Prediction Of Players Research Articles

Performance Analysis of a Cricketer by Data Visualization

Abstract: Indian Premier League is a very competitive tournament where team selection is a very tricky and tedious procedure. Analysis of sports data and Prediction of each player’s performance helps in filtering the best players. A novel method employing the techniques of Data Analytics and Data Visualization is used in this research paper to extract individual player performance from huge statistics and datasets. An application is created to bridge the space between selecting team, coaches, and team management and to give a better interpretation on player steadiness, scoring and further capabilities. In this paper, pandas library is used for data analysis and manipulation tool, Microsoft azure is used for performance prediction and HTML, CSS, flask for the front-end application. Additionally, various machine learning algorithms are applied on the same data to find the best fit. The proposed application can be beneficial for team managements and decision making Keywords: Indian Premier League, Data analytics, Data Visualization, Prediction of player’s Performance, Microsoft Azure

Performance prediction at single-action level to a first-person shooter video game

Serious games, professional entertainment (e.g. e-sport), or the immersive simulation of critical scenarios by means of virtual reality belong to the scope of the video game industry. With implications in economic (gambling) and professional careers (e-sports), researchers have focused on the high-level analysis of the win/lose chances and on the player’s profile (good/bad performers). At the low-level analysis, the prediction of player’s performance at single-action level, such as in the case of hits in a first-person shooter (FPS) video game, to the best of our knowledge, has not been undertaken yet. In this study we hypothesize that VR video games, embed enough contextual information to predict performance in an FPS game at single-action level. For this purpose, we developed an FPS video game and a single-shoot level prediction model based on virtual world contextual information. Eighteen students of the University of Granada without previous experience in the game played for 45–50 min and generated 600–1200 events each. Every event, which was composed of twenty-contextual-player-centred components of the virtual scenario, were transmitted on-line to a remote server to perform predictions. Data from fifteen out of eighteen participants were used to train the model prediction model. After training, the model predicted “hit”/”miss” with a mean accuracy of 74.1%. In a broad vision, our results suggest that immersive virtual environments bear enough contextual information for accurate predictions even at single-action level. In a closed-loop design, this finding could be used (e.g. in defence, professional e-sports, etc.) to anticipate participant’s actions/decisions before they are taken, and modify the virtual scenario (e.g. abort mission, change environmental conditions) or drive the player (e. g. suggest options, relieve of command, etc.) according to the purpose of the mission.

Empirical Bayesball Remixed: Empirical Bayes Methods for Longitudinal Data

SummaryEmpirical Bayes methods for Gaussian and binomial compound decision problems involving longitudinal data are considered. A recent convex optimization reformulation of the nonparametric maximum likelihood estimator of Kiefer and Wolfowitz (Annals of Mathematical Statistics 1956; 27: 887–906) is employed to construct nonparametric Bayes rules for compound decisions. The methods are illustrated with an application to predict baseball batting averages, and the age profile of batting performance. An important aspect of the empirical application is the general bivariate specification of the distribution of heterogeneous location and scale effects for players that exhibits a weak positive association between location and scale attributes. Prediction of players' batting averages for 2012 based on performance in the prior decade using the proposed methods shows substantially improved performance over more naive methods with more restrictive treatment of unobserved heterogeneity. Comparisons are also made with nonparametric Bayesian methods based on Dirichlet process priors, which can be viewed as a regularized, or smoothed, version of the Kiefer–Wolfowitz method. Copyright © 2016 John Wiley & Sons, Ltd.

A Study of Prisoner’s Dilemma Game Model with Incomplete Information

Prisoners’ dilemma is a typical game theory issue. In our study, it is regarded as an incomplete information game with unpublicized game strategies. We solve our problem by establishing a machine learning model using Bayes formula. The model established is referred to as the Bayes model. Based on the Bayesian model, we can make the prediction of players’ choices to better complete the unknown information in the game. And we suggest the hash table to make improvement in space and time complexity. We build a game system with several types of game strategy for testing. In double- or multiplayer games, the Bayes model is more superior to other strategy models; the total income using Bayes model is higher than that of other models. Moreover, from the result of the games on the natural model with Bayes model, as well as the natural model with TFT model, it is found that Bayes model accrued more benefits than TFT model on average. This demonstrates that the Bayes model introduced in this study is feasible and effective. Therefore, it provides a novel method of solving incomplete information game problem.

On the impossibility of predicting the behavior of rational agents

A foundational assumption in economics is that people are rational: they choose optimal plans of action given their predictions about future states of the world. In games of strategy this means that each player's strategy should be optimal given his or her prediction of the opponents' strategies. We demonstrate that there is an inherent tension between rationality and prediction when players are uncertain about their opponents' payoff functions. Specifically, there are games in which it is impossible for perfectly rational players to learn to predict the future behavior of their opponents (even approximately) no matter what learning rule they use. The reason is that in trying to predict the next-period behavior of an opponent, a rational player must take an action this period that the opponent can observe. This observation may cause the opponent to alter his next-period behavior, thus invalidating the first player's prediction. The resulting feedback loop has the property that, a positive fraction of the time, the predicted probability of some action next period differs substantially from the actual probability with which the action is going to occur. We conclude that there are strategic situations in which it is impossible in principle for perfectly rational agents to learn to predict the future behavior of other perfectly rational agents based solely on their observed actions.