OBJECTIVES: To examine the usefulness of selected physiological and perceptual measures to monito... more OBJECTIVES: To examine the usefulness of selected physiological and perceptual measures to monitor fitness, fatigue and running performance during a pre-season, 2-week training camp in eighteen professional Australian Rules Football players (21.9±2.0 years). DESIGN: Observational. METHODS: Training load, perceived ratings of wellness (e.g. fatigue, sleep quality) and salivary cortisol were collected daily. Submaximal exercise heart rate (HRex) and a vagal-related heart rate variability index (LnSD1) were also collected at the start of each training session. Yo-Yo Intermittent Recovery level 2 test (Yo-YoIR2, assessed pre-, mid- and post-camp, temperate conditions) and high-speed running distance during standardized drills (HSR, >14.4kmh(-1), 4 times throughout, outdoor) were used as performance measures. RESULTS: There were significant (P<0.001 for all) day-to-day variations in training load (coefficient of variation, CV: 66%), wellness measures (6-18%), HRex (3.3%), LnSD1 (19...
International Journal of Sports Physiology and Performance, 2014
To determine the physical activity measures and skill-performance characteristics that contribute... more To determine the physical activity measures and skill-performance characteristics that contribute to coaches&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; perception of performance and player performance rank in professional Australian Football (AF). Prospective, longitudinal. Physical activity profiles were assessed via microtechnology (GPS and accelerometer) from 40 professional AF players from the same team during 15 Australian Football League games. Skill-performance measure and player-rank scores (Champion Data Rank) were provided by a commercial statistical provider. The physical-performance variables, skill involvements, and individual player performance scores were expressed relative to playing time for each quarter. A stepwise multiple regression was used to examine the contribution of physical activity and skill involvements to coaches&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; perception of performance and player rank in AF. Stepwise multiple-regression analysis revealed that 42.2% of the variance in coaches&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; perception of a player&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s performance could be explained by the skill-performance characteristics (player rank/min, effective kicks/min, pressure points/min, handballs/min, and running bounces/ min), with a small contribution from physical activity measures (accelerations/min) (adjusted R2 = .422, F6,282 = 36.054, P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .001). Multiple regression also revealed that 66.4% of the adjusted variance in player rank could be explained by total disposals/min, effective kicks/min, pressure points/min, kick clangers/min, marks/min, speed (m/min), and peak speed (adjusted R2 = .664, F7,281 = 82.289, P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .001). Increased physical activity throughout a match (speed [m/min] β - 0.097 and peak speed β - 0.116) negatively affects player rank in AF. Skill performance rather than increased physical activity is more important to coaches&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; perception of performance and player rank in professional AF.
OBJECTIVES: To examine the usefulness of selected physiological and perceptual measures to monito... more OBJECTIVES: To examine the usefulness of selected physiological and perceptual measures to monitor fitness, fatigue and running performance during a pre-season, 2-week training camp in eighteen professional Australian Rules Football players (21.9±2.0 years). DESIGN: Observational. METHODS: Training load, perceived ratings of wellness (e.g. fatigue, sleep quality) and salivary cortisol were collected daily. Submaximal exercise heart rate (HRex) and a vagal-related heart rate variability index (LnSD1) were also collected at the start of each training session. Yo-Yo Intermittent Recovery level 2 test (Yo-YoIR2, assessed pre-, mid- and post-camp, temperate conditions) and high-speed running distance during standardized drills (HSR, >14.4kmh(-1), 4 times throughout, outdoor) were used as performance measures. RESULTS: There were significant (P<0.001 for all) day-to-day variations in training load (coefficient of variation, CV: 66%), wellness measures (6-18%), HRex (3.3%), LnSD1 (19...
International Journal of Sports Physiology and Performance, 2014
To determine the physical activity measures and skill-performance characteristics that contribute... more To determine the physical activity measures and skill-performance characteristics that contribute to coaches&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; perception of performance and player performance rank in professional Australian Football (AF). Prospective, longitudinal. Physical activity profiles were assessed via microtechnology (GPS and accelerometer) from 40 professional AF players from the same team during 15 Australian Football League games. Skill-performance measure and player-rank scores (Champion Data Rank) were provided by a commercial statistical provider. The physical-performance variables, skill involvements, and individual player performance scores were expressed relative to playing time for each quarter. A stepwise multiple regression was used to examine the contribution of physical activity and skill involvements to coaches&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; perception of performance and player rank in AF. Stepwise multiple-regression analysis revealed that 42.2% of the variance in coaches&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; perception of a player&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s performance could be explained by the skill-performance characteristics (player rank/min, effective kicks/min, pressure points/min, handballs/min, and running bounces/ min), with a small contribution from physical activity measures (accelerations/min) (adjusted R2 = .422, F6,282 = 36.054, P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .001). Multiple regression also revealed that 66.4% of the adjusted variance in player rank could be explained by total disposals/min, effective kicks/min, pressure points/min, kick clangers/min, marks/min, speed (m/min), and peak speed (adjusted R2 = .664, F7,281 = 82.289, P &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .001). Increased physical activity throughout a match (speed [m/min] β - 0.097 and peak speed β - 0.116) negatively affects player rank in AF. Skill performance rather than increased physical activity is more important to coaches&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; perception of performance and player rank in professional AF.
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