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AbstractWheelchair basketball, regulated by the International Wheelchair Basketball Federation, is a sport designed for individuals with physical disabilities. This article presents a data-driven tool that effectively determines optimal team lineups based on past performance data and metrics for player effectiveness. Our proposed methodology involves combining a Bayesian longitudinal model with an integer linear problem to optimize the lineup of a wheelchair basketball team. To illustrate our approach, we use real data from a team competing in the Rollstuhlbasketball Bundesliga, namely the Doneck Dolphins Trier. We consider three distinct performance metrics for each player and incorporate uncertainty from the posterior predictive distribution of the longitudinal model into the optimization process. The results demonstrate the tool’s ability to select the most suitable team compositions and to calculate posterior probabilities of several players playing together in the best composition. [ABSTRACT FROM AUTHOR]

This study investigates the performance of unorthodox players in T20 cricket, focusing on both batsmen and bowlers. The analysis evaluates the strike rates and batting averages of unorthodox batsmen to identify those who score quickly while maintaining consistency. For bowlers, the study compares economy rates and bowling averages to assess their effectiveness in limiting runs while taking wickets. Using data collected from 2005 to 2024, the research highlights players like Suryakumar Yadav, Andre Russell, and Saeed Ajmal, who excel in their respective metrics. Additionally, the study explores correlations between batting strike rate and average, as well as bowling economy and average, revealing statistically significant relationships that provide insights into player performance. [ABSTRACT FROM AUTHOR]

Copyright of Perpectivas is the property of Revista Perspectivas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: Analyzing the performance of Wheelchair Basketball players according to Functional Classification and game indicators to provide relevant information regarding the identification of details which can determine indicial and team success in each of the different Functional Classifications. Game statistics are used for the analysis of the performance of athletes. This allows the strengths and weaknesses of the players to be understood, and to design and implement training sessions for performance enhancement. Therefore, the objective of this study was to analyse the game statistics of the 2021 and 2023 European Champions and the 2023 World Champions in Wheelchair Basketball (WB), both male and female. For this purpose, the game statistics of all the championships were collected, and the differences according to gender and competition analysed through a statistical and inferential analysis, considering the functional classification of the players, based on the Mann–Whitney U test for the comparison of gender, and the Kruskal–Wallis H test for the comparison between championships. The results showed that players with a classification higher than 3.0 have higher three-point and two-point throws, while players with a classification of 1.0 have lower effectiveness in shooting. Depending on the championship, a greater number of three-point throws and respective effectiveness was found in the 2023 World Championships, with the male athletes presenting a greater number of points. This information will support WB coaches in designing and implementing training sessions and patterns of play and movements in competitive moments to improve individual and team performance. [ABSTRACT FROM AUTHOR]

Background. Random Forest is a popular machine learning algorithm used for classification and regression tasks. The study purpose is to investigate the use of Random Forest machine learning to predict the winning chances of teams in the 2024 Indian Premier League (IPL) season. Objectives. By analyzing comprehensive player statistics, including matches played, batting and bowling averages, as well as fielding contributions, the study aims to understand the factors that influence team success in T20 cricket and provide actionable insights for team management, betting markets, and cricket enthusiasts. Material and methods. The study involved 10 cricket teams competing in the IPL 2024 season. Player statistics and match outcomes data from previous IPL seasons were collected and cleaned, with missing values addressed through imputation. The features were based on player statistics, including both aggregate measures and calculated metrics. A Random Forest is chosen as the machine learning model, trained using preprocessed data, with features derived from player statistics as input and match outcomes as the target variable. The dataset was split into training and validation sets, using methodologies such as cross-validation to ensure a robust model performance. Results. The Random Forest model demonstrates strong predictive performance, with a low Mean Squared Error (MSE) of 8.2174, Root Mean Squared Error (RMSE) of 2.8666, and a high R-Squared value of 0.9173, indicating approximately 91.73% variance explained in the data. Chennai Super Kings emerge as frontrunners with a predicted performance percentage of 83.4%, while teams like Punjab Kings and Lucknow Super Giants show lower predicted performance percentages, suggesting potential areas for its improvement. Conclusions. This study reveals the effectiveness of Random Forest machine learning in forecasting IPL match outcomes based on player statistics. It provides valuable insights into team dynamics and offers actionable recommendations for team management and cricket enthusiasts. The findings enrich our understanding of IPL match dynamics, contribute to the evolution of cricket analytics, and promote greater engagement with sport, ultimately enhancing the fan experience in the IPL. [ABSTRACT FROM AUTHOR]

