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Applied Sciences
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Computer Science in Sport
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Computer Science in Sport

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 39147

Special Issue Editor

Dr. Christian W. Dawson

E-Mail Website
Guest Editor
Computer Science Department, Loughborough University, Loughborough LE11 3TU, UK
Interests: environmental modelling; artificial neural networks; rainfall-runoff modelling; sports performance analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer science in sport is a cross-disciplinary topic that brings together the problem-solving capabilities of computer science with various theoretical and practical aspects of all sports and physical activities. Applications cover a diverse range, including the analysis of individuals and teams in competition and training; equipment design and assessment (which can include playing surfaces and clothing); biomechanics; physiological analysis; injury prediction and prevention; and tactical analysis and modelling. Areas of computer science that have been utilized include image processing, data mining, artificial intelligence, virtual reality, wearable devices, ubiquitous computing and sensor technologies—to name a few.

This Special Issue aims to bring together the latest research and ideas in this cross-disciplinary area. Its focus is on the capturing of individual and team performance during training and competition and using these data to enhance performance in the future.

Dr. Christian W. Dawson
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computer Science
  • Team Sports
  • Individual Performance Analysis
  • Biomechanics
  • Physiology

Published Papers (7 papers)

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Research

9 pages, 2422 KiB  
Article
Using Computer Simulation to Investigate Which Joint Angle Changes Have the Most Effect on Ball Release Speed in Overarm Throwing
by Nurhidayah Omar, Maurice R. Yeadon and Mark A. King
Appl. Sci. 2019, 9(5), 999; https://doi.org/10.3390/app9050999 - 11 Mar 2019
Cited by 1 | Viewed by 3759
Abstract
Efficient throwing mechanics is predicated on a pitcher’s ability to perform a sequence of movements of body segments, which progresses from the legs, pelvis, and trunk to the smaller, distal arm segments. Each segment plays a vital role in achieving maximum ball velocity [...] Read more.
Efficient throwing mechanics is predicated on a pitcher’s ability to perform a sequence of movements of body segments, which progresses from the legs, pelvis, and trunk to the smaller, distal arm segments. Each segment plays a vital role in achieving maximum ball velocity at ball release. The perturbation of one joint angle has an effect on the ball release speed. An eight-segment angle-driven simulation model of the trunk, upper limbs and ball was developed to determine which joint angle changes have the most influence on ball release speed in overarm throwing for an experienced pitcher. Fifteen overarm throwing trials were recorded, and the joint angle time histories of each trial were input into the simulation model. Systematically replacing each joint angle time history with a constant value showed that overarm throwing was sensitive (≥5 m/s effect on ball release speed) to trunk extension/flexion and upper arm external/internal rotation, and very sensitive (≥10 m/s effect) to forearm extension/flexion. Computer simulation allows detailed analysis and complete control to investigate contributions to performance, and the key joint angle changes for overarm throwing were identified in this analysis. Full article
(This article belongs to the Special Issue Computer Science in Sport)
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11 pages, 3236 KiB  
Article
Accuracy and Inter-Unit Reliability of Ultra-Wide-Band Tracking System in Indoor Exercise
by Alejandro Bastida-Castillo, Carlos David Gómez-Carmona, Ernesto De la Cruz-Sánchez, Xavier Reche-Royo, Sergio José Ibáñez and José Pino Ortega
Appl. Sci. 2019, 9(5), 939; https://doi.org/10.3390/app9050939 - 06 Mar 2019
Cited by 105 | Viewed by 5439
Abstract
The purpose of this study was to assess the accuracy of positional data and the inter-unit reliability of an ultra-wide-band (UWB) tracking system. Four well-trained males performed five courses designed for the analysis of x- and y-coordinate accuracy analysis, specifically related to the [...] Read more.
