 Subjects -> SPORTS AND GAMES (Total: 199 journals)
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- A Deep Learning Approach Based on Interpretable Feature Importance for
Predicting Sports Results
Abstract: Football match result prediction is a challenging task that has been the subject of much research. Traditionally, predictions have been made by team managers, fans, and analysts based on their knowledge and experience. However and recently there has been an increased interest in predicting match outcomes using statistical techniques and machine learning. These algorithms can learn from historical data to identify complex relationships between different variables, and then make predictions about the outcome of future matches. Accordingly, forecasting plays a pivotal role in assisting managers and clubs in making well-informed decisions geared toward securing victories in leagues and tournaments. In this paper, we presented an approach, which is generally applicable in all areas of sports, to forecast football match results based on three stages. The first stage involves identifying and collecting the occurred events during a football match. As a multiclass classification problem with three classes, each match can have three possible outcomes. Then, we applied multiple machine learning algorithms to compare the performance of those different models, and choose the one that performs the best. As a final step, this study goes through the critical aspect of model interpretability. We used the SHapley Additive exPlanations (SHAP) method to decipher the feature importance within our best model, focusing on the factors that influence match predictions. Experiment results indicate that the Multilayer Perceptron (MLP), a neural network algorithm, was effective when compared to various other models and produced competitive results with prior works. The MLP model has achieved 0.8342 for accuracy. The particular significance of this study lies in the use of the SHAP method to explain the predictions made by the MLP model. Specifically, by exploiting its graphical representation to illustrate the influence of each feature within our dataset in predicting the outcome of a football match.
PubDate: Wed, 19 Mar 2025 00:00:00 GMT
- Deep Learning with 3D ResNets for Comprehensive Dual-Lane Speed Climbing
Video Analysis
Abstract: Analyzing dual-lane speed climbing videos provides critical insights into data- driven performance evaluation in sports climbing. This study introduces an enhanced deep learning approach based on 3D ResNets to classify and analyze speed climbing states. Leveraging an annotated dataset of 872 high-resolution videos covering 15 state combinations, the model integrates optimized 3D convolutions and residual connections, achieving significant improvements in classification accuracy and computational efficiency. With a test accuracy of 92.78%, the model significantly outperforms 2D CNNs and C3D models. Additionally, its lightweight architecture and reduced computational complexity equip it with the potential for real-time deployment in controlled environments. While challenges such as data imbalance and limited generalization remain, this research provides a robust technical framework for speed climbing video analysis and lays the groundwork for broader applications in spatiotemporal modeling and intelligent sports analytics.
PubDate: Sun, 23 Feb 2025 00:00:00 GMT
- Estimating the Relevance of First Offensive Shot Tactics in Table Tennis
via Simulation Based on a Finite Markov Chain Model
Abstract: Finite Markov chain modelling is a commonly used type of stochastic modelling employed in performance analysis of net games. Finite Markov chains are based on a state transition model which can be used to depict the game structure of net games as a succession of states which are defined as equivalence classes for game situations, e.g. service and return. Furthermore, the theory of finite Markov chains allows for the calculation of model variables which are of significant interest not only for validation but also for performance analysis, like wining probabilities or expected rally lengths starting from different states. By simulation, of a more-or-less of tactical behaviors one may study the impact of these tactics on overall success. A novel state transition model for table tennis is introduced in this study as extension of an existing model in the literature containing only the first offensive shot. The new model additionally contains subsequent shots since they may be perceived as being influenced by the first offensive shot. A sample of 105 single matches (49 female, 56 male) at the 2020 Tokyo Olympics was examined. The validation of the Markov property resulted in satisfactory results. The relevance of 26 transitions denoting specific tactical behaviors was obtained using simulation and subsequently compared between sexes. Results provide insights concerning the game structure of table tennis with a particular emphasis on the transition from the initial phase of rallies to the first offensive shot.
