<abstract><title style='display:none'>Abstract</title>The wage of a football player is a function of numerous aspects such as the player’s skills, performance in the previous seasons, age, trajectory of improvement, personality, and more. Based on these aspects, salaries of football players are determined through negotiation between the team management and the agents. In this study we propose an objective quantitative method to determine football players’ wages based on their skills. The method is based on the application of pattern recognition algorithms to performance (e.g., scoring), behavior (e.g., aggression), and abilities (e.g., acceleration) data of football players. Experimental results using data from 6,082 players show that the Pearson correlation between the predicted and actual salary of the players is ~0.77 (p &lt; .001). The proposed method can be used as an assistive technology when negotiating players salaries, as well as for performing quantitative analysis of links between the salary and the performance of football players. The method is based on the performance and skills of the players, but does not take into account aspects that are not related directly to the game such as the popularity of the player among fans, predicted merchandise sales, etc, which are also factors of high impact on the salary, especially in the case of the team lead players and superstars. Analysis of player salaries in eight European football leagues show that the skills that mostly affect the salary are largely consistent across leagues, but some differences exist. Analysis of underpaid and overpaid players shows that overpaid players tend to be stronger, but are inferior in their reactions, vision, acceleration, agility, and balance compared to underpaid football players.</abstract>

Computational Estimation of Football Player Wages

<abstract><title style='display:none'>Abstract</title> Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.</abstract>

Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

<abstract><title style='display:none'>Abstract</title>Identifying potential future elite athletes is important in many sporting events. The successful identification of potential future elite athletes at an early age would help to provide high-quality coaching and training environments in which to optimize their development. However, a large variety of different skills and qualities are needed to succeed in elite sports, making talent identification generally a complex and multifaceted problem. Due to the rarity of elite athletes, datasets are inherently imbalanced, making classical statistical inference difficult. Therefore, we approach talent identification as an anomaly detection problem. We trained a nonlinear one-class support vector machine (one-class SVM) on a dataset (N=951) collected from 14-year-old junior soccer players to detect potential future elite players. The mean area under the receiver operating characteristic curve (AUC-ROC) over the tested hyperparameter combinations was 0.763 (std 0.007). The most accurate model was obtained when physical tests, measuring, for example, technical skills, speed, and agility, were used. According to our results, the proposed approach could be useful to support decision-makers in the process of talent identification.</abstract>

Talent identification in soccer using a one-class support vector machine

<abstract><title style='display:none'>Abstract</title> 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</abstract>

Predictive Modelling of Training Loads and Injury in Australian Football

<abstract><title style='display:none'>Abstract</title>This study analyzed the effect of a disproportionate amount of time between rounds of a playoff series—known as the “rest vs. rust” debate in the popular sports media—on the likelihood of winning each game of the subsequent round of the tournament. We utilized NBA Finals data from 1984 – 2018, and analyzed this phenomenon using ordered logistic regression with a categorical dependent variable representing the margin of victory of each game. In addition to several control variables, variables reflecting the difference in the time between series for the two teams were used to measure this effect. The results indicate that having additional time between rounds of the series provides a statistically significant advantage; interestingly, though, it has more of an impact on the second game of the subsequent round than it does for the first game. Teams may utilize the results of this study when deciding on how to schedule and intensify their practice sessions, by providing appropriate rest and maintaining rhythm to increase the likelihood of winning each game.</abstract>

Rest vs. Rust: The Effect of Disproportionate Time Between Rounds of a Playoff Series

<abstract><title style='display:none'>Abstract</title>The purpose of the present study was to demonstrate an inductive approach for dynamically modelling sport-related injuries with a probabilistic graphical model. Dynamic Bayesian Network (DBN), a well-known machine learning method, was employed to illustrate how sport practitioners could utilize a simulatory environment to augment the training management process. 23 University of Iowa female student-athletes (from 3 undisclosed teams) were regularly monitored with common athlete monitoring technologies, throughout the 2016 competitive season, as a part of their routine health and well-being surveillance. The presented work investigated the ability of these technologies to model injury occurrences in a dynamic, temporal dimension. To verify validity, DBN model accuracy was compared with the performance of its static counterpart. After 3 rounds of 5-fold cross-validation, resultant DBN mean accuracy surpassed naïve baseline threshold whereas static Bayesian network did not achieve baseline accuracy. Conclusive DBN suggested subjectively-reported stress two days prior, subjective internal perceived exertions one day prior, direct current potential and sympathetic tone the day of, as the most impactful towards injury manifestation.</abstract>

Decision Support System for Mitigating Athletic Injuries

<abstract>
<title style='display:none'>Abstract</title>
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.
</abstract>

The Success-Score in Professional Football: a metric of playing style or a metric of match outcome?

