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Comparison of the Evaluation of Performance Preconditions in Tennis with the Use of Equal and Expertly Judged Criteria Weights

Open Access
|Aug 2021

References

  1. Ahamad, G., Naqvi, K., & Beg, S. (2013). A Model for Talent Identification In Cricket Based on OWA Operator. International Journal of Information Technology & Management Information System, 4(2), 40–55.
  2. Ahmed, F., & Kilic, K. (2019). Fuzzy Analytic Hierarchy Process: A performance analysis of various algorithms. Fuzzy Sets and Systems, 362, 10–128. https://doi.org/10.1016/j.fss.2018.08.009">https://doi.org/10.1016/j.fss.2018.08.00910.1016/j.fss.2018.08.009
  3. Ağılönü, A., & Balli, S. (2009). Developing computer aided model for selecting talent players in badminton. International Journal of Human Sciences, 6(2), 293–301.
  4. Bottoni, A., Giafelici, A., Tamburri, R., & Faina, M. (2011). Talent selection criteria for Olympic distance triathlon. Journal of Human Sport & Exercise, 6(2), 293–304.10.4100/jhse.2011.62.09
  5. Božić-Štulić, D., Kruzic, S., Gotovac, S., & Papić, V. (2018). Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks. Journal of Communications Software and Systems, 14(1), 82–90. https://doi.org/10.24138/jcomss.v14i1.441">https://doi.org/10.24138/jcomss.v14i1.44110.24138/jcomss.v14i1.441
  6. Budak, G., Kara, I., & Tansel, Y., I. (2017). Weighting the positions and skills of volleyball sport by using AHP: a real life application. Journal of Sports and Physical Education. 4(1), 23–29.10.9790/6737-0401012329
  7. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, N. J.: L. Erlbaum Associates.
  8. Couceiro, M., Martins, F., Clement, F., Dias, G., & Mendes, R. (2014). On fuzzy approach for the evaluation of golf players. Maejo International Journal of Science and Technology, 8(01), 86–99.
  9. Das, S., Chowdhury, S. R., & Saha, H. (2012). Accuracy Enhancement in a Fuzzy Expert Decision Making System Through Appropriate Determination of Membership Functions and Its Application in a Medical Diagnostic Decision Making System. Journal of Medical Systems, 36(3), 1607–1620.10.1007/s10916-010-9623-821107889
  10. Durović, N., Dizdar, D., & Zagorac, N. (2015). Importance of Hierarchical Structure Determining Tennis Performance for Modern Defensive Baseliner. Collegium Antropologicum, 39, Suppl. 1, 103–108.
  11. Fernandez-Fernandez, J., Ulbricht, A., & Ferrauti, A. (2014). Fitness testing of tennis players: How valuable is it? British Journal of Sports Medicine, 48, 22–31.10.1136/bjsports-2013-093152399522824668375
  12. Ferrauti, A., Maier, P., & Weber, K. (2014). Handbuch für Tennistraining: Leistung, Athletik, Gesundheit. Aachen: Meyer & Meyer.
  13. Filipčič, A., & Filipčič, T. (2005). The relationship of tennis-specific motor abilities and the competition efficiency of young female tennis players. Kinesiology, 37(2), 164–172.
  14. Güllich A., & Krüger M. (2013). Sport. Das Lehrbuch für das Sportstudium. Berlin: Springer-Verlag.10.1007/978-3-642-37546-0
  15. Hohmann, A., Lames, M., & Letzelter, M. (2007). Einführung in die Trainingswissenschaft. Wiebelsheim: Limpert Verlag.
  16. Holeček, P., & Talašová, J. (2010). FuzzME: A new software for multiple-criteria fuzzy evaluation. Acta Universitatis Matthiae Belii ser. Mathematics, 16, 35–51.
  17. Hubáček, O., Zháněl, J., & Polách, M. (2015). Comparison of probabilistic and fuzzy approach to evaluating condition performance level in tennis. Kinesiologia Slovenica Journal, 21(1), 26–36.
  18. Leist, K.-H. (1996). Fuzzy: Modellierung verschiedenartigen Systeme und Prozesse unter Heranziehung unscharfer Mengen, Analyse und Verarbeitung unscharfer Daten. Perspektiven einer kurzfristigen Einarbeitung. In Quade, K. (Red.). Anwendungen der Fuzzy-Logik und Neuronaler Systeme, 19–21. Köln: Bundesinstitut für Sportwissenschaft, Sport und Buch Strauss.
  19. Nasiri, M., M., Ranjbar, M., Tavana, M., Arteaga, F., J., S., & Yazdanparast, R. (2019). A novel hybrid method for selecting soccer players during transfer season. Expert systems. 36, 1–19.10.1111/exsy.12342
  20. Noori, M, & Sadeghi, H. (2017). Designing smart model in volleyball talent identification via fuzzy logic based on main and weighted criteria resulted from the analytic hierarchy process. Journal of Advanced Sport Technology, 1(2), 16–24.
  21. Novatchkov, H., & Baca, A. (2013). Fuzzy logic in sports: A review and illustrative case study in the field of strength training. International Journal of Computer Applications. 71(6), 8–14.10.5120/12360-8675