PubDate: Sun, 23 Feb 2025 00:00:00 GMT
- Two clusterings to capture basketball players’ shooting tendencies using
tracking data: clustering of shooting styles and the shots themselves
Abstract: Studies to understand the shooting preferences of basketball players relied exclusively on data on shot location, which did not lead to concrete understandings because they contained no information on how they moved to that location. Therefore, this study tried to cluster the players’ shooting tendencies using the tracking data of the players’ movements during the game. To do this, we first created hand-crafted shot features that included information on the pre-shot movement. Using those features, the dissimilarity of shooting tendencies between players was computed by considering the shot set of each player as a probability distribution and calculating the Wasserstein distance between them. The clustering based on their dissimilarity resulted in more clusters than in previous studies and allowed for specific shooting styles to be defined. Clustering using Gower distance as a dissimilarity measure for shot features, including a categorical feature, extracted clusters of shots that are useful for understanding players’ more detailed shooting tendencies. These results prove that it is not only the shot location but also how the player moved before the shot that is important to capture the player’s shooting preferences.
PubDate: Sun, 23 Feb 2025 00:00:00 GMT
- Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’
Performance Readiness Using Machine Learning
Abstract: The role of firefighters has evolved from traditional tasks like rescuing cats from trees and extinguishing house fires to more complex land, sea, and air rescues. The increasing demands for public safety necessitate rigorous training and high fitness levels for firefighters to manage their daily tasks effectively. In this study, final assessments of fitness and anthropometric parameters were gathered from 746 Malaysian firefighter recruits. A k-means clustering algorithm was utilized to group the performance levels of the firefighters whilst a quadratic discriminant analysis model was employed to predict the grouping of firefighters based on these parameters. Feature importance analysis was used to identify the most significant parameters contributing to model performance. Concurrently, the Mann-Whitney test was used to determine the essential anthro-fitness parameters differentiating between the groups of firefighters. The k-means clustering identified two performance groups: excellent and average anthro-fitness readiness (EFR and AFR) groups. The model demonstrated a mean performance accuracy of 91% for training and 87% for independent tests. Feature importance analysis revealed that inclined pull-ups, standing broad jump, shuttle run, 2.4 km run, age, and sit-ups were the most significant parameters. The Mann-Whitney test showed that the EFR group outperformed the AFR group in all anthro-fitness parameters except for height, weight, and age, which showed no significant difference. This study highlights the critical role of specific fitness and anthropometric parameters in distinguishing high-performing firefighters. By identifying the most significant contributors to overall fitness, fire departments can better prepare their personnel to meet the increasing public safety demands. The high accuracy of the predictive model also suggests its potential application in ongoing firefighter assessments and training optimization.
PubDate: Wed, 05 Feb 2025 00:00:00 GMT
- Erratum to: Energy Cost of Running Under Hypogravity in Well-Trained
Runners and Triathletes: A Biomechanical Perspective – International
Journal of Computer Science in Sport 18(2), Pages 60–80 (2019)
PubDate: Fri, 18 Oct 2024 00:00:00 GMT
- A Review on the Application of Artificial Intelligence in Basketball
Sports
Abstract: Basketball exerts a significant global influence, marked by intense competition and widespread participation, contributing substantially to the global economy. Recent advancements in computer technology and artificial intelligence (AI) have propelled research in basketball, leading to notable achievements in various aspects of the sport. A thorough literature review on the application of computer and AI technologies in basketball reveals four key areas: virtual reality technology, data capture and recognition, performance analysis and prediction, and basketball flight trajectory prediction. These studies enhance player and team training, analyze player characteristics, devise game strategies, recognize on-court data patterns, predict match outcomes, and reduce injury risks. Evidence from most studies indicates that computer and AI technologies have significantly improved player instruction and training, demonstrating remarkable potential for development in analysis and prediction. Nevertheless, this research is still in its infancy; more efforts are needed to translate these findings into practical applications.
