Sports Data Mining

By: Robert P. Schumaker, Osama K. Solieman, Hsinchun Chen

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Hardcover | 30 September 2010

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152 Pages

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23.5 x 15.88 x 1.27

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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.

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Sports Data Mining: The Field	p. 1
Chapter Overview	p. 1
Definition	p. 2
History	p. 5
Societal Dimensions	p. 8
The International Landscape	p. 10
Criticisms	p. 12
Questions for Discussion	p. 13
Sports Data Mining Methodology	p. 15
Chapter Overview	p. 15
Scientific Foundation	p. 16
Traditional Data Mining Applications	p. 18
Deriving Knowledge	p. 20
Questions for Discussion	p. 21
Data Sources for Sports	p. 23
Chapter Overview	p. 23
Introduction	p. 23
Professional Societies	p. 24
The Society for American Baseball Research	p. 24
Association for Professional Basketball Research	p. 24
Professional Football Researchers Association	p. 25
Sport-Related Associations	p. 25
The International Association on Computer Science in Sport	p. 25
The International Association for Sports Information	p. 26
Special Interest Sources	p. 26
Baseball	p. 26
Basketball	p. 26
Football	p. 27
Cricket	p. 27
Soccer	p. 27
Multiple Sports	p. 28
Conclusions	p. 28
Questions for Discussion	p. 28
Research in Sports Statistics	p. 29
Chapter Overview	p. 29
Introduction	p. 29
Sports Statistics	p. 29
History and Inherent Problems of Statistics in Sports	p. 30
Bill James	p. 31
Dean Oliver	p. 32
Baseball Research	p. 32
Building Blocks	p. 33
Runs Created	p. 33
Win Shares	p. 35
Linear Weights and Total Player Rating	p. 35
Pitching Measures	p. 36
Basketball Research	p. 37
Shot Zones	p. 37
Player Efficiency Rating	p. 38
Plus/Minus Rating	p. 38
Measuring Player Contribution to Winning	p. 39
Rating Clutch Performances	p. 39
Football Research	p. 40
Defense-Adjusted Value Over Average	p. 40
Defense-Adjusted Points Above Replacement	p. 41
Adjusted Line Yards	p. 41
Emerging Research in Other Sports	p. 41
NCAA Bowl Championship Series	p. 42
NCAA Men's Basketball Tournament	p. 42
Soccer	p. 43
Cricket	p. 43
Olympic Curling	p. 44
Conclusions	p. 44
Questions for Discussion	p. 44
Tools and Systems for Sports Data Analysis	p. 45
Chapter Overview	p. 45
Introduction	p. 45
Sports Data Mining Tools	p. 46
Advanced Scout	p. 46
Synergy Online	p. 47
Sports Vis	p. 47
Sports Data Hub	p. 48
Scouting Tools	p. 49
Digital Scout	p. 49
Inside Edge	p. 49
Sports Fraud Detection	p. 50
Las Vegas Sports Consultants	p. 52
Offshore Gaming	p. 53
Conclusions	p. 53
Questions for Discussion	p. 53
Predictive Modeling for Sports and Gaming	p. 55
Chapter Overview	p. 55
Introduction	p. 55
Statistical Simulations	p. 56
Baseball	p. 56
Basketball's BBall	p. 57
Other Sporting Simulations	p. 58
Machine Learning	p. 58
Soccer	p. 58
Greyhound and Thoroughbred Racing	p. 59
Commercial Products	p. 60
Conclusions	p. 63
Questions for Discussion	p. 63
Multimedia and Video Analysis for Sports	p. 65
Chapter Overview	p. 65
Introduction	p. 65
Searchable Video	p. 66
SoccerQ	p. 67
Blinkx	p. 68
Clipta	p. 68
Sports VHL	p. 69
Truveo	p. 69
Bluefin Lab	p. 69
Motion Analysis	p. 69
Conclusions	p. 70
Questions for Discussion	p. 70
Web Sports Data Extraction and Visualization	p. 71
Chapter Overview	p. 71
Introduction	p. 71
Web Data Sources	p. 72
Baseball	p. 72
Basketball	p. 74
Cricket	p. 77
Football	p. 78
Hockey	p. 81
Soccer	p. 82
Other Sport Sources	p. 83
Extracting Data	p. 84
Programs	p. 85
Conclusions	p. 87
Questions for Discussion	p. 87
Open Source Data Mining Tools for Sports	p. 89
Chapter Overview	p. 89
Introduction	p. 89
WEKA	p. 89
Rapidminer	p. 91
Conclusions	p. 92
Questions for Discussion	p. 92
Greyhound Racing Using Neural Networks: A Case Study	p. 93
Chapter Overview	p. 93
Introduction	p. 93
Setting Up the Experiments	p. 94
Testing ID3	p. 96
Testing the Backpropagation Neural Network	p. 98
The Results	p. 98
Conclusions	p. 99
Questions for Discussion	p. 100
Greyhound Racing Using Support Vector Machines: A Case Study	p. 101
Chapter Overview	p. 101
Introduction	p. 101
Relevant Literature	p. 102
Research Methodology	p. 103
Data Acquisition	p. 105
Support Vector Machines Algorithm	p. 105
Results	p. 106
Conclusions	p. 108
Questions for Discussion	p. 108
Betting and Gaming	p. 109
Chapter Overview	p. 109
Introduction	p. 109
The Effects on Gambling on Sports	p. 109
Sportsbooks and Offshore Betting	p. 111
Arbitrage Methods	p. 112
Cautions and Gambling Pitfalls	p. 113
Conclusions	p. 113
Questions for Discussion	p. 114
Conclusions	p. 115
Chapter Overview	p. 115
Sports Data Mining Challenges	p. 115
Sports Data Mining Audience	p. 116
Future Directions	p. 117
References	p. 119
Index	p. 127
Table of Contents provided by Ingram. All Rights Reserved.

Sports Data Mining

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Hardcover

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