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- The Kos Angle, an optimizing parameter for football expected goals (xG)
models
Abstract: The utilization of metrics such as expected goals (xG) has the potential to provide teams with a competitive edge. By incorporating xG into their analysis and decision-making processes, teams can gain valuable insights. This study proposes a new approach to football xG modeling using Kos Angle which represents the shooting angle, from which we substract the angles occupied by players inside the shot angle. The objective of this study is to evaluate the impact of the Kos Angle feature on the performance of football xG models. After developing the mathematical formula of the Kos Angle, we selected additional features and built different xG models. Subsequently, the impact of the Kos Angle feature on the models’ performances was evaluated, revealing an increase in Recall and Precision and a decrease in Brier score and RMSE. We also found that the Kos Angle accounted for a significant portion of the models’ predictive power. By providing a more realistic representation of shot situations, the addition of the Kos Angle feature allows the improvement of xG models performances, which can give a more valuable insights to football professionals who rely on xG metrics and their variations.
PubDate: Wed, 13 Sep 2023 00:00:00 GMT
- Workload Monitoring Tools in Field-Based Team Sports, the Emerging
Technology and Analytics used for Performance and Injury Prediction: A
Systematic Review
Abstract: Training load (TL) is frequently documented among team sports and the development of emerging technology (ET) is displaying promising results towards player performance and injury risk identification. The aim of this systematic review was to identify ETs used in field-based sport to monitor TL for injury/performance prediction and provide sport specific recommendations by identifying new data generation in which coaches may consider when tracking players for an increased accuracy in training prescription and evaluation among field-based sports. Data was extracted from 60 articles following a systematic search of CINAHL, SPORTDiscus, Web of Science and IEEE XPLORE databases. Global positioning system (GPS) and accelerometers were common external TL tools and Rated Perceived Exertion (RPE) for internal TL. A collection of analytics tools were identified when investigating injury/performance prediction. Machine Learning showed promising results in many studies, identifying the strongest predictive variables and injury risk identification. Overall, a variety of TL monitoring tools and predictive analytics were utilized by researchers and were successful in predicting injury/performance, but no common method taken by researchers could be identified. This review highlights the positive effect of ETs, but further investigation is desired towards a ‘gold standard” predictive analytics tool for injury/performance prediction in field-based team sports.
PubDate: Mon, 04 Sep 2023 00:00:00 GMT
- Hierarchical Bayesian analysis of racehorse running ability and jockey
skills
Abstract: In this paper, we proposed a new method of evaluating horse ability and jockey skills in horse racing. In the proposed method, we aimed to estimate unobservable individual effects of horses and jockeys simultaneously with regression coefficients for explanatory variables such as horse age and racetrack conditions and other parameters in the regression model. The data used in this paper are records on 1800m races (excluding steeplechases) held by the Japan Racing Association from 2016 to 2018, including 4063 horses and 143 jockeys. We applied the hierarchical Bayesian model to stably estimate such a large amount of individual effects. We used the Markov chain Monte Carlo (MCMC) method coupled with Ancillarity- Sufficiency Interweaving Strategy for Bayesian estimation of the model and choose the best model with Widely Applicable Information Criterion as a model selection criterion. As a result, we found a large difference in the ability among horses and jockeys. Additionally, we observed a strong relationship between the individual effects and the race records for both horses and jockeys.
PubDate: Sat, 19 Aug 2023 00:00:00 GMT
- Systematic Analysis of Position-Data-based Key Performance Indicators
Abstract: In the past 20 years, performance analysis in soccer has accumulated a wide variety of key performance indicators (KPI’s) aimed at reflecting a team’s strength and success. Thanks to rapidly advancing technologies and data analytics more sophisticated metrics, requiring high resolution data acquisition and big data methods, are developed. This includes many position-data-based KPI’s, which incorporate precise spatial and temporal information about every player and the ball on the field.The present study contributes to this research by performing a large-scale comparison of several metrics mainly based on player positions and passing events. Their association with team’s success (derived from goals scored) and team’s strength (estimated from pre-game betting odds) is analysed.The systematic analysis revealed relevant results for further KPI research: First, the magnitude of overall correlation coefficients was higher for relative metrics than for absolute metrics. Second, the correlation of metrics with the strength of a team is stronger than the correlation with the game success of a team. Third, correlation analysis with team strength indicated more positive associations, while correlation analysis with success is most likely confounded by the intermediate score line of a game and revealed more negative associations.