PubDate: Fri, 18 Oct 2024 00:00:00 GMT
- Exploring Premier League Clubs Performance and Home-Away Differences Based
on Passing Network Analysis
Abstract: Whereas many studies have investigated the home advantage in football, only few studies focused on different passing patterns of home and away teams. Therefore, the aim of this study was to use two holistic indicators of social network analysis to explore potential differences: transitivity and density. As these metrics are not born in sport science, a further contribution of this study was to evaluate if these can serve as performance indicators. Based on a sample of the complete 2017/18 Premier League season, this study shows that higher ranked teams show significantly higher values for density (Z = 12.00; p < .001; r = 0.795) and transitivity (Z = 7.08; p < .001; r = 0.469) with large effect sizes. The differences of the teams’ performances for home and away games were not pronounced, and only with a small effect size (density: Z = 5.20; p< .001; r=0.267; transitivity: Z = 1.73; p = 0.084; r=0.089). Overall, results contribute to the current knowledge base in two ways: First, we could show that density and transitivity are correlated with performance, which makes sense as they can be interpreted as a team’s coopration variability. Second, we could show that the degree of successful collaboration is not significantly higher for matches played at home.
PubDate: Fri, 18 Oct 2024 00:00:00 GMT
- Advancing Artistic Swimming Officiating and Performance Assessment: A
Computer Vision Study Using MediaPipe
Abstract: Artistic Swimming (AS) requires complete execution and synchronization of movements for performance evaluation. The interest in objective and subjective performance analysis worldwide in sports via valid and reliable Artificial Intelligence (AI) tools is spreading depending on the required analysis parameters to design a novel system. This study investigated a novel application of the MediaPipe-based computer vision tool validation by examining biomechanical aspects and the objective performance impact in ballet leg and barracuda AS techniques. Twenty experienced AS athletes participated and executed these techniques under controlled conditions. Thirty-six recorded video trials were captured and analyzed via computer vision using MediaPipe, Kinovea, and AutoCAD (gold standard), with correlations calculated to assess the reliability of measurements and tools. The results indicated a non-significant difference (p<0.05) among the software tools, supported by one-way ANOVA and Bland-Altman tests. Notably, in ballet leg technique, maintaining alignment between the upper body trunk and knee in a line had a small correlation with other leg deviations; however, this aspect had a moderate negative correlation in scoring. Overall, this study suggests MediaPipe efficiency in computer vision for AS officiating and performance analysis, offering a reliable, real-time alternative to traditional methods and providing perceptions of AS techniques.
PubDate: Mon, 16 Sep 2024 00:00:00 GMT
- Comparison between six-week exergaming, conventional balance and no
exercise training program on older adults’ balance and gait speed
Abstract: We evaluated differences between a six-week exergame-training and a conventional balance training program on the balance and gait speed of older adults’ (>65 years). Forty-two healthy participants were recruited from independent living and community centers and randomized to one of three groups: exergaming balance training (EBT), conventional balance training (CBT), or control (no training). The participants completed two balance measurements (Fullerton Advanced Balance Scale (FAB) and center of pressure (COP) excursion), and gait speed at pre, post-intervention, and after a three-week follow-up. Both EBT and CBT groups improved their scores on the FAB, COP displacement, and gait speed post-intervention (p<0.05) and these changes were maintained and did not return to pre-training values after three weeks of detraining. The control group scores for FAB and gait velocity values declined (p<0.001) but not COP excursions during the study. This six-week exergame training program improved balance control and gait speed in community-dwelling seniors in a similar fashion to conventional training. Participants’ physical abilities scores improved and were maintained following three weeks of detraining. Exergame-based training therefore may be considered as an intervention that can address balance control and gait speed in older adults. As well improved scores can be maintained with transient or sporadic activity.