  22. Ozceylan, E. (2016). A mathematical model using AHP priorities for soccer player selection: A case study. South African journal of industrial engineering. 27(2), 190–205.10.7166/27-2-1265
  23. Papahristodoulou, Ch. (2012). Optimal Football Strategies: AC Milan versus FC Barcelona. Business Performance Measurement and Management. Cambridge Scholars, 371–393.
  24. Papić, V., Rogulj, N., & Pleština, V. (2009). Identification of sport talents using a web-oriented expert system with a fuzzy module. Expert Systems with Applications, 36(5), 8830–8838.10.1016/j.eswa.2008.11.031
  25. Papić, V., Rogulj, N., & Pleština V. (2011). Expert system for identification of sport talents: Idea, implementation and results. In P. Vizureanu (Ed.), Expert systems for human, materials and automation, 3–16. Rijeka: InTech.10.5772/19203
  26. Pudaruth, S., Seesaha, R., & Rambacussing, L. (2013). Generating Horse Racing Tips at the Champs De March Using Fuzzy Logic. International Journal of Computer Science and Technology, 4, 7–11.
  27. Roberts-Thomson, C., L., Lokshin, A., M., & Kuzkin, V. A. (2014). Jump detection using fuzzy logic. In IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES), 125–131. DOI: 10.1109/CIES.2014.701184110.1109/CIES.2014.7011841
  28. Saaty, T.L. (1980). The Analytic Hierarchy Process. McGraw-Hill, New York.10.21236/ADA214804
  29. Saaty, T.L. (1990). How to make a decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48, 9–26.10.1016/0377-2217(90)90057-I
  30. Sagar, S., & Babu, K. A. (2012). Removing impulse random noise from color video using fuzzy filter. International journal of engineering research and development, 3(3), 7–10.
  31. Shin, C., Y., & Wang, P., P. (2010). Economic applications of fuzzy subset theory and fuzzy logic: A brief survey. New Mathematics and Natural Computation, 6(3), 301–320.10.1142/S1793005710001773
  32. Schönborn, R. (2010). Optimales Tennistraining: der Weg zum erfolgreichen Tennis vom Anfänger bis zur Weltspitze. Balingen: Spitta Verlag.
  33. Singh, G., Bhatia, N., & Singh, S. (2011). Fuzzy cognitive maps based cricket player performance evaluator. International Journal of Enterprise Computing and Business Systems. 1(2), 1–15.
  34. Stoklasa, J., Jandová, V., & Talašová, J. (2013). Weak consistency in Saaty´s AHP – evaluating creative work outcomes of Czech art colleges. Neural network world, 1(13), 61–77.10.14311/NNW.2013.23.005
  35. Trawinski, K. (2010). A fuzzy classification system for prediction of the results of the basketball games. In International Conference on Fuzzy Systems, 1–7. DOI: 10.1109/FUZZY.2010.55843910.1109/FUZZY.2010.5584399
  36. Ulbricht, A., Fernandez-Fernandez, J., Mendez-Villanueva, A., & Ferrauti, A. (2016). Impact of Fitness Characteristics on Tennis Performance in Elite Junior Tennis Players. Journal of Strength & Conditioning Research, 30(4), 989–998.10.1519/JSC.0000000000001267
  37. Zadeh, L. A. (1965). Fuzzy-Sets. Inform and Control, 8, 338–353.10.1016/S0019-9958(65)90241-X
  38. Zderčík, A., Nykodým, J., Talašová, J., Holeček, P., & Bozděch, M. (2020). The application of fuzzy logic in the diagnostics of performance preconditions in tennis. In J. Cacek, Z. Sajdlová & K. Šimková (Eds.), Proceedings of the 12th International Conference on Kinanthropology „Sport and Quality of Life“, Brno, Czech Republic, November 7–9, 2019 (pp. 42–49). Brno: Masaryk University.10.5817/CZ.MUNI.P210-9631-2020-5
  39. Zeng, W., & Li, J. (2014). Fuzzy Logic and Its Application in Football Team Ranking. The Scientific World Journal. 10.1155/2014/291650">http://dx.doi.org/10.1155/2014/29165010.1155/2014/291650408329025032227
  40. Zháněl, J., Leist, K.-H., Kadlčíková, K., & Talašová, J. (1999). Possibilities of application of fuzzy sets in evaluation of motor performance. In V. Strojnik, & A. Ušaj (Eds.), Proceedings of the 6th Scientific ConferenceTheories of Human Motor Performance and their Reflections in Practice“, Ljubljana, Slovenia, September 1–4, 1999 (pp. 421–424. Ljubljana: University of Ljubljana.
  41. Zháněl, J., Černošek, M., Zvonař, M., Nykodým, J., Vespalec, T. & López Sánchez, G. F. (2015). Comparison of the level of top tennis players’ performance preconditions (case study). Comparación del nivel de condiciones previas de rendimiento de tenistas de élite (estudio de caso). APUNTS – Educació física i esports, 122(4), 52–60.10.5672/apunts.2014-0983.cat.(2015/4).122.06
Language: English
Page range: 79 - 91
Published on: Aug 10, 2021
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2021 J. Zháněl, P. Holeček, A. Zderčík, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.