This system is built on objective parameters designed as a mechanism for competitive parity. It grants higher odds to franchises that had poor competitive results the previous season, allowing them to acquire the best prospects in their sport. However, in practice, this original intent is often altered by trades and future assets. Draft pick protections and “pick swaps” frequently mean that rebuilding teams do not control their own destiny, shifting the benefit of young talent toward organizations with better sporting performance but higher strategic capital. But let’s focus on the parameters being evaluated, as physical condition seems to play a central role.

Prior to the selection of young players, there is an almost mythologized rite of passage: the Combine. A fundamental part of the Draft process where, every year, the major U.S. leagues summon the best young athletes for an exhaustive battery of tests (anthropometric, physical, technical, psychological, etc.) to measure and analyze the athletic and technical potential of the world’s top prospects. The goal? To provide franchises with high-quality information before they make one of the most critical (and million-dollar) decisions in world sports: choosing a new young player for their roster.

The idea was born in NFL offices in the early 1980s. It was Tex Schramm, an executive for the Dallas Cowboys, who pushed for the National Invitational Camp in Tampa in 1982 to ensure an efficient and standardized process [1]. Centralizing the process ensures athletes are evaluated under the same conditions and equipment, creating a common baseline to compare the potential of athletes from different programs and geographies. Theoretically, the Combine is the ideal showcase where an athlete’s physical performance can help them drastically increase their value—and therefore their position—in the selection process [1, 2].

And this point will be our focus over the next few posts. In this first entry, we will discuss how they are organized, what tests are performed in the different leagues, and reflect on their real value. We will start with a description of the anthropometric and physical testing process, seeking to understand the value they bring to decision-making while reviewing their empirical support. Let’s dive in.

The Scientific (and Economic) Objective of the Combines

In theory, the purpose is purely scientific: a controlled environment to measure potential objectively. An idyllic moment where sports scientists become the protagonists, attempting to translate laboratory methods into practice. Ensuring nothing is missed, while the plan is clearly directed at identifying the most relevant characteristics for each sporting discipline.

However, it is impossible to ignore the media machinery. What began as an efficiency tool has transformed into a televised spectacle that generates narratives before a single professional minute is even played. It fuels “water cooler” conversations. And let’s be clear: we enjoy that too.

But this duality forces us to ask some uncomfortable questions. To what extent does the spectacle eclipse the rigor of the evaluation and cloud our judgment when making decisions? How are human biases managed when millions of dollars are at stake? The fundamental question we can highlight is: now that we have more precise technology and decades of data, have we truly improved our ability to predict success? Or has the refinement of instruments only led us to measure variables with more accuracy that may not be the most determining factors for an elite career?

This first post is purely narrative (how the Combines were born, what happens in them, and what limitations we see). The goal of the upcoming posts is to explore the evolution of the athletic profile and question the validity of these tests against professional reality. We will evaluate if there is a real correlation with the clearest performance indicators (such as starting status or minutes played). We will see then if we are capable of separating data from noise and doing science away from the spectacle.

Different Paths to the Same Destination

While the NFL set the precedent, it would be a mistake to think the other leagues simply copied their model. On the contrary, each sport has undergone an adaptation process, selecting the most suitable tests to evaluate the physical capacities that truly define success in their discipline. While some power and agility tests form a common pillar, it is the differences and unique tests that reveal the true athletic essence of each sport. Below, we deconstruct this battery of tests comparatively.

The Standard for Upper Body Strength

The Bench Press, with its fixed load of 225 pounds (102 kg), has established itself as the standard barometer for upper-body strength-endurance in the NFL and NHL Combines. Unlike an absolute maximum strength evaluation (1RM), this test seeks to measure an athlete’s ability to produce work repeatedly, simulating the physical demand of a lineman who must repel successive contacts throughout a game.

However, scientific rigor invites reflection: decades of accumulated evidence show that the number of repetitions has a weak correlation with actual on-field performance or career longevity for most positions. Despite this modest predictive validity, the exercise survives as an indicator of physical maturity and commitment to strength training; a poor result often acts as a “red flag” regarding the prospect’s work ethic, while exceptional performance fuels the media narrative of the “physical specimen,” even if its real transfer to playing technique is often limited.

How much force can you apply in a short time?

