rss_2.0Sciendo RSS Feed for International Journal of Computer Science in Sporthttps://sciendo.com/journal/IJCSShttps://www.sciendo.comhttps://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/63c6e5c8ba938b7823b785e8/cover-image.jpghttps://sciendo.com/journal/IJCSS140216https://sciendo.com/article/10.2478/ijcss-2023-0004<abstract> <title style='display:none'>Abstract</title> <p>Simulating and predicting tournament outcomes has become an increasingly popular research topic. The outcomes can be influenced by several factors, such as attack, defence and home advantage strength values, as well as tournament structures. However, the claim that different structures, such as knockout (KO), round-robin (RR) and hybrid structures, have their own time restraints and requirements has limited the evaluation of the best structure for a particular type of sports tournament using quantitative approaches. To address this issue, this study develops a decision support system (DSS) using Microsoft Visual Basic, based on the object-oriented programming approach, to simulate and forecast the impact of the various tournament structures on soccer tournament outcomes. The DSS utilized the attack, defence and home advantage values of the teams involved in the Malaysia Super League 2018 to make better prediction. The rankings produced by the DSS were then compared to the actual rankings using Spearman correlation to reveal the simulated accuracy level. The results indicate that a double RR produces a higher correlation value than a single RR, indicating that more matches played provide more data to create better predictions. Additionally, a random KO predicts better than a ranking KO, suggesting that pre-ranking teams before a tournament starts does not significantly impact the prediction. The findings of this study can help tournament organizers plan forthcoming games by simulating various tournament structures to determine the most suitable one for their needs.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2023-00042023-05-27T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2023-0003<abstract> <title style='display:none'>Abstract</title> <p>Change of direction speed, reaction time, sprint speed, and explosive strength are important factors that determine athletes’ performance in the majority of sports. From the practical standpoint, it is of interest to investigate to what extent they differ among athletes of team and individual sports. We compared 7 handball, 11 basketball, and 15 male table tennis players in four reaction time tests, 505 Agility test, 5m and 20m sprints, squat, countermovement, and drop jumps. Basketball players performed better in reaction time to fast generating stimuli (12.6%, p=.001) and countermovement jump height (14.5%, p=.05) than handball players. In addition, they achieved a higher reactive strength index (25%, p=.01) than table tennis players. Handball players were faster in the 505 Agility test compared to table tennis players (4.6%, p=.04). Results revealed that performance of basketball players is mainly determined by explosive strength, handball players by change of direction speed, and table tennis by speed of response to visual stimuli. These differences may be ascribed to long-term adaptation to sport-specific stimuli. Novel assessment methods and devices should better determine key performance factors of athletes with regard to sport-specific tasks.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2023-00032023-04-06T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2023-0002<abstract> <title style='display:none'>Abstract</title> <p>NBA teams rely heavily on their star players, though an ever-increasing tendency shows that proper ball movement is key for building a successful offense. According to experts, one of the most crucial individual contributions for this aspect is ‘making the extra pass’ – meaning to pass on a decent shooting opportunity to create an even better one. However, judging this ability is subjective, even a precise definition is missing. In this analysis, we conceptualize the event and design a method to measure this skill on an individual player level. Using this model, we analyze directly assisted shots – whether they could have been turned down to make the extra pass. In-season statistics are used to calculate the scoring efficiency of the player from the particular zone given the distance of the closest defender. Our method helps to automatically find individual situations where the extra pass could have been played to gain a margin in Expected Points and scaled up to a whole season, we are able to identify which areas of the court are the most often overlooked. By detecting these missed opportunities of extra passes, experts can easily point out situations where better teamwork can lead to better scoring opportunities.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2023-00022023-03-08T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2023-0001<abstract> <title style='display:none'>Abstract</title> <p>In baseball, every play on the field is quantitatively evaluated and the statistics have an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of a batter’s hitting contribution. However, this measure ignores the game situation, such as the runners on base, which coaches and batters are known to consider when employing multiple hitting strategies, yet, the effectiveness of these strategies is unknown. This is probably because (1) we cannot obtain the batter’s strategy and (2) it is difficult to estimate the effect of the strategies. Here, we propose a new method for estimating the effect using counterfactual batting simulation. The entire framework consists of two phases: (i) generate a counter-factual batter’s ability based on their actual performances and (ii) simulate games with the batting simulator. To realize (i), we propose a deep learning model that transforms batting ability when batting strategy is changed. This method can estimate the effects of various strategies, which has been traditionally difficult with actual game data. We found that, when the switching cost of batting strategies can be ignored, the use of different strategies increased runs. When the switching cost is considered, the conditions for increasing runs were limited. Our results suggest that players and coaches should be careful when employing multiple batting strategies given the trade-offs thereof. We also discuss practical baseball use-cases to use this simulation.