PubDate: Wed, 05 Jun 2024 00:00:00 GMT
- The application of Machine and Deep Learning for technique and skill
analysis in swing and team sport-specific movement: A systematic review
Abstract: There is an ever-present need to objectively measure and analyze sports motion for the determination of correct patterns of motion for skill execution. Developments in performance analysis technologies such as inertial measuring units (IMUs) have resulted in enormous data generation. However, these advances present challenges in analysis, interpretation, and transformation of data into useful information. Artificial intelligence (AI) systems can process and analyze large amounts of data quickly and efficiently through classification techniques. This study aimed to systematically review the literature on Machine Learning (ML) and Deep Learning (DL) methods applied to IMU data inputs for evaluating techniques or skills in individual swing and team sports. Electronic database searches (IEEE Xplore, PubMed, Scopus, and Google Scholar) were conducted and aligned with the PRISMA statement and guidelines. A total of 26 articles were included in the review. The Support Vector Machine (SVM) was identified as the most utilized model, as per 7 studies. A deep learning approach was reported in 6 studies, in the form of a Convolutional Neural Network (CNN) architecture. The in-depth analysis highlighted varying methodologies across all sports inclusive of device specifications, data preprocessing techniques and model performance evaluation. This review highlights that each step of the ML modeling process is iterative and should be based on the specific characteristics of the movement being analyzed.
PubDate: Wed, 05 Jun 2024 00:00:00 GMT
- A Pilot Study in Sensor Instrumented Training (SIT) - Ground Contact Time
for Monitoring Fatigue and Curve Running Technique
Abstract: This study examines the possibilities of sensor-instrumented training (SIT) in mid-distance running training sessions. Within this framework, variations of ground contact time (GCT) between straight and curved running, as well as GCT as a fatigue indicator, are explored. Seven experienced runners, with two elite female athletes, participated in two training protocols: 15 sets of 400 m with 1-minute rest and five sets of 300 m with 3-minute rest. GCT was calculated using two inertial measurement units (IMU) attached to the athletes’ feet. The running speed of all athletes was measured with wearable GPS devices. GCT showed variations between inner and outer feet, notably during curve running (300m: 2.56%; 400m: 2.35%). However, for the 300m runs, statistically insignificant GCT differences were more pronounced in straight runs (3.54%) than in curve runs (2.56%), contrasting with the typical assumption of higher differences in curve running. A fatigue-indicating pattern is visible in GCT, as well as speed curves. Other data of this study are consistent with prior research that has observed differences between the inner and outer foot during curve running, while our understanding of the development throughout the training session is enhanced. Using SIT can be a valuable tool for refining curve running technique. By incorporating novel sensing technology, the possibilities enhance our understanding of running kinematics and offer an excellent application of SIT in sports.
PubDate: Wed, 05 Jun 2024 00:00:00 GMT
- The Success-Score in Professional Football: a metric of playing style or a
metric of match outcome'
Abstract: In the growing field of data analysis in soccer tracking data is analyzed utilizing increasingly complex methods to account for the dynamic, multifactorial nature of the game. One promising approach is the Success-Score combining ball control and space control. The resulting metric is hypothesized to indicate performance levels and to distinguish performance from playing style. Position datasets from one season of the German Bundesliga were analyzed by calculating Success-Scores based on different interval lengths for two different areas. The relative goalscoring frequency above resp. below the 80th percentile and the rank order correlation between goals and Success-Scores was used to assess the relevance of the Success- Score for goalscoring. The influence of the Success-Score on match outcome, accounting for possession and opponent quality was analyzed via mixed linear models. Results indicated a relation between goalscoring and the Success-Scores, as well as a considerable influence of the Success-Scores on match outcome. The mixed linear models allowed to conclude that Success-Scores capture performance rather than just playing style. The results highlight the potential of the general concept of the Success-Score, combining space and ball control. However, the practical value of the Success-Score in its current implementation appears limited and requires further development.
