Different Types Of Players Research Articles

Basketball self-evaluation matrix: discrepancy between self-confidence and decision-making performance on psychological profiling of players.

In basketball training, self-evaluation plays a crucial role in the decision-making and execution of movements of players. The self-evaluation of players is influenced by their perception of own basketball ability and self-confidence state. This study aimed to explore potential discrepancies between self-confidence levels of players and their decision-making performance, while also characterizing different types of players. Data was collected from 20 youth basketball players who participated in a decision-making video task and a self-confidence assessment. Based on data from their self-confidence and decision-making awareness assessment, the K-means cluster analysis was used to categorize the players into different groups. Then, ANOVA and post hoc Scheffe test were conducted to compare these clusters. The cluster analysis identified four distinct profiles of players and the results of the ANOVA and post hoc Scheffe tests revealed significant differences between the four clusters. The "High Self-confidence & Low Decision-making Awareness" players might display an overconfident mindset, while the "High Self-confidence & High Decision-making Awareness" players potentially demonstrated the better performance and maintained a consistent and confident attitude. The "Low Self-confidence & High Decision-making Awareness" players appeared to lack confidence and needed to foster greater faith in their abilities. Finally, the "Low Self-confidence & Low Decision-making Awareness" players required a long-term and comprehensive training program to improve their skills. These preliminary findings informed the development of a self-evaluation matrix, designed to help coaches better understand player profiles and design tailored interventions. Moreover, this study contributes on sport calibration and enhances understanding of the behavioral and psychological states of players.

Local stability and convergence factors of nonlinear duopoly games with different types of players

Local stability and convergence factors of nonlinear duopoly games with different types of players

Research on the prediction of sports competition results based on momentum modeling

This study examines the role of “momentum” in sports competitions. Using a trained model to predict all samples, we analyzed the correlation and effect of “momentum” with athletes' scores through Pearson's correlation coefficient and one-way linear regression. The results showed that “momentum” was moderately correlated with athletes' scores and had a significant effect on the outcome of the game. We also built a judgmental descriptive model, aggregated and normalized the data, and performed binary logistic regression analysis. The results show that the model has good accuracy in predicting whether an athlete will be able to win a race. In addition, we validated it using machine learning algorithms and plotted ROC curves and AUC values. The results show that traditional machine learning models perform well in predicting the outcome of a single match. Finally, suggested strategies are proposed for different types of players, including studying the opponent's game video in depth, emphasizing on psychological preparation, making specific tactical plans, and maintaining flexibility and adaptation to improve performance in the game.

Research on the Influencing Factors of NBA Players’ Salaries

Currently, most of the studies are discussing the imbalance of NBA salaries, and only a few will actually take a closer look at what aspects of a player's capability can significantly affect their salary, which is what would be mainly discussed in this report. For NBA this mature business league, earning high salaries is the common goal of all players. However, due to the limitations such as the NBA salary cap, each team is limited in the total amount of salary they can allocate to their players. For team owners, determining a player’s value through scoring, defenses, and shooting percentage is vital. Players, need to learn what they can do to earn more, such as improving their defensive efficiency or grabbing more rebounds. Each team needs different types of players, such as three-point shooters, organizers, or players who own leadership. Next season, the salary cap for NBA teams will be around $123 million and each team will have around 13 players. Team management will need to evaluate the value of every individual. For example, they will evaluate a three-point shooter in terms of three-point percentage, percentage of shots taken, number of threes per game, etc. The main purpose of this paper is to analyze the potential reasons for the income distribution of the league, which is conducive to enhancing the understanding of the pattern for the player side, and help the owners to take advantage of it, and better allocate their resources.

Heatmap Analysis to Differentiate Diverse Player Types in Table Tennis—A Training and Tactical Strategy Development Potential

(1) Background: Computer-based analyses have been widely used to study aspects of various team and racket sports. However, such analyses have so far eluded table tennis, except in special competition forms, under fixed laboratory conditions or have only tracked the ball. The aim was to detect a basic, global, positional behavior, independent of the score. (2) Methods: We investigated the player position of professional male table tennis players with respect to handedness (right/left), playing system (offensive/defensive) and racket holding (shakehand/penholder) to determine the applicability of automated analysis systems. We used existing video data of competitive matches (N = 198 sets; 2006–2020) and transliterated them into an x–y coordinate system. From this, we were able to conduct a heatmap analysis for different types of players. (3) Results: The comparison between right- and left-handed players resulted in a significant difference in the positioning of the x coordinate (D = 0.5663; p = 0.001). Both groups positioned themselves on average in their own backhanded half of the table (Re: x = −0.22 m, Li: x = 0.39 m). (4) Conclusions: Our results have yielded valuable insights into the importance of analyzing positional behavior in a differentiated manner depending on handedness, playing strategy and racket holding posture.

Aspiration-driven co-evolution of cooperation with individual behavioral diversity.

In evolutionary game, aspiration-driven updates and imitation updates are the two dominant game models, and individual behavior patterns are mainly categorized into two types: node player and link player. In more recent studies, the mixture strategy of different types of players has been proven to improve cooperation substantially. Motivated by such a co-evolution mechanism, we combine aspiration dynamics with individual behavioral diversity, where self-assessed aspirations are used to update imitation strategies. In this study, the node players and the link players are capable to transform into each other autonomously, which introduces new features to cooperation in a diverse population as well. In addition, by driving all the players to form specific behavior patterns, the proposed mechanism achieves a survival environment optimization of the cooperators. As expected, the interaction between node players and link players allows the cooperator to avoid the invasion of the defector. Based on the experimental evaluation, the proposed work has demonstrated that the co-evolution mechanism has facilitated the emergence of cooperation by featuring mutual transformation between different players. We hope to inspire a new way of thinking for a promising solution to social dilemmas.

CAPACITAÇÃO PSICOLÓGICA DE JOGADORES DE FUTEBOL A NÍVEL DE COMPETIÇÃO

ABSTRACT Introduction: Competitive sports compete for technology, tactics and physical fitness. There is also competition for the psychological quality of athletes. There is an intrinsic connection between athletes’ mental state in competition and their psychological characteristics. Objective: Explore the influence of soccer players’ psychological characteristics on the level of competition. Methods: This paper investigates 220 soccer players using a questionnaire. In this database, variance analysis, multiple comparisons, factor analysis and other statistical analysis methods were used to ascertain the psychological status of Chinese soccer players. Finally, the factors affecting the psychological characteristics of soccer players were analyzed. Results: There were differences in the psychological characteristics of soccer players at different positions and levels. Conclusion: There are specific variations in psychological status among different types of players. The factors that influence the anxiety level of soccer players are a multidimensional system. It contains both subjective and objective content. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Pre-service Teachers' Player Types and Their Relation to Self-efficacy With Digital Media

Gamification in educational contexts is often used to increase learners’ intrinsic motivation and their self-efficacy beliefs, two constructs which are positively related with each other as well as to scholastic outcomes. As self-efficacy beliefs are conceptualized context-specific, it is important to consider various users’ needs by adapting the learning environment to their corresponding gamification player types (GPT). Studies show that different GPT are characterized by different motivational aspects (intrinsically motivated, extrinsically motivated, disruptive GTP) Therefore, the aim of the present study is the examination of six different GPT according to the HEXAD typology (Tondello et al., 2016) for pre-service teachers regarding their distribution and their relation to self-efficacy with digital media. Altogether N = 75 pre-service teachers were assessed in terms of their GPT and their self-efficacy with digital media. It was hypothesized that the GPT distribution for pre-service teachers is comparable to the distribution reported by Tondello et al. (2016). Additionally, it was exploratively examined to what extent the different types of players differ in their self-efficacy with digital media. Results show that the GPT distribution in the current sample differs significantly from the expected distribution. Pre-service teachers seem to show specific GPT characteristics due to their profession. Concerning self-efficacy with digital media, pre-service teachers show a high subjective perception of their self-efficacy with digital media, but contrarious to the hypothesis no significant relation was found between GPT and self-efficacy with digital media. The results indicate that pre-service teachers feel confident to the use of digital media in class and that pre-service teachers form a group with a specific distribution of player types and therefore have specific needs in gamified learning environments. Due to the reported high amount of intrinsic player types, game design elements which promote intrinsic motivation should be in the focus of prospective analyses.

A REWARD MECHANISM FOR MOBILE CROWD-SENSING BASED ON BLOCK CHAIN TECHNOLOGY

MCS stands for "mobile crowd sensing" and is a new sensor situation for cyber-physical social systems. Smart cities, personal healthcare, and environmental monitoring all use MCS. By recruiting people and offering prizes, the MCS programme obtains sensory data from the target area. Participants must be encouraged to participate and sensory data must be provided through reward methods. While MCS apps provide Mechanisms of reward, sensory data, and personal data are vulnerable to malevolent task initiators/participants and hackers. This paper models the MCS situation as a sensory data market with two types of participants: monthly and instant payment participants, in order to offer a fair incentive mechanism. By evaluating two different types of players and task initiators, this research presents an incentive mechanism backed by a three-level Stackelberg game. Evaluation handles two issues through theoretical analysis and simulation. The MCS performance and reward scheme is based on block chain.

