rss_2.0International Journal of Computer Science in Sport FeedSciendo RSS Feed for International Journal of Computer Science in Sporthttps://sciendo.com/journal/IJCSShttps://www.sciendo.comInternational Journal of Computer Science in Sport 's Coverhttps://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/60970b54375d2744aa44f4b1/cover-image.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20210920T041016Z&X-Amz-SignedHeaders=host&X-Amz-Expires=604800&X-Amz-Credential=AKIA6AP2G7AKDOZOEZ7H%2F20210920%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=4fb121a059a23d6c2bdfccc3a70412605f33ae52de54c4264db834c57ff56c2f200300Can Elite Australian Football Player’s Game Performance Be Predicted?https://sciendo.com/article/10.2478/ijcss-2021-0004<abstract> <title style='display:none'>Abstract</title> <p>In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.</p> </abstract>ARTICLE2021-08-10T00:00:00.000+00:00Comparing bottom-up and top-down ratings for individual soccer playershttps://sciendo.com/article/10.2478/ijcss-2021-0002<abstract> <title style='display:none'>Abstract</title> <p>Correctly assessing the contributions of an individual player in a team sport is challenging. However, an ability to better evaluate each player can translate into improved team performance, through better recruitment or team selection decisions. Two main ideas have emerged for using data to evaluate players: Top-down ratings observe the performance of the team as a whole and then distribute credit for this performance onto the players involved. Bottom-up ratings assign a value to each action performed, and then evaluate a player based on the sum of values for actions performed by that player. This paper compares a variant of plus-minus ratings, which is a top-down rating, and a bottom-up rating based on valuing actions by estimating probabilities. The reliability of ratings is measured by whether similar ratings are produced when using different data sets, while the validity of ratings is evaluated through the quality of match outcome forecasts generated when the ratings are used as predictor variables. The results indicate that the plus-minus ratings perform better than the bottom-up ratings with respect to the reliability and validity measures chosen and that plus-minus ratings have certain advantages that may be difficult to replicate in bottom-up ratings.</p> </abstract>ARTICLE2021-05-08T00:00:00.000+00:00Strictness vs. flexibility: Simulation-based recognition of strategies and its success in soccerhttps://sciendo.com/article/10.2478/ijcss-2021-0003<abstract> <title style='display:none'>Abstract</title> <p>Introduction: Recognition and optimization of strategies in sport games is difficult in particular in case of team games, where a number of players are acting “independently” of each other. One way to improve the situation is to cluster the teams into a small number of tactical groups and to analyze the interaction of those groups. The aim of the study is the evaluation of the applicability of SOCCER© simulation in professional soccer by analyzing and simulation of the tactical group interaction.</p> <p>Methods: The players’ positions of tactical groups in soccer can be mapped to formation-patterns and then reflect strategic behaviour and interaction. Based on this information, Monte Carlo-Simulation allows for generating strategies, which – at least from the mathematical point of view – are optimal. In practice, behaviour can be orientated in those optimal strategies but normally is changing depending on the opponent team’s activities. Analyzing the game under the aspect of such simulated strategies revealed how strictly resp. flexible a team follows resp. varies strategic patterns.</p> <p>Approach: A Simulation- and Validation-Study on the basis of 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and to optimize such strategic team behaviour in professional soccer.</p> <p>Results: The Validation-Study demonstrated the applicability of our tactical model. The results of the Simulation-Study revealed that offensive player groups need less tactical strictness in order to gain successful ball possession whereas defensive player groups need tactical strictness to do so.</p> <p>Conclusion: The strategic behaviour could be recognized and served as basis for optimization analysis: offensive players should play with a more flexible tactical orientation to stay in possession of the ball, whereas defensive players should play with a more planned orientation in order to be successful. The strategic behaviour of tactical groups can be recognized and optimized using Monte Carlo-based analysis, proposing a new and innovative approach to quantify tactical performance in soccer.