Given the importance of accurate team rankings in American college football (CFB) – due to heavy title and playoff implications – strides have been made to improve metrics for team performance evaluation, going from basic averages (e.g. points scored per game) to metrics that adjust for a team's strength of schedule, but one aspect that's yet to be accounted for is the ability of team's offense and defense to complement one another, termed 'complementary football'. American football is unique because the same team's offensive and defensive units typically consist of separate player sets that don't share the field simultaneously, which tempts one to evaluate them independently. Yet, some aspects of your team's defensive (offensive) performance may directly impact the complementary unit, e.g. turnovers forced by your defense could lead to easier scoring chances for your offense. Our main goal is to identify the most consistently influential features of complementary football in a data-driven way, subsequently adjusting each team's offensive (defensive) performance for that of their complementary unit. To achieve that, for the 2009–2019 CFB seasons, we incorporate natural splines with group penalty approaches, conducting partially constrained optimization to guarantee the full adjustment for the strength of schedule and home-field factor. [ABSTRACT FROM AUTHOR]

Carl Morris 1938–2023 was well-known for his pioneering research in Bayesian multiparameter inference and prediction. Morris was also known for his development of statistical thinking and methodology in sports. This paper provides an overview of Morris' contributions in sports. This includes Morris' experience in sports as a youth, summaries of some of Morris' best-known contributions using sports data, his influence working with students, and some of Morris' thinking about the interplay of statistics and sports. [ABSTRACT FROM AUTHOR]

Analytics, the application of advanced statistics to sport, has become increasingly prominent. This paper will focus on their application in basketball. I will argue that the introduction of analytics is an attempt to expand the logic of modernity into sport, an area which, for Elias, its demands had historically been temporarily suspended. Instead we can see some of the key tenets of analytics – which I will explore via reference to The Mid-Range Theory, a recent book promoting their use in basketball – as reproducing the modernist demand for control, order, prediction and efficiency. In turn, I will also explore how the critique of analytics reflects the critique of modernity, most notably in its attempt to disenchant the social world and create a new iron cage of efficiency. Therefore, the case of analytics in basketball shows us that what Wagner called modernity as interpretation still has significant social purchase. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Sport & Exercise / Türk Spor ve Egzersiz Dergisi is the property of Turkish Journal of Sport & Exercise and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Turkish Journal of Sport & Exercise / Türk Spor ve Egzersiz Dergisi is the property of Turkish Journal of Sport & Exercise and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is common to be interested in rankings or order relationships among entities. In complex settings where one does not directly measure a univariate statistic upon which to base ranks, such inferences typically rely on statistical models having entity-specific parameters. These can be treated as random effects in hierarchical models characterizing variation among the entities. In this paper, we are particularly interested in the problem of ranking basketball players in terms of their contribution to team performance. Using data from the National Basketball Association (NBA) in the United States, we find that many players have similar latent ability levels, making any single estimated ranking highly misleading. The current literature fails to provide summaries of order relationships that adequately account for uncertainty. Motivated by this, we propose a Bayesian strategy for characterizing uncertainty in inferences on order relationships among players and lineups. Our approach adapts to scenarios in which uncertainty in ordering is high by producing more conservative results that improve interpretability. This is achieved through a reward function within a decision theoretic framework. We apply our approach to data from the 2009-2010 NBA season. [ABSTRACT FROM AUTHOR]

In today's world, Human Activity Recognition (HAR) through video streams is actively used in every aspect of our life, such as automated surveillance systems and sports statistics are computed according to the videos with the help of HAR. Activity detection is not a new subject, and several methods are available. However, the most recent and most promising techniques rely on Convolutional Neural Networks (CNNs). CNNs primary usage is based on a single image frame to perform logical or categorical identification of an object, scene, or activity. We exploit this feature to adapt CNN on video streams to achieve HAR. In this study, we present a Personalized HAR (PHAR) framework that increases activity recognition accuracy with Object Detection (OD). First, we demonstrate the state-of-the-art HAR and OD methods in the literature. Then we illustrate our framework with two new Single Person Human Activity Recognition models. Finally, the performance of the new framework is evaluated with the well-known activity detection methods. Results show that our new PHAR model with 95% accuracy ratio outperforms the CNN-LSTM-based reference model (90%). Moreover, a new metric Average Accuracy Score (AAS) is described in this study, PHAR models approximately have 94% AAS, which is better than the reference model with 89% AAS. [ABSTRACT FROM AUTHOR]

