Going Up and Out: A Systems Thinking Approach to Athlete Health

In this post, guest author Scott McLean explains the characteristics of a complex systems and how systems thinking can be utilised in the context of athlete health.

Guest author, Scott McLean.

Athlete health has become one of the most discussed issues in modern sport, yet despite increasingly more availability to technology and data, prevention protocols and support systems, athletes’ health continues decline (1). This is due, in part, to limited knowledge regarding factors beyond the athlete that contribute to these incidents.

Sports practice and research has often been approached as if they operate in predictable ways, using reductionist methods that break complex problems into smaller, manageable pieces, based on the assumption that fixing each part will fix the whole (2).

Sport is a complex and dynamic system composed of numerous interacting and interdependent components (3,4). These include physiological, psychological, and technical elements of athletes, as well as broader social, economic, and even political influences (2). The interactions between these components are not predictable and can lead to unexpected outcomes which are difficult to predict and/or control (5).

While reductionist methods have advanced our understanding in areas like equipment design and skill development (6), they may not be suitable for fully understanding the inherent complexity of sport. Many of the persistent and complex challenges in sport e.g. athlete health, require approaches better suited to understanding and managing systemic complexity (6).

Characteristics of Complex Systems

At this point, it is important to understand what complexity is and how we might define it. In essence, complexity science attempts to understand and respond to phenomena that are dynamic and unpredictable, multi-dimensional, and comprise interrelated components.

Essential to complexity science,  is a focus on the interactions among components within the ‘complex system’, rather than on the contribution of components in isolation (7). For example, focusing solely on the biomechanics of an athlete for injury prevention overlooks the interaction of other factors like training load, sleep, psychological stress, genetics and environmental conditions. It is these interactions where emergent (or unknown) properties, such as injuries, are created.

As such, complexity is elusive and difficult to define. Cilliers (5) outlined eight complex system characteristics which are presented in Table 1, along with relevant examples in a sporting context.

Table 1. Cilliers complex systems characteristics (5) applied to sport (adapted from McLean et al (8)).

Complex system characteristic	Definition	Example in sport
Multiple interacting components	Complex systems consist of multiple components or agents that interact with each other. The diversity and number of these components contribute to the system's complexity, making it more than just the sum of its parts.	In a football team, players, coaches, tactics, support staff, technology, and even fans interact in complex ways to influence the outcome of performances.
Non-linear interactions and emergence	Interactions between components are abundant and can be non-linear in nature, meaning that there is an asymmetry between input and output, and small events can produce large outcomes and vice versa. Emergent properties arising from interactions mean that ‘the action of the whole is more than the sum of its parts’.	In basketball, a single three-point shot can dramatically shift the momentum of the game, and lead to emergent phenomena like a sudden boost in team morale, which might, in turn, affect the overall performance of the team positively.
Open system	Complex systems are open systems, meaning  it is difficult to define their boundary and what they interact with and influence in their environment, whilst being influenced by their environment in return.	Golf tournaments are open systems that are influenced by a myriad of external factors such as weather conditions, terrain, and crowd behaviour.
Ignorance of components	Components within the system are ignorant in that they respond only to local information and do not fully comprehend the behaviour of the overall system or the effects of their actions on the behaviour of the overall system.	A football club increases training intensity to improve performance, while more media and sponsorship duties are scheduled. Each component acts on local priorities, ignoring the cumulative toll on the player’s health which negatively impacts the player.
Path dependence	Their past is co-responsible for present behaviour in that decisions and actions made previously influence the here and now. This characteristic is referred to by others as sensitive dependence on initial conditions.	The history of sport science has been shown to be driving multiple current behaviours within sports science. Many of the values or approaches adopted during the early years of sport science research are continuing to drive behaviour in a manner that may be detrimental to outcomes. These include the value placed on quantifiable data, reductionism, and linear thinking.
Information received primarily from neighbours	Information received by components mainly derives from neighbouring components and how long-range interactions are limited. However, as components often interact with many other components, it is possible to influence non-neighbouring components through just a few interactions;	In rowing, team members primarily rely on immediate neighbours for pace and rhythm. This local interaction influences the overall performance of the team.
Recurrent loops in interactions	The effect of an activity can feedback onto itself either directly or through other components. These feedback loops can be positive or negative, and both are necessary	Cyclists often use real-time performance metrics e.g. speed, cadence, or power outputs to adjust their race strategy. This feedback loop can either improve or degrade performance, depending on various factors like current physical condition or environmental factors.
Dynamicity	Complex systems are dynamic, meaning they change over time. This could be due to internal factors, such as adaptation or evolution, or external factors like environmental changes. The dynamic nature of these systems makes them inherently difficult to model with static methods.	In team invasion sports, the strategies are highly dynamic, constantly adjusting to the opponent's actions and to real-time events such as injuries or penalties. As such, team invasion sports are vivid example of the dynamic nature of complex systems.