</p> </abstract>ARTICLE2021-05-08T00:00:00.000+00:00Sports Information Systems: A systematic reviewhttps://sciendo.com/article/10.2478/ijcss-2021-0001<abstract> <title style='display:none'>Abstract</title> <p>Many professional sport organizations are currently in the process of finding or already using <italic>sports information systems (SIS)</italic> to integrate data from different information and measurement systems. The problem is that requirements are very heterogeneous. That is why no consistent definition of <italic>SIS</italic> and their categories exist, and it is often not clear which fields and functions <italic>SIS</italic> must cover. This work aims to provide a structured comparison of commercial <italic>SIS</italic> available on the market to provide an overview of the relevant features and characterize categories. Following PRISMA guidelines, a systematic search for relevant <italic>SIS</italic> providers was conducted. A catalog of 164 review items was created to define relevant features of <italic>SIS</italic> and to conduct semi-standardized interviews with product representatives. Overall 36 eligible <italic>SIS</italic> from 11 countries were identified and 21 of them were interviewed. The analysis of the interviews has shown that there are features that are present in all <italic>SIS</italic>, whereas others differ or are generally less represented. As a result, different <italic>SIS</italic> categories have been defined. The study suggests a more differentiated categorization of <italic>SIS</italic> is necessary and terms need to be defined more precisely. This review should be considered when companies designing <italic>SIS</italic> or sport organizations select <italic>SIS</italic>.</p> </abstract>ARTICLE2021-05-08T00:00:00.000+00:00Comparison of the Evaluation of Performance Preconditions in Tennis with the Use of Equal and Expertly Judged Criteria Weightshttps://sciendo.com/article/10.2478/ijcss-2021-0005<abstract> <title style='display:none'>Abstract</title> <p>Tennis performance is influenced by various factors, among which physical performance factors play an important role. The aim of the study was an analysis of possibilities of the use of Saaty’s method for assessing the level of performance prerequisites and comparing the results obtained using equal weights and various weights. The research on Czech female players (U12; n = 211) was based on the results of the TENDIAG1 test battery (9 items) and the results were processed by FuzzME software and relevant statistical methods (correlation coefficient r, Student´s t-test, effect size index d). The results of Saaty’s method show that the most important athletic performance criteria for tennis coaches are the leg reaction time and the running speed, while the least important are endurance and strength. The evaluation using various criteria weights offers a finer scale for assessing athletes’ performance prerequisites despite the proven high degree of association between the results obtained with equal and various weights and the insignificant difference of mean values. The results have shown possibilities for the use of a fuzzy approach in sports practice and motivate further research towards broadening the structure or the number of evaluation criteria.</p> </abstract>ARTICLE2021-08-10T00:00:00.000+00:00Intra-seasonal Variability of Ball Speed and Coordination of Two Team-Handball Throwing Techniques in Elite Male Adolescent Players.https://sciendo.com/article/10.1515/ijcss-2016-0001<abstract><title style='display:none'>Abstract</title><p>In sports biomechanics and motor control, a thorough study of coordination variability is important to understanding how the human movement system is organized. From a more applied sport science perspective, knowledge about performance variability is essential regarding the evaluation of true sport specific effects of any intervention. While there are many reports of intervention studies in team-handball, no description of the amount of normal variability is available. This study investigated variability of two important throwing techniques in team-handball within elite junior players over a 4-month period during a competitive season. To evaluate ball speed variability, the intra-individual coefficient of variation was calculated. The 95<sup>th</sup> percentile of ball speed variability over all players was 7%, which can be used as an effect size estimate in future research. For coordination variability, a qualitative description based on the output of neural networks was used. All participants presented multiple coordination patterns, representing multi-stability on a month-to-month timescale and switched between stable states without the manipulation of any control variable. Some limitations in the methodology and applications of neural networks in the present study and in biomechanics and motor control in general are highlighted. When more researchers adopt these methodologies, a more coherent framework for their application can emerge.