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 1800-m 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. [ABSTRACT FROM AUTHOR]

With the development of modern science and technology, sports training has begun to develop in the direction of new ideas, new technologies and new equipment. Wearable devices with wireless data transmission and real-time feedback functions are more and more widely used in sports training, providing a simpler and more scientific way to explore the biological significance of sports training. In this paper, the wearable Internet of Things is applied to the intelligent system simulation of physical training for sports majors. With the support of the Internet of Things technology, wearable devices can quickly capture the movement data of students' physical training and quickly transmit this information to the intelligent system of physical training for sports majors, so that the system can be updated in real time. Wearable devices are high-tech products developed in the context of big data. The use of wearable devices to achieve sports training and data collection is only an external manifestation of sports scientific monitoring. The collection and statistics of the original sports data obtained through more scientific information processing technology is the focus of sports scientific monitoring. At present, the use of wearable devices in sports training also has some measurement errors, which affect the validity and credibility of data. Data processing is not systematic and perfect, and the compatibility and connectivity are low. Statistical analysis is a common way to analyze sports training data at home and abroad. This paper discusses the physical training data based on the status update of the wearable object network and uses the optimized data processing method to improve the accuracy of the data. [ABSTRACT FROM AUTHOR]

Statistics in sports plays a key role in predicting winning strategies and providing objective performance indicators. Despite the growing interest in recent years in using statistical methodologies in this field, less emphasis has been given to the multivariate approach. This work aims at using the Bayesian networks to model the joint distribution of a set of indicators of players' performances in basketball in order to discover the set of their probabilistic relationships as well as the main determinants affecting the player's winning percentage. From a methodological point of view, the interest is to define a suitable model for non-Gaussian data, relaxing the strong assumption on normal distribution in favour of Gaussian copula. Through the estimated Bayesian network, we discovered many interesting dependence relationships, providing a scientific validation of some known results mainly based on experience. At last, some scenarios of interest have been simulated to understand the main determinants that contribute to rising in the number of won games by a player. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an R software package for simulating plays and drives using play-by-play data from the National Football League. The simulations are generated by sampling play-by-play data from previous football seasons. The sampling procedure adds statistical rigor to any decisions or inferences arising from examining the simulations. We highlight that the package is particularly useful as a data-driven tool for evaluating potential in-game strategies or rule changes within the league. We demonstrate its utility by evaluating the oft-debated strategy of "going for it" on fourth down and investigating whether or not teams should pass more than the current standard. [ABSTRACT FROM AUTHOR]

Several studies deal with the development of advanced statistical methods for predicting football match results. These predictions are then used to construct profitable betting strategies. Even if the most popular bets are based on whether one expects that a team will win, lose, or draw in the next game, nowadays a variety of other outcomes are available for betting purposes. While some of these events are binary in nature (e.g. the red cards occurrence), others can be seen as binary outcomes. In this paper we propose a simple framework, based on score-driven models, able to obtain accurate forecasts for binary outcomes in soccer matches. To show the usefulness of the proposed statistical approach, two experiments to the English Premier League and to the Italian Serie A are provided for predicting red cards occurrence, Under/Over and Goal/No Goal events. [ABSTRACT FROM AUTHOR]

The sports statistics community is rapidly evolving. R is an open-source software in constant development that has gained a lot of popularity within statistical communities and sports analytics. This work presents a systematic review of the available R CRAN sport packages following PRISMA guidelines. We consider all packages active as of 18 February 2021. A total of 81 sport R packages created since 2006 were detected. Of these, 35.9% were authored by an American national, 69.1% include a dataset, 43.2% provide vignettes, and 65.4% have been updated at least once. The sport with the highest representation is basketball (n = 14, 17.3%), followed by soccer (n = 12, 14.8%) and packages related to physical activity (n = 11, 13.6%). There are no sports packages directed solely for the female gender, while 59.3% of packages are focused on professional athletes. Fifty packages (61.7%) are related to the sports performance analysis category, and web scraping (n = 43, 53.1%) was the main functionality in the collected packages. The creation of new R packages in the area of sports could help solve questions and tasks that still remain a challenge in this field, while continuing to help to improve the level of statistical education and computational thinking skills. [ABSTRACT FROM AUTHOR]