PubDate: Thu, 18 Apr 2024 00:00:00 GMT
- Spin measurement system for table tennis balls based on asynchronous
non-high-speed cameras
Abstract: The spin of the ball plays a crucial role in table tennis tactics. However, it has rarely been measured and reported for the broadcast audience to better understand table tennis matches. This paper introduces a system designed to measure the spin of a table tennis ball without using electrically synchronized shutters or high-speed cameras. The system employs multiple unsynchronized cameras to detect the logos printed on the ball and estimates its three-dimensional translational motion to determine the spin rate (rotational velocity expressed in the revolutions per unit time) and spin axis (imaginary line around which the ball rotates). An experimental analysis indicated median errors of 0.78 rps and 12.5° in spin rate and axis, respectively. Additionally, the system exhibited sufficient resolution to analyze the spin rate and axis of a service ball in table tennis, distinguishing between spin axes that differ by 30° with 95.8% confidence. The developed system was used in the Japanese T-League to report the spin of several services after the live streaming of matches. The developed system successfully measured the spins of 92.1% of the served balls, confirming that the system has sufficient capability to feedback spin data immediately after a match.
PubDate: Sat, 09 Mar 2024 00:00:00 GMT
- Automatic Detection of Faults in Simulated Race Walking from a Fixed
Smartphone Camera
Abstract: Automatic fault detection is a major challenge in many sports. In race walking, judges visually detect faults according to the rules. Hence, automatic fault detection systems will help a training of race walking without experts’ visual judgement. Some studies have attempted to use sensors and machine learning to automatically detect faults. However, there are problems associated with sensor attachments and equipment such as a high-speed camera, which conflict with the visual judgement of judges, and the interpretability of the fault detection models. In this study, we proposed an automatic fault detection system for non-contact measurement. We used pose estimation and machine learning models trained based on the judgements of multiple qualified judges to realize fair fault judgement. We verified them using smartphone videos of normal race walking and walking with intentional faults in several athletes including the medalist of the Tokyo Olympics. The results show that the proposed system detected faults with an average accuracy of over 90%. We also revealed that the machine learning model detects faults according to the rules. In addition, the intentional faulty walking movement of the medalist was different from that of other walkers. This finding informs realization of a more general fault detection model.
PubDate: Sat, 09 Mar 2024 00:00:00 GMT
- The Use of Momentum-Inspired Features in Pre-Game Prediction Models for
the Sport of Ice Hockey
Abstract: We make a unique contribution to momentum research by proposing a way to quantify momentum with performance indicators (i.e., features). We argue that due to measurable randomness in the NHL, sequential outcomes’ dependence or independence may not be the best way to approach momentum. Instead, we quantify momentum using a small sample of a team’s recent games and a linear line of best-fit to determine the trend of a team’s performances before an upcoming game. We show that with the use of SVM and logistic regression these momentum- based features have more predictive power than traditional frequency-based features in a pre-game prediction model which only uses each team’s three most recent games to assess team quality. While a random forest favors the use of both feature sets combined. The predictive power of these momentum-based features suggests that momentum is a real phenomenon in the NHL and may have more effect on the outcome of games than suggested by previous research. In addition, we believe that how our momentum-based features were designed and compared to frequency-based features could form a framework for comparing the short-term effects of momentum on any individual sport or team.
PubDate: Sat, 24 Feb 2024 00:00:00 GMT
- Pacing Patterns of Half-Marathon Runners: An analysis of ten years of
results from Gothenburg Half Marathon
Abstract: The Gothenburg Half Marathon is one of the world’s largest half marathon races with over 40 000 participants each year. In order to reduce the number of runners risking over-straining, injury, or collapse, we would like to provide runners with advice to appropriately plan their pacing. Many participants are older or without extensive training experience and may particularly benefit from such pacing assistance. Our aim is to provide this with the help of machine learning. We first analyze a large publicly available dataset of results from the years 2010 - 2019 (n = 423 496) to identify pacing patterns related to age, sex, ability, and temperature of the race day. These features are then used to train machine learning models for predicting runner’s finish time and to identify which runners are at risk of making severe pacing errors and which ones seem set to pace well. We find that prediction of finish time improves over the current baseline, while identification of pacing patterns correctly identifies over 70% of runners at risk of severe slowdowns, albeit with many false positives.