The ability to generate maximum force in the shortest time possible is perhaps the most fundamental athletic quality in team sports. This allows a player to gain a few milliseconds, separate from a rival, or rise above them to generate the necessary advantage. This is why the Combines perform various tests to quantify this capacity. Jump tests are the common denominator across all major leagues. Specifically, we find the Vertical Jump and the Broad Jump (standing long jump). The vertical jump measures the capacity of the lower limbs on the vertical axis, while the broad jump does so on the horizontal axis.

• The NFL and NHL use these two tests as a baseline indicator of general power. In the NFL, it correlates with explosiveness from a static stance for linemen and the jumping ability of receivers and defensive backs. In the NHL, it is considered a good indirect indicator of skating stride power [2].

• The NBA goes a step further in specificity. In addition to the standard vertical jump, it includes the Max Vertical Leap (a vertical jump with a running start). This variant is much more specific to basketball, where players rarely jump from a static position, but rather after movement.

Why these tests and not others? They are simple, reliable, and quick to execute. Historically, they have been performed with the Vertec, although in recent years we have seen the incorporation of force plates at the base of the static vertical jump—a tool that provides greater depth in the study of the neuromuscular characteristics of athletes.

Moving in Small Spaces While Making Decisions

Straight-line speed is useful, and it is measured in the Combines, but the ability to brake, change direction, and re-accelerate is what differentiates a good athlete from an elite one in team sports. This is one of the main reasons leagues have implemented tests that evaluate the time it takes for a player to get from point A to point B. The straight-line sprint is a common test across leagues, with the distance covered being the distinguishing element. Secondly, speed tests including movements in different planes have been implemented, implying the need to accelerate and decelerate. Despite this, whether we are talking about linear sprints or change-of-direction tests, the focus has centered exclusively on quantifying time, leaving out biomechanical evaluation (both kinetic and kinematic) and even qualitative movement analysis.

Cone Drills and Agility Runs (Shuttle Runs)

The neuromuscular system’s ability to change direction efficiently while maintaining body control is key in team sports. This is where the adaptation of tests becomes most evident:

• NFL: Uses two tests for different purposes. The first is the 5-10-5 Shuttle (or Pro Agility), measuring quickness in the same plane with 180-degree turns. The second is the 3-Cone Drill, highly valued because it simulates a 90-degree change of direction in both directions (left then right) while maintaining speed—a crucial movement for pass rushers trying to bypass a blocker.

• NBA: Employs the Lane Agility Drill. This is considered the most sport-specific agility test across all leagues. It uses the dimensions of the “paint” to simulate defensive slides and offensive/defensive movements near the basket. However, we should be skeptical of the specific mechanics used to “beat” the test and reflect on whether they are truly specific. Contrary to popular belief, defensive actions have evolved from side steps to cross steps to close defensive space.

• NHL: Proposes the Pro Agility Test (identical to the NFL’s 5-10-5), as lateral quickness is fundamental on ice. Recently, variants like the Transition Agility Test have emerged, performed on skates with a stick in hand. This measures transition ability, edge control, and multi-directional agility, capturing the essence of hockey: alternating between forward/lateral skating and quick turns.

While other standardized tests like the T-Test or Illinois Agility Test exist in the literature, leagues prefer these drills because they believe the movement patterns more faithfully mimic the real demands of their respective sports.

Unique Tests for Each Sport

These league-exclusive tests best demonstrate which physical capacity is most critical and differentiating for each discipline.

• NHL: The Anaerobic Engine (Wingate and VO2 Max): The Wingate Test consists of a 30-second maximal sprint on a stationary bike against high resistance to measure peak anaerobic power. It is complemented by the VO2 Max test for aerobic capacity. Why? Hockey is defined by “shifts”: 45–60 seconds of maximal effort followed by recovery. The Wingate simulates a shift perfectly, while VO2 Max measures the “engine’s” ability to recover between efforts.

• MLB: Biomechanics Labs for Better Selection: The newest Combine is the least focused on generic physical tests and the most focused on movement biomechanics. Systems like TrackMan or Hawkeye analyze pitchers’ deliveries (velocity, spin rate, movement efficiency) and hitters’ mechanics (exit velocity, launch angle). Success for a pitcher depends more on the physics of their arm than a vertical jump.

