Basketball can be described as a moderate-to-long duration exercise including repeated bouts of high-intensity activity interspersed with periods of low to moderate active recovery or passive rest. A match is characterized by repeated explosive activities, such as sprints, jumps, shuffles and rapid changes in direction. In top-level modern basketball, players are frequently required to play consecutive matches with limited time to recover. To ensure adequate recovery after any basketball activity (i.e., match or training), it is necessary to know the type of fatigue induced and, if possible, its underlying mechanisms. Despite limited scientific evidence to support their effectiveness in facilitating optimal recovery, certain recovery strategies are commonly utilized in basketball. It is particularly important to optimize recovery because players spend a much greater proportion of their time recovering than they do in training. Therefore, the main aim of this report is to facilitate useful information that may lead to practical application, based on the scientific evidence and applied knowledge specifically in basketball.

AbstractDecision making in sport involves forecasting and selecting choices from different options of action, care, or management. These processes are conditioned by the available information (sometimes limited, fallible, or excessive), the cognitive limitations of the decision-maker (heuristics and biases), the finite amount of available time to make the decision, and the levels of risk and reward. Decision support systems have become increasingly common in sporting contexts such as scheduling optimization, skills evaluation and classification, decision-making assessment, talent identification and team selection, or injury risk assessment. However no specific, formalised framework exists to help guide either the development or evaluation of these systems. Drawing on a variety of literature, this paper proposes a decision support system development framework for specific use in high-performance sport. It proposes three separate criteria for this purpose: 1) Context Satisfaction, 2) Output Quality, and 3) Process Efficiency. Underpinning these criteria there are six specific components: Feasibility, Delivered knowledge, Decisional guidance, Data quality, System error, and System complexity. The proposed framework offers a systematic approach for users to ensure that each of the six components are considered and optimised before, during, and after developing the system. A DSS development framework for high-performance sport should help to improve both short and long term decision-making in a variety of sporting contexts.

Teams experiencing highly competitive densities may be particularly exposed to performance breakdown and injury risk. The aim of this study was to analyse the association between fixture congestion cycles (playing back-to-back games, playing on one day's rest, playing on two day's rest, playing on three or more day's rest) and performance of NBA basketball teams. A total of 82 games from all teams participating in NBA 2016/2017 regular season were considered. Game-related statistics by fixture congestion cycles and game outcome were examined using the Pearson's Chi-Square test, Discriminant Analysis and Binary logistic regression. The results revealed that the likelihood of winning a game increased significantly from playing back-toback games to having one day rest in between. Shooting efficacy-related statistics presented a considerable discriminatory power of the different fixture congestion cycles. In conclusion, fixture congestion cycles showed a significant impact on the game outcome and team performance. The findings may add value in the re-design of game schedules in the NBA as well as inform coaches to critically manage training load in order to enhance performance and reduce the risk of injury.

Background: Periodization implies the systematic planning of training and competition with the goal of reaching the best possible performance in the most important competition. In team sports, this consists of finding a flight-and-practice schedule that maximizes the opportunities to perform the periodized contents (e.g., trips, practices, games, and days off). This process is conducted whilst considering known constraints (e.g., competitive schedule, roster availability, weather, especial events, holidays, or emotional effect of days away). The way a scheduling decision support system (DSS) leads users to make a decision should allow for flexibility, whilst minimizing users' confusion and facilitating the understanding of the recommendation given by the scheduling decision support system. Traditional approaches to solving scheduling problems use either simulation models, analytical models, heuristic approaches or a combination of these methods. When it comes to evaluate how the scheduling DSS is performing, three overarching aspects need to be reviewed: context satisfaction, process efficiency, and output quality. Appropriate training periodization and scheduling of trips and training sessions are critical for teams to optimize training and recovery processes in order to maximize health and performance. This article presents a methodological framework for designing decision-support systems for scheduling in professional team sports.