</p></abstract>ARTICLE2016-07-27T00:00:00.000+00:00Predictive models of the 2015 Rugby World Cup: accuracy and applicationhttps://sciendo.com/article/10.1515/ijcss-2016-0003<abstract><title style='display:none'>Abstract</title><p>The current investigation compared 12 models of outcomes of international rugby union matches and then used the most accurate model to investigate performances within the 2015 Rugby World Cup. The underlying linear regression models were used within a simulation package that introduced random variability about performance evidenced by the residual distribution of the regression analyses. Each model was used within 10,000 simulations of the 2015 Rugby World Cup from which match outcome and team progression statistics were recorded. The most accurate model with respect to the actual 2015 tournament was developed using data from all seven previous tournaments rather than restricting cases to the most recent three tournaments. The model was more accurate when the data used violated the assumptions of linear regression rather than transforming variables to satisfy the assumptions. The model included World ranking points as a predictor variable and was more accurate than corresponding models that represented relative home advantage as well. The most accurate model used separate models for the pool and knockout stage matches although the 9 models that separating these match types were less accurate on average than when the two match types were considered together. This model was used to investigate properties of the 2015 Rugby World Cup. The tournament disadvantaged three teams in the World’s top 5 who were drawn in the same pool. Teams ranked in the World’s top 7 did not perform as well as predicted while teams ranked 16<sup>th</sup> and below performed better than predicted suggesting that the strength in depth in international rugby union is increasing. There was a small effect of having additional recovery days from the previous match compared to the opponents which was worth 4.1 points. The information produced by this research should be considered by those design tournaments such as the Rugby World Cup.</p></abstract>ARTICLE2016-07-27T00:00:00.000+00:00Performance Analysis in Table Tennis - Stochastic Simulation by Numerical Derivationhttps://sciendo.com/article/10.1515/ijcss-2016-0002<abstract><title style='display:none'>Abstract</title><p>The aim of this study was to identify the impact of different tactical behaviors on the winning probability in table tennis. The performance analysis was done by mathematical simulation using a Markov chain model. 259 high-level table tennis games were evaluated by means of a new simulation approach using numerical derivation to remove the necessity to perform a second modeling step in order to determine the difficulty of tactical behaviors. Based on the derivation, several mathematical constructs like directional derivations and the gradient are examined for application in table tennis. Results reveal errors and long rallies as the most influencing game situations, together with the positive effect of risky play on the winning probability of losing players.</p></abstract>ARTICLE2016-07-27T00:00:00.000+00:00Computer Science in Sport – Research and Practice: A book reviewhttps://sciendo.com/article/10.1515/ijcss-2016-0004<abstract><title style='display:none'>Abstract</title><p>Sports informatics and computer science in sport are perhaps the most exciting and fast-moving disciplines across all of sports science. The tremendous parallel growth in digital technology, non-invasive sensor devices, computer vision and machine learning have empowered sports analytics in ways perhaps never seen before. This growth provides great challenges for new entrants and seasoned veterans of sports analytics alike. Keeping pace with new technological innovations requires a thorough and systematic understanding of many diverse topics from computer programming, to database design, machine learning algorithms and sensor technology. Nevertheless, as quickly as the state of the art technology changes, the foundation skills and knowledge about computer science in sport are lasting. Furthermore, resources for students and practitioners across this range of areas are scarce, and the new-release textbook <italic>Computer Science in Sport: Research and Practice</italic> edited by Professor Arnold Baca, provides much of the foundation knowledge required for working in sports informatics. This is certainly a comprehensive text that will be a valuable resource for many readers.