Player Attributes Research Articles

Penerapan Algoritma Backpropagation dalam Memprediksi Kemenangan dalam Bermain Mobile Legends

Victory in Mobile Legends is influenced by various factors, such as player skills, strategy, and character selection. To predict game outcomes, the backpropagation algorithm is applied to process historical gameplay data and create an accurate predictive model. This study aims to apply the backpropagation algorithm to predict victory based on player attributes, including team role, experience level, and past performance. The research method involves training and testing the model using data from multiple gameplay sessions with varied outcomes. Findings show that the backpropagation algorithm can predict game results with high accuracy, especially when the data includes a more comprehensive range of attributes. The implications of this study suggest that a backpropagation-based predictive model can help players understand their chances of winning and optimize their gameplay strategies. Furthermore, future developments in this algorithm could provide benefits for similar applications in other digital gaming fields.

Innovative player evaluation: Dual-possibility Pythagorean fuzzy hypersoft sets for accurate international football rankings

This study introduces an advanced approach for ranking international football players, addressing the inherent uncertainties in performance evaluations. By integrating dual possibility theory and Pythagorean fuzzy sets, the model accommodates varying degrees of ambiguity and imprecision in player attributes. Additionally, the use of hypersoft set theory enriches the analysis by capturing the multifaceted nature of player evaluations. The proposed aggregation operators refine the synthesis of diverse information sources, leading to a comprehensive and nuanced assessment. This research significantly enhances player evaluation methodologies, providing a more adaptable framework for a fair assessment of international football talent. A practical example illustrates the application of dual-possibility Pythagorean fuzzy hypersoft sets (DP-PFHSS). A numerical technique is proposed for solving multi-criteria decision-making (MCDM) challenges with known dual possibility information using the proposed aggregation operators. This decision-making algorithm effectively determines a football player's worth, contributing to the overall ranking and evaluation process. The approach aids in scouting and recruitment by facilitating talent identification and informed player signings. Graphical analysis, comparing existing and proposed methods using average and geometric operators, demonstrates the superiority of the proposed approach in the players evaluation, indicating that F1 is in the top ranking.

Player Types in Gamified Learning: Conceptualization, Validation, and Profiling

Personalized gamification is often considered a superior approach to a one-size-fits-all method. This study addresses three limitations of previous personalized gamification research: (1) conceptual overlap in previous player type models, (2) lack of empirically validated questionnaires, and (3) inadequate profiling methods that only consider the dominant player type. We conducted two interlocking studies. Study One focused on accurately conceptualizing and measuring player types. It synthesized key attributes from previous models and proposed a new model, along with a questionnaire that underwent rigorous development and validation. Study Two focused on comprehensive player type profiling. It employed a new method, cognitive diagnostic models, which produced finer-grained profiles enabling multiple player attributes. Key contributions include a new player type model with five main archetypes, a valid questionnaire for the proposed model, and a feasible method for player type profiling. These contributions advance our understanding of player types and facilitate future personalized gamification practices.

Visualizing Player Attributes: Transforming Data into Insightful Graphs, Heatmaps and Radar Plots

The concept behind this approach involves showcasing the strengths and weaknesses of players through the utilization of graphs, heatmaps, and various Radar Plots. Numerous websites gather vast amounts of data for various purposes, which we will extract through web scraping. Additionally, there will be a substantial dataset available, consisting of files in formats such as CSV and more. All this collected data will be transformed into visually engaging diagrams. The utilization of graphs enhances the readability and improves the overall experience of interpreting the data, as people generally prefer visual presentations over raw numbers.

Is it in the Game? Reflections of Race-Based Stereotypes in EA SPORTS FIFA 22

Scholars have long worked to explore the existence of race-based stereotypes and narratives within sports media. Video game scholars share in this curiosity of understanding the extent to which racial stereotypes are being embedded into games and the role that they may play in cultivating or perpetuating racial stereotypes among their players. Utilizing framing and schema theories as a guide for understanding the media’s role in developing stereotypes, the present study conducted a content analysis of player data present in EA SPORTS’ FIFA 22 to investigate whether racial stereotypes were being embedded in the most popular sports video game (SVG) title through its player attributes. Each athlete represented in the game is given a 0–99 rating across a total of 35 attributes with the intention of reflecting the player’s real-life ability in these areas. Through an analysis of the avatar’s skin tones alongside their respective values for each of the attributes selected for analysis, the study found significant differences between athletes of different skin tones across nearly all of the attributes present that reinforce the Brain vs Brawn narratives scholars have located in many sporting contexts. Conclusions and suggestions of next steps are discussed.

Gendered Consequences of COVID-19 Among Professional Tennis Players.

The COVID-19 pandemic increased the risk of travelling, working, and participating in public events. To test whether there were gendered differences in the response to COVID-19, we examine the behavior of male and female professional tennis players. We use data from major tennis tournaments which included a rather large number of athletes withdrawing from play. After controlling for past performance, wealth, and other relevant player attributes, we find that female tennis players were more likely to withdraw. This suggests that high-earning women may have greater risk aversion, especially related to COVID-19, than their male counterparts. Importantly, women were more risk-averse when it comes to international travel.