To understand the Historical European Martial Arts (HEMA) community, its weapon disciplines, organisational structure and gender, a HEMA census was conducted in 2021 for Germany and Austria and in 2019 for Germany. The 2021 census captures 3186 HEMA practitioners training in 124 locations in Germany and 801 practitioners training in 34 locations in Austria. It is the most comprehensive and detailed dataset of its kind to date. It shows that most HEMA groups are organised as an independent Verein or as part of an umbrella Verein.1 The pandemic had an average effect on the number of practitioners and primarily impacted commercial schools with a 20% reduction in membership. 20% (Germany) and 27.5% (Austria) of all practitioners are identified as female, while 0.75% (Germany) and 3.9% (Austria) of practitioners are identified as nonbinary. Longsword is the most popular weapon discipline, being trained at over 88% of locations in both countries. Runner-up disciplines trained at 40% to 44% of locations include Long Knife, Dagger, Sword & Buckler, Historical Wrestling (Germany), and Dagger, Sword & Buckler and Polearms (Austria). Two-handed swords saw the biggest increase in training locations during the pandemic. HEMA hotspots are in South Germany (Bavaria and Baden-Württemberg), where nearly 39% of all German practitioners train, and in Eastern Austria (Vienna, Lower Austria and Burgenland), where over 55% of all Austrian practitioners train.

Many popular sports involve matches between two teams or players where each team have the possibility of scoring points throughout the match. While the overall match winner and result is interesting, it conveys little information about the underlying scoring trends throughout the match. Modeling approaches that accommodate a finer granularity of the score difference throughout the match is needed to evaluate in-game strategies, discuss scoring streaks, teams strengths, and other aspects of the game. We propose a latent Gaussian process to model the score difference between two teams and introduce the Trend Direction Index as an easily interpretable probabilistic measure of the current trend in the match as well as a measure of post-game trend evaluation. In addition we propose the Excitement Trend Index—the expected number of monotonicity changes in the running score difference—as a measure of overall game excitement. Our proposed methodology is applied to all 1143 matches from the 2019–2020 National Basketball Association season. We show how the trends can be interpreted in individual games and how the excitement score can be used to cluster teams according to how exciting they are to watch. [ABSTRACT FROM AUTHOR]

Evolving media landscapes toward increasingly diverse and competitive environments in both traditional and new media requires producers regularly examine the quality of their productions. One growing line of research identifies the increasing presence and significance of statistics in sports media programming. This experiment measures the effect of statistics on enjoyment and perceived credibility by sport consumers while considering level of fanship, media source, and variations in placement within Instagram posts. Results uncover evidence that validates previous observations about statistics in media while contradicting others. Specifically, findings reveal that statistics enhance enjoyment and improve perceived credibility. Observations were consistent across fanship level. However, additional findings also suggest media source and placement of statistics influences both enjoyment and credibility as well. For both dependent variables, statistics in both the Instagram caption and image yielded significantly greater enjoyment and credibility than some other conditions including posts without statistics at all. The impact of these and other findings on sports media industry and scholarship, along with limitations and directions for future research, are discussed. [ABSTRACT FROM AUTHOR]

In double round-robin sports tournaments, the total of points awarded divided by the total of the points earned at home consists in metric to access the home advantage that is easy to use, to interpret and is widely known. However, when a team award a small number of points this metric is underestimated, vary expressively, among others fragilities. Thus, our objective was to build a new metric to measure home advantage keeping the positive aspects of the previous one and improving all the listed fragilities. It was proposed the new metric and a test to be used in sportive championships. As the study application, it was used data from Brazilian Championship Series-A (2003-2020). As our main result, the new metric was not negatively correlated to the awarded points. The test of the metric let to verify on which occasion there is enough information to affirm that exists home advantage, avoiding the effects of a small number of awarded points. Based on this, of the 370 participations of 43 teams in 18 editions, one participation presented home disadvantage, 259 presented home advantage and 110 did not present home effect. Besides, the metric permitted to visualize the individual behavior of each team throughout the league. [ABSTRACT FROM AUTHOR]

Data Volley is one of the most widely used sports analysis software for professional volleyball statistics analysis. To develop the automatic data volley system, the vision-based game data acquisition is a key technology, which includes the 3D multiple objects tracking, event detection and quality evaluation. This paper combines temporal and spatial features of the game information to achieve the game data acquisition. First, the time-vary fission filter is proposed to generate the prior state distribution for tracker initialization. By using the temporal continuity of image features, the variance of team state distribution can be approximated so that the initial state of each player can be filtered out. Second, the team formation mapping with sequential motion feature is proposed to deal with the detection of event type, which represents the players' distribution from the spatial concept and the temporal relationship. At last, to estimate the quality, the relative spatial filters are proposed by extracting and describing additional features of the subsequent condition in different situations. Experiments are conducted on game videos from the Semifinal and Final Game of 2014 Japan Inter High School Games of Mens Volleyball in Tokyo Metropolitan Gymnasium. The results show 94.1% rounds are successfully initialized, the event type detection result achieves the average accuracy of 98.72%, and the success rate of the events' quality evaluation achieves 97.27% on average. [ABSTRACT FROM AUTHOR]

The existence (or not) of the hot hand in sport continues to attract the attention of economists and psychologists. The paper presents analysis to test the belief prevalent in golfing circles that golfers go in and out of form quickly, while 'class' remains relatively constant. By going in and out of form, the golfer is effectively experiencing a longer-run hot hand: one can speculate that periods of confidence breed good performance. To test for the existence of 'form', we present a new application of the Ornstein–Uhlenbeck model and use it to identify both a golfer's class and form when modelling golf scores. The findings suggest that short-term form does exist in golf and that this hot hand lasts for about four weeks. [ABSTRACT FROM AUTHOR]

Sport je důležitou součástí našich životů a také velice významně přispívá k identitě a struktuře moderních měst. Pokud jde o popularitu sportu, zejména fotbalu, stal se významným moderním prvkem, kde se odehrávají specifické druhy ekonomické i sociální interakce. Cílem tohoto článku je navrhnout způsob hodnocení výkonnosti fotbalových klubů na základě metody DEA a Malmquistova indexu. Pro empirickou analýzu byly vybrány profesionální české fotbalové kluby hrající nejvyšší fotbalovou soutěž s názvem Fotuna:Liga. K analýze relativní účinnosti klubů byl použit BCC a CCR model. Studie byla provedena na vzorku 20 klubů hrajících v sezónách 2015/16 - 2019/20. Do modelů byly zahrnuty 2 vstupní proměnné a jedna výstupní proměnná. Pro vybrané kluby byly následně vypočítány hodnoty Malmquistova indexu. Pomocí Malmquistova indexu bylo následně možné kvantifikovat celkovou změnu produktivity faktorů a rozložit ji na technologickou změnu a změnu technické účinnosti. Výsledky ukazují, že české fotbalové kluby dosáhly ve sledovaném období relativně vysoké úrovně efektivity a že nejvyššího skóre efektivity dosáhly tradiční kluby. Tyto výsledky by následně mohly manažerům klubů pomoci zlepšit výkonnost jejich týmů.

In this work, we present a data set on the survival times and mortality rates of all 4374 professional basketball players who participated in the National Basketball Association (NBA) from its inception in 1946 until July 2019 [1]. It contains the data of 412 active and 3962 former players. The data were recorded from different internet sources and include information on each player's position, ethnicity, handedness, ages at NBA debut and career end, height, weight, or number of NBA games. The results of the analysis of a previous data set with the same variables of all NBA players from 1946 to 2015 were recently published by Martinez et al. in 2019 [2]. The information provided in the data set can be useful to better understand the mortality risk among NBA players.

In recent years, the physical quality of middle school students in China has generally declined, which has attracted the attention of the state and the Ministry of education. With the development of Internet technology, China's physical education teaching environment has gradually become intelligent. This article mainly studies the mobile terminal equipment and methods of martial arts movement correction in the intelligent physical education environment. 21 young martial arts athletes were selected as the research objects. In the experiment, functional screening (FMS) was used to test the martial arts athletes, followed by FMS tests and scores. A video camera was used to record the motion test from the subjects' sagittal and frontal planes. Using wireless sensor technology to collect the athletes' motion signals, after the twelfth week, the FMS, SEBT, and the number of successful routines were tested on the two groups of athletes, respectively. The pre-test data and the post-correction data of the two test indicators were compared and analyzed. There is no significant difference. The necessary statistics and integration of the obtained data are carried out by using the calculation methods in sports statistics. The experimental data showed that the average total scores of FMS screening test of Changquan athletes and Taijiquan athletes were 14.71 ± 1.52 and 16.20 ± 1.32, respectively. The results of the research on the mobile terminal equipment and methods of martial arts movement correction in the intelligent sports environment show that the mobile terminal equipment can improve the independent training ability of athletes, and at the same time has a good correction effect on irregular movements. [ABSTRACT FROM AUTHOR]