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2023-00012023-01-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0007<abstract> <title style='display:none'>Abstract</title> <p>Periods of intensified training may increase athletes’ fatigue and impair their recovery status. Therefore, understanding internal and external load markers-related to fatigue is crucial to optimize their weekly training loads. The current investigation aimed to adopt machine learning (ML) techniques to understand the impact of training load parameters on the recovery status of athletes. Twenty-six adult soccer players were monitored for six months, during which internal and external load parameters were daily collected. Players’ recovery status was assessed through the 10-point total quality recovery (TQR) scale. Then, different ML algorithms were employed to predict players’ recovery status in the subsequent training session (S-TQR). The goodness of the models was evaluated through the root mean squared error (RMSE), mean absolute error (MAE), and Pearson’s Correlation Coefficient (r). Random forest regression model produced the best performance (RMSE=1.32, MAE=1.04, r = 0.52). TQR, age of players, total decelerations, average speed, and S-RPE recorded in the previous training were recognized by the model as the most relevant features. Thus, ML techniques may help coaches and physical trainers to identify those factors connected to players’ recovery status and, consequently, driving them toward a correct management of the weekly training loads.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00072023-01-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0010<abstract> <title style='display:none'>Abstract</title> <p>The purpose of this study was to evaluate three distinct methods of teaching the three-step ball throw simulating the javelin throw technique to primary school students. The sample consisted of 131 primary school students of 5th and 6th grade (Mage = 11.4, SD = 0.47 years) randomly divided into three groups. The control group (CON) received typical instruction, the first experimental group (EXP) followed a blended learning intervention which included an interactive learning activity software and the second experimental group (EXPVF) followed the same blended learning method with an additional direct video feedback system. A pre/post-test design was implemented to evaluate students’ technique, using as criteria five selected technique elements of the three-step ball throw. Wilcoxon signed-rank test analysis showed that all three groups performed significantly better after the intervention in all five criteria. However, Kruskal-Wallis H test analysis with post-hoc test revealed that the results for EXPVF group were significantly better than the other two groups in all elements, while the EXP group showed significantly better results in three of the five elements compared with the CON group. In conclusion, students appeared to benefit more in their three-step ball throw technique through blended learning and direct video feedback.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00102023-01-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0009<abstract> <title style='display:none'>Abstract</title> <p>Typical performance indicators in professional quantitative soccer analysis simplify complex matters, resulting in loss of information. Hence, a novel approach to characterize the performance of soccer teams was investigated: Success-Scores, combining space control with ball control and the correlation between the two.</p> <p>Success-Score Profiles were calculated for 14 games from the German Bundesliga. The dataset was split into two groups: all data points above resp. below the 80^th percentile of Success-Scores. Subsequently, the relative goalscoring frequency in those two groups was compared. All data points were sorted according to their Success-Score and split into equally sized eighths. These groups were tested for a rank order correlation with the number of scored goals. Finally, the Success-Scores of two teams with different success levels as well as their opponents’ Success-Scores were compared.</p> <p>Results indicated significantly higher goalscoring frequencies above the 80th percentile for Success-Scores and a statistically significant rank order correlation between the Success-Scores and the number of scored goals, r_s(6) = 0.73, p = .04. The more successful team showed significantly higher Success-Scores.</p> <p>This novel performance indicator shows significant connections to success defined as scoring goals and final ranking in elite soccer and therefore shows potential in reconizing underlying performance.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00092023-01-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0008<abstract> <title style='display:none'>Abstract</title> <p>Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data contain time information, it is natural to consider the temporal dimension in form of time series. However, in sports, the effective use of time series data mining techniques is still under development. The main goal of this paper is therefore to serve as an introduction to time series data mining and a glossary for interested researchers from the sports community. The paper gives an overview about current data mining tasks and tries to identify their potential research direction for further investigation. Furthermore, we want to draw more attention with respect to the importance of mining approaches with sport data and their particular challenges beyond usual time series data mining tasks.