This meta-analysis aimed to examine the effects of plyometric training on physical fitness attributes in handball players. A systematic literature search across PubMed, SCOPUS, SPORTDiscus, and Web of Science identified 20 studies with 563 players. Plyometric training showed significant medium-to-large effects on various attributes: countermovement jump with arms (ES = 1.84), countermovement jump (ES = 1.33), squat jump (ES = 1.17), and horizontal jump (ES = 0.83), ≤ 10-m linear sprint time (ES = -1.12), > 10-m linear sprint time (ES = -1.46), repeated sprint ability with change-of-direction time (ES = -1.53), agility (ES = -1.60), maximal strength (ES = 0.52), and force-velocity (muscle power) (ES = 1.13). No significant impact on balance was found. Subgroup analysis indicated more pronounced agility improvements in players ≤ 66.6 kg compared to > 66.6 kg (ES = -1.93 vs. -0.23, p = 0.014). Additionally, greater improvements were observed in linear sprint and repeat sprint ability when comparing training durations of > 8 weeks with those ≤ 8 weeks (ES = -2.30 to -2.89 vs. ES = -0.92 to -0.97). In conclusion, plyometric training effectively improves various physical fitness attributes, including jump performance, linear sprint ability, maximal strength, muscle power and agility.

BACKGROUND: The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using.
METHODS: Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes.
RESULTS: Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI.
CONCLUSIONS: The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods.