References
- Basketball-Reference. (2019). NBA & ABA single season leaders and records for player efficiency rating. Retrieved from http://www.basketball-reference.com/leaders/per_season.html
- Basketball-Reference. (2020). San Antonio Spurs. Retrieved from https://www.basketball-reference.com/teams/SAS/
- Berri, D.J., Van Gilder, J., & Fenn, A.J. (2014). Is the sports media color-blind? International Journal of Sport Finance, 9(2), 130-148.
- Berry, S.M., Reese, C.S., & Larkey, P.D. (1999). Bridging different eras in sports. Journal of the American Statistical Association, 94(447), 661-676. doi: https://doi.org/10.1080/01621459.1999.10474163
- Blanco, V., Salmerón, R., & Gómez-Haro, S. (2018). A multicriteria selection system based on player performance: Case study - The Spanish ACB basketball league. Group Decision and Negotiation, 27, 1029-1046. doi: https://doi.org/10.1007/s10726-018-9583-9
- Chen, Y., Dai, J., & Zhang, C. (2019). A neural network model of the NBA Most Valued Player selection prediction. In Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence (pp. 16-20). New York, NY: Association for Computing Machinery.
- Chu, D., & Wang, C. (2019). Empirical study on relationship between sports analytics and success in regular season and postseason in Major League Baseball. Journal of Sports Analytics, 5(3), 205-222. doi: https://doi.org/10.3233/JSA-190269
- Coleman, B.J., DuMond, J.M., & Lynch, A.K. (2008). An examination of NBA MVP voting behavior: Does race matter? Journal of Sports Economics, 9(6), 606-627. doi: https://doi.org/10.1177/1527002508320653
- Dadelo, S., Turskis, Z., Zavadskas, E.K., & Dadeliene, R. (2014). Multi-criteria assessment and ranking system of sport team formation based on objective-measured values of criteria set. Expert Systems with Applications, 41(14), 6106-6113. doi: https://doi.org/10.1016/j.eswa.2014.03.036
- Harremoës, P. (2019). Replication papers. Publications, 7(3), 53. doi: https://doi.org/10.3390/publications7030053
- Hervé, M. (2019). RVAideMemoire: Testing and plotting procedures for biostatistics. R package version 0.9-74.
- Hu, J., Zhang, H., & Qiu, J. (2019). Prediction of MVP attribution in NBA regular match based on BP neural network model. In Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (pp. 1-5). New York, NY: Association for Computing Machinery.
- Jiang, Z., & Jia, Z. R. (2018). Effects of students’ basketball club participation motivation on club cohesion organizational commitment as the mediator. Journal of Interdisciplinary Mathematics, 21(2), 519-528. doi: https://doi.org/10.1080/09720502.2018.1462026
- Kalman, S., & Bosch, J. (2020). NBA lineup analysis on clustered player tendencies: A new approach to the positions of basketball & modeling lineup efficiency of soft lineup aggregates. Paper presented at the 14th Annual MIT Sloan Sports Analytics Conference, Boston, MA. Retrieved from http://www.sloansportsconference.com/wp-content/ uploads/2020/02/Kalman_NBA_Line_up_Analysis.pdf
- Karaköprü, U.O., & Kabadurmuş, Ö. (2019). Using multi-criteria decision making methods to make logistics decisions in sports clubs. Alphanumeric Journal, 7(1), 129-142. doi: https://doi.org/10.17093/alphanumeric.425766
- Lin, M. (2018). Contour tracking algorithm for dynamic image of basketball shooting arm. Journal of Discrete Mathematical Sciences and Cryptography, 21(2), 299-304. doi: https://doi.org/10.1080/09720529.2018.1449303
- Lin, M.J., & Chang, C.C. (2011). Testing Coase theorem: The case of free agency in NBA. Applied Economics, 43(20), 2545-2558. doi: https://doi.org/10.1080/00036840903299722
- Lin, Z.J. (2017). Behavior prediction of ball carriers in basketball match video. Journal of Discrete Mathematical Sciences and Cryptography, 20(1), 103-113. doi: https://doi.org/10.1080/09720529.2016.1178905
- Mangiafico, S. (2019). rcompanion: Functions to support extension education program evaluation. R package version 2.0.10.
- Martin, B.A.C. (2016). MCDM: Multi-criteria decision making methods for crisp data. R package version 1.20.
- Moreno, P., & Lozano, S. (2014). A network DEA assessment of team efficiency in the NBA. Annals of Operation Research, 214(1), 99-124. doi: https://doi.org/10.1007/s10479-012-1074-9
- NBA Advanced Stats. (2019). NBA Advanced Stats. Retrieved from https://stats.nba.com/players/advanced/?sort=GP&dir=-1
- NBA Advanced Stats. (2020). Players tracking – Speed & distance. Retrieved from https://stats.nba.com/players/speed-distance/
- NBA. (2020). NBA Defensive Player of the Year award winners. Retrieved from https://www.nba.com/history/awards/defensive-player-of-the-year
- NBA. (2020). NBA MVP award winners. Retrieved from https://www.nba.com/history/awards/mvp
- Nikjo, B., Rezaeian, J., & Javadian, N. (2015). Decision making in best player selection: An integrated approach with AHP and Extended TOPSIS methods based on WeFA framework in MAGDM problems. International Journal of Research in Industrial Engineering, 4, 1-14.
- Pelton, K. (2015). The great analytics ranking. Retrieved from http://www.espn.com/espn/feature/story?id=12331388&_slug_=the-great-analytics-rankings&redirected=true
- Peng, Y., Wang, G., & Wang, H. (2012). User preferences based software defect detection algorithms selection using MCDM. Information Sciences, 191, 3-13. doi: https://doi.org/10.1016/j.ins.2010.04.019
- Pradhan, S. (2018). Ranking regular seasons in the NBA’s Modern Era using grey relational analysis. Journal of Sports Analytics, 4(1), 31-63. doi: https://doi.org/10.3233/JSA-160165
- Radovanović, S., Radojičić, M., Jeremić, V., & Savić, G. (2013). A novel approach in evaluating efficiency of basketball players. Management, 67, 37-45.
- Wang, P., Zhu, Z., & Wang, Y. (2016). A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design. Information Sciences, 345, 27-45. doi: https://doi.org/10.1016/j.ins.2016.01.076
- Zhang, G., & Zhang, D. (2018). Model construction of technical test and evaluation of “young basketball players”. Journal of Discrete Mathematical Sciences and Cryptography, 21(6), 1449-1454. doi: https://doi.org/10.1080/09720529.2018.1527813