Sports knowledge management and data mining

Introduction Vast amounts of sports data are routinely collected about players, coaching decisions and game events. Making sense of this data is important to those seeking an edge. By transforming this data into actionable knowledge, scouts, managers and coaches can have a better idea of what to expect from opponents and be able to use a player draft more effectively. With millions of dollars riding on the many decisions made within a sports franchise (Lewis, 2003), the sports environment is ideal for data mining and knowledge management approaches. While the application these approaches to the sports environment may be unique and the focus of this chapter, the topics of data mining and knowledge management should certainly be well known to the reader and form the basis of the approaches we discuss.

[1]  U. Johansson,et al.  Neural networks mine for gold at the greyhound racetrack , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[2]  Shane G. Henderson,et al.  A Simulation Model for Predicting Yacht Match Race Outcomes , 2004, Oper. Res..

[3]  Yi Ding,et al.  Segmental Hidden Markov Models for View-based Sport Video Analysis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Hsinchun Chen,et al.  Intelligence and Security Informatics , 2006, Lecture Notes in Computer Science.

[5]  Scott Berry.  27. Introduction to the Methodologies and Multiple Sports Articles , 2005, Anthology of Statistics in Sports.

[6]  Wei-Ta Chu,et al.  Extraction of Baseball Trajectory and Physics-Based Validation for Single-View Baseball Video Sequences , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[7]  David Scott,et al.  Prediction of Physical Performance Using Data Mining. (Measurement) , 2003 .

[8]  Henry D. Fetter.  Taking on the Yankees: Winning and Losing in the Business of Baseball, 1903-2003 , 2003 .

[9]  Rodney J. Paul,et al.  Bettor Misperceptions in the NBA , 2005 .

[10]  Ba Tu Truong,et al.  Automatic genre identification for content-based video categorization , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[11]  D. Garrick,et al.  The effect of simulated censored data on estimates of heritability of longevity in the Thoroughbred racing industry. , 2006, Genetics and molecular research : GMR.

[12]  Lloyd A. Smith,et al.  Data mining in sports: predicting Cy Young award winners , 2007 .

[13]  Hsinchun Chen,et al.  Evaluating a news-aware quantitative trader: The effect of momentum and contrarian stock selection strategies , 2008, J. Assoc. Inf. Sci. Technol..

[14]  Hsinchun Chen,et al.  Expert prediction, symbolic learning, and neural networks. An experiment on greyhound racing , 1994, IEEE Expert.

[15]  Shu-Ching Chen,et al.  An enhanced query model for soccer video retrieval using temporal relationships , 2005, 21st International Conference on Data Engineering (ICDE'05).

[16]  Judith Pinn Carlisle.  Escaping the Veil of Maya: Wisdom and the Organization , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[17]  Philip K Schot,et al.  Sit-to-Stand Performance of Older Adults following Strength Training , 2003, Research quarterly for exercise and sport.

[18]  Charles E. Vickery,et al.  The relationship of subsequent racing performance to foreleg flight patterns during racing speed workouts of unraced 2-year-old Thoroughbred racehorses at auctions , 2005 .

[19]  A. P. Rotshtein,et al.  Football Predictions Based on a Fuzzy Model with Genetic and Neural Tuning , 2005 .

[20]  B. Jay Coleman,et al.  Identifying the NCAA Tournament "Dance Card" , 2001, Interfaces.

[21]  Jim Albert.  Streaky Hitting in Baseball , 2008 .

[22]  N. Bontis,et al.  Meta-review of knowledge management and intellectual capital literature: citation impact and research productivity rankings , 2004 .

[23]  Mike Wright,et al.  A MARKOV CHAIN APPROACH TO OPTIMAL PINCH HITTING STRATEGIES IN A DESIGNATED HITTER RULE BASEBALL GAME , 2003 .

[24]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[25]  Noboru Babaguchi,et al.  Learning Personal Preference From Viewer's Operations for Browsing and Its Application to Baseball Video Retrieval and Summarization , 2007, IEEE Transactions on Multimedia.

[26]  M. O. Thirunarayanan.  Degrees that expire: an idea whose time has come , 2007, UBIQ.

[27]  J. Stasko,et al.  SportVis: Discovering Meaning in Sports Statistics Through Information Visualization , 2006 .

[28]  Stephen Wright,et al.  Measure of success. , 2008, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[29]  Stephen W Marshall,et al.  Risk Factors and Risk Statistics for Sports Injuries , 2007, Clinical journal of sport medicine : official journal of the Canadian Academy of Sport Medicine.

[30]  C. Barros,et al.  Performance evaluation of the English Premier Football League with data envelopment analysis , 2006 .

[31]  Peter Knight,et al.  Knowledge management best practices in national sport organisations , 2007 .

[32]  Serial correlation in the wagering market for professional basketball. , 1995 .