PubDate: Sat, 03 Feb 2024 00:00:00 GMT
- Attack with Empty Goal (7 vs 6) in Team Handball - Analysis of Men’s
EHF Euro 2022
Abstract: Team handball is constantly evolving. Since the beginning of the century some changes has been introduced but no rule has been as controversial and not consensual as the one introduced in 2016 that allows the change of a goalkeeper for a field player (Empty goal) allowing teams to play 7 vs. 6 (Prudente et al., 2022). With this study we intend to analyze and characterize the attack with empty goal (7 vs. 6) of the 12 best ranked teams in Men’s EHF Euro 2022. Observational Methodology was used and it was built, validated by experts and subsequently used a mixed ad hoc instrument combining a 12 criteria field format with 77 category category system to observe and register data. Data were gathered from 28 matches involving teams classified in the first twelve places in the 2022 Men’s EHF Euro 2022. These were recorded from TV broadcasts, and the total number of offensive sequences carried out in an organized attack game method 7 vs. 6 with empty goal (n = 121) was analyzed. For data analysis, prospective and retrospective sequential analysis and the technique of polar coordinates was used. The main results show a stronger association between: a) No Goal by Technical Fault and succeeded direct goal attempt; b) Direct Goal Attempt and Goal. Results also show that best ranked teams used less 7 vs. 6 attack system. According to the main results, teams that used 7 vs. 6 and lost the ball by technical fault had a stronger association with a direct goal attempt by the opponent team. That is positively associated with goal. This leads to a practical recommendation that teams that want to use 7 vs. 6 should practice this special option in order to achieve more efficiency, reducing the number of technical faults and consequently the opponents goal to goal attempts.
PubDate: Mon, 06 Nov 2023 00:00:00 GMT
- Success-Score in Professional Soccer – Is there a sweet spot in the
analysis of space and ball control'
Abstract: In contrast to simple performance indicators in the practical application of quantitative analysis in professional soccer, the inclusion of certain contextual elements can improve both the predictive quality and interpretability of these. Therefore, the Success-Score is intended to identify the factors relevant to success by linking ball control and space control.Position datasets from 14 games of the Bundesliga were used to calculate Success-Scores for several interval lengths for the penalty area and the 30-meter-zone. The relative goalscoring frequency above resp. below the 80th percentile, the rank correlation in terms of goals scored pursuant to the sorting of the Success-Score as well as possible distinctions in the Success-Score between two teams of different quality were examined.Results revealed that interval lengths and the area under investigation largely affect the resulting Success-Score and its distribution. The Success-Score applied to the 30-meter-zone seems preferable when analyzing goalscoring. Dependent on the target of analysis, methodological and theoretical considerations need to be balanced in a sweet spot of the interval length.
PubDate: Thu, 26 Oct 2023 00:00:00 GMT
- A comparison of tournament systems for the men’s World Handball
Championship
Abstract: The men’s Handball World Championship commences with eight round robin groups of four teams before the “main round” of four groups of six teams. These groups of six each include the top three teams from pairs of initial groups. The tournament draw uses pots of eight which risks two teams in the top four appearing in the same group of the main round. A further issue is that teams finishing between third and sixth in the main round groups are awarded tournament places between ninth and 24th without any further matches. Therefore, the purpose of this investigation was to compare the current tournament system with alternatives using pots of four teams in the draw, and / or adding a knockout stage to place teams from ninth to 24th. These four tournament systems were simulated 100,000 times, using underlying regression models for the goals scored based on their World ranking points. Introducing pots of four increased the chance of reaching the quarter-finals for teams ranked one to four and nine to 12 by 1.3% and 1.6% respectively. It is recommended that the draw uses pots of four teams associated with pairs of initial groups that lead to common main draw groups.
PubDate: Fri, 06 Oct 2023 00:00:00 GMT
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