Purpose: To explore complex system behavior and subsequent team performance in professional rugby union.
Methods: Here, we present 2 studies. In the first, we used global positioning system technology to measure player clustering during stoppages in play in nearly 100 games of professional rugby union to explore team (complex system) behavior and performance. In the second, we measured stress hormones (cortisol and testosterone) prior to team meetings and analyzed these relative to amount of time and the frequency with which players looked at peer presenters, as well as subsequent training performance, to explain how stress may lead to behaviors observed in the first study and subsequent match performance.
Results: No link between player clustering during stoppages of play and performance was observed. When players (complex system agents) demonstrated greater levels of stress (as indicated by greater cortisol-awakening response and a greater decline in testosterone-to-cortisol ratio across the morning), they tended to look at peer presenters more; however, training quality declined (P = .02). Correlational analysis also showed that training quality was related to testosterone-to-cortisol ratio (P = .04).
Conclusions: Team behavior is complex and can be unpredictable. It is possible that under stress, complex system agents (ie, rugby union players) look at (and cluster toward) their teammates more; however, meaningful interaction may not necessarily occur. Furthermore, while complex system (team) analysis may be valuable strategically in rugby union in the context of describing behavior, without understanding "how" or "why" intrateam/interagent behaviors emerge it may have little meaning.
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http://dx.doi.org/10.1123/ijspp.2023-0085 | DOI Listing |
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W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
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Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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Department of Experimental Medicine, University of Salento, Via Lecce-Monteroni, 73047 Lecce, Italy.
The immune response to SARS-CoV-2 infection is highly complex, involving a dynamic interplay between the innate and adaptive immune systems [...
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