• NBA: Real Competition (5 vs 5 games): While other leagues have positional drills, the NBA places significant weight on 5-on-5 games. Basketball is fluid and dependent on decision-making and “Basketball IQ.” Games allow scouts to see how a player communicates on defense and makes decisions under pressure.

The Real Use of Generated Knowledge

Rather than a universal formula, each franchise develops its own scoring algorithms to create an integral valuation profile. However, when these algorithms are put through the scientific ringer, we find little statistical support in any major league [3, 4].

The foundation of evaluation remains game tape. Scientific literature supports this: previous competitive performance (college stats) is a much more robust predictor of success than any isolated physical test. Combine results act as a catalyst or a brake, but with surprisingly low explanatory power. Studies show physical tests explain only 2.6% of the variability in a player’s first-year performance [5, 6, 7].

The paradox is that the Combine discriminates who enters the league, but not who triumphs in it. Machine learning models show an 83% success rate in predicting if a player will debut, but fail miserably at predicting how many minutes (snaps) they will actually play [8].

To make this knowledge “useful,” teams must apply a positional specificity filter, as evidence shows “islands of value”:

• Running Backs: The 10-yard dash is a solid indicator of future yards per carry [5].

• Wide Receivers: Vertical jump and height are the best predictors of production [5].

• Quarterbacks: Speed and vertical power explain up to 62% of their rushing yards but say little about passing accuracy [2].

Ultimately, the true “insurance policy” isn’t the stopwatch; it’s the medical exam. Medical grades are the ultimate veto factor. While a great jump adds points, a history of major injury points accurately toward a short career with few games played [9, 10].

Limitations and the Need for Longitudinal Tracking

One of the greatest risks of the Combine is treating its results as immutable truths. Performance is a dynamic construct. We believe it is imperative that leagues establish longitudinal tracking that follows the player throughout their career.

Professional success depends on the interaction between chronic load (accumulated stress) and the organism’s restoration capacity. As seasons progress, biological aging and tissue degeneration occur. Monitoring these changes is critical: a rookie who was the fastest in his class may become high-risk if his ability to recover from competitive stress drops drastically.

By establishing a constant monitoring system, franchises could move from reactive management (treating injuries) to proactive talent management. Only through this evolutionary analysis can we understand if we are looking at a “flash in the pan” or an athlete capable of sustained high performance.

Questions for Upcoming Posts

In our next installment, we will analyze historical data to study trends in different tests and understand if athletes are getting bigger, stronger, and/or faster [17]. We will focus on the NBA.

• How many new players entering the league actually participated in the Combine?

• Do players who participate in the Combine secure the top Draft spots?

• How many players who test at the Combine never play a single game?

• If we could generate a global “athletic profile,” would it correlate with rookie-year performance?

• Should we weight test results based on the player’s presumed position/role?

We don’t have all the answers yet, but here is a small preview of the analyses we’ve conducted using NBA anthropometric and physical data from the last 20 years. See figures below regarding the evolution of height, weight, and physical performance.

References

1. NFL Football Operations. The History of the Draft. Accessed August 8, 2025. https://operations.nfl.com/journey-to-the-nfl/the-nfl-draft/the-history-of-the-draft/

2. NBA Draft Combine. Wikipedia. https://en.wikipedia.org/wiki/NBA_draft_combine

3. NFL Scouting Combine. Wikipedia. Actualizado el 29 de julio de 2025. https://en.wikipedia.org/wiki/NFL_Scouting_Combine

4. Lyons B, Hoffman B, Michel J, Williams K. On the Predictive Efficiency of Past Performance and Physical Ability: The Case of the National Football League. Hum Perform. 2011;24:158-172.

5. Hartman M. Competitive Performance Compared to Combine Performance as a Valid Predictor of NFL Draft Status. J Strength Cond Res. 2011;25.

6. Cook J, Ryan G, Snarr R, Rossi S. The Relationship Between the NFL Scouting Combine and Game Performance Over a 5-Year Period. J Strength Cond Res. 2020.

7. Kuzmits F, Adams A. The NFL Combine: Does It Predict Performance in the National Football League?. J Strength Cond Res. 2008;22:1721-1727.

