Sports Data Mining
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 Wi
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Series Editors Ramesh Sharda Oklahoma State University, Stillwater, OK, USA Stefan Voß University of Hamburg, Hamburg, Germany
For other titles published in this series; go to http://www.springer.com/series/6157
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Robert P. Schumaker Hsinchun Chen
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Osama K. Solieman
Sports Data Mining
Robert P. Schumaker Cleveland State University Cleveland, OH 44115, USA [email protected]
Osama K. Solieman Tucson, AZ 85704, USA [email protected]
Hsinchun Chen University of Arizona Tucson, AZ 85721, USA [email protected]
ISSN 1571-0270 ISBN 978-1-4419-6729-9 e-ISBN 978-1-4419-6730-5 DOI 10.1007/978-1-4419-6730-5 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2010933519 # Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Aims Sports data mining has experienced rapid growth in recent years. Beginning with fantasy league players and sporting enthusiasts seeking an edge in predictions, tools and techniques began to be developed to better measure both player and team performance. These new methods of performance measurement are starting to get the attention of major sports franchises including baseball’s Boston Red Sox and Oakland Athletics as well as soccer’s AC Milan. Before the advent of data mining, sports organizations relied almost exclusively on human expertise. It was believed that domain experts (coaches, managers and scouts) could effectively convert their collected data into usable knowledge. As the different types of data collected grew in scope, these organizations sought to find more practical methods to make sense of what they had. This led first to the addition of in-house statisticians to create better measures of performance and better decision-making criteria. The second step was to find more practical methods to extract valuable knowledge using data mining techniques. Sports organizations are sitting on a wealth of data and need ways to harness it. This monograph will highlight current measurement inadequacies and showcase techniques to make better usage of collected data. Properly leveraging sports data mining techniques can result in better team performance by matching players to certain situations, identifying individual player contributi
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