Data Mining in Elite Sports: A Review and a Framework

REFERENCES

• Agrawal , R. and Srikant , R. Mining sequential patterns . Proceedings of the Eleventh International Conference on Data Engineering . Taipei , Taiwan. Edited by: Yu , P. S. and Chen , A. L. P. pp. 3 – 14 . IEEE Computer Society .
   Google Scholar
• Agrawal , R. , Imielinski , T. and Swami , A. 1993 . Mining association rules between sets of items in large databases . ACM SIGMOD Record , 22 : 207 – 216 . doi: 10.1145/170036.170072
   Google Scholar
• Alpaydin , E. 2010 . Introduction to machine learning , 2nd , Cambridge , MA : MIT Press .
   Google Scholar
• Anagnostopoulos , T. , Anagnostopoulos , C. B. , Hadjiefthymiades , S. , Kalousis , A. and Kyriakakos , M. Path prediction through data mining . IEEE International Conference on Pervasive Services . Istanbul , Turkey: IEEE. pp. 128 – 135 .
   Google Scholar
• Berkhin , P. 2006 . “ A survey of clustering data mining techniques ” . In Grouping multidimensional data , Edited by: Kogan , J. , Nicholas , C. and Teboulle , M. 25 – 71 . Berlin , , Germany : Springer .
   Google Scholar
• Bhandari , I. , Colet , E. , Parker , J. , Pines , Z. , Pratap , R. and Ramanujam , K. 1997 . Advanced Scout: Data mining and knowledge discovery in NBA data . Data Mining and Knowledge Discovery , 1 : 121 – 125 . doi: 10.1023/A:1009782106822
   Web of Science ®Google Scholar
• Bishop, D. (2003). Performance analysis: What is performance analysis, and how can it be integrated within the coaching process to benefit performance? Peak Performance, 4–7. http://www.pponline.co.uk/encyc/sports-performance-analysis- (http://www.pponline.co.uk/encyc/sports-performance-analysis-) (Accessed: 1 December 2010 ).
   Google Scholar
• Chen , I. , Homma , H. , Jin , C. and Yan , H. H. 2007 . Identification of elite swimmers’ race patterns using cluster analysis . International Journal of Sports Science and Coaching , 2 : 293 – 303 . doi: 10.1260/174795407782233083
   Google Scholar
• Clausen , H. 2012 . Important concepts of machine learning , 1st , New Delhi , , India : World Technologies .
   Google Scholar
• Cox , T. F. and Dunn , R. T. 2002 . An analysis of decathlon data . Journal of the Royal Statistical Society , 51 : 179 – 187 . doi: 10.1111/1467-9884.00310
   Google Scholar
• Edelmann-Nusser , J. , Hohmann , A. and Henneberg , B. 2002 . Modelling and prediction of competitive performance in swimming upon neural networks . European Journal of Sport Science , 2 : 1 – 10 . doi: 10.1080/17461390200072201
   Google Scholar
• Fayyad , U. , Piatetsky-Shapiro , G. and Smyth , P. 1996 . The KDD process for extracting useful knowledge from volumes of data . Communications of the ACM , 39 ( 11 ) : 27 – 34 . doi: 10.1145/240455.240464
   Web of Science ®Google Scholar
• Freund , R. J. , Wilson , W. J. and Sa , P. 2006 . Regression analysis , Burlington , , MA : Academic .
   Google Scholar
• Johnson , M. B. , Edmonds , W. A. , Jain , S. and Cavados , J., Jr. 2009 . Analysis of elite swimming performances and their respective between-gender differences over time . Journal of Quantitative Analysis in Sports , 5 ( 4 ) doi: 10.2202/1559-0410.1186
   Google Scholar
• Jones , A. M. and Whipp , B. J. 2002 . Bioenergetic constraints on tactical decision making in middle distance running . British Journal of Sports Medicine , 32 : 102 – 104 . doi: 10.1136/bjsm.36.2.102
   Web of Science ®Google Scholar
• Kahn, J. (2003). Neural network prediction of NFL games. http://homepages.caewisc.edu/~ece539/project/f03/kahn.pdf?q-nf1-search-statistics (http://homepages.caewisc.edu/~ece539/project/f03/kahn.pdf?q-nf1-search-statistics)
   Google Scholar
• Kenny , I. C. , Sprevak , D. , Sharp , C. and Boreham , C. 2005 . Determinants of success in the Olympic decathlon: Some statistical evidence . Journal of Quantitative Analysis in Sports , 1 ( 1 ) article 3
   Google Scholar
• Kline , C. E. , Durstine , J. L. , Davis , J. M. , Moore , T. A. , Devlin , T. M. , Zielinski , M. R. and Youngstedt , S. D. 2007 . Circadian variation in swim performance . Journal of Applied physiology , 102 : 641 – 649 . doi: 10.1152/japplphysiol.00910.2006
   PubMed Web of Science ®Google Scholar
• Lamb , P. , Bartlett , R. and Robins , A. 2010 . Self-organising maps: An objective method for clustering complex human movement . International Journal of Computer Science in Sport , 9 : 20 – 29 .