8. LaPlaca D, McCullick B. NFL Scouting Combine Tests Correlated to NFL Player Performance. J Strength Cond Res. 2020.

9. Szekely B, Sinnott C, Halow S, Ryan G. NFL Career Success as Predicted by NFL Scouting Combine. ArXiv. 2023.

10. Wang D, Weiss L, et al. Athletes With Musculoskeletal Injuries Identified at the NFL Combine and Prediction of Outcomes: A Systematic Review. Orthop J Sports Med. 2018;6.

11. Brophy R, Chehab E, et al. Predictive value of orthopedic evaluation and injury history at the NFL combine. Med Sci Sports Exerc. 2008;40(8):1368-72.

12. Teramoto M, Cross C, Willick S. Predictive Value of NFL Scouting Combine on Future Performance of RBs and WRs. J Strength Cond Res. 2016;30:1379–1390.

13. Vincent L, Blissmer B, Hatfield D. National Scouting Combine Scores as Performance Predictors in the NFL. J Strength Cond Res. 2019;33:104–111.

14. Rishis E, Johnston K, Baker J. On the predictive validity of the NFL combine: does it forecast future success?. J Sports Sci. 2023;41:217-231.

15. Asprey W, et al. A 10-year Evaluation of the NFL Combine: Career Longevity for NFL Offensive players. J Hum Perform. 2020;7:8.

16. Teramoto M, et al. Predictive Validity of NBA Draft Combine on Future Performance. J Strength Cond Res. 2018;32:396–408.

17. García-Rubio J, et al. Citius, Altius, Fortius; Is It Enough to Achieve Success in Basketball?. Int J Environ Res Public Health. 2020;17.

This week we published a new scientific article in which we study the relationship between game performance and physical demands in basketball players. This is an objective that authors have pursued before, but on this occasion, we do so with a different methodological approach.

<aside> <img src=”/icons/thought-dialogue_gray.svg” alt=”/icons/thought-dialogue_gray.svg” width=”40px” />

We recommend reading a previous publication from which many of these questions arose. Is there a relationship between physical demand and performance in basketball?

</aside>

Contextualization

Is physical load a cause or a consequence of performance?

Although both concepts are inseparable to understand the game, our study proposes a change of perspective. It is suggested to analyze the data under the assumption that the observed physical demands are the result of game actions, and not the other way around. The physical load recorded during a game should not be interpreted as a direct indicator of an athlete’s maximum physical condition, as it is conditioned by the chaotic nature of the game, the coach’s tactics, and the player’s talent.

The study treats performance (e.g., shots attempted) as the independent variable (cause) and physical demand (e.g., number of jumps) as the dependent variable (consequence).

How to fairly compare the load of players with different minutes on the court?

In sports with unlimited substitutions, comparing a player who plays 30 minutes with another who plays 5 is a methodological challenge. Instead of setting minimum time thresholds (which can exclude decisive players) or relativizing data per minute (which can mask the effect of accumulated fatigue), this study proposes a statistical solution.

Partial correlations are used to analyze the relationship between performance and load, while statistically controlling for the effect of minutes played. This method not only helps to mitigate differences in on-court time but also implicitly recognizes that fatigue increases as the game progresses, a factor that influences tactical decisions and the ability to execute high-intensity actions. It is proposed as a starting point for a collective reflection to improve analysis in the future.

Objectives

In this study, we posed two key questions 🤔:

1. What is the relationship between the performance statistics of a game and the physical demands (measured with positioning systems and inertial sensors) in basketball players?

2. How do these relationships change according to the playing position: guards, forwards, and centers?

Main findings

Our main findings open up an important discussion for coaches, strength and conditioning staff, and analysts:

📈 Performance and physical load are intrinsically linked. This is not a novel discovery. But scoring more points is associated with covering more distance and, above all, with a higher number of high-intensity accelerations. Assists, in turn, correlate with sharp decelerations, a key action for creating advantages. And what about rebounds?

🔬 Inertial measurement units (IMUs) are more revealing than positioning systems. Metrics like the number of jumps or high-intensity horizontal impacts showed stronger correlations with performance (points, two-point attempts, rebounds) than distance or speed data. This shows that many decisive actions in basketball occur with little displacement but at a very high physical cost. But it’s not the same for everyone.

👤 Playing position changes everything. The demands are unique to each role.

• Centers: Their performance is strongly linked to jumps and high-intensity impacts, quantifying the “battle in the paint” for the first time.