The purpose of this study was to assess the accuracy of positional data and the inter-unit reliability of an ultra-wide-band (UWB) tracking system. Four well-trained males performed five courses designed for the analysis of x- and y-coordinate accuracy analysis, specifically related to the positional distance variation between the UWB data and the fixed reference lines of a basketball court. This was achieved using geographic information system (GIS) mapping software that calculated, for each interval and participant, the distance from the main axis of displacement and from the opposite side of the court each 0.5 s (x and y coordinate). The accuracy of the results was satisfactory, with a mean absolute error of all estimations for the x-position of 5.2 ± 3.1 cm and for the y-position of 5.8 ± 2.3 cm. Regarding inter-unit reliability, the intra-class correlation coefficient (ICC) value was high for the x-coordinate (0.65) and very high for the y-coordinate (0.85). The main findings of the study were: (i) The accuracy of UWB tracking systems can be considered suitable for practical applications in sport analyses; (ii) position estimations are very precise and acceptable for tactical analyses; (iii) the error of the position estimations does not change significantly across different courses; and (iv) the use of different devices does not significantly affect the measurement error. Full article
(This article belongs to the Special Issue Computer Science in Sport)
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18 pages, 5833 KiB  
Article
Statistical Analysis of Table-Tennis Ball Trajectories
by Ralf Schneider, Lars Lewerentz, Karl Lüskow, Marc Marschall and Stefan Kemnitz
Appl. Sci. 2018, 8(12), 2595; https://doi.org/10.3390/app8122595 - 12 Dec 2018
Cited by 5 | Viewed by 5179
Abstract
In this work, the equations of motion for table-tennis balls were numerically solved on graphics processing units (GPUs) using Compute Unified Device Architecture (CUDA) for systematical statistical studies of the impact of ball size and weight, as well as of net height, on [...] Read more.
In this work, the equations of motion for table-tennis balls were numerically solved on graphics processing units (GPUs) using Compute Unified Device Architecture (CUDA) for systematical statistical studies of the impact of ball size and weight, as well as of net height, on the distribution functions of successful strokes. Half a billion different initial conditions involving hitting location, initial spin, and velocities were analyzed to reach sufficient statistical significance for the different cases. In this paper, an advanced statistical analysis of the database generated by the simulation is presented. Full article
(This article belongs to the Special Issue Computer Science in Sport)
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15 pages, 2269 KiB  
Article
Matching Golfers’ Movement Patterns during a Golf Swing
by Aimée C. Mears, Jonathan R. Roberts and Stephanie E. Forrester
Appl. Sci. 2018, 8(12), 2452; https://doi.org/10.3390/app8122452 - 01 Dec 2018
Cited by 3 | Viewed by 4636
Abstract
The golf swing is a multidimensional movement requiring alternative data analysis methods to interpret non-linear relationships in biomechanics data related to golf shot outcomes. The purpose of this study was to use a combined principal component analysis (PCA), fuzzy coding, and multiple correspondence [...] Read more.
The golf swing is a multidimensional movement requiring alternative data analysis methods to interpret non-linear relationships in biomechanics data related to golf shot outcomes. The purpose of this study was to use a combined principal component analysis (PCA), fuzzy coding, and multiple correspondence analysis (MCA) data analysis approach to visualise associations within key biomechanics movement patterns and impact parameters in a group of low handicap golfers. Biomechanics data was captured and analysed for 22 golfers when hitting shots with their own driver. Relationships between biomechanics variables were firstly achieved by quantifying principal components, followed by fuzzy coding and finally MCA. Clubhead velocity and ball velocity were included as supplementary data in MCA. A total of 35.9% of inertia was explained by the first factor plane of MCA. Dimension one and two, and subsequent visualisation of MCA results, showed a separation of golfers’ biomechanics (i.e., swing techniques). The MCA plot can be used to simply and quickly identify movement patterns of a group of similar handicap golfers if supported with appropriate descriptive interpretation of the data. This technique also has the potential to highlight mismatched golfer biomechanics variables which could be contributing to weaker impact parameters. Full article