This paper deals with the challenging themes of the way sporting teams and athletes are ranked in sports competitions. Starting from the paradigmatic case of soccer, we advance a new method for ranking teams in the official national championships through computational statistics methods based on Kendall correlations and radar charts. In detail, we consider the goals for and against the teams in the individual matches as a further source of score assignment beyond the usual win-tie-lose trichotomy. Our approach overcomes some biases in the scoring rules that are currently employed. The methodological proposal is tested over the relevant case of the Italian “Serie A” championships played during 1930–2023. [ABSTRACT FROM AUTHOR]

This article is a statistical investigation into the 2017 Major League Baseball scandal involving the Houston Astros, the World Series championship winner that the same year. The Astros were alleged to have stolen their opponents' pitching signs in order to provide their batters with a potentially unfair advantage. This work finds compelling evidence that the Astros on-field performance was significantly affected by their sign-stealing ploy and quantifies the effects. The three main findings in the article are (i) the Astros' odds of swinging at a pitch were reduced by approximately 27% (OR: 0.725, 95% CI: (0.618, 0.850)) when the sign was stolen, (ii) when an Astros player swung, the odds of making contact with the ball increased roughly 80% (OR: 1.805, 95% CI: (1.342, 2.675)) on non-fastball pitches, and (iii) when the Astros made contact with a ball on a pitch in which the sign was known, the ball's exit velocity (launch speed) increased on average by 2.386 (95% CI: (0.334, 4.451)) miles per hour. [ABSTRACT FROM AUTHOR]

Based on the application characteristics of the world's elite tennis players' skills and tactics, this paper establishes the evaluation model of skill and tactic level. According to the principle of normal distribution, taking the overall average as the reference value and the overall standard deviation as the discrete distance, the deviation method commonly used in sports statistics is adopted to formulate the grade evaluation standard. The standard is divided into five evaluation grades: excellent, good, passing, poor, and extremely poor. The equivalent evaluation scale of diagnostic indexes of tennis skill and tactic level is established. It also discusses many factors that characterize the skills and tactics of tennis singles. At the same time, it introduces the grey relational algorithm, constructs the evaluation model of skills and tactic level, and makes an example application. Through experimental analysis, it is concluded that this model can be applied not only to the diagnosis of individual athletes but also to the evaluation of a sports team. This article is aimed at further enriching the analysis system of tennis skills and tactics. [ABSTRACT FROM AUTHOR]

Sport climbing, which made its Olympic debut at the 2020 Summer Games, generally consists of three separate disciplines: speed climbing, bouldering, and lead climbing. However, the International Olympic Committee (IOC) only allowed one set of medals each for men and women in sport climbing. As a result, the governing body of sport climbing, rather than choosing only one of the three disciplines to include in the Olympics, decided to create a competition combining all three disciplines. In order to determine a winner, a combined scoring system was created using the product of the ranks across the three disciplines to determine an overall score for each climber. In this work, the rank-product scoring system of sport climbing is evaluated through simulation to investigate its general features, specifically, the advancement probabilities and scores for climbers given certain placements. Additionally, analyses of historical climbing contest results are presented and real examples of violations of the independence of irrelevant alternatives are illustrated. Finally, this work finds evidence that the current competition format is putting speed climbers at a disadvantage. [ABSTRACT FROM AUTHOR]

Selective exposure provides a useful lens for exploring how attention to competing information within exemplification theory (i.e., exemplars versus base-rate data) can impact viewing outcomes. Sports highlights reflect an ideal context for examining this selectivity, as exemplars are often presented concomitantly alongside base-rate measures of performance. To examine this selectivity, an experiment was conducted where viewers watched a series of exemplars (or sports highlights) presented alongside statistical measures of athlete performance while eye tracking gauged visual attention. Although viewers allocated greater overall attention to exemplars, domain-specific interest predicted increased attention to base-rate data, which in turn was associated with increased recall. This relationship was moderated by viewers' actual, objective sport knowledge. [ABSTRACT FROM AUTHOR]

Copyright of Búsqueda is the property of Corporacion Universitaria del Caribe (CECAR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)