• Guards: Shooting efficiency is associated with high-speed running, likely in fast-break situations.

• Forwards: They present a more hybrid profile, where both total distance and explosive actions are important.

Practical Applications

Understanding the physical demands reflected in game statistics has great practical value. The findings of this study offer several direct applications:

For teams without technology: The game statistics (box score) can serve as a useful approximation of the physical load experienced by a player. A high volume of actions (shots, rebounds, assists) with the same playing time suggests a higher physical demand, which can help contextualize fatigue and manage rotations.

Technology selection: The results suggest that inertial measurement units (IMUs) are particularly effective for monitoring the demands of basketball, showing stronger and more numerous correlations with performance than positioning systems (LPS). Given their greater accessibility and lower cost, IMUs represent a valuable tool.

Individualization according to the role: The main practical value of the study is its contribution to the individualization of load management, using specific metrics for each position as a first step towards role-based analysis.

• Centers: Their performance is intrinsically linked to vertical actions. Their monitoring should prioritize IMU-derived metrics such as the number of jumps and high-intensity impacts (Hi-HI), which correlated with their offensive production and rebounding success. Their physical preparation should, therefore, focus on plyometric power and the strength to withstand contact.

• Guards: The efficiency of guards is associated with high-speed movements. Monitoring high-speed running (HSR) and the frequency of high-intensity accelerations and decelerations is recommended, as these relate to a better shooting percentage and greater playmaking.

• Forwards: Their hybrid role requires a balanced monitoring approach, considering both the total distance covered and their volume of explosive actions, such as jumps and player load (PL), to obtain a complete understanding of their workload.

In summary, these findings empower professionals to move from generic monitoring to a specific, evidence-based strategy, adapting training and tracking to the unique demands of each position and, in the future, of each role in the game.

New questions

This study not only provides findings and practical applications that are considered relevant but also opens the door to new lines of research and offers tools for direct application by coaches, strength and conditioning staff, and analysts.

While the results are promising, they should be considered exploratory, as correlation does not imply causation. To validate and delve deeper into these relationships, future research should advance in several key directions:

More sophisticated methods: It is crucial to incorporate video analysis synchronized with performance and physical load data, also including opponent data for greater context. In the long term, the data could be integrated into machine learning algorithms to build predictive models of basketball performance.

From position to role: Perhaps the most important step for the future is to refine player categorization beyond traditional positions. As is common practice in professional basketball, it is essential to identify the specific roles within the same position (e.g., differentiating a “stretch-forward” from a “post-up forward”). This approach would allow for a much more detailed and granular understanding of the physical demands associated with performance, facilitating the creation of highly individualized monitoring and training strategies.

References

Espasa-Labrador J, Martínez-Rubio C, García F, Fort-Vanmeergaehe A, Guarch J, Calleja-González J. Exploring the Relationship Between Game Performance and Physical Demands in Youth Male Basketball Players. Journal of Functional Morphology and Kinesiology. 2025; 10(3):293. https://doi.org/10.3390/jfmk10030293

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]

☔ The presence of clouds does not guarantee rain.

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].

🧠 Understand injury risk as a complex and probabilistic system [3].

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.

🌪️The wind blows every day, but only alerts us when it changes more than usual.

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.

What can the physical trainer or sports scientist do?

• Accept uncertainty: we cannot predict every injury, but we can recognize contexts of increased vulnerability.

• Monitor trends, not isolated events: bad training does not injure, but accumulation can.

• Combine data and experience: data provides patterns. Technician’s eye, context.

• Think scenarios, not single answers: what if this player trains today, what if we give him rest, what approach can we take that given the current situation will reduce the potential risk?

• Contextualize information: the same load, HRV or fatigue value does not mean the same thing for everyone. History, position, competitive phase, external pressure or emotional state can radically modify the interpretation.

The role of the modern trainer increasingly resembles that of the meteorologist: to read signals, interpret probabilities and help the team make better decisions under uncertainty.

Will it rain, will our player get injured?

We don’t know for sure. But if we see the sky darkening, the humidity rising and the wind shifting, it might be a good idea to bring an umbrella.

In sports, as in weather, it’s not about eliminating risk, it’s about anticipating it better. And for that, we need to stop thinking about single causes and start thinking about probabilities within complex systems.

