Female basketball game performance is influenced by menstrual cycle phase, age, perceived demands and game-related contextual factors.

This study evaluated the influence of physical and perceived game demands, menstrual cycle phase, perceived recovery, individual and game-related contextual factors on competitive performance in professional, female basketball players.11 professional female players (age: 20.6 ± 2.7 years) were monitored for game-related statistics (Performance Index Rating, PIR; rebounds, REB; effective field goal %, eFG%; turnovers, TO), objective (PlayerLoad per minute, PL·min-1) and subjective (RPE) game loads, pre-game perceived recovery (Total Quality Recovery, TQRpre), menstrual phase (follicular; luteal) and game-related contextual factors (game location; game outcome; score differential; opponent level) during 12 official games. Separate linear mixed models were used to evaluate the influence of RPE, PL·min-1, TQRpre, menstrual phase, contextual factors, and individual characteristics (age; playing position) on game-related statistics.Higher PIR and eFG% were found for older players and those who reported higher RPE (all p < 0.05). Higher age also led to less TO (p = 0.042). eFG% was higher when players reported higher TQRpre ;(p = 0.010). Better shooting (eFG%) and rebounding (REB) performances were found during the follicular menstrual phase (p < 0.05). More REB were collected in won games (p = 0.002).This study suggests that the co-influences of perceptual, menstrual-related, individual and game-related contextual factors should be considered to optimise female basketball players' performance.

An ecological investigation of average and peak external load intensities of basketball skills and game-based training drills.

This study quantified average and peak external intensities of various basketball training drills. Thirteen youth male basketball players (age: 15.2 ± 0.3 years) were monitored (BioHarness-3 devices) to obtain average and peak external load per minute (EL · min-1; peak EL · min-1) during team-based training sessions. Researchers coded the training sessions by analysing the drill type (skills, 1vs1, 2vs2, 3vs0, 3vs3, 4vs0, 4vs4, 5vs5, 5vs5-scrimmage), court area per player, player's involvement in the drill (in percentage), playing positions (backcourt; frontcourt) and competition rotation status (starter; rotation; bench). Separate linear mixed models were run to assess the influence of training and individual constraints on average and peak EL · min-1. Drill type influenced average and peak EL · min-1 (p < 0.05), but with different directions of effects. EL · min-1 was higher in skills and 4vs0 drills, while higher peak EL · min-1 values were obtained in 5vs5 and 5vs5-scrimmage. Similarly, EL · min-1 was higher when involvement % increased (p = 0.001), while there was an opposite trend for peak EL · min-1 (lower with higher involvement %). Court area per player influenced peak (p = 0.025) but not average demands. No effects were found for playing position or competition rotation status (all p > 0.05), except for a moderately higher EL · min-1 in starters compared to bench players. The external load intensities of basketball training drills substantially vary depending on the load indicator chosen, the training content, and task and individual constraints. Practitioners should not interchangeably use average and peak external intensity indicators to design training but considering them as separate constructs could help to gain a better understanding of basketball training and competition demands.

Training load, recovery and game performance in semiprofessional male basketball: influence of individual characteristics and contextual factors.

This study examined the effects of individual characteristics and contextual factors on training load, pre-game recovery and game performance in adult male semi-professional basketball. Fourteen players were monitored, across a whole competitive season, with the session-RPE method to calculate weekly training load, and the Total Quality Recovery Scale to obtain pre-game recovery scores. Additionally, game-related statistics were gathered during official games to calculate the Performance Index Rating (PIR). Individual characteristics and contextual factors were grouped using k-means cluster analyses. Separate mixed linear models for repeated measures were performed to evaluate the single and combined (interaction) effects of individual characteristics (playing experience; playing position; playing time) and contextual factors (season phase; recovery cycle; previous game outcome; previous and upcoming opponent level) on weekly training load, pre-game recovery and PIR. Weekly load was higher in guards and medium minute-per-game (MPG) players, and lower for medium-experienced players, before facing high-level opponents, during later season phases and short recovery cycles (all p < 0.05). Pre-game recovery was lower in centers and high-experience players (p < 0.05). Game performance was better in high-MPG players (p < 0.05) and when facing low and medium-level opponents (p < 0.001). Interestingly, players performed better in games when the previous week's training load was low (p = 0.042). This study suggests that several individual characteristics and contextual factors need to be considered when monitoring training load (playing experience, playing position, playing time, recovery cycle, upcoming opponent level), recovery (playing experience, playing position) and game performance (opponent level, weekly training load, pre-game recovery) in basketball players during the competitive season.

Game Related Statistics Discriminate National and Foreign Players According to Playing Position and Team Ability in the Women's Basketball EuroLeague.

The aim of the present study was to examine the differences in game-related statistics between national and foreign female basketball players in the Women's EuroLeague, according to playing positions and team ability. The official box-scores of 112 games from the 2016-2017 season of the Women's EuroLeague (FIBA) were examined. Players were categorised based upon country of nationality versus competition (i.e., foreign or national), playing positions (i.e., Guards, Forwards, Centers), and team ability (i.e., four groups using a cluster of k-means analysis according to the winning percentage of each team during the competition). A structural coefficient (SC) above |0.30| was used to identify the variables that best differentiated the national and foreign players. Results showed that foreign players had a better performance according to team ability and playing position for most of the performance indicators, with higher values for minutes played, percentage of successful 2-point field-goals, percentage of successful free-throws, and percentage of assists. Moreover, foreign players performed better in variables associated with offensive situations, while national players were prevailing with indicators associated with defensive actions. These results have highlighted the unique contributions of foreign and national players, based upon playing position and team ability, to team success in the Euroleague. This information will assist the recruitment process of national and foreign athletes for coaches to develop successful elite female basketball teams.