The collection, analysis and exploitation of footballer attributes: A systematic review

There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.

Opening up the court: analyzing player performance across tennis Grand Slams

Abstract In tennis, the Australian Open, French Open, Wimbledon, and US Open are the four most prestigious events (Grand Slams). These four Grand Slams differ in the composition of the court surfaces, when they are played in the year, and which city hosts the players. Individual Grand Slams come with different expectations, and it is often thought that some players achieve better results at some Grand Slams than others. It is also thought that differences in results may be attributed, at least partially, to surface type of the courts. For example, Rafael Nadal, Roger Federer, and Serena Williams have achieved their best results on clay, grass, and hard courts, respectively. This paper explores differences among Grand Slams, while adjusting for confounders such as tour, competitor strength, and player attributes. More specifically, we examine the effect of the Grand Slam on player performance for matches from 2013 to 2019. We take two approaches to modeling these data: (1) a mixed-effects model accounting for both player and tournament features and (2) models that emphasize individual performance. We identify differences across the Grand Slams at both the tournament and individual player level.

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

Defining the attributes for specific playing positions in football match-play: A complex systems approach

ABSTRACT Introduction Talent identification and development programmes that retain and develop athletes are integral to the sporting success of National Governing Bodies in football. Currently, player attributes are studied in isolation without considering the complexity of interacting components of football performance. The current study applied a systems analysis framework to describe, in a structured and systematic way, the attributes for specific playing positions in football match-play. Methods: Eight high-level football coaches were interviewed. Models of the interacting attributes were developed for each position using Work Domain Analysis (WDA). Results: Six WDA models detail the attributes associated with each playing position. The models identified purposes of playing positions (restrict opposition actions, organise and coordinate, building up of offence, goal scoring and stretch opposition), performance-related measures and the functions required to achieve the playing position purposes (game play, psychological functions, time and space and player actions). Critically, the relationships between the purposes, measures, functions, processes, and objects are described in the models. Discussion The WDA models describe the interacting attributes of different playing positions in football match-play. The findings demonstrate football performance is complex, consisting of multiple interacting and dynamic components. Implications to support coaches in talent identification and development are discussed.

Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.

Outline analyses of the called strike zone in Major League Baseball

We extend statistical shape analytic methods known as outline analysis for application to the strike zone, a central feature of the game of baseball. Although the strike zone is rigorously defined by Major League Baseball’s official rules, umpires make mistakes in calling pitches as strikes (and balls) and may even adhere to a strike zone somewhat different than that prescribed by the rule book. Our methods yield inference on geometric attributes (centroid, dimensions, orientation and shape) of this “called strike zone” (CSZ) and on the effects that years, umpires, player attributes, game situation factors and their interactions have on those attributes. The methodology consists of first using kernel discriminant analysis to determine a noisy outline representing the CSZ corresponding to each factor combination, then fitting existing elliptic Fourier and new generalized superelliptic models for closed curves to that outline and finally analyzing the fitted model coefficients using standard methods of regression analysis, factorial analysis of variance and variance component estimation. We apply these methods to PITCHf/x data comprising more than three million called pitches from the 2008–2016 Major League Baseball seasons to address numerous questions about the CSZ. We find that all geometric attributes of the CSZ, except its size, became significantly more like those of the rule-book strike zone from 2008–2016 and that several player attribute/game situation factors had statistically and practically significant effects on many of them. We also establish that the variation in the horizontal center, width and area of an individual umpire’s CSZ from pitch to pitch is smaller than their variation among CSZs from different umpires.

K-리그 관람자의 U-20 축구대표팀 스타선수 속성과 경기태도, 충성도 및 행동의도간의 관계

K-리그 관람자의 U-20 축구대표팀 스타선수 속성과 경기태도, 충성도 및 행동의도간의 관계

Women's Lacrosse Players’ perceptions of teammate leadership: Examining athlete leadership behaviors, attributes, and interactions

Athletes fulfill both on the field (task) and off the field (social) team roles. For this reason, recent research on athlete leadership has concluded there is no one best type of athlete leader. In the current study, role differentiation theory was applied to investigate how peers perceive teammate leadership roles and behaviors within a women’s lacrosse program at a NCAA Division I university. Each player (N = 30) participated in a survey in which they were tasked with rating every teammate on the following leadership behaviors: technical, interpersonal, and contagious energy. Individual player attributes of were also considered in the analysis of a cross-classified nested model that resulted in 870 total ratings that predicted overall athlete leadership. Results suggest behaviors of technical, interpersonal, and contagious energy all impact the perception of teammates’ overall leadership. Coaches and athletes can use these results to be reassured that both on field and off field leadership behaviors are important for athlete leadership development. Furthermore, a discussion of how behaviors of social roles and leadership behaviors can be transferable for athletes’ in life after sport is discussed.