We hope you like it! 👏🏻

References

1. Pol, R., Hristovski, R., Medina, D., Balagué, N., & Seirul-lo, F. (2019). From microscopic to macroscopic sports injuries: Applying the complex dynamic systems approach to sports medicine: A narrative review. British Journal of Sports Medicine, 53(19), 1214-1220.

2. Fonseca, S. T., Souza, T. R., Verhagen, E., van Emmerik, R. E. A., Bittencourt, N. F. N., & Oliveira, R. S. (2020). Sports injury forecasting and complexity: A synergetic approach. Sports Medicine, 50(10), 1757-1770.

3. Road to Performance. Injury risk: context and assessment. Road to Performance. Published online. https://www.roadtoperformance.com/Riesgo-de-lesi-n-contexto-y-valoraci-n-ab1c1d07ed384474ab46b4463bcc3490

This metaphor, so obvious on the road, seems to vanish with astonishing ease when we enter our field: the analysis of sports performance. We have internalized it to such an extent that we have forgotten to question our own processes. In recent years, we have given data great power, to the point of basing a large part of the decisions in high-performance sports on it. We do not intend to diminish its value in the slightest, as it is very useful, but it cannot be everything. This is mainly because the data we collect, just like what we see in the car’s rearview mirror, is only a static picture of the past. It describes a part of the reality that was, provided we can say with some level of certainty that the data collected captures the essence of that reality under study.

Allow us to share an anecdote that has partly motivated this editorial. Not long ago, a high-level basketball scouting manager confessed the following to us: “Statistics in basketball are used to save you from watching games. But if you really want to know how a player or a team behaves, you have to sit down and watch the games.” This seemingly obvious statement is devastating in its simplicity and largely describes the situation we find ourselves in. It is through the observation of the game that we can better understand its dynamics and thus delve into the study of futures, the next stimulus, the next drill, the next training session, or the next game.

From yesterday's High-Speed Running to tomorrow's total distance: the linearity trap

This drift toward detail is visualized very well if we look at biomedical research over the last century. Let’s consider how we have moved from studying the functioning of complete physiological systems to analyzing the behavior of a single cell in a Petri dish. This reductionist approach has been an undeniable engine of progress, allowing us to decipher fundamental mechanisms. This is not a criticism, but the observation of a success. However, it has also generated a monumental challenge: understanding how those truths discovered in the isolation of a laboratory translate back to the chaotic and interconnected ecosystem of the living organism. By focusing on a part, we run the risk of misinterpreting the whole. And that is exactly our crossroads.

Let’s move the example to our terrain, to the analysis of data collected in load monitoring processes. Currently, we have technological solutions—positioning systems, inertial sensors, etc.—that provide us with a giant, high-definition rearview mirror, allowing us to see the curves we have already navigated in great detail. We can even “zoom in” practically infinitely to decompose each of those curves into a thousand different pieces. We can observe, centimeter by centimeter, where the player was, what accelerations their torso underwent, or how long a change of direction lasted.

And if we have reached this point, we must be aware that we are one step away from falling into a dangerous trap. We have stopped seeing the curve; we have lost perspective of the whole, of the context. We move to the analysis of the gesture with exquisite precision, treating it as an isolated event, forgetting that it forms part of an adaptive system. This forces us to ask some questions: What is the real impact of that hyper-analyzed variable on the global state of the player? Will the change we propose in that small piece have the linear and predictable effect we expect on the whole? We are convinced that analysis has value, but we must be cautious.

And that drift toward detail, that reductionism that has allowed us to understand the isolated piece, is precisely what feeds the mirage of linearity. By zooming in on a single variable—an acceleration, the distance covered at high speed, etc.—we fall into the temptation of believing that we have isolated the fundamental cause of performance or risk. We assume that if we modify that piece, the effect on the whole will be direct, predictable, and proportional.

We forget that in complex systems, the interactions between components often matter more than the components themselves. By focusing on the curve, not only do we lose sight of the road, but we forget that the condition of the road modifies the way we approach each curve and the experience of driving itself. And here we have already run into the mirage of linearity.

The person responsible for load management observes the data and assumes, almost by inertia, that if a load X produced a result Y, a load X+1 will produce a predictably superior result. If today we did more, the response is more intense; the more meters covered at high intensity in competition, the more time players will need to recover. This leads us, in many cases, to shorter and less intense tasks. And what certainty do we have that this is more beneficial?

