Time series data analysis for sport skill

Maeda, Toshiyuki; Fujii, Masanori; Hayashi, Isao

doi:10.1109/isda.2012.6416570

Cited by 9 publications

(4 citation statements)

References 1 publication

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“…In team sport, time series analysis has been utilised in Australian football to identify and describe the segments of physical (and skilled) output during matches [73]. Similarly, time series analysis has been utilised to profile the skilled output in team sport matches [98] and predict team success in the English Premier League [99]. The visualisation of metrics from athlete tracking systems, including raw trace data that can be analysed via time series segmentation, requires the visual encoding of thousands of data points.…”

Section: Time Series Segmentationmentioning

confidence: 99%

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis

et al. 2022

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Seeking to obtain a competitive advantage and manage the risk of injury, team sport organisations are investing in tracking systems that can quantify training and competition characteristics. It is expected that such information can support objective decision-making for the prescription and manipulation of training load. This narrative review aims to summarise, and critically evaluate, different tracking systems and their use within team sports. The selection of systems should be dependent upon the context of the sport and needs careful consideration by practitioners. The selection of metrics requires a critical process to be able to describe, plan, monitor and evaluate training and competition characteristics of each sport. An emerging consideration for tracking systems data is the selection of suitable time analysis, such as temporal durations, peak demands or time series segmentation, whose best use depends on the temporal characteristics of the sport. Finally, examples of characteristics and the application of tracking data across seven popular team sports are presented. Practitioners working in specific team sports are advised to follow a critical thinking process, with a healthy dose of scepticism and awareness of appropriate theoretical frameworks, where possible, when creating new or selecting an existing metric to profile team sport athletes.

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Section: Time Series Segmentationmentioning

confidence: 99%

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis

et al. 2022

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“…Moreover, machine learning techniques have also been used for e.g. classifying skill of individual players in table tennis using C4.5, naïve Bayes and random forests (Maeda et al, 2012) and for classifying subjects in running/walking using artificial neural networks and support vector machines (Fischer et al, 2011).…”

Section: Classification and Predictionmentioning

confidence: 99%

Situation Modeling and Visual Analytics for Decision Support in Sports

Dahlbom

Riveiro

2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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High performance is the goal in most sporting activities, for elite athletes as well as for recreational practitioners, and the process of measuring, evaluating and improving performance is one fundamental aspect to why people engage in sports. This is a complex process which possibly involves analyzing large amounts of heterogeneous data in order to apply actions that change important properties for improved outcome. The number of computer based decision support systems in the context of data analysis for performance improvement is scarce. In this position paper we briefly review the literature, and we propose the use of information fusion, situation modeling and visual analytics as suitable tools for supporting decision makers, ranging from recreational to elite, in the process of performance evaluation.

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“…We had evaluated those into three play levels as expert/intermediate/novice, and classify the models using data mining technologies [3], [4]. We furthermore had an attempt to apply our research framework to other sports skills, such a personal sports skill identification using time series motion picture data, focused on volleyball [5].…”

Section: Introductionmentioning

confidence: 99%

Sports Skill Analysis using Motion Frequency

Yajima¹,

Matsuda²,

Maeda³

2016

Fifth International Conference on Advances in Computing, Control and Networking - ACCN 2016

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Abstract-This paper addresses sports skill discrimination using motion picture data, focused on volleyball attack skill. We attempt to certify the hypothesis that expert skills have relatively low frequency motions rather than novice skills as the similarity of human postural control. For this purpose we proceed experiments and analyze sports skills as for frequency of motion using time series motion pictures of volleyball attacks. In this paper, volleyball play is analyzed with motion picture data recorded by hi-speed cam-coder, where we do not use physical information such as body skeleton model, and so on. Time series data are obtained from the motion picture data with four marking points, and analyzed using Fast Fourier Transform (FFT) and clustering data mining method. As the experiment results, we have found that y-axes of novice data may have more highfrequency data, and that implies novice motions may have high frequency motions, and that may support our hypothesis.

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Time series data analysis for sport skill

Cited by 9 publications

References 1 publication

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis

Situation Modeling and Visual Analytics for Decision Support in Sports

Sports Skill Analysis using Motion Frequency

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