The characteristics described in Table 1 can be found in all sports and all levels. To truly understand the complexity of sport, it is important to understand the limitations of reductionism and adopt a holistic, systems-thinking approach. This broader perspective not only offers more detailed insights but opens new possibilities to explore and enhance sport.

If complexity is the problem, then systems thinking is the answer. - Senge (9)

What is Systems Thinking?

Systems thinking is an approach that views problems not as isolated events but as part of a larger interconnected network. Rather than focusing on components in isolation, such as the mechanism of injury to an athlete, systems thinking asks how these outcomes emerge from relationships between system components, system structures, feedback loops, contextual and environmental conditions.

In sport, systems thinking views the athlete not just as an isolated entity but as part of a broader system: influenced by coaching decisions, organisational culture, League policies, medical protocols, media narratives, and even national and international governance structures, among others.

Systems thinking resists the temptation to reduce and simplify. It embraces complexity.

As my favourite systems thinker Donella Meadows suggests "We can't control systems or figure them out. But we can dance with them" (10). 

This well-known quote captures Meadows’ view on engaging with complex systems, rather than trying to control them or fully comprehend every detail, we should interact with them in a flexible and responsive manner, like dancing, where we attune to the rhythm and adjust our movements accordingly.

For sport, this might mean not focussing on every possible measurement and controlling every variable, but having an awareness of the broader system, listening to feedback, adjusting in real-time, and acknowledging the interdependence of physical, psychological, contextual and organisational factors. I think the really good practitioners in sport do this.

This dynamic, systems-awareness may reduce injury risk, not through rigid control, but through flexibility, attentiveness, and collaboration. As Meadows puts it, “pay attention to what is important, not just what is quantifiable”.

Going Up and Out: Seeing the Whole System

Systems thinking offers a shift of focus beyond surface-level symptoms to the deeper structures and interactions that shape outcomes, revealing new and often overlooked opportunities for intervention.

The argument is not to abandon reductionist methods, which remain valuable and necessary, but to recognise their limitations when confronting the complex, dynamic, and multi-causal nature of sport (11). Systems thinking equips us with an approach to engage with persistent problems in sport and one that complements traditional approaches by situating them within the bigger picture.

Systems thinking approaches help us identify leverage points in sport (12), where small strategic interventions can yield substantial systemic impact. The future of optimal athlete health will not be brought about solely through more technology and data, improved prevention and rehabilitation protocols. Athlete health will be optimised through an understanding of how, where and when to intervene within the broader sports system that contributes and influences health issues. This will require us to go up and out rather than down and in.

Case Study

The following case study summarises a practical example of how the application of a systems thinking-based approach can identify systemic contributory factors to athlete health incidents. We recently completed a proof-of-concept project that aimed to develop a novel systems thinking-based incident reporting and learning tool for injury, illness, and psychosocial incidents in sport. Results are not yet published, so here I provide a high-level summary of the findings only.

Incident causation models

Models and methods of incident causation that capture the systemic conditions contributing to adverse events are essential for developing athlete health interventions. Rasmussen’s Risk Management Framework (RMF) (Figure 1) (13) and its associated method, Accident Mapping (AcciMap) method, are widely regarded as state-of-the-art tools for understanding how multiple factors interact to produce adverse incidents (14), in this case, athlete health incidents.

Figure 1. Rasmussen’s RMF (13) with example stakeholders, adapted to sport (6). Arrows represent Vertical integration which refers to the alignment and coordination of decision-making across different levels of the system. Descending arrows indicate systemic controls (e.g. laws, policy, procedures, guidelines etc.), moving down the system. Ascending arrows represent systemic feedback (e.g. data, statistics, reporting etc.) moving back up the system performance.