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00082023-01-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0006<abstract> <title style='display:none'>Abstract</title> <p>Classifying multi-agent cooperative behavior is a fundamental problem in various scientific and engineering domains. In team sports, many cooperative plays can be manually labelled by experts. However, it requires high labour costs and a large amount of unlabelled data is not utilised. This paper examines semi-supervised learning methods for the classification of strategic cooperative plays (called screen plays) in basketball using a smaller labelled dataset and a larger unlabelled dataset. We compared the classification performance of two basic semi-supervised learning methods: self-training and label-propagation. Results show that the classification performance of the semi-supervised learning approaches improved upon the conventional supervised approach (SVM: support vector machine) for minor types of screen-plays (flare, pin, back, cross, and hand-off screen). For the feature importance, we found that self-training obtained similar or higher Sharpley values than SVM. Our approach has the potential to reduce manual labelling costs for detecting various cooperative behaviors.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00062022-11-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0005<abstract> <title style='display:none'>Abstract</title> <p>Virtual reality (VR) is a tool used in sports to train specific situations under standardized conditions. However, it remains unclear whether improved performances from VR training can be transferred into real world (RW). Therefore, the current study compares beginner training of balance beam tasks in VR (simulated balance beam height, n = 17) with similar training in RW (n = 15). Both groups completed 12 training sessions (each 20 min) within six weeks in their respective environment. The training aimed to learn the one leg full turn on a balance beam with a height of 120 cm. Criteria were defined to analyze the movement quality before and after the intervention. Statistical analyses showed similar improvements in movement quality in RW for both training groups after the intervention (p &lt; .05). These results indicate that the skills adapted in VR could be transferred into RW and that the VR training was as effective as the RW training in improving the movement quality of balance beam elements. Thereby, VR provides the advantages of a reduced risk of injury due to a simulated beam height, a faster beam height adjustment, and spacial independence from specific gyms.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00052022-11-17T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0004<abstract> <title style='display:none'>Abstract</title> <p>Cloud-based services revolutionize how applications are designed and provisioned in more and more application domains. Operating a cloud application, however, requires careful choices of configuration settings so that the quality of service is acceptable at all times, while cloud costs remain reasonable. We propose an analytical queuing model for cloud resource provisioning that provides an approximation on end-to-end application latency and on cloud resource usage, and we evaluate its performance. We pick an emerging use case of cloud deployment for validation: sports analytics. We have created a low-cost, cloud-based soccer player tracking system. We present the optimization of the cloud-deployed data processing of this system: we set the parameters with the aim of sacrificing as least as possible on accuracy, i.e., quality of service, while keeping latency and cloud costs low. We demonstrate that the analytical model we propose to estimate the end-to-end latency of a microservice-type cloud native application falls within a close range of what the measurements of the real implementation show. The model is therefore suitable for the planning of the cloud deployment costs for microservice-type applications as well.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00042022-06-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0003<abstract> <title style='display:none'>Abstract</title> <p>This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings include (a) genetic algorithm and particle swarm optimization have been extensively used in the literature, (b) meta-heuristics have been widely applied in the sports of cricket and soccer, (c) the limitations and challenges of using meta-heuristics in sports. Through awareness and discussion on implementation of meta-heuristics, sports analytics research can be rich in the future.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00032022-06-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0001<abstract> <title style='display:none'>Abstract</title> <p>The aim of this study is to integrate an inertial sensor inside a hammer to allow a realtime feedback. In the first step we build our own prototype to measure the radial acceleration. In the second step there is a validation with an infrared camera system. It is a comparison between the radial acceleration along the wire axis, that is measured by the sensor against the velocity that is delivered by the infrared camera system. As a result, significant correlation was observed between the measured velocity and the acceleration (r = 0.99, p &lt; 0.001). These suggest that this system can used in the training to improve the technique of the hammer throw.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00012022-03-24T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2022-0002<abstract> <title style='display:none'>Abstract</title> <p>The Association of Tennis Professionals (ATP) distributes a considerable amount of money in prizes each year. Studies have shown that only the top 100 ranked players can self-finance; hence, it is convenient to introduce changes to the prize distribution to promote a more sustainable system. A Linear Programming model to distribute the tournament’s budget under a new concept for the fair distribution of prize money is proposed. Additionally, to distribute the prizes, a function based on the effort of the players is designed. The model was applied to tournaments to demonstrate the impact on improving the player’s prizes distribution.