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Sports Data Mining / by Robert P. Schumaker, Osama K. Solieman, Hsinchun Chen
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Integrated Series in Information Systems ; 26 | SpringerLink BücherPublisher: Boston, MA : Springer Science+Business Media, LLC, 2010Description: Online-Ressource (XIV, 138 p, online resource)ISBN:- 9781441967305
- 006.312
- 796.02856312
- QA76.9.D343
Contents:
Summary: 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.PPN: PPN: 1650219741Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SBE
Sports Data Mining; Preface; Aims; Audience; Future; Contents; List of Figures; List of Tables; Chapter 1: Sports Data Mining: The Field; Chapter Overview; Chapter Overview; Chapter Overview; Chapter Overview; 1 Definition; 2 History; 3 Societal Dimensions; 4 The International Landscape; 5 Criticisms; 6 Questions for Discussion; Chapter 2: Sports Data Mining Methodology; Chapter Overview; 1 Scientific Foundation; 2 Traditional Data Mining Applications; 3 Deriving Knowledge; 4 Questions for Discussion; Chapter 3: Data Sources for Sports; Chapter Overview; 1 Introduction
2 Professional Societies2.1 The Society for American Baseball Research; 2.2 Association for Professional Basketball Research; 2.3 Professional Football Researchers Association; 3 Sport-Related Associations; 3.1 The International Association on Computer Science in Sport; 3.2 The International Association for Sports Information; 4 Special Interest Sources; 4.1 Baseball; 4.2 Basketball; 4.3 Football; 4.4 Cricket; 4.5 Soccer; 4.6 Multiple Sports; 5 Conclusions; 6 Questions for Discussion; Chapter 4: Research in Sports Statistics; Chapter Overview; 1 Introduction; 2 Sports Statistics
2.1 History and Inherent Problems of Statistics in Sports2.2 Bill James; 2.3 Dean Oliver; 3 Baseball Research; 3.1 Building Blocks; 3.2 Runs Created; 3.3 Win Shares; 3.4 Linear Weights and Total Player Rating; 3.5 Pitching Measures; 4 Basketball Research; 4.1 Shot Zones; 4.2 Player Efficiency Rating; 4.3 Plus/Minus Rating; 4.4 Measuring Player Contribution to Winning; 4.5 Rating Clutch Performances; 5 Football Research; 5.1 Defense-Adjusted Value Over Average; 5.2 Defense-Adjusted Points Above Replacement; 5.3 Adjusted Line Yards; 6 Emerging Research in Other Sports
6.1 NCAA Bowl Championship Series6.2 NCAA Men´s Basketball Tournament; 6.3 Soccer; 6.4 Cricket; 6.5 Olympic Curling; 7 Conclusions; 8 Questions for Discussion; Chapter 5: Tools and Systems for Sports Data Analysis; Chapter Overview; 1 Introduction; 2 Sports Data Mining Tools; 2.1 Advanced Scout; 2.2 Synergy Online; 2.3 SportsVis; 2.4 Sports Data Hub; 3 Scouting Tools; 3.1 Digital Scout; 3.2 Inside Edge; 4 Sports Fraud Detection; 4.1 Las Vegas Sports Consultants; 4.2 Offshore Gaming; 5 Conclusions; 6 Questions for Discussion; Chapter 6: Predictive Modeling for Sports and Gaming; 1 Introduction
2 Statistical Simulations2.1 Baseball; 2.2 Basketball´s BBall; 2.3 Other Sporting Simulations; 3 Machine Learning; 3.1 Soccer; 3.2 Greyhound and Thoroughbred Racing; 3.3 Commercial Products; 3.3.1 Synergy Online; 3.3.2 The Dr. Z System; 3.3.3 Front Office Football; 3.3.4 Visual Sports; 4 Conclusions; 5 Questions for Discussion; Chapter 7: Multimedia and Video Analysis for Sports; Chapter Overview; 1 Introduction; 2 Searchable Video; 2.1 SoccerQ; 2.2 Blinkx; 2.3 Clipta; 2.4 SportsVHL; 2.5 Truveo; 2.6 Bluefin Lab; 3 Motion Analysis; 4 Conclusions; 5 Questions for Discussion
Chapter 8: Web Sports Data Extraction and Visualization
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