Coaches Ranking: A Time-Irrelevant Data Mining Solution with Flexibility and Objectivity

Article Preview

Abstract:

The ranking of coaches is of great significance for sports teams to find talented coaches. Traditional ranking mechanism is relatively subjective which is usually accomplished by voting. In this paper, we establish a ranking mechanism based on both subjective and objective factors. We use data mining method to classify and analyze the data. The regression tree and the decision tree (CART) are used to narrow down the number of coaches to a reasonable scale. We put forward our AHP model to rank the “candidates” and propose a coach rating analysis system (CRAS) for evaluating the accuracy of system. We verify our ranking system more comprehensive in terms of the evaluation of the coaches through analyzing the result. The proposed mechanism is also significant to find potential coaches.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

5010-5013

Citation:

Online since:

July 2014

Export:

Price:

* - Corresponding Author

[1] Data of the coaches on http: /www. sports-reference. com.

Google Scholar

[2] Data of the coaches on http: /en. wikipedia. org/wiki/College_football.

Google Scholar

[3] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth: From data mining to knowledge discovery in databases, AI magazine, vol. 17 (1996), p.37.

Google Scholar

[4] R. R. Vempada and K. S. Rao: National Conference on Communications (NCC) (IIT Kharagpur, India, Feb 3-5, 2012), pp.1-5.

Google Scholar

[5] T. L. Saaty: Fundamentals of decision making and priority theory with the analytic hierarchy process vol. 6 (Rws Publications, American 2000).

Google Scholar

[6] A. SIGKDD, Data mining curriculum, ed, (2012).

Google Scholar

[7] Information on http: /en. wikipedia. org/wiki/Elo_rating_system#Mathematical_details.

Google Scholar

[8] A. Halevy, P. Norvig, and F. Pereira: The unreasonable effectiveness of data: Intelligent Systems, IEEE, vol. 24 (2009), pp.8-12.

DOI: 10.1109/mis.2009.36

Google Scholar

[9] M. Bramer: Principles of data mining (Springer, Germany 2013).

Google Scholar

[10] Top 10 coaches list on http: /www. ncaa. com.

Google Scholar