💡 Note: These analyses are generally performed using averages and standard deviations. Perhaps these are not the most adequate statistics when we speak of living systems that change their response to apparently similar stimuli. But we will explore this in another editorial.

But this is not exclusive to the field, as endless recognized scientific journals publish articles that correlate accumulated load with past injuries; yet, in essence, it is a forensic analysis. We study the airplane’s black box after the accident. This forces us to ask an uncomfortable question: are we using this data to understand complexity or, actually, to feel that we control it? The fundamental problem with this approach, and the antidote against extreme reductionism, is remembering that athletes are not machines. They are complex and adaptive biological systems. Ignoring this is ignoring the elephant in the room. The key to understanding this lies in concepts that should be the pillar of our praxis. One of them is hormesis: the principle by which a stressor in the right dose provokes a response that strengthens the system, breaking any simple linear logic.

Let’s think of two players executing exactly the same session. For one, who has rested well and has high confidence, it is an optimal stimulus. For the other, who carries mental fatigue and sleeps poorly, that same load can be the push toward injury. The external load registered by the positioning systems will be identical; the system receiving it, and therefore the result, is radically different. The data on the screen does not tell us that story.

This brings us directly to the nature of the systems we work with, magnificently explored in works like Thinking in Systems by Donella Meadows or In a Flight of Starlings by Giorgio Parisi. Living systems, like an athlete or a team, have properties that challenge our desire to simplify:

• Emergence: Performance is not the sum of its parts. It is a quality that emerges from the interaction of countless variables. How do we measure the influence of the coach’s confidence or locker room chemistry in the load data we see on the computer?

• Adaptation: The system learns and changes. Today’s load modifies tomorrow’s athlete. Therefore, yesterday’s data applies to a “version” of the athlete that, strictly speaking, no longer exists.

• Self-organization: The system finds solutions and coordinates without centralized control. Think of how a team adjusts its press on the field or finds offensive synergies that were not explicitly designed on the whiteboard, but arise from the interaction and mutual trust of the players.

• Non-linearity: Small changes can provoke disproportionate results (the famous “sensitivity to initial conditions” or butterfly effect). The history of science, as Peter L. Bernstein narrates in his history of risk, is the history of the human struggle to domesticate this uncertainty.

Seeing and managing the elephant in the room

So, what do we do? The proposal is not to abandon statistics or the study of load values; quite the opposite. We must understand their relevance and their place. As Alfred Korzybski stated, “the map is not the territory.” Our data are maps of the past, but they are not data from a complex and changing reality. Their value does not lie in giving us certainties, but in helping us to formulate better questions. Shouldn’t that be our main skill when we work in high-performance team sports?

The great challenge is to start balancing the scales. We must complement our deep knowledge of the past with methods that allow us to explore the future, accepting uncertainty as an inherent characteristic of the system, not as an enemy to be defeated. And to do this, we must be self-critical. We talk a lot about complex systems, but the pressure of day-to-day life pushes us to look for a simplification in a traffic light that tells us with its colors what decision to make. It’s human. We try to reduce the complexity of the world’s reality into smaller pieces so that we can handle it.

Recognizing this tension is the first step. The next is to dare to look up from the rearview mirror. The question that remains for us is: in our daily routine, do the tools we use foster our capacity for observation and judgment, or are they designed to replace them? How much time do we dedicate each day to asking ourselves questions? Do we need to develop new skills to move forward? The answer will define whether we are mere descriptors of the past or true architects of future performance.

Referencias

• Bernstein PL. Against the Gods: The Remarkable Story of Risk. John Wiley & Sons; 1998.

• Introducción a los Sistemas Complejos. Polymatas. Publicado online. Accedido el 11 de junio de 2025. https://www.polymatas.com/introduccion-sistemas-complejos/

• Korzybski A. Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics. International Non-Aristotelian Library Publishing Company; 1933.

• Meadows DH. Thinking in Systems: A Primer. Chelsea Green Publishing; 2008.

• Parisi G. In a Flight of Starlings: The Wonders of Complexity. Allen Lane; 2023.

• Taleb NN. The Black Swan: The Impact of the Highly Improbable. Random House; 2007.