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Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to sports research, coaching and optimization of athletic performance. The strength of AI with regards to applied sports research, coaching and athletic performance involve the automation of time-consuming tasks, processing and analysis of large amounts of data, and recognition of complex patterns and relationships. However, it is also essential to be aware of the weaknesses associated with the integration of AI into this field. For instance, it is imperative that the data employed to train the AI system be both diverse and complete, in addition to as unbiased as possible with respect to factors such as the gender, level of performance, and experience of an athlete. Other challenges include e.g., limited adaptability to novel situations and the cost and other resources required. Opportunities include the possibility to monitor athletes both long-term and in real-time, the potential discovery of novel indicators of performance, and prediction of risk for future injury. Leveraging these opportunities can transform athletic development and the practice of sports science in general. Threats include over-dependence on technology, less involvement of human expertise, risks with respect to data privacy, breaching of the integrity and manipulation of data, and resistance to adopting such new technology. Understanding and addressing these SWOT factors is essential for maximizing the benefits of AI while mitigating its risks, thereby paving the way for its successful integration into sport science research, coaching, and optimization of athletic performance.
PurposeOur aims were to examine (i) the internal load during simulated soccer match-play by elite youth players; and (ii) the time-course of subsequent recovery from perceived and performance fatigability.
MethodsEleven male youth players (16 ± 1 years, 178 ± 7 cm, 67 ± 7 kg) participated in a 2 × 40-min simulated soccer match, completing 30 rounds (160 s each) with every round including multidirectional and linear sprinting (LS20m), jumping (CMJ) and running at different intensities. During each round, LS20m, CMJ, agility, heart rate (HR), oxygen uptake (VO2), energy expenditure (EE), substrate utilization and perceived exertion RPE were assessed. In addition, the blood level of lactate (Lac) was obtained after each of the five rounds. Creatine kinase (CK) concentration, maximal voluntary isometric knee extension and flexion, CMJ, number of skippings in 30 s, and subjective ratings on the Acute Recovery and Stress Scale (ARSS) were examined before and immediately, 24 and 48 h after the simulation.
Results: During the game %HR\(_{peak}\) (p < 0.05, d = 1.08), %VO2\(_{peak}\) (p < 0.05; d = 0.68), Lac (p < 0.05, d = 2.59), RPE\(_{total}\) (p < 0.05, d = 4.59), and RPE\(_{legs}\) (p < 0.05, d = 4.45) all increased with time during both halves (all p < 0.05). Agility improved (p < 0.05, d = 0.70) over the time-course of the game, with no changes in LS20m (p ≥ 0.05, d = 0.34) or CMJ (p ≥ 0.05, d = 0.27). EE was similar during both halves (528 ± 58 vs. 514 ± 61 kcal; p = 0.60; d = 0.23), with 62% (second half: 65%) carbohydrate, 9% (9%) protein and 26% (27%) fat utilization. With respect to recovery, maximal voluntary knee extension (p ≥ 0.05, d = 0.50) and flexion force (p ≥ 0.05, d = 0.19), CMJ (p ≥ 0.05, d = 0.13), number of ground contacts (p ≥ 0.05, d = 0.57) and average contact time (p ≥ 0.05, d = 0.39) during 30-s of skipping remained unaltered 24 and 48 h after the game. Most ARSS dimensions of load (p < 0.05, d = 3.79) and recovery (p < 0.05, d = 3.22) returned to baseline levels after 24 h of recovery. Relative to baseline values, CK was elevated immediately and 24 h after (p < 0.05, d = 2.03) and normalized 48 h later.
ConclusionIn youth soccer players the simulated match evoked considerable circulatory, metabolic and perceptual load, with an EE of 1042 ± 118 kcal. Among the indicators of perceived and performance fatigability examined, the level of CK and certain subjective ratings differed considerably immediately following or 24–48 h after a 2 × 40-min simulated soccer match in comparison to baseline. Accordingly, monitoring these variables may assist coaches in assessing a U17 player’s perceived and performance fatigability in connection with scheduling training following a soccer match.
Incremental exercise testing is frequently used as a tool for evaluating determinants of endurance performance. The available reference values for the peak oxygen uptake \((VO_{2peak})\), % of \(VO_{2peak}\) , running speed at the lactate threshold \((v_{LT})\), running economy (RE), and maximal running speed \((v_{peak})\) for different age, gender, and disciplines are not sufficient for the elite athletic population. The key variables of 491 young athletes (age range 12–21 years; 250 males, 241 females) assessed during a running step test protocol \((2.4 m s^{−1} ; increase 0.4 m s^{−1} 5 min^{−1})\) were analysed in five subgroups, which were related to combat-, team-, endurance-, sprint- and power-, and racquet-related disciplines. Compared with female athletes, male athletes achieved a higher \(v_{peak}\) (P = 0.004). The body mass, lean body mass, height, abs. \(VO_{2peak} (ml min^{−1})\), rel. \(VO_{2peak} (ml kg^{−1} min^{−1})\), rel. \(VO_{2peak} (ml min^{−1} kg^{−0.75})\), and RE were higher in the male participants compared with the females (P < 0.01). The % of \(VO_2\) at \(v_{LT}\) was lower in the males compared with the females (P < 0.01). No differences between gender were detected for the \(v_{LT}\) (P = 0.17) and % of \(VO_2\) at \(v_{LT}\) (P = 0.42). This study is one of the first to provide a broad spectrum of data to classify nearly 500 elite athletes aged 12–21 years of both gender and different disciplines.