PubDate: Fri, 16 Jun 2023 00:00:00 GMT
- Detecting Outliers in Cardiopulmonary Exercise Testing Data of Ski Racers
– A Comparison of Methods and their Effect on the Performance of Fatigue
Prediction
Abstract: In sports science, cardiopulmonary data is used to assess exercise intensity, performance and health status of athletes and derive relevant target values. However, sensors may produce flawed data and data may include a wide variety of artifacts, which could potentially lead to false conclusions. Thus, appropriate and customized pre-processing algorithms are a vital prerequisite for producing reliable and valid analysis results. To find adequate outlier detection methods for this type of data, we compared three algorithms by applying them on seven ergospirometric measures of junior ski racing athletes and applied a model to predict fatigue during skiing based on the pre-processed data. While values that lie outside a realistic spectrum were consistently labelled as outliers by all methods, and mean values and standard deviations changed in similar ways, methods differed from each other when it comes to changing trends, recurring patterns, and subsequent outliers. Decomposing the sensor data into different components (trend, seasonality, remainder) before dealing with outliers increased average predictive performance the most. However, pre-processing remarkably improved prediction results for certain study participants and not for others. Thus, handling outliers correctly prior to deriving information from ergospirometric data is recommended but more research should be conducted to find methods that achieve more consistent improvement.
PubDate: Sun, 04 Jun 2023 00:00:00 GMT
- A Decision Support System for Simulating and Predicting the Impacts of
Various Tournament Structures on Tournament Outcomes
Abstract: 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.
PubDate: Sat, 27 May 2023 00:00:00 GMT
- Sport-specific differences in key performance factors among handball,
basketball and table tennis players
Abstract: 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.
PubDate: Thu, 06 Apr 2023 00:00:00 GMT
- Modeling the extra pass in basketball – an assesment of one of the most
crucial skills for creating great ball movement
Abstract: 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.
PubDate: Wed, 08 Mar 2023 00:00:00 GMT
- Estimating the effect of hitting strategies in baseball using
counterfactual virtual simulation with deep learning
Abstract: 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.
PubDate: Tue, 17 Jan 2023 00:00:00 GMT
- Analysis of Relationship between Training Load and Recovery Status in
Adult Soccer Players: a Machine Learning Approach
Abstract: 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.
PubDate: Tue, 17 Jan 2023 00:00:00 GMT
- The Impact of Blended Learning and Direct Video Feedback on Primary School
Students’ Three-Step Ball Throwing Technique
Abstract: 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.
PubDate: Tue, 17 Jan 2023 00:00:00 GMT
- Success-Score in Professional Soccer – Validation of a Dynamic Key
Performance Indicator Combining Space Control and Ball Control within
Goalscoring Opportunities
Abstract: 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.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 80th 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.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, rs(6) = 0.73, p = .04. The more successful team showed significantly higher Success-Scores.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.
PubDate: Tue, 17 Jan 2023 00:00:00 GMT
- Time Series Data Mining for Sport Data: a Review
Abstract: 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.
PubDate: Tue, 17 Jan 2023 00:00:00 GMT
- Cooperative play classification in team sports via semi-supervised
learning
Abstract: 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.
PubDate: Thu, 17 Nov 2022 00:00:00 GMT
- The Impact of Virtual Reality Training on Learning Gymnastic Elements on a
Balance Beam with Simulated Height
Abstract: 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 < .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.
PubDate: Thu, 17 Nov 2022 00:00:00 GMT
- Optimizing and dimensioning a data intensive cloud application for soccer
player tracking
Abstract: 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.
PubDate: Wed, 15 Jun 2022 00:00:00 GMT
- Meta-heuristics meet sports: a systematic review from the viewpoint of
nature inspired algorithms
Abstract: 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.
PubDate: Wed, 15 Jun 2022 00:00:00 GMT
- Wireless inertial sensor system for hammer throwing
Abstract: 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 < 0.001). These suggest that this system can used in the training to improve the technique of the hammer throw.
PubDate: Thu, 24 Mar 2022 00:00:00 GMT
- An optimization model for the fair distribution of prize money in ATP
tournaments
Abstract: 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.
PubDate: Thu, 24 Mar 2022 00:00:00 GMT
- Offseason Fitness Tests a Collegiate Basketball Strength Coach Should
Choose to Predict In-Season Perfomance Based on Sex
Abstract: 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.
PubDate: Sun, 28 Nov 2021 00:00:00 GMT
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