PB2B

Chao, K., Chen, C., & Li, C. (2013). A PRELIMINARY STUDY OF BUSINESS INTELLIGENCE IN SPORTS: A PERFORMANCE-SALARY MODEL OF PLAYER VIA ARTIFICIAL NEURAL NETWORK. International Journal of Electronic Business Management, 11(1), 13-22.




Content: “Artificial neural network is a teaching tool with the principle of processing information through simulating human brain” Based off the article there is a lot of data and statistics thrown around because it mentions as the evaluation of a players performance from when they are about to hit free agency and they years that they won’t hit free agency. There are many type of performance options that are evaluated in the paper and it is based of different goals of the players and how much they play.




Construction: For the paper they actually listed the structure of how the paper was written. Instead of just having regular headings they divided it up by chapters. Chapter 2: Literature Review, in this chapter, we will review relevant regulations on MLB player's salary and literatures about artificial neural networks technology. What the paper did strongly as well was to explain how the salaries and contracts of the players worked for those people that may not be familiar with how the process is. There is also an explanation on artificial neural network so readers can get a basic understanding of the concept.

Chapter 3: Research Methods, this chapter will include detail illustrations and explanations on the model analyzing the relationship between pitcher's performance and salary. Chapter 4: Experiment, as to the model proposed in this study, further experiments will be conducted through the collection of data. Chapter 5 discusses the model and outlines several future research directions. Conclusion of this study is in Chapter 6; and references will be stated in the later part of this paper.




Keywords: Artificial Neural Network, Sports Business, Professional Baseball, Organization Management




Intended/Primary Audience: MLB Players, MLB Organizations/Teams




Peripheral/secondary audience(s): Fans, other sports organizations such as NFL, NBA?




Research Gap: That may be a disconnect between the way to measure the value of a player and how much their contract should be worth depending on the performance. How does one know what amount is far according to that performance.




Research Methods: In this regard, this study develops an approach to estimate proper salary level for players. The approach analyzes the performance and contractual salaries of free agencies on the year before signing the contract during 2006-2010 in MLB, and combines with neural networks technology to evaluate players' actual performance and their salaries.




Scholar’s Argument: player's performance may not only affect the personal performance and performance of the team, more importantly, but also have profound impact on the business operation of the tem and the league. Therefore, it's very important both to the players and the team whether the team can provide proper salary to the players to ensure their best performance






A review of virtual environments for training in ball sports by Miles, Helen C; Pop, Serban R; Watt, Simon J; Lawrence, Gavin P; John, Nigel W Computers & Graphics, 10/2012, Volume 36, Issue 6




Content: The paper is based on training in virtual reality situations and measuring and improving players performances using that type of technology.




Construction: The affordances of the paper is that it again gives some background information on facilities that use these types of training methods and it shows data and how that works and what type of data and information can be collected from the simulations and can be used and given to the coach of the team




Keywords: Sports analysis, Review, Training, Artificial Intelligence, Virtual Reality




Intended/Primary Audience: Sport Teams, Sports Enthusiasts, Professional Players




Peripheral/secondary audience(s): Fans of sports




Research Gap: How do you make these scenarios and situations as realistically as possible and actually challenging so it can be like real life. And how can you be sure if this actually improves the performance of a player on the field




Research Methods: focuses on just ball sports, and was carried out as part of a current project developing training tools for rugby. A VE needs to provide realistic rendering of the sports scene to achieve good perceptual fidelity


Scholar’s Argument: goal is to provide multiple scenarios to players at different levels of difficulty, providing them with improved skills that can be applied directly to the real sports arena. The typical hardware and software components needed are identified in the paper, and important psychological factors that should be considered are discussed