An exploration of young professional football players’ perceptions of the talent development process in England

The identification and development of players in English professional football has become an increasingly significant topic of debate given the historical perceived underperformance of the English national team at international tournaments. To enhance understanding of the challenges and barriers experienced by English youth footballers, the authors explore the developmental experiences of English professional football players from different levels of the English football pyramid. Professional players (N = 11) from football clubs in the top four professional divisions in England took part in individual semi-structured interviews, which were analysed inductively using thematic analysis. The data revealed three interrelated themes that were perceived to mediate player identification and development pathways at professional clubs. Pathways for young players to progress and experience first-team competitive football differed when the level of the league that the players operated within was considered, with significant issues also raised relating to the suitability of the under 21 league structure, the importance attached to the educational welfare of young players, and variations in the identification of player attributes. This study sheds new light on the priorities and processes of talent development and education provision in English football.

Establishing consensus of position-specific predictors for elite youth soccer in England

ABSTRACTPurpose: To construct a valid and reliable methodology for the development of position-specific predictors deemed appropriate for talent identification purposes within elite youth soccer in England.Method: N = 10 panel experts participated in a three-step modified e-Delphi poll to generate consensus on a series of generic youth player attributes. A follow-up electronic survey completed by coaches, scouts and recruitment staff (n = 99) ranked these attributes to specific player-positions.Results: A final list of 44 player attributes found consensus using the three-step modified e-Delphi poll. Findings indicated that player-positional attributes considered most important in the youth phase are more psychological and technical than physiological or anthropometric. Despite ‘hidden’ attributes (e.g., coachability, flair, versatility, and vision) finding consensus on the e-Delphi poll, there was no evidence to support these traits when associated with a specific playing position.Conclusion: For those practitioners responsible for talent recruitment, our findings may provide greater understanding of the multiple attributes required for some playing positions. However, further ecological research is required to assess the veracity of our claims.

A MATHEMATICAL MODEL USING AHP PRIORITIES FOR SOCCER PLAYER SELECTION: A CASE STUDY

The process of player selection in multi-player sports like soccer is a complex multi-criteria problem. In this paper, a two-phase approach is proposed for soccer player selection. In the first phase, the attributes of each player – based on their position within a soccer team – is prioritised using the Analytic Hierarchic Process (AHP). In the second phase, a 0-1 integer linear programming model is developed using the weights of player attributes, and the top performers are determined for inclusion in the team. Finally, a case study on the Turkish soccer club called Fenerbahce is used to illustrate the applicability and performance of the proposed approach.

Development and Validation of the Poker Skills Measure (PSM).

Existing research has demonstrated that poker is a game predominated by skill. Little is known about the specific characteristics of good poker players however, likely due in part to the lack of a readily available measure of poker skill. In the absence of an available and easily administered poker skill measure, laboratory studies of poker player attributes have used questionable methodologies to assess skill including peer- and self-report. The aim of the current research was to create a valid, reliable, and easily administered measure of poker playing skill. A sample of 100 University of Lethbridge undergraduate students and Lethbridge community members completed the newly created Poker Skills Measure (PSM) and an objective measure of poker playing performance (playing virtual poker). External validity of the measure was demonstrated via significant associations—expected and detected—between the PSM and the objective playing measure. Specifically, significant positive associations were found between PSM scores and hands won, pre- and post flop aggression, and a significant negative relationship was detected between PSM scores and number of hands played. Within the current sample, acceptable internal consistency (Cronbach's α = .82) and very good test re-test reliability (r = .78) was achieved with the 35 item PSM. Future directions are discussed.

“White men can't jump.” But can they throw? Social perception in European basketball

In the present article, we investigate the influence of sociocultural stereotypes on the impression formation of basketball players and coaches. In Experiment 1 (n = 32), participants were shown a picture of a black or white basketball player prior to observation of a point-light video of a player executing a basketball free throw. The participant was informed that the player depicted in the picture was executing the free throw. Results indicated that ethnicity of the target player significantly influenced participant evaluations, demonstrating specific stereotypes about black and white basketball players when evaluating performance. In Experiment 2 (n = 30), results derived from the Implicit Association Test indicated that black players are implicitly associated with athletic player attributes. The results are in line with social schema theory and demonstrate that - similar to findings that have been reported in the United States - a subpopulation of German basketball players and coaches hold specific stereotypes about the abilities of black and white basketball athletes. These stereotypes bias impression formation when coaches and players make assessments of basketball performance.

Time to Play?

The online session behavior of users in a multiplayer online role playing game is examined. Three dimensions of online sessions are studied: frequency of sessions, distribution of start and stop times, and duration. These measures are modeled and examined econometrically to understand their relationships with attributes of the users and their avatars. Frequency is analyzed by using a negative binomial regression to uncover relationships with contributing factors, such as the experience level of players and the number of avatars per user account. Start and stop time distributions are examined aggregately with discrete choice models that capture the probability of starting a session in relation to the sociodemographics of users. Duration models are estimated by using data on the characteristics of events that occur within the duration and other user sociodemographics and player attributes. Since sessions are not explicitly indicated within the data set, sessions were stitched together on the basis of an assumed threshold for offline duration. Overall, these models and results provide insight into temporal characteristics of online sessions, including their aggregate traffic characteristics, such as frequency, headways, and duration length. These results also provide information on the nature of online game playing from a user perspective.