</p></abstract>ARTICLE2016-07-27T00:00:00.000+00:00Feature Selection to Win the Point of ATP Tennis Players Using Rally Informationhttps://sciendo.com/article/10.2478/ijcss-2020-0003<p>In tennis, the accumulation of data has progressed and research on tactical analysis has been conducted. Estimating strategically important factors would have the benefit of providing players with useful advice and helping audience members understand what tennis players are good at. Previous research has been conducted into ways of predicting Association of Tennis Professionals (ATP) tennis match outcomes as well as estimating factors that are important for victories using machine learning models. The challenge of previous research is that the victory factor lacks concreteness. Since we thought the root of the abovementioned problem was that previous researchers used game summary as a feature and did not consider the process of rallies between points, this research focused on calculating the frequency of single shots, two-shot patterns, and specific effective shot patterns from each point rally of ATP singles matches. We then used those data to predict point winners and useful features using L1-regularized logistic regression. The highest accuracy obtained was 66.5%, and the area under the curve (AUC) was 0.689. The most prominent feature we found was the ratio of specific shots by specific players. From these results, our method could reveal more concretely tactical factors than previous studies.</p>ARTICLE2020-06-29T00:00:00.000+00:00A Team-Compatibility Decision Support System for the National Football Leaguehttps://sciendo.com/article/10.2478/ijcss-2020-0005<p>Many factors are considered when making a hiring decision in the National Football League (NFL). One difficult decision that executives must make is who they will select in the offseason. Mathematical models can be developed to aid humans in their decision-making processes because these models are able to find hidden relationships within numeric data. This research proposes the <bold>H</bold>euristic <bold>E</bold>valuation of <bold>A</bold>rtificially <bold>R</bold>eplaced <bold>T</bold>eammates (HEART) methodology, which is a mathematical model that utilizes machine learning and statistical-based methodologies to aid managers with their hiring decisions. The goal of HEART is to determine expected and theoretical contribution values for a potential candidate, which represents a player’s ability to increase or decrease a team’s forecasted winning percentage. In order to validate the usefulness of the methodology, the results of a 2007 case study were presented to subject matter experts. After analyzing the survey results statistically, five of the eight decision-making categories were found to be “very useful” in terms of the information that the methodology provided.</p>ARTICLE2020-06-29T00:00:00.000+00:00Performance of machine learning models in application to beach volleyball data.https://sciendo.com/article/10.2478/ijcss-2020-0002<p>Driven by the increased availability of position and performance data, automated analyses are becoming the daily routine in many top-level sports. Methods from the domains of data mining and machine learning are more frequently used to generate new insights from massive amounts of data. This study evaluates the performance of four current models (multi-layer perceptron, convolutional network, recurrent network, gradient boosted tree) in classifying tactical behaviors on a beach volleyball dataset consisting of 1,356 top-level games. A three-way between-subjects analysis of variance was conducted to determine the effects of model, input features and target behavior on classification accuracy. Results show significant differences in classification accuracy between models as well as significant interaction effects between factors. Our models achieve classification performance similar to previous work in other sports. Nonetheless, they are not yet at the level to warrant practical application in day to day performance analysis in beach volleyball.</p>ARTICLE2020-06-29T00:00:00.000+00:00Validation of gyroscope sensors for snow sports performance monitoringhttps://sciendo.com/article/10.2478/ijcss-2020-0004<p>Wearable sensors that can be used to measure human performance outcomes are becoming increasingly popular within sport science research. Validation of these sensors is vital to ensure accuracy of extracted data. The aim of this study was to establish the validity and reliability of gyroscope sensors contained within three different inertial measurement units (IMU). Three IMUs (OptimEye, I Measure U and Logger A) were fixed to a mechanical calibration device that rotates through known angular velocities and positions. RMS scores for angular displacement, which were calculated from the integrated angular velocity vectors, were 3.85° ± 2.21° and 4.34° ± 2.57° for the OptimEye and IMesU devices, respectively. The RMS error score for the Logger A was 22.76° ± 23.22°, which was attributed to a large baseline shift of the angular velocity vector. After a baseline correction of all three devices, RMS error scores were all below 3.90°. Test re-test reliability of the three gyroscope sensors were high with coefficient of variation (CV%) scores below 2.5%. Overall, the three tested IMUs are suitable for measuring angular displacement of snow sports manoeuvres after baseline corrections have been made. Future studies should investigate the accuracy and reliability of accelerometer and magnetometer sensors contained in each of the IMUs to be used to identify take-off and landing events and the orientation of the athlete at those events.