The RMF conceptualises systems as hierarchies of interacting structures in which all stakeholders share responsibility for systems safety and performance. It emphasises shared responsibility for the performance of the system, and that decisions and actions at higher levels, such as governance, policy, and management shape conditions at the operational level, and that these effects are fed back up the system to influence decisions making (13).

Building on this, the AcciMap method graphically maps stakeholders decisions, and actions across system levels, offering a clear visual representation of how events unfold through the system’s interconnected layers (15). See our recent editorial in the British Journal of Sports Medicine (16). These tools enable a more comprehensive understanding of incident causation, supporting interventions that are systemically informed rather than symptom focused.

Figure 2 shows a selection of the high-level contributory factors that were identified in the analysis of thirteen injury, illness, and psychosocial incidents in sport. Only the high-level contributory factors are shown here, but each high-level factor contains sub-factors. For example, ‘Athlete physical condition’ had several sub factors such as, fitness, load, previous injury etc.

The contributory factors (boxes) identified in the analysed incidents are placed at the level of the RMF where the decision(s) or action(s) took place. For example, competition scheduling at the international level was identified to contribute to injury; and coaching methodology at the Direct supervisor & management level was identified to contribute to psychosocial and injury incidents.

Critically, relationships exist between many of these contributory factors. For example, competition scheduling influences logistics, which influences travel bookings, which influences planning and preparation, which influences athlete physical condition etc. Relationships not included for figure clarity.

Figure 2. High level summary AcciMap of identified in thirteen injury, illness, and psychosocial incidents in sport.

The findings indicate that:

• Injury, Illness, and psychosocial incidents in sport are influenced by a range of contributory factors from across the entire sports system.

• At higher levels of the RMF, identified contributory factors included governance, funding, travel bookings, coaching methodology, and judgement and decision-making.

• At lower levels of the RMF, contributor factors of athlete physical and mental condition, judgement and decision-making, team and coach relationships, contact, social media, personal and lifestyle factors were identified.

• Despite there being only thirteen incidents, the analysis has captured a range of data that is not typically captured and/or associated with injury, illness, and psychosocial incidents.  

• These findings offer new opportunities for improving data collection for injury, illness, and psychosocial incidents in sport from across the broader system.

• By understanding the systemic influences on injury, illness, and psychosocial incidents we can develop better prevention interventions that will have greater leverage on the broader system.

• The findings suggest that there is no root cause to athlete health issues, they emerge from the interaction of multiple contributory factors from across the system.

Given the outcomes presented in the case study, developing and applying systems-based incident reporting and learning tools holds strong potential to inform targeted, evidence-based interventions, improve policy and practice, and ultimately reduce the occurrence and severity of injury, illness, and psychosocial incidents in sport.

Final Thoughts

To better understand and respond to injury in sport, we must shift our focus up and out, rather than down and in. While traditional approaches often zoom in on isolated risk factors e.g. muscle strength or joint angles, this narrow view (while still important) can miss the broader, dynamic context in which injuries occur.

Systems thinking enables us to take that step back and see the broader picture: how individual, team, organisational, and environmental factors interact over time. It allows practitioners to explore how training decisions, communication, competition schedules, and recovery processes interact, often in unexpected ways to influence injury causation. By adopting a systems view, we move from treating injury as the product of single points of failure, to understanding it as an emergent outcome of a complex and interconnected system.

Dr Scott McLean is the Director of Australian based sports consulting company- Leverage Point Consulting. Scott is also an Adjunct Associate Professor at the Centre for Human Factors and Sociotechnical Systems at the University of the Sunshine Coast (UniSC). Scott is recognised as a global leader in systems thinking and complexity in sport. As an academic, he has over 100 publications, including two books, numerous peer reviewed journal articles, book chapters, reports, conference articles and abstracts. Please feel free to get in touch with Scott via LinkedIn.

Acknowledgements

For the current case study, the Centre for Human Factors and Sociotechnical Systems at the University of the Sunshine Coast partnered with the Australian Institute of Sport. The study was funded through a UniSC internal funding scheme. Acknowledgement also goes to the significant contributions of project co-investigators Paul Salmon, Liam Toohey, and Karl Dodd.

