The main purpose of this study was to describe the most demanding scenarios of match play in basketball through a number of physical demand measures (high-intensity accelerations and decelerations, relative distance covered, and relative distance covered in established speed zones) for four different rolling average time epochs (30, 60, 180, and 300 s) during an official international tournament. A secondary purpose was to identify whether there were significant differences in physical demand measures among playing positions (centers, guards, and forwards) and levels (two best classified teams in the tournament and remaining teams), match scoring (winning, losing, and drawing), and playing periods (match quarter) at the moment of the most demanding scenarios. Data were collected from 94 male under 18 (U18) elite basketball players (age: 17.4 ± 0.7 years; stature: 199.0 ± 11.9 cm; body mass: 87.1 ± 13.1 kg) competing in a Euroleague Basketball Tournament. Measures were compared via a Bayesian inference analysis. The results revealed the presence of position-related differences [Bayesian factor (BF) > 10 (at least strong evidence) and standardized effect size (δ) > 0.6 (at least moderate)] so that centers covered a lower relative distance at speed zone 1 and had lower high-intensity accelerations and decelerations than guards. However, the Bayesian analysis did not demonstrate the existence of significant differences in any physical demand measure in relation to the playing level, match scoring, and playing periods at the moment of the most demanding scenarios. Therefore, this study provides coaches and strength and conditioning specialists with a most demanding scenario reference on physical demands that can be used as an upper limit threshold in the training and rehabilitation monitoring processes.

The purpose of this study was to compare the most demanding scenarios (MDS) encountered by professional basketball players across game quarters and playing positions during official match-play. Ten professional bas- ketball players were monitored during 11 matches using a local positioning system. Peak physical demands were measured via total distance, distance >18 and >21 km·h -1 , number of sprints >18 and >21 km·h -1 , and number of accelerations and decelerations >2 and >3 m∙s -2 captured over 30, 60, 120, 180, and 300-s rolling averages. Linear mixed models and effect sizes (ES) were used to compare MDS encountered between game quarters and playing positions. Between Quarters 1 and 2, there was a reduction in the total distance (ES = 0.64-1.39) for all playing groups along with a reduction in distance >21 km·h -1 in centres (ES = 0.77-0.81) and a reduction in accelerations and decelerations >2 m∙s -2 in guards (ES = 0.66-0.78) across longer sample periods (180-300-s). Between Quarters 1 and 4, reductions in the total distance were evident for forwards and centres (ES = 0.64-0.91) as well as distance and sprints >21 km·h -1 in centres (ES = 0.64-0.97). Regarding positional differences, guards and forwards covered a higher total distance than centres across most quarters and sample periods (ES = 0.22-1.44). Our data suggest a decrease in MDS with game progression in basketball. In addition, MDS appear to be duration-specific and posi- tion-dependent in basketball. Therefore, practitioners should consider these differences in MDS based on game quarters and positional demands to optimise individual and team performance.

Understanding the most demanding scenarios of basketball match-play can optimise training prescription. We established physical demand differences in total distance covered, distance covered at high-speed running, distance covered at highintensity accelerations and decelerations, number of high-speed running actions and number of high-intensity accelerations comparing the traditional average method with the most demanding scenarios based on 1-minute rolling averages. Physical demand parameters were analysed from 21 elite basketball players according to playing position during a friendly game via local positioning system microtechnology. The results showed that players covered a total distance of 141.3 m•min −1 (p < 0.001; ES = 7.80) and 25.4 m•min −1 (p < 0.001; ES = 4.52) at high-speed running using rolling averages, compared to 66.3 and 3.2 m min −1 , respectively, using the traditional average approach. These data represent a very large increase of 113.1% for total distance per minute and 686.4% for high-speed running distance per minute, 252% for the number of high-intensity accelerations and 290.5% for the number of high-intensity decelerations, respectively, demonstrating the relevance of this novel approach. In conclusion, this investigation indicated that the traditional average method underestimates peak physical demands over a 1-minute period during a basketball game. Thus, the average approach should be complemented by analysing the most demanding scenarios in order to have a better understanding of physical demands during basketball competition.

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The purpose of this study was to compare peak external intensities across game quarters in basketball. Eight semi-professional male players were monitored using accelerometers. For all quarters, peak intensities were determined via moving averages for PlayerLoad/minute (PL·min-1) using sample durations of 15 s, 30 s, 1 min, 2 min, 3 min, 4 min, and 5 min. Linear mixed models and effect sizes (ES) were used to compare peak intensities between quarters for each sample duration. Small decreases in peak PL·min-1 occurred between Quarters 1 and 4 for all sample durations (ES = 0.21-0.49). Small decreases in peak PL·min-1 were apparent between quarters 1 and 2 for 30-s, 1-min, and 3-min sample durations (ES = 0.24-0.33), and between quarters 3 and 4 for 2-5-min sample durations (ES = 0.20-0.24). Peak intensities decline across quarters with game progression in basketball, providing useful insight for practitioners to develop game-specific training and tactical strategies.