<abstract>
<title style='display:none'>Abstract</title>
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.
</abstract>

Automatic Detection of Faults in Simulated Race Walking from a Fixed Smartphone Camera

<abstract>
<title style='display:none'>Abstract</title>
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.
</abstract>

Spin measurement system for table tennis balls based on asynchronous non-high-speed cameras

<abstract>
<title style='display:none'>Abstract</title>
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.
</abstract>

The Use of Momentum-Inspired Features in Pre-Game Prediction Models for the Sport of Ice Hockey

<abstract>
<title style='display:none'>Abstract</title>
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.
</abstract>

Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon

<abstract>
<title style='display:none'>Abstract</title>
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.
</abstract>

Attack with Empty Goal (7 vs 6) in Team Handball - Analysis of Men’s EHF Euro 2022

<abstract><title style='display:none'>Abstract</title>In the last thirty years, an increasing interest in sport sciences regarding the analysis of expert athletes’ gaze behavior has become apparent. This narrative review provides an overview of the use of eye tracking systems in high-performance sports from 1987 to 2016. A systematic search of the PubMed, Scopus, SPORTDiscus, and WebofScience databases was conducted. The search was performed using the keywords <italic>eye tracking</italic>, <italic>eye movement</italic>, <italic>gaze behavior/patterns</italic>, and <italic>visual search strategies</italic> in combination with <italic>high-performance sports</italic>, <italic>elite athletes</italic>, <italic>high-class athletes</italic>, <italic>sport experts</italic>, and <italic>top-athletes</italic>. It yielded a total of 86 studies of which almost half were conducted computer-based or in front of a screen. Most studies dealt with the analysis of gaze behavior during dead ball situations while also focusing on differences between expert athletes and novices. More high-quality intervention studies are essential to determine if there are ideal gaze strategies and, if yes, how it is possible to learn/implement these.</abstract>

Eye tracking in high-performance sports: Evaluation of its application in expert athletes

<abstract><title style='display:none'>Abstract</title>Decision making in sport involves forecasting and selecting choices from different options of action, care, or management. These processes are conditioned by the available information (sometimes limited, fallible, or excessive), the cognitive limitations of the decision-maker (heuristics and biases), the finite amount of available time to make the decision, and the levels of risk and reward. Decision support systems have become increasingly common in sporting contexts such as scheduling optimization, skills evaluation and classification, decision-making assessment, talent identification and team selection, or injury risk assessment. However no specific, formalised framework exists to help guide either the development or evaluation of these systems. Drawing on a variety of literature, this paper proposes a decision support system development framework for specific use in high-performance sport. It proposes three separate criteria for this purpose: 1) Context Satisfaction, 2) Output Quality, and 3) Process Efficiency. Underpinning these criteria there are six specific components: Feasibility, Delivered knowledge, Decisional guidance, Data quality, System error, and System complexity. The proposed framework offers a systematic approach for users to ensure that each of the six components are considered and optimised before, during, and after developing the system. A DSS development framework for high-performance sport should help to improve both short and long term decision-making in a variety of sporting contexts.</abstract>

A development framework for decision support systems in high-performance sport

<abstract><title style='display:none'>Abstract</title> AIM: The current investigation aimed to create an objective rating of Gaelic football teams and to examine factors relating to a team's rating. METHOD: A modified version of the Elo Ratings formula (Elo, 1978) was used to rate Gaelic football teams. A total of 1101 competitive senior Inter County matches from 2010-2015 were incorporated into calculations. Factors examined between teams included population, registered player numbers, previous success at adult and underage levels, financial income from the GAA, team expenses and number of clubs in a county. RESULTS: The Elo Ratings formula for Gaelic football was found to have a strong predictive ability, correctly predicting the result in 72.90% of 642 matches over a 6 year period. Strong positive correlations were observed between previous success at senior level, Under 21 level, Under 18 level and current Elo points. Moderate correlations exist between population figures and current Elo points. Moderate correlations are also evident between the number of registered players in a county and the county’s Elo rating points. CONCLUSION: Gaelic football teams can be objectively rated using a modified Elo Ratings formula. In order to develop a successful senior team, counties should focus on the development of underage players, particularly up to U18 and U21 level.</abstract>

A Rating System For Gaelic Football Teams: Factors That Influence Success

<abstract><title style='display:none'>Abstract</title>The increasing availability of data from sports events has led to many new directions of research, and sports analytics can play a role in making better decisions both within a club and at the level of an individual player. The ability to objectively evaluate individual players in team sports is one aspect that may enable better decision making, but such evaluations are not straightforward to obtain. One class of ratings for individual players in team sports, known as plus-minus ratings, attempt to distribute credit for the performance of a team onto the players of that team. Such ratings have a long history, going back at least to the 1950s, but in recent years research on advanced versions of plus-minus ratings has increased noticeably. This paper presents a comprehensive review of contributions to plus-minus ratings in later years, pointing out some key developments and showing the richness of the mathematical models developed. One conclusion is that the literature on plus-minus ratings is quite fragmented, but that awareness of past contributions to the field should allow researchers to focus on some of the many open research questions related to the evaluation of individual players in team sports.</abstract>

A comprehensive review of plus-minus ratings for evaluating individual players in team sports

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International Journal of Computer Science in Sport

 The International Journal of Computer Science in Sport (IJCSS) is published in association with the International Association of Computer Science in Sport (IACSS).  It is a...

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 The International Journal of Computer Science in Sport (IJCSS) is published in association with the International Association of Computer...

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