively.
For example, a kinematic and kinetic analysis during a drop jump, a change of direction or any other task may reveal relevant biomechanical deficits or asymmetries. These, in interaction with other factors (such as fatigue, accumulated load or muscle weakness), may increase the risk of injury. In that sense, specific strength or movement mechanics assessments remain valuable tools. But we must accept their limitation; they are not predictive on their own. They are not going to tell us with certainty who will be injured, who will not, or when it will happen. Their usefulness lies in offering us signs of vulnerability within a broader picture that must be read in a dynamic and probabilistic key. Just as in meteorology it is not enough to know that the pressure has dropped, but it is necessary to know what its usual value is in that region and time of year, in sport we cannot interpret an isolated metric without knowing its normal behavior in that player.
Each variable, be it training load, heart rate variability, performance in a jump test or subjective perception levels, has its own natural variation, a kind of "usual climate". Only when we know this individual history can we identify relevant fluctuations that are outside their normal range. It is not a matter of reacting to any change, but of detecting significant deviations from the usual pattern of that athlete, and doing so in real time. That is the key to understanding contexts of increased vulnerability.
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🌪️El wind blows every day, but only alerts us when it changes more than usual.
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When we say that a player has an increased risk of injury, we are not saying that he will get injured. We are saying that, given his current conditions, the likelihood has increased compared to his usual state. And that this requires attention. This shift in mindset is key. It moves us away from looking for blame and toward better managing risk.