Frequently Asked Questions (FAQs)

What is a complex system?

Complexity refers to the behaviour of systems made up of many interconnected parts, where the interactions between those parts give rise to patterns or outcomes that cannot be understood by examining the parts in isolation. An example of complexity in sports injury might be the interaction between training load, fatigue, poor sleep, technique, decision making, psychological stress, but also, factors like communication breakdowns between staff, club or organisation decision making or processes. No single cause explains the injury, but the combination creates a tipping point.

What is systems thinking?

An approach to complex problem-solving that focuses on understanding how elements within a system influence one another over time. Systems thinking resists the temptation to reduce and simplify. It embraces complexity.

How can systems thinking support reducing sports injury risk?

Systems thinking can support reducing sports injury risk by helping practitioners look beyond isolated causes and understand how multiple interacting factors contribute to injury. Rather than focusing solely on one element in isolation systems thinking encourages a holistic view of the athlete's broader environment. If you think you have found the ‘root cause of an injury’, think what influenced it, then what influenced that, and so on. There are final observable outcomes, but there is no ‘root cause’. 

References

1. Ekstrand, J., Bengtsson, H., Waldén, M., Davison, M., Khan, K. M., & Hägglund, M. (2023). Hamstring injury rates have increased during recent seasons and now constitute 24% of all injuries in men’s professional football: the UEFA Elite Club Injury Study from 2001/02 to 2021/22. British journal of sports medicine, 57(5), 292-298.

2. McLean, S., Kerhervé, H. A., Stevens, N., & Salmon, P. M. (2021). A systems analysis critique of sport-science research. International Journal of Sports Physiology and Performance, 16(10), 1385-1392.

3. Bittencourt, N. F., Meeuwisse, W. H., Mendonça, L. D., Nettel-Aguirre, A., Ocarino, J. M., & Fonseca, S. T. (2016). Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition—narrative review and new concept. British journal of sports medicine, 50(21), 1309-1314.

4. Salmon, P. M., & McLean, S. (2021). Complexity in the beautiful game: implications for football research and practice. Science and Medicine in Football, 4(2), 162-167.

5. Cilliers, P. (1998).  Complexity and postmodernism.  Routledge: Boca-Raton, FL.

6. McLean, S., Robertson, S., & Salmon, P. M. (2025). Complexity and systems thinking in sport. Journal of Sports Sciences, 43(1), 1-5.

7. Ottino, J. M. (2003). Complex systems. American Institute of Chemical Engineers. AIChE Journal, 49(2), 292.

8. Mclean, S., Naughton, M., Read, G., Stanton, N. A., Hulme, A., Walker, G., & Salmon, P. (2024). Systems Thinking Methods in Sport: Practical Guidance and Case Study Applications. Taylor & Francis.

9. Senge, P. M. (1997). The fifth discipline. Measuring business excellence, 1(3), 46-51.

10. Meadows, D. H. (2008). Thinking in systems: A primer. chelsea green publishing.

11. Drust, B. (2025). A reflection on systems thinking as a research tool in sport and exercise science. Journal of Sports Sciences, 43(1), 6-7.

12. Naughton, M., Salmon, P. M., & McLean, S. (2024). Where do we intervene to optimize sports systems? Leverage Points the way. Journal of Sports Sciences, 42(7), 566-573.

13. Rasmussen J. Risk management in a dynamic society: a modelling problem. Safety science 1997;27(2-3):183-213.

14. Salmon, P. M., Hulme, A., Walker, G. H., Waterson, P., Berber, E., & Stanton, N. A. (2020). The big picture on accident causation: A review, synthesis and meta-analysis of AcciMap studies. Safety science, 126, 104650.

15. Svedung I, Rasmussen J. Graphic representation of accident scenarios: mapping system structure and the causation of accidents. Safety science 2002;40(5):397-417.

16. Hendricks, S., Naughton, M., Salmon, P. M., West, S. W., Paul, L., Jones, B., ... & McLean, S. (2025). ‘Tackling’ safety through a systems thinking approach: building safety culture within sport. British Journal of Sports Medicine, 59(10), 695-697.