   Google Scholar
• Liao , T. Tactics analysis on women swimming athletes in the 800m freestyle swimming race using speed coefficient theory . Proceedings of International Workshop on Knowledge Discovery and Data Mining . Edited by: Lo , Q. and Sim , B. K. M. pp. 453 – 456 . Washington , DC : IEEE Compter Society . doi: 10.1109/WKDD.2008.145
   Google Scholar
• Ofoghi , B. , Stefano , D. , Zeleznikow , J. and MacMahon , C. May 29–June 2 2012 . “ Modelling relationships between swimming attributes for performance prediction ” . In American College of Sports Medicine May 29–June 2 , San Francisco , California USA
   Google Scholar
• Ofoghi , B. , Zeleznikow , J. and MacMahon , C. 2011a . Probabilistic modelling to give advice about rowing split measures to support strategy and pacing in race planning . International Journal of Performance Analysis in Sport , 11 : 239 – 253 .
   Web of Science ®Google Scholar
• Ofoghi , B. , Zeleznikow , J. and MacMahon , C. A machine learning approach to triathlon component analysis . Proceedings of the International Symposium on Computer Science in Sport . Shanghai , China. Edited by: Jiang , Y. and Zhang , H. pp. 30 – 33 .
   Google Scholar
• Ofoghi , B. , Zeleznikow , J. , MacMahon , C. and Dwyer , D. A machine learning approach to predicting winning patterns in track cycling omnium . Proceedings of the International Federation for Information Processing (IFIP) Conference on Advances in Information and Communication Technology . Brisbane , Australia. Edited by: Bramer , M. pp. 67 – 76 . Springer Berlin Heidelberg .
   Google Scholar
• Ofoghi , B. , Zeleznikow , J. , MacMahon , C. and Dwyer , D. Has the addition of the elimination race to the track cycling omnium benefitted sprinters or endurance riders? . Proceedings of the International Symposium on Computer Science in Sport . Edited by: Jiang , Y. and Zhang , H. pp. 34 – 37 . Washington , , DC : IEEE Computer Society. .
   Google Scholar
• Pollard , G. and Pollard , G. 2010 . Four ball best ball 1 . Journal of Sports Science and Medicine , 9 : 86 – 91 .
   PubMed Web of Science ®Google Scholar
• Ransdell , L. B. , Vener , J. and Huberty , J. 2009 . Masters athletes: An analysis of running, swimming and cycling performance by age and gender . Journal of Exercise Science and Fitness , 7 : S61 – S73 . doi: 10.1016/S1728-869X(09)60024-1
   Web of Science ®Google Scholar
• Rowley , J. 2007 . The wisdom hierarchy: representations of the DIKW hierarchy . Journal of Information Science , 33 : 163 – 180 . doi: 10.1177/0165551506070706
   Web of Science ®Google Scholar
• Schumaker , R. P. , Solieman , O. K. and Chen , H. 2010 . Sports data mining. Integrated series in information systems , Vol. 26 , New York, NY : Springer .
   Google Scholar
• Shao , S. Application of BP neural network model in sports aerobics performance evaluation . Proceedings of the 2009 Pacific-Asia Conference on Knowledge Engineering and Software Engineering . Washington , DC . Edited by: Xiong , F. and Li , Q. pp. 33 – 35 . IEEE Computer Society .
   Google Scholar
• Smith , R. M. and Spinks , W. L. 1995 . Discriminant analysis of biomechanical differences between novice, good and elite rowers . Journal of Sports Sciences , 13 : 377 – 385 .
   PubMedGoogle Scholar
• Smyth , G.K. 2002 . Nonlinear regression . Encyclopedia of Environmetrics , 3 : 1405 – 1411 .
   Google Scholar
• Stranieri , A. and Zeleznikow , J. 2005 . Knowledge discovery from legal databases: Law and Philosophy Library , Vol. 69 , Dordrecht, The Netherlands : Springer .
   Google Scholar
• Sun , J. , Yu , W. and Zhao , H. Study of association rule mining on technical action of ball games . Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2010 . Washington , DC . pp. 539 – 542 . IEEE Computer Society .
   Google Scholar
• Vaz , L. , Rooyen , M. V. and Sampaio , J. 2010 . Rugby game-related statistics that discriminate between winning and losing teams in IRB and super twelve close games . Journal of Sports Science and Medicine , 9 : 51 – 55 .
   PubMed Web of Science ®Google Scholar
• Vezos , N. , Gourgoulis , V. , Aggeloussis , N. , Kasimatis , P. , Christoforidis , C. and Mavromatis , G. 2007 . Underwater stroke kinematics during breathing and breath-holding front crawl swimming . Journal of Sport Science and Medicine , 6 : 58 – 62 .
   PubMed Web of Science ®Google Scholar
• Wilson , B. , Mason , B. , Cossor , J. , Arellano , R. , Chatard , J. and Riewald , S. Relationships between stroke efficiency measures and freestyle swimming performance: An analysis of freestyle swimming events at the Sydney 2000 Olympics . Edited by: Blackwell , J. R. and Sanders , R. H. pp. 79 – 82 . San Francisco , CA : University of San Francisco . Proceedings of Swim Sessions: XIX International Symposium on Biomechanics in Sports (
   Google Scholar
• Woolf , A. , Ansley , L. and Bidgood , P. 2007 . Grouping of decathlon disciplines . Journal of Quantitative Analysis in Sports , 3 ( 4 ) article 5 doi: 10.2202/1559-0410.1057
   Google Scholar
• Zwols , Y. and Sierksma , G. 2009 . Training optimization for the decathlon . Journal of Operations Research , 57 : 812 – 822 . doi: 10.1287/opre.1080.0616
   Web of Science ®Google Scholar