Search Results

Purpose: With female Australian football (AF) gaining popularity, understanding match demands is becoming increasingly important. The aim of this study was to compare running performances of rotated and whole-quarter state-level female AF players during match quarters. Methods : Twenty-two state-level female AF midfielders wore Global Positioning System units during 14 games to evaluate activity profiles. The Yo-Yo Intermittent Recovery Test Level 1 (Yo-Yo IR1) was used as a measure of high-intensity running ability. Data were categorized into whole quarter, rotation bout 1, and rotation bout 2 before being further divided into quartiles. Players were separated into high- or low-Yo-Yo IR1 groups using a median split based on their Yo-Yo IR1 performance. Short (4–6 min), moderate (6–12 min), and long (12–18 min) on-field bout activity profiles were compared with whole-quarter players. Results: High Yo-Yo IR1 performance allowed players to cover greater relative distances (ES = 0.57–0.88) and high-speed distances (ES = 0.57–0.86) during rotations. No differences were reported between Yo-Yo IR1 groups when players were required to play whole quarters (ES ≤ 0.26, likelihood ≤64%). Players who were on field for short to moderate durations exhibited greater activity profiles than whole-quarter players. Conclusions: Superior high-speed running ability results in a greater activity profile than for players who possess lower high-speed running ability. The findings also highlight the importance of short to moderate (4–12 min) rotation periods and may be used to increase high-intensity running performance within quarters in female AF players.

Purpose: The transition of female Australian football (AF) players from amateur to semielite competitions has the potential for athletes to be underprepared for match play. To gain an understanding of the match demands of female football, the aims of this study were to highlight the physical qualities that discriminate selected and nonselected female AF players, investigate activity profiles of female AF players, and gain an understanding of the influence of physical qualities on performance in female AF Methods: Twenty-two female AF state academy players (mean [SD]: age = 23.2 [4.5] y) and 27 nonselected players (mean [SD]: age = 23.4 [4.9] y) completed a Yo-Yo intermittent recovery test level 1, countermovement jump, and 30-m sprint tests were completed prior to the competitive season. During 14 matches, players wore global positioning system units to describe the running demands of match play. Results: Selected players were faster over 30 m (ES = 0.57; P = .04) and covered greater distances on the Yo-Yo IR1 (ES = 1.09; P < .001). Selected midfielders spent greater time on the field and covered greater total distances (ES = 0.73–0.85; P < .01). Players faster over 5 m (r = −.612) and 30 m (r = −.807) and who performed better on the Yo-Yo IR1 (r = .489) covered greater high-speed distances during match play. Conclusions: An emphasis should be placed on the development of physical fitness in this playing group to ensure optimal preparation for the national competition.

Purpose: This study described and analyzed practices and perceptions of rhythmic gymnastics coaches, medical staff, and athletes on training-load management. Methods: Online surveys were distributed among professionals and gymnasts involved in rhythmic gymnastics training across the world. One hundred (50 coaches, 12 medical staff, and 38 gymnasts) participants from 25 different countries completed the surveys. Results: Respondents stated using coaches’ perception on a daily basis as a method of monitoring external (57%) and internal (58%) load, recovery/fatigue (52%), and performance (64%). Variables and methods (eg, wearable devices, athlete self-reported measures, session rating of perceived exertion), and metrics (eg, acute and chronic load) commonly reported in the training-load literature and other sports were not frequently used in rhythmic gymnastics. The majority of coaches (60.3% [17%]) perceived that maladaptation rarely or never occurred. Medical staff involvement in sharing and discussing training-load information was limited, and they perceived that the measurement of athletes’ recovery/fatigue was poor. Gymnasts noted good quality in relation to the measurement of performance. Most participants (≥85%) believed that a specific training-load management model for rhythmic gymnastics could be very or extremely effective. Conclusions: In conclusion, rhythmic gymnastics coaches’ perception is the most commonly used strategy to monitor load, recovery/fatigue, and performance; although, this could be a limited method to guarantee effective training-load management in this sport.

Purpose: To determine (1) how change-of-direction (COD) workloads influence PlayerLoad (PL) variables when controlling total distance covered and (2) relationships among collision workloads and PL variables during rugby league match play. Methods: Participants completed 3 protocols (crossover design) consisting of 10 repetitions of a 60-m effort in 15 s. The difference between protocols was the COD demands required to complete 1 repetition: no COD (straight line), 1° × 180° COD, or 3° × 180° COD. During rugby league matches, relationships among collision workloads, triaxial vector-magnitude PlayerLoad (PL_VM), anteroposterior + mediolateral PL (PL_2D), and PL_VM accumulated at locomotor velocities below 2 m·s⁻¹ (ie, PL_SLOW) were examined using Pearson correlations (r) with coefficients of determination (R ²). Results: Comparing 3° × 180° COD to straight-line drills, PL_VM·min⁻¹ (d = 1.50 ± 0.49, large, likelihood = 100%, almost certainly), PL_2D·min⁻¹ (d = 1.38 ± 0.53, large, likelihood = 100%, almost certainly), and PL_SLOW·min⁻¹ (d = 1.69 ± 0.40, large, likelihood = 100%, almost certainly) were greater. Collisions per minute demonstrated a distinct (ie, R ² < .50) relationship from PL_VM·min⁻¹ (R ² = .30, r = .55) and PL_2D·min⁻¹ (R ² = .37, r = .61). Total distance per minute demonstrated a very large relationship with PL_VM·min⁻¹ (R ² = .62, r = .79) and PL_2D·min⁻¹ (R ² = .57, r = .76). Conclusions: PL variables demonstrate (1) large increases as COD demands intensify, (2) separate relationships from collision workloads, and (3) moderate to very large relationships with total distance during match play. PL variables should be used with caution to measure collision workloads in team sport.