We live in a world where everything is connected and, as such, our decision-making processes rely on information that is extremely global, dynamic and affected by very different levels of dimensions, factors and variables. Famous theoretical physicist Stephen Hawking advised the twenty-first century grads by saying: “Embrace complexity!” Here are some takeaway messages that complexity science thinking can provide:
1- Be open to embrace new perspectives and try connecting to people with different backgrounds and experiences. An ancient Indian fable called “The blind men and the elephant” describes how six blind men come across an elephant for the first time in their lives and tried to conceptualise it just by touching it. As a result, there was a complete disagreement on what an elephant is, illustrating how we tend to take our individual perspectives as the unique version of reality.
2- Seeing the connectedness in the world requires developing creativity. Research shows that in early ages most of the children can be classified as creative geniuses[1], however, our society engages most of us in an education and labor process that favors noncreative behavior. Contrary to the common beliefs, creativity is not genetically predetermined, it’s a skill to be developed symbiotically with others within the environment, as we explore our talents. Time to be developed?
3- One of the most dangerous trends in sports analytics is the illusion that with enough technology and data, people can predict the unpredictable. The game flow is influenced by actions-reactions from all players that will trigger known, but also frequently, unknown scenarios. Personalized training can help empowering the players to deal with these constant needs. Unpredictable is not untrainable.
4- Each competition scenario takes part under very unique landscapes that result from the interaction of multiple variables (time, space, opponents, crowd, teammates, …). Variability is a key ingredient to personalize performance in every moment of the preparation, in contrast with more monotonous and standard repetition-based training approaches that may mostly help to build (probably false states) of self-confidence.
5- Analyzing game performance is about describing trends in past events and try to understand how they may impact the preparation of the next competition. Key concepts are the game pace, as measured by ball possessions, and what Dean Oliver[2] describes as the four factors, when evaluating the efficiency of an offense or defense: shooting percentage (eFG%), turnover rate (TOV%), offensive rebounding percentage (OREB%), and getting to the foul line (FTR). Keep it simple.
6- Higher training workloads may not correspond to the best performances in competition[3]. Most frequently, a contrary trend can be found, suggesting that elite players cover shorter distances at lower average velocities, probably as a result of making less mistakes when deciding when and where to move and possibly expending less energy to reach their destinations. Nevertheless, it seems also clear that players who are unloaded too much have a higher risk of injury.
Authors: Jaime Sampaio, Francesco Cuzzolin, Julio Calleja-Gonzalez, Igor Jukic, Baris Kocaoglu, Sergej M. Ostojic, Mar Rovira.
References:
[1] George Land, Beth Jarman (1992), Breakpoint and Beyond: Mastering the Future – Today, HarperBusiness.
[2] Dean Oliver (2004) Basketball on paper: rules and tools for performance analysis. Potomac books.
[3] Vazquez-Guerrero J, Casals M, Corral-López J & Sampaio J (2020) Higher training workloads may not correspond to the best performances of elite basketball players. Research in Sports Medicine, 28(4), 540-552.
