Sports Data Mining

Sports Data Mining

Details Book

  • Autor: Robert P. Schumaker
  • Editor: Springer-Verlag New York Inc.
  • Relaese Date: 06 November 2012
  • ISBN: 146142691X
  • Format Book: PDF, Epub, DOCx, TXT
  • Number of page: 138 pages
  • File Size: 51MB
  • Rating:


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Description Sports Data Mining de Robert P. Schumaker:

Data mining is the process of extracting hidden patterns from data, and it's commonly used in business, bioinformatics, counter-terrorism, and, increasingly, in professional sports. First popularized in Michael Lewis' best-selling Moneyball: The Art of Winning An Unfair Game, it is has become an intrinsic part of all professional sports the world over, from baseball to cricket to soccer. While an industry has developed based on statistical analysis services for any given sport, or even for betting behavior analysis on these sports, no research-level book has considered the subject in any detail until now. Sports Data Mining brings together in one place the state of the art as it concerns an international array of sports: baseball, football, basketball, soccer, greyhound racing are all covered, and the authors (including Hsinchun Chen, one of the most esteemed and well-known experts in data mining in the world) present the latest research, developments, software available, and applications for each sport. They even examine the hidden patterns in gaming and wagering, along with the most common systems for wager analysis. Dr. Robert Schumaker is an Assistant Professor in Information Systems at Iona College. Rob's overall research interests involve the uses of technology to acquire, deliver and make predictions in a variety of Business-related environments. These interests further branch into computer mediated communications, design science, human computer interfaces, machine learning algorithms, natural language processing, technology acceptance models and textual data mining. His recent research has focused on Sports Knowledge Management and Data Mining of relevant data from Sports-related databases and producing accurate predictions that can provide an edge to sports organizations and gamblers alike. Using the Moneyball style philosophy, this project analyzes the use of different machine learning techniques to predict outcomes of sporting events. He has authored or co-authored many journal articles, including ACM Transactions on Information Systems, Decision Support Systems, IEEE Systems, Man and Cybernetics - Part A and Communications of the ACM. Osama K. Solieman attended the University of Arizona graduating with a BS in Computer Science in 2003. In 2006, he received a MS in Management Information Systems where he was also the lead researcher on a database project for the Department of Electrical

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Comments

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