(This article belongs to the Special Issue Computer Science in Sport)
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14 pages, 355 KiB  
Article
Predictive Modeling of VO2max Based on 20 m Shuttle Run Test for Young Healthy People
by Krzysztof Przednowek, Zbigniew Barabasz, Maria Zadarko-Domaradzka, Karolina H. Przednowek, Edyta Nizioł-Babiarz, Maciej Huzarski, Klaudia Sibiga, Bartosz Dziadek and Emilian Zadarko
Appl. Sci. 2018, 8(11), 2213; https://doi.org/10.3390/app8112213 - 10 Nov 2018
Cited by 10 | Viewed by 5160
Abstract
This study presents mathematical models for predicting VO2max based on a 20 m shuttle run and anthropometric parameters. The research was conducted with data provided by 308 young healthy people (aged 20.6 ± 1.6). The research group includes 154 females (aged [...] Read more.
This study presents mathematical models for predicting VO2max based on a 20 m shuttle run and anthropometric parameters. The research was conducted with data provided by 308 young healthy people (aged 20.6 ± 1.6). The research group includes 154 females (aged 20.3 ± 1.2) and 154 males (aged 20.8 ± 1.8). Twenty-four variables were used to build the models, including one dependent variable and 23 independent variables. The predictive methods of analysis include: the classical model of ordinary least squares (OLS) regression, regularized methods such as ridge regression and Lasso regression, artificial neural networks such as the multilayer perceptron (MLP) and radial basis function (RBF) network. All models were calculated in R software (version 3.5.0, R Foundation for Statistical Computing, Vienna, Austria). The study also involved variable selection methods (Lasso and stepwise regressions) to identify optimum predictors for the analysed study group. In order to compare and choose the best model, leave-one-out cross-validation (LOOCV) was used. The paper presents three types of models: for females, males and the whole group. An analysis has revealed that the models for females ( RMSE C V = 4.07 mL·kg−1·min−1) are characterised by a smaller degree of error as compared to male models ( RMSE C V = 5.30 mL·kg−1·min−1). The model accounting for sex generated an error level of RMSE C V = 4.78 mL·kg−1·min−1. Full article
(This article belongs to the Special Issue Computer Science in Sport)
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14 pages, 11627 KiB  
Article
A System for Analysing the Basketball Free Throw Trajectory Based on Particle Swarm Optimization
by Krzysztof Przednowek, Tomasz Krzeszowski, Karolina H. Przednowek and Pawel Lenik
Appl. Sci. 2018, 8(11), 2090; https://doi.org/10.3390/app8112090 - 29 Oct 2018
Cited by 11 | Viewed by 7729
Abstract
This paper describes a system for the automatic detection and tracking of a ball trajectory during a free throw. The tracking method is based on a particle swarm optimization (PSO) algorithm. The proposed method allows for the measurement of selected parameters of a [...] Read more.
This paper describes a system for the automatic detection and tracking of a ball trajectory during a free throw. The tracking method is based on a particle swarm optimization (PSO) algorithm. The proposed method allows for the measurement of selected parameters of a basketball free throw trajectory. Ten parameters (four distances, three velocities, and three angle parameters) were taken into account. The research material included 200 sequences captured by a 100 Hz monocular camera. The study was based on a group of 30 basketball players who played in the Polish Second Division during the 2015/2016 season and the Youth Polish National Team in 2017. The experimental results showed the differences between the parameters in both missed and hit throws. The proposed system may be used in the training process as a tool to improve the technique of the free throw in basketball. Full article
(This article belongs to the Special Issue Computer Science in Sport)
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18 pages, 8905 KiB  
Article
Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering
by Arthur Leroy, Andy MARC, Olivier DUPAS, Jean Lionel REY and Servane Gey
Appl. Sci. 2018, 8(10), 1766; https://doi.org/10.3390/app8101766 - 30 Sep 2018
Cited by 14 | Viewed by 5641
Abstract
Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as [...] Read more.
Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool. Full article
(This article belongs to the Special Issue Computer Science in Sport)
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