AIMC Topic: Athletic Performance
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The use of technical-tactical and physical performance indicators to classify between levels of match-play in elite rugby league.
Science & medicine in football
Sep 8, 2020
This study aimed to identify which physical and technical-tactical performance indicators (PI) can classify between levels of rugby league match-play. Data were collected from 46 European Super League (ESL) and 36 under-19 Academy (Academy) level mat...
Importance of anthropometric features to predict physical performance in elite youth soccer: a machine learning approach.
Research in sports medicine (Print)
Aug 23, 2020
The present study aimed to determine the contribution of soccer players' anthropometric features to predict their physical performance. Sixteen players, from a professional youth soccer academy, were recruited. Several anthropometric features such as...
Complementing subjective with objective data in analysing expertise: A machine-learning approach applied to badminton.
Journal of sports sciences
Jun 17, 2020
This study aimed to assess which combination of subjective and empirical data might help to identify the expertise level. A group of 10 expert coaches classified 40 participants in 5 different expertise groups based on the video footage of the rallie...
Using Artificial Intelligence for Pattern Recognition in a Sports Context.
Sensors (Basel, Switzerland)
May 27, 2020
Optimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding...
Auto detecting deliveries in elite cricket fast bowlers using microsensors and machine learning.
Journal of sports sciences
Feb 26, 2020
Cricket fast bowlers are at a high risk of injury occurrence, which has previously been shown to be correlated to bowling workloads. This study aimed to develop and test an algorithm that can automatically, reliably and accurately detect bowling deli...
The impact of different training load quantification and modelling methodologies on performance predictions in elite swimmers.
European journal of sport science
Feb 4, 2020
The use of rolling averages to analyse training data has been debated recently. We evaluated two training load quantification methods (five-zone, seven-zone) fitted to performances over two race distances (50 and 100 m) using four separate longitudin...
Identifying playing talent in professional football using artificial neural networks.
Journal of sports sciences
Jan 15, 2020
The aim of the current study was to objectively identify position-specific key performance indicators in professional football that predict out-field players league status. The sample consisted of 966 out-field players who completed the full 90 minut...
Use of Machine Learning to Model Volume Load Effects on Changes in Jump Performance.
International journal of sports physiology and performance
Oct 14, 2019
PURPOSE: To use an artificial neural network (ANN) to model the effect of 15 weeks of resistance training on changes in countermovement jump (CMJ) performance in male track-and-field athletes.
Predicting centre of mass horizontal speed in low to severe swimming intensities with linear and non-linear models.
Journal of sports sciences
Feb 6, 2019
We aimed to compare multilayer perceptron (MLP) neural networks, radial basis function neural networks (RBF) and linear models (LM) accuracy to predict the centre of mass (CM) horizontal speed at low-moderate, heavy and severe swimming intensities us...
Sprint Assessment Using Machine Learning and a Wearable Accelerometer.
Journal of applied biomechanics
Feb 5, 2019
Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v and τ, which indicate a sprinter's maximal theoretical velocity and the time it takes to approach v, respecti...
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