</p>ARTICLE2020-06-29T00:00:00.000+00:00Ordinal versus nominal regression models and the problem of correctly predicting draws in soccerhttps://sciendo.com/article/10.1515/ijcss-2017-0004<p>Ordinal regression models are frequently used in academic literature to model outcomes of soccer matches, and seem to be preferred over nominal models. One reason is that, obviously, there is a natural hierarchy of outcomes, with victory being preferred to a draw and a draw being preferred to a loss. However, the often used ordinal models have an assumption of proportional odds: the influence of an independent variable on the log odds is the same for each outcome. This paper illustrates how ordinal regression models therefore fail to fully utilize independent variables that contain information about the likelihood of matches ending in a draw. However, in practice, this flaw does not seem to have a substantial effect on the predictive accuracy of an ordered logit regression model when compared to a multinomial logistic regression model.</p>ARTICLE2017-07-22T00:00:00.000+00:00Issues in Using Self-Organizing Maps in Human Movement and Sport Sciencehttps://sciendo.com/article/10.1515/ijcss-2017-0001<p>Self-Organizing Maps (SOMs) are steadily more integrated as data-analysis tools in human movement and sport science. One of the issues limiting researchers’ confidence in their applications and conclusions concerns the (arbitrary) selection of training parameters, their effect on the quality of the SOM and the sensitivity of any subsequent analyses. In this paper, we demonstrate how quality and sensitivity may be examined to increase the validity of SOM-based data-analysis. For this purpose, we use two related data sets where the research question concerns coordination variability in a volleyball spike. SOMs are an attractive tool for analysing this problem because of their ability to reduce the highdimensional time series to a two-dimensional problem while preserving the topological, non-linear relations in the original data. In a first step, we systematically search the SOM parameter space for a set of options that produces significantly lower continuity, accuracy and combined map errors and we discuss the sensitivity of SOM-based analyses of coordination variability to changes in training parameters. In a second step, we further investigate the effect of using different numbers of trials and variables on the SOM quality and sensitivity. These sensitivity analyses are able to validate the conclusions from statistical tests. Using this type of analysis can guide researchers to select SOM parameters that optimally represent their data and to examine how they affect the subsequent analyses. This may also enforce confidence in any conclusions that are drawn from studies using SOMs and enhance their integration in human movement and sport science.</p>ARTICLE2017-07-22T00:00:00.000+00:00Network structure of UEFA Champions League teams: association with classical notational variables and variance between different levels of successhttps://sciendo.com/article/10.1515/ijcss-2017-0003<p>The aim of this study was to analyse the general properties of the network of elite football teams that participated in UEFA Champions League 2015–2016. Analysis of variance of the general network measures between performances in competition was made. Moreover, the association between performance variables (goals, shots, and percentage of ball possession) and general network measures also was tested. The best sixteen teams that participated in UEFA Champions League 2015–2016 were analysed in a total of 109 official matches. Statistically significant differences between maximum stages in competition were found in total links (p = 0.003; ES = 0.087), network density (p = 0.003; ES = 0.088), and clustering coefficient (p = 0.007; ES = 0.078). Total links (r = 0.439; p = 0.001), network density (r = 0.433; p = 0.001) and clustering coefficient (r = 0.367; p = 0.001) had a moderate positive correlations with percentage of ball possession. This study revealed that teams that achieved the quarterfinals and finals had greater values of general network measures than the remaining teams, thus suggesting that higher values of homogeneity in network process may improve the success of the teams. Moderate correlations were found between ball possession and the general network measures suggesting that teams with more capacity to perform longer passing sequences may involve more players in a more homogeneity manner.