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2022-00022022-03-24T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2021-0009<abstract> <title style='display:none'>Abstract</title> <p>Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2021-00092021-11-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2021-0011<abstract> <title style='display:none'>Abstract</title> <p>To aid the implementation of athlete surveillance systems relative to logistical circumstances, easy-to-access information that summarises the extent to which methods of acquiring data are used in practice to monitor athletes is required. In this scoping review, Social Network Analysis and Mining (SNAM) techniques were used to summarise and identify the most prevalent combinations of methods used to monitor athletes in research studying team, individual, field- and court-based sports (357 articles; SPORTDiscus, MEDLINE, CINHAL, and WebOfScience; 2014-2018 inc.) . The most prevalent combination in team and field-based sports were HR and/or sRPE (internal) and GPS, whereas in individual and court-based sports, internal methods (e.g., HR and sRPE) were most prevalent. In court-based sports, where external methods were occasionally collected in combination with internal methods of acquiring data, the use of accelerometers or inertial measuring units (ACC/IMU) were most prevalent. Whilst individual and court-based sports are less researched, this SNAM-based summary reveals that court-based sports may lead the way in using ACC/IMU to monitor athletes. Questionnaires and self-reported methods of acquiring data are common in all categories of sport. This scoping review provides coaches, sport-scientists and researchers with a data-driven visual resource to aid the selection of methods of acquiring data from athletes in all categories of sport relative to logistical circumstances. A guide on how to practically implement a surveillance system based on the visual summaries provided herein, is also presented.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2021-00112021-11-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2021-0008<abstract> <title style='display:none'>Abstract</title> <p>Typical player management processes focus on managing an athlete’s physical, physiological, psychological, technical and tactical preparation and performance. Current literature illustrates limited attempts to optimize such processes in sports. Therefore, this study aimed to analyze the application of Business Process Management (BPM) in healthcare (a service industry resembling sports) and formulate a model to optimize data driven player management processes in professional sports. A systematic review, adhering to PRISMA framework was conducted on articles extracted from seven databases, focused on using BPM to digitally optimize patient related healthcare processes. Literature reviews by authors was the main mode of healthcare process identification for BPM interventions. Interviews with process owners followed by process modelling were common modes of process discovery. Stakeholder and value-based analysis highlighted potential optimization areas. In most articles, details on process redesign strategies were not explicitly provided. New digital system developments and implementation of Business Process Management Systems were common. Optimized processes were evaluated using usability assessments and pre-post statistical analysis of key process performance indicators. However, the scientific rigor of most experiments designed for such latter evaluations were suboptimal. From the findings, a stepwise approach to optimize data driven player management processes in professional sports has been proposed.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2021-00082021-11-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2021-0010<abstract> <title style='display:none'>Abstract</title> <p>Quantification of athletic performance via analysis of scores of off-season fitness tests has become an essential part of the modern strength and conditioning coach (SCC). Player Efficiency Rating (PER) and Efficiency index (EFF) are two of the most used in-season basketball performance metrics in the US. We collected data from male and female basketball players of a National Collegiate Athletic Association (NCAA) program. Based on sex, we examined a) if unadjusted PER (uPER) and EFF reflect different amounts of information and b) which fitness tests predict those two indices more accurately. Our results showed lower means and less variability of the fitness tests scores in women than men. The correlation between uPER and EFF in men was moderate and strong in women. In men, no strong correlation was found between any fitness test and EFF, while full court sprint was strongly correlated with uPER. In women, strong correlations were detected between a) the T-drill and EFF and b) the foul court sprint, the vertical jump, and the T-drill and uPER. The collegiate SCCs should consider that off-season scores of a) the foul court drill may predict uPER more accurately in both men and women and b) the T-drill may predict both EFF and uPER more precisely in women.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2021-00102021-11-28T00:00:00.000+00:00https://sciendo.com/article/10.2478/ijcss-2021-0006<abstract> <title style='display:none'>Abstract</title> <p>This research introduces a new novel method for mathematically optimizing team sport training models to enhance two measures of athletic performance using an evolutionary computation based approach. A common training load model, consisting of daily training load prescriptions, was optimized using an evolutionary multi-objective algorithm to produce improvements in the mean match-day running intensity across a competitive season. The optimized training model was then compared to real-world observed training and performance data to assess the potential improvements in performance that could be achieved. The results demonstrated that it is possible to increase and maintain a stable level of match-day running performance across a competitive season whilst adhering to model-based and real-world constraints, using an intelligently optimized training design compared a to standard human design, across multiple performance criteria (BF+0 = 5651, BF+0 = 11803). This work demonstrates the value of evolutionary algorithms to design and optimize team sport training models and provides support staff with an effective decision support system to plan and prescribe optimal strategies to enhance in-season athlete performance.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2021-00062021-09-25T00:00:00.000+00:00https://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>ARTICLEtruehttps://sciendo.com/article/10.2478/ijcss-2021-00022021-05-08T00:00:00.000+00:00en-us-1