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From baseball to greyhound racing and beyond, Sports Data Mining presents the latest research, developments, software and applications for data mining in sports. The text examines hidden patterns in gaming and wagering, along with the most common systems for wager analysis.

Full Description

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  • ISBN13: 978-1-4419-6729-9
  • 152 Pages
  • User Level: Science
  • Publication Date: September 10, 2010
  • Available eBook Formats: PDF
Full Description
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.
Table of Contents

Table of Contents

  1. Chapter 1. Sports Data Mining.
  2. Chapter 2. Sports Data Mining Methodology.
  3. Chapter 3. Data Sources for Sports.
  4. Chapter 4. Research in Sports Statistics.
  5. Chapter 5. Tools and Systems for Sports Data Analysis.
  6. Chapter 6. Predictive Modeling for Sports and Gaming.
  7. Chapter 7. Multimedia and Video Analysis for Sports.
  8. Chapter 8. Web Sports Data Extraction and Visualization.
  9. Chapter 9. Open Source Data Mining Tools for Sports.
  10. Chapter 10. Greyhound Racing Using Neural Networks.
  11. Chapter 11. Greyhound Racing Using Support Vector Machines.
  12. Chapter 12. Betting and Gaming.
  13. Chapter 13. Conclusions.

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