McGahan, J, Burns, C, Lacey, S, Gabbett, T, and Neill, CO. Investigation in to the positional running demands of elite Gaelic football players: How competition data can inform training practice. J Strength Cond Res 34(7): 2040-2047, 2020-This study investigated the positional running demands of elite Gaelic football players during match play and compared these demands with typical training activities used to prepare players for competition. Global positioning system (GPS) data were obtained from 30 elite Gaelic football players (26.9 ± 3.5 years, 182.8 ± 6.1 cm, 84.6 ± 8.1 kg) across a full season (13 competitive games and 78 training sessions). Only players who completed the full match and respective training sessions were included (n = 107 match files and n = 1,603 training files). Data were collected using 4-Hz GPS units (VX Sport, Lower Hutt, New Zealand). Mean high speed (≥17 km·h; m·min), mean speed (m·min), percentage at high speed (%), and mean sprint efforts (≥17 km·h; no.·min) were recorded. Running variables were analyzed across the 5 outfield positional lines in Gaelic football (full back [FB], half back [HB], midfield [MF], half forward, and full forward [FF]). For mean high-speed running and mean speed, significant relationships (range r = 0.811-0.964 and r = 0.792-0.998, respectively) were found between competition and game-based training for players in the FB, HB, MF, and FF lines (p ≤ 0.05). Analyses of mean sprint efforts and percentage at high speed found positive correlations between competition and training activities across each of the positional lines. Appropriately designed training activities can ensure that the position-specific demands of elite Gaelic football competition are met using a game-based training approach. Collectively, these findings demonstrate the value of and provide support for the use of a game-based training approach as a method of preparing players for the physical demands of competition in elite Gaelic football.
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http://dx.doi.org/10.1519/JSC.0000000000002492 | DOI Listing |
Int J Sports Physiol Perform
December 2024
Department of Orthopedic Surgery, Sports Orthopedic Research Center Copenhagen (SORC-C), Amager-Hvidovre Hospital, Institute of Clinical Medicine, Copenhagen University, Hvidovre, Denmark.
Purpose: The purpose of this research was to report isometric hip adduction and abduction strength reference values of men's and women's Gaelic football and rugby union players and compare values between sexes and between sports.
Methods: This cross-sectional cohort study consisted of 331 club-level athletes. Maximum isometric hip adduction squeeze and abduction press strength values were measured with a ForceFrame across several testing positions.
Ir J Med Sci
December 2024
Amsterdam UMC location University of Amsterdam, Department of Orthopedic Surgery and Sports Medicine, Meibergdreef 9, Amsterdam, The Netherlands.
Background: Knee injuries are common among elite intercounty Gaelic games players (collectively GAA players).
Aims: The primary aim was to examine knee pain, function, and quality of life in retired elite male GAA players. Secondary objectives were to (i) report the incidence of previous knee surgery and total knee replacement, (ii) assess medication usage, and (iii) investigate any associations between a history of knee injury and/or knee surgery and knee pain, function, and quality of life among retired elite male GAA players.
Data Brief
December 2024
School of Computing, Dublin City University, Dublin, Ireland.
Research in field sports often measures the performance of players during competitive games with individual and time-based descriptive statistics. Data is generated using GPS technologies, capturing simple data such as time (seconds) and position (latitude and longitude). While the data capture is highly granular and in relatively high volumes, the raw data are unsuited to any form of analysis or machine learning functions.
View Article and Find Full Text PDFEur J Appl Physiol
December 2024
Department of Biomedical Sciences, University of Padua, Via Marzolo, 3, 35131, Padua, Italy.
Purpose: Body composition can be estimated using anthropometric-based regression models, which are population-specific and should not be used interchangeably. However, the widespread availability of predictive equations in the literature makes selecting the most valid equations challenging. This systematic review compiles anthropometric-based predictive equations for estimating body mass components, focusing on those developed specifically for athletes using multicomponent models (i.
View Article and Find Full Text PDFJ Sport Rehabil
December 2024
Health Research Institute, University of Limerick, Limerick, Ireland.
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