[33]  M. Lewis,et al.  Moneyball: The Art of Winning an Unfair Game , 2003 .

[34]  Di Dong,et al.  Integrating Data Mining Processes within the Web Environment for the Sports Community , 2007, 2007 IEEE International Conference on Integration Technology.

[35]  Ian Witten,et al.  Data Mining , 2000 .

[36]  Hsinchun Chen,et al.  Intelligence and Security Informatics for International Security: Information Sharing and Data Mining (Integrated Series in Information Systems) , 2006 .

[37]  Paul Volcker,et al.  The Report of the Independent Members of the Commissioner''s Blue Ribbon Panel on Baseball Economics , 2000 .

[38]  Hsinchun Chen,et al.  Knowledge Management Systems: A Text Mining Perspective , 2001 .

[39]  Thomas Gilovich,et al.  The Cold Facts about the “Hot Hand” in Basketball , 1989 .

[40]  M. Beyerlein,et al.  Knowledge transfer and management consulting: A look at “The firm” , 2000 .

[41]  Mark E. GLICKMAN,et al.  A State-Space Model for National Football League Scores , 1998 .

[42]  Tim B. Swartz,et al.  A Two-Stage Bayesian Model for Predicting Winners in Major League Baseball , 2021, Journal of Data Science.

[43]  Akihisa Kodate,et al.  Sports video categorizing method using camera motion parameters , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[44]  D. J. Kelley,et al.  Predicting Baseball Home Run Records Using Exponential Frequency Distributions , 2006, physics/0608228.

[45]  M. Boisot,et al.  Data, information and knowledge: have we got it right? , 2004 .

[46]  Stephen Wright,et al.  Measure of success. , 2008, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[47]  Fred Stentiford,et al.  Recent Trends in Video Analysis: A Taxonomy of Video Classification Problems , 2002, IMSA.

[48]  Paul Lionel Zimmerman.  The New Thinking Man's Guide to Pro Football , 1984 .

[49]  Hans C. van Houwelingen,et al.  The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .

[50]  Hal S. Stern,et al.  On the Probability of Winning a Football Game , 1991 .

[51]  A. Ginart,et al.  Self-evolution in knowledge bases , 2005, IEEE Autotestcon, 2005..

[52]  M. Zeleny.  Management support systems: Towards integrated knowledge management , 1987 .

[53]  Suh-Yin Lee,et al.  A Video Information System for Sport Motion Analysis , 1997, J. Vis. Lang. Comput..

[54]  P. Allsopp,et al.  Rating teams and analysing outcomes in one‐day and test cricket , 2004 .

[55]  Justin Wolfers,et al.  Point Shaving: Corruption in NCAA Basketball , 2006 .

[56]  Håvard Rue,et al.  Prediction and retrospective analysis of soccer matches in a league , 2000 .

[57]  Hsinchun Chen,et al.  Web mining: Machine learning for web applications , 2005, Annu. Rev. Inf. Sci. Technol..

[58]  Joseph Adler.  Baseball Hacks , 2006 .

[59]  Hua-Tsung Chen,et al.  Pitch-by-Pitch Extraction from Single View Baseball Video Sequences , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[60]  Inderpal S. Bhandari,et al.  Advanced Scout: Data Mining and Knowledge Discovery in NBA Data , 2004, Data Mining and Knowledge Discovery.

[61]  W FellinghamGilbert,et al.  Using Box-Scores to Determine a Position's Contribution to Winning Basketball Games , 2007 .

[62]  Andrew C. Thomas.  The Impact of Puck Possession and Location on Ice Hockey Strategy , 2006 .

[63]  Dean Oliver,et al.  Basketball on Paper: Rules and Tools for Performance Analysis , 2003 .

[64]  M. Fischetti.  Working knowledge. , 2003, Scientific American.

[65]  Mark Pawlewski,et al.  Video genre classification using dynamics , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[66]  P. Bierly,et al.  Organizational learning, knowledge and wisdom , 2000 .

[67]  Boon-Lock Yeo,et al.  Analysis And Presentation Of Soccer Highlights From Digital Video , 1995 .

[68]  Michael R. Middleton,et al.  Information as a Resource , 2002 .

[69]  Ruud H. Koning,et al.  Balance in competition in Dutch soccer , 2000 .

[70]  Dorothy E. Leidner,et al.  Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues , 2001, MIS Q..

[71]  C. Benito.  Annual Review of Information Science and Technology (ARIST) , 2003 .

[72]  Michael J. Schell.  Baseball's All-Time Best Hitters: How Statistics Can Level the Playing Field , 2005 .

[73]  C. Papahristodoulou.  Team Performance in UEFA Champions League 2005-06 , 2006 .

[74]  Shuliang Wang,et al.  Data Mining and Knowledge Discovery , 2005, Mathematical Principles of the Internet.

[75]  Спорт.  Society for American Baseball Research , 2010, 100 Years of Math Milestones.