The effect of perceptions competition and learning costs on cooperation in spatial evolutionary multigames

It is a great challenge to correctly understand the evolution and incentives of cooperation. In recent years, the spatial evolutionary multigames have attracted extensive attention from researchers. Actually, for players, not only themselves but also their neighbors will affect their perceptions, which has not been considered in previous works. In addition, changing perceptions has a learning process, which will cause some costs. Inspired by the above facts, we explore the impact of perceptions competition mechanisms and learning costs on the evolution of cooperation in the multigames dilemma, which consists of prisoner game and snowdrift game. Especially, we further explore how different groups can coexist in such a setting. It is found that the heterogeneity of payoffs contributes to network reciprocity and can maintain cooperative behaviors even under unfavorable conditions. Compared with the static multigames mechanism, our perceptions competition mechanism has a higher proportion of cooperators under the same temptation value b and sucker payoff θ, and the cooperators can survive within a larger range of parameters. At the same time, a small cost can effectively maintain a high proportion of cooperators, while an appropriate cost will keep players at a high level of both the proportion of cooperators and the coexistence range. The continuous increase of costs will make our evolutionary mechanism ineffective and turn to static multigames. We also find that the introduction of costs will help different types of players coexist, and with the increase of costs, the coexistence area will first decrease and then increase. Moreover, our main findings are equally applicable to complex networks such as BA networks and random networks.

Privacy considerations for online advertising: a stakeholder’s perspective to programmatic advertising

PurposePrivacy considerations have become a topic with increasing interest from academics, industry leaders and regulators. In response to consumers’ privacy concerns, Google announced in 2020 that Chrome would stop supporting third-party cookies in the near future. At the same time, advertising technology companies are developing alternative solutions for online targeting and consumer privacy controls. This paper aims to explore privacy considerations related to online tracking and targeting methods used for programmatic advertising (i.e. third-party cookies, Privacy Sandbox, Unified ID 2.0) for a variety of stakeholders: consumers, AdTech platforms, advertisers and publishers.Design/methodology/approachThis study analyzes the topic of internet user privacy concerns, through a multi-pronged approach: industry conversations to collect information, a comprehensive review of trade publications and extensive empirical analysis. This study uses two methods to collect data on consumer preferences for privacy controls: a survey of a representative sample of US consumers and field data from conversations on web-forums created by tech professionals.FindingsThe results suggest that there are four main segments in the US internet user population. The first segment, consisting of 26% of internet users, is driven by a strong preference for relevant ads and includes consumers who accept the premises of both Privacy Sandbox and Unified ID (UID) 2.0. The second segment (26%) includes consumers who are ambivalent about both sets of premises. The third segment (34%) is driven by a need for relevant ads and a strong desire to prevent advertisers from aggressively collecting data, with consumers who accept the premises of Privacy Sandbox but reject the premises of UID 2.0. The fourth segment (15% of consumers) rejected both sets of premises about privacy control. Text analysis results suggest that the conversation around UID 2.0 is still nascent. Google Sandbox associations seem nominally positive, with sarcasm being an important factor in the sentiment analysis results.Originality/valueThe value of this paper lies in its multi-method examination of online privacy concerns in light of the recent regulatory legislation (i.e. General Data Protection Regulation and California Consumer Privacy Act) and changes for third-party cookies in browsers such as Firefox, Safari and Chrome. Two alternatives proposed to replace third-party cookies (Privacy Sandbox and Unified ID 2.0) are in the proposal and prototype stage. The elimination of third-party cookies will affect stakeholders, including different types of players in the AdTech industry and internet users. This paper analyzes how two alternative proposals for privacy control align with the interests of several stakeholders.

Sustainable society based on social gamification using Nova Empire ecology mining

In recent years, with the development of social app, the Internet has created a different interactive mode from the previous daily life, which makes the development of society gradually presents the trend of “gamification”. “Social gamification” makes the life systematically integrated into the competition activities. Therefore, we propose a new idea for the realization of a sustainable society under the trend of “social gamification” by combining strategy game ecology mining for the first time. Specifically, we use big data mining to analyze the attributes of the smurf (subsidiary account), and then extract discriminative features and compute a score for each attribute using machine learning, which could accurately locate all the smurf in the game. By banning the smurf, the game ecology is optimized to improve the player’s game experience. Meanwhile, this paper proposes appropriate operation strategies for players at different stages based on multi-level clustering. In the real society, smurf represents the citizens who disturb social order, and different types of players represent the pyramid structure of social classes. The measures in the game provide references for the construction of a sustainable society.

The interplay of micro-transaction type and amount of playing in video game evaluations

I examine how the possibility of acquiring different features through in-game purchases (micro-transactions) affect intentions to play and recommend a game. I propose that individuals react more negatively to micro-transactions involving functional rather than aesthetic features. This is because functional, but not aesthetic features, alter the game balance – an essential element of competitive multiplayer games. Further, drawing on the difference between time and money as indicators of preference, I argue that this effect is stronger among those who spend more time playing games, but is not affected by how much money players spend on games. Three online studies (N = 574) with video game players provide converging support for our hypotheses. I conclude with a discussion of balancing game reputation with game revenue in light of different types of players.

Does the conformity save us when information advantage fails?

Evolutionary spatial Prisoner’s Dilemma Game (PDG) has been widely applied to study the possible mechanisms of cooperation promotion. In this paper, we discuss the conformity, information advantage and the coupling between them. We assume there is global conformity for all the players when no decision in the best-takes-all strategy provides a better payoff. By assuming conformity, the neighborhood diversity is then introduced by allowing normal (advantage) players to see 4 (8) neighbors. With these modifications in place, the model is still a classical game theory model with a deterministic property, and is capable of describing how different types of players with different conformities can interact among themselves without the violation of payoff priority. We show that the global conformity, though subtle in the pursuing the payoff, significantly changes the cooperation. On comparing 4 extreme cases with different combinations of conformity and advantage, the results suggest that conformity is more important in the promotion of cooperation. In addition, the simulation of conformal conflict presents a scenario wherein the cooperators may extend to the other side of the conformal boundary.

Cyber-Physical Security Evaluation in Manufacturing Systems with a Bayesian Game Model

Cyber-physical security has been taken more seriously recently in manufacturing systems because of highly increasing integration of cyber and network systems with physical production known as cyber-physical manufacturing systems (CPMS). This paper presents a novel proactive approach to forecast the interrelation of attackers and cyber-physical manufacturing systems to suggest a proper defense strategy to the system managerial. In this research, the zero-sum game payoff function developed in our previous studies is transformed into an epistemic type Bayesian game that addresses uncertainties on types of players. The risk preference behavior also is considered as a characteristic of different types of players and to form the Bayesian utility function. Finally, a numerical example is analyzed with a structured method by developing the Bayesian game, computing Bayes-Nash equilibria, and finding quantal response equilibrium. This approach enables managers to decide a proper defense strategy in advance of experiencing cyber-physical dilemma when there are not enough previous data.

Stochastic differential game in high frequency market

This paper presents an application of a linear quadratic stochastic differential game to a model in finance, which describes trading behaviors of different types of players in a high frequency stock market. Stability of the high frequency market is a central issue for financial markets. Building a model that expresses the trading behaviors of the different types of players and the price actions in turmoil is important to set regulations to maintain fair markets. Firstly, we represent trading behaviors of the three types of players, algorithmic traders, general traders, and market makers as well as the mid price process of a risky asset by a linear quadratic stochastic differential game. Secondly, we obtain a Nash equilibrium by solving a forward–backward stochastic differential equation (FBSDE) derived from the stochastic maximum principle. Finally, we present numerical examples of the Nash equilibrium and the corresponding price action of the risky asset, which agree with the empirical findings on trading behaviors of players in high frequency markets. This model can be used to investigate the impact of regulation changes on the market stability as well as trading strategies of the players.

The role of the Belgian government in enhancing the integration of healthcare for Mentally Ill Offenders: an analysis of the reform plan

Background: In 2016 The Belgian government launched a Reform program to optimize and integrate care for Mentally Ill Offenders MIO. Collaboration networks get the ‘mandate’ to implement the program, adapted to locoregional contexts. This paper aims to make a plan analysis of the government reform program and tries to analyze the characteristics of the mandate. Methods: Qualitative data were collected using document analysis and semi structured interviews with policy delegates cabinet and administration and regional healthcare- and justice coordinators. Data were analyzed using a continuous comparative method, including researcher triangulation. Results: The current policy framework sets a number of guiding principles to reorganize the field and care for MIO. Key principles are the focus on integration of care of services form different sectors and organizations, reintegration of MIO into society, developing smooth patient trajectories for MIO and emptying the prisons with MIO by means of introducing new types of facilities. The responsibility to implement this is mandated to policy implementation networks per regional Court of Appeal region. The Master Plan holds no detailed rules or procedures to guide the collaboration. The plan incorporates previously established locoregional steering groups and strengthens the role of steering group coordinators to enhance collaboration. These latter are the linking-pin between locoregional activity and the policymakers. The locoregional coordinators are supervised by a national coordinator. These coordinators have to facilitate the dialogue between organizations to realize the guiding principles, facilitate a common understanding of integrated care and facilitate intersectoral patient trajectories of different types of MIO’s. Discussion: The strategy of the Reform plan is in line with recommendations in literature to tackle wicked problems. The reform plan aims at developing common goals through locoregional dialogue of intersectoral partners. It tries to guide this process by creating a structure in which formally appointed linking pin-persons play a central role. The mandate is characterized by a low level of formalization. It mainly focusses on enhancing dialogue and interaction in the hope that mutual learning will emerge. The policy framework is however not yet in the stage of promoting the idea of networks as a whole. Conclusion: The current policy framework in the Belgian field of MIO is an example of a policy network model. It aims to facilitate dialogue and interaction between different types of players and assumes that local players will have the competence to implement the integration of care. Limitations: This plan analysis is part of a larger research project performing an implementation study of the reform and emerging networks. Suggestions for further research: To develop effective mandated networks research is needed on governance mechanisms and whole networks within the characteristics of the institutional field.

PLAY WITH FRIENDS, AGAINST MACHINE OR ALL ALONE? SOCIAL IN-GAME MECHANICS AS A PREDICTOR OF GAMER LIFETIME VALUE

Social games where players play together with others in multiplayer mode are currently emerging and attracting attention in the media after the success of e.g. Pokemon GO. Nevertheless, little is known about the profitability of social players. Previous gaming research that has profiled different types of players has focused on games played alone, without social mechanics (cf. Vahlo et al., 2017). From a marketing perspective, it is also interesting to study the effects of merely watching game play or browsing new game ads as a mode of entertainment, and what kind of effect that has on future behaviour. Passive participation as a type of aesthetic entertainment has been acknowledged in eSports context (Seo, 2013). This paper analyses big data of a gaming company on an individual player level, including different types of single play and multiplayer gaming sessions and in-game purchase behaviour data. We compare the effects of social and non-social ingame mechanics on how individuals spend money in-game over time, frequency of play, sessions as well as the length of played sessions in minutes during the gamers’ whole lifecycle. The anonymized repository data includes gaming behaviour, in-game purchase behaviour related to the use of one specific digital game in the USA. The game can be played both alone, and with others as a team. We used structural equation modeling to analyse the behavior of 23 049 randomly selected players, who have played the game for at least one week. The data included the individual players’ total session history during their play lifecycle. Interestingly, social play with other people is a strong predictor of money spent in the long term. Social play also prompts long-term interest in the game, as friends invite and encourage each other to play with or against other virtual teams. Nevertheless, social play is not for everyone! The results help to optimize player journeys and to make strategic decisions that support long term profitability of gaming companies.

An Economic Model of Human Cooperation Based on Indirect Reciprocity and Its Implication on Environmental Protection.

There has been an urgent challenge for environmental protection due to issues like population increase, climate change, and pollution. To address this challenge, sustained human cooperation is critical. However, how cooperation in human beings evolves is one of the 125 most challenging scientific questions, as announced by Science in its 125th anniversary. In this paper, we contribute to answering this question by building an economic game model based on indirect reciprocity and altruism behavior. In our model, there are three types of participants: cooperator, defector, and discriminator. In every round of the game, the cooperator chooses cooperation, the defector chooses non-cooperation, and the choice of the discriminator depends on the choice of his partner in the last round. Our analysis and main result shows that there is no stable evolution equilibrium in this game, which implies that the proportions of different types of players will keep changing instead of reaching a stable equilibrium. In other words, there is no guarantee that cooperation will be dominant in this game. An implication of this result is that to achieve cooperation and protect the environment more effectively, cooperators and discriminators in our society should be provided with incentives.

Stochastic Differential Game in High Frequency Market

This paper presents an application of a linear quadratic stochastic differential game to a financial model that describes trading behaviors of different types of players in a high frequency stock market. Stability of the stock market in a high frequency environment is a central issue in financial markets. Building a model that expresses the trading behaviors of the different types of players and the price actions in a high frequency market helps introduce better regulations for a healthy market. Firstly, we represent trading behaviors of the three types of players, algorithmic traders, general traders, and market makers as well as the mid-price process of a risky asset by a linear quadratic stochastic differential game. Secondly, we obtain a Nash equilibrium for open loop admissible strategies by solving a forward-backward stochastic differential equation (FBSDE) derived from the stochastic maximum principle. Finally, we present numerical examples of the Nash equilibrium for open loop admissible strategies and the corresponding price action of the risky asset, which agree with the empirical findings on the mechanism of high frequency markets. This model can be used to investigate the impact of regulation changes on the market stability as well as trading strategies of the players.