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Sports Data Mining : Integrated Series in Information Systems - Robert P. Schumaker

Sports Data Mining

Integrated Series in Information Systems

Hardcover

Published: 30th September 2010
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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.

Sports Data Mining: The Fieldp. 1
Chapter Overviewp. 1
Definitionp. 2
Historyp. 5
Societal Dimensionsp. 8
The International Landscapep. 10
Criticismsp. 12
Questions for Discussionp. 13
Sports Data Mining Methodologyp. 15
Chapter Overviewp. 15
Scientific Foundationp. 16
Traditional Data Mining Applicationsp. 18
Deriving Knowledgep. 20
Questions for Discussionp. 21
Data Sources for Sportsp. 23
Chapter Overviewp. 23
Introductionp. 23
Professional Societiesp. 24
The Society for American Baseball Researchp. 24
Association for Professional Basketball Researchp. 24
Professional Football Researchers Associationp. 25
Sport-Related Associationsp. 25
The International Association on Computer Science in Sportp. 25
The International Association for Sports Informationp. 26
Special Interest Sourcesp. 26
Baseballp. 26
Basketballp. 26
Footballp. 27
Cricketp. 27
Soccerp. 27
Multiple Sportsp. 28
Conclusionsp. 28
Questions for Discussionp. 28
Research in Sports Statisticsp. 29
Chapter Overviewp. 29
Introductionp. 29
Sports Statisticsp. 29
History and Inherent Problems of Statistics in Sportsp. 30
Bill Jamesp. 31
Dean Oliverp. 32
Baseball Researchp. 32
Building Blocksp. 33
Runs Createdp. 33
Win Sharesp. 35
Linear Weights and Total Player Ratingp. 35
Pitching Measuresp. 36
Basketball Researchp. 37
Shot Zonesp. 37
Player Efficiency Ratingp. 38
Plus/Minus Ratingp. 38
Measuring Player Contribution to Winningp. 39
Rating Clutch Performancesp. 39
Football Researchp. 40
Defense-Adjusted Value Over Averagep. 40
Defense-Adjusted Points Above Replacementp. 41
Adjusted Line Yardsp. 41
Emerging Research in Other Sportsp. 41
NCAA Bowl Championship Seriesp. 42
NCAA Men's Basketball Tournamentp. 42
Soccerp. 43
Cricketp. 43
Olympic Curlingp. 44
Conclusionsp. 44
Questions for Discussionp. 44
Tools and Systems for Sports Data Analysisp. 45
Chapter Overviewp. 45
Introductionp. 45
Sports Data Mining Toolsp. 46
Advanced Scoutp. 46
Synergy Onlinep. 47
Sports Visp. 47
Sports Data Hubp. 48
Scouting Toolsp. 49
Digital Scoutp. 49
Inside Edgep. 49
Sports Fraud Detectionp. 50
Las Vegas Sports Consultantsp. 52
Offshore Gamingp. 53
Conclusionsp. 53
Questions for Discussionp. 53
Predictive Modeling for Sports and Gamingp. 55
Chapter Overviewp. 55
Introductionp. 55
Statistical Simulationsp. 56
Baseballp. 56
Basketball's BBallp. 57
Other Sporting Simulationsp. 58
Machine Learningp. 58
Soccerp. 58
Greyhound and Thoroughbred Racingp. 59
Commercial Productsp. 60
Conclusionsp. 63
Questions for Discussionp. 63
Multimedia and Video Analysis for Sportsp. 65
Chapter Overviewp. 65
Introductionp. 65
Searchable Videop. 66
SoccerQp. 67
Blinkxp. 68
Cliptap. 68
Sports VHLp. 69
Truveop. 69
Bluefin Labp. 69
Motion Analysisp. 69
Conclusionsp. 70
Questions for Discussionp. 70
Web Sports Data Extraction and Visualizationp. 71
Chapter Overviewp. 71
Introductionp. 71
Web Data Sourcesp. 72
Baseballp. 72
Basketballp. 74
Cricketp. 77
Footballp. 78
Hockeyp. 81
Soccerp. 82
Other Sport Sourcesp. 83
Extracting Datap. 84
Programsp. 85
Conclusionsp. 87
Questions for Discussionp. 87
Open Source Data Mining Tools for Sportsp. 89
Chapter Overviewp. 89
Introductionp. 89
WEKAp. 89
Rapidminerp. 91
Conclusionsp. 92
Questions for Discussionp. 92
Greyhound Racing Using Neural Networks: A Case Studyp. 93
Chapter Overviewp. 93
Introductionp. 93
Setting Up the Experimentsp. 94
Testing ID3p. 96
Testing the Backpropagation Neural Networkp. 98
The Resultsp. 98
Conclusionsp. 99
Questions for Discussionp. 100
Greyhound Racing Using Support Vector Machines: A Case Studyp. 101
Chapter Overviewp. 101
Introductionp. 101
Relevant Literaturep. 102
Research Methodologyp. 103
Data Acquisitionp. 105
Support Vector Machines Algorithmp. 105
Resultsp. 106
Conclusionsp. 108
Questions for Discussionp. 108
Betting and Gamingp. 109
Chapter Overviewp. 109
Introductionp. 109
The Effects on Gambling on Sportsp. 109
Sportsbooks and Offshore Bettingp. 111
Arbitrage Methodsp. 112
Cautions and Gambling Pitfallsp. 113
Conclusionsp. 113
Questions for Discussionp. 114
Conclusionsp. 115
Chapter Overviewp. 115
Sports Data Mining Challengesp. 115
Sports Data Mining Audiencep. 116
Future Directionsp. 117
Referencesp. 119
Indexp. 127
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781441967299
ISBN-10: 144196729X
Series: Integrated Series in Information Systems
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 138
Published: 30th September 2010
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 23.5 x 15.5  x 1.27
Weight (kg): 0.36