</p>ARTICLE2017-07-22T00:00:00.000+00:00A Pilot Study on Offensive Success in Soccer Based on Space and Ball Control – Key Performance Indicators and Key to Understand Game Dynamicshttps://sciendo.com/article/10.1515/ijcss-2017-0005<p>The intention of Key Performance Indicators (KPI) is to map complex system-behaviour to single values for scaling, rating and ranking systems or system components. Very often, however, this mapping only reduces important information about tactical behaviour or playing dynamics without replacing it by useful ones. The presented approach tries to bridge the gap between complex dynamics and numerical indicators in the case of offensive effectiveness in soccer in two steps. First, a model is developed which visualises offensive actions in a process-oriented way by using information units to represent offensive performance – i.e. Key Performance Indicators. Second, this model is organised in relation to time intervals, which enables to measure the effectiveness for a whole half-time as well as for arbitrary intervals of any desired lengths.</p>ARTICLE2017-07-22T00:00:00.000+00:00Using Several Types of Virtual Characters in Sports - a Literature Surveyhttps://sciendo.com/article/10.2478/ijcss-2018-0001<p> This article discusses the development and application of virtual environments (VEs) in the domain of exercise as well as research in recreational and high-performance sports. A special focus is put on the use of virtual characters (VCs). For its elaboration, the following criteria parameters were chosen: scene content and the role of the VC, output device, kind of additional feedback, level of expertise of the tested participants, kind of user’s movement (reaction), kind of the visualization of the user’s body, kind of study and kind of evaluation. We explored the role of VCs embodying virtual opponents, teammates, or coaches in sports. We divided these VCs in passive and autonomous characters. Passive VCs are not affected by the user, whereas autonomous VCs adapt autonomously to the user’s movements and positions. We identified 44 sport related VEs, thereof 22 each in the domain of recreational sports and high-performance sports: of the identified 44 VEs, 19 VEs are without VC, 20 VEs with passive VCs, and 5 VEs with autonomous VCs. We categorized studies examining expert athletes in high-performance sports as well as studies analyzing novices, beginners or advanced athletes in recreational sports. Nevertheless, all identified systems are suitable for athletes of recreational and high-performance level</p>ARTICLE2018-07-28T00:00:00.000+00:00Comparison of Different Time-Frequency Analyses Techniques Based on sEMG-Signals in Table Tennis: A Case Studyhttps://sciendo.com/article/10.2478/ijcss-2018-0004<p> The surface EMG signal in the action of dynamic contraction has more movement interference compared to sustained static contractions. In addition, the recruitment and de-recruitment of motor units causes a faster change in the surface EMG signal’s proprieties. Therefore, more complex techniques are required to extract information from the surface EMG signal. The standardized protocol for surface myoelectric signal measurement in table tennis was a case study in this research area. The Autoregressive method based on the Akaike Information Criterion, the Wavelet method based on intensity analysis, and the Hilbert-Huang transform method were used to estimate the muscle fatigue and non-fatigue condition. The result was that the Hilbert-Huang transform method was shown to be more reliable and accurate for studying the biceps brachii muscle in both conditions. However, the Wavelet method based on intensity analysis is more reliable and accurate for the pectoralis major muscle, deltoideus anterior muscle and deltoideus medialis muscle. The results suggest that different time-frequency analysis techniques influence different muscle analyses based on surface EMG signals in fatigue and non-fatigue conditions</p>ARTICLE2018-07-28T00:00:00.000+00:00Predictive Modelling of Training Loads and Injury in Australian Footballhttps://sciendo.com/article/10.2478/ijcss-2018-0002<p> To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and relative training load metrics were calculated for each player each day. Injury prediction models (regularised logistic regression, generalised estimating equations, random forests and support vector machines) were built for non-contact, non-contact time-loss and hamstring specific injuries using the first two seasons of data. Injury predictions were then generated for the third season and evaluated using the area under the receiver operator characteristic (AUC). Predictive performance was only marginally better than chance for models of non-contact and non-contact time-loss injuries (AUC&lt;0.65). The best performing model was a multivariate logistic regression for hamstring injuries (best AUC=0.76). Injury prediction models built using training load data from a single club showed poor ability to predict injuries when tested on previously unseen data, suggesting limited application as a daily decision tool for practitioners. Focusing the modelling approach on specific injury types and increasing the amount of training observations may improve predictive models for injury prevention</p>ARTICLE2018-07-28T00:00:00.000+00:00en-us-1