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. A full draft TOC is attached.

With combined (NFL; MLB; NBA; NHL) team values in the US running at more than $42 billion (NFL alone was at $33.3 billion in 2008!), and European soccer teams at over $10 billion, professional team sports worldwide is a massive business that is about to experience its first real contraction in over ten years. Combine that with the proven effectiveness -- and growing use -- of statistical analysis to produce winning teams (and thus higher revenues), and then consider the sharp growth in college programs in sports business: an eager market awaits this book in the sports business market alone. It will also appeal to researchers in data mining broadly; the sports statistics service industry that’s developed in the last ten years; and anyone studying any of the pari-mutuel wagering sports around the world.