Araştırma Makalesi
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Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods

Yıl 2025, Cilt: 15 Sayı: 3, 911 - 930, 30.09.2025

Öz

In the NBA, offensive and defensive performances play a critical role in the success of teams and players. A team's collective performance is decisive on the road to championship. Team performance in the NBA is more important than individual talents. In this context, the regular season matches of all teams in the Eastern and Western conferences in the NBA in the 2023/2024 season were considered in the study. It is aimed at analyzing the one-season offensive and defensive performances of the teams in the Eastern and Western conferences of the NBA using TOPSIS and BORDA counting methods, which are multi-criteria decision-making (MCDM) methods. In this direction, the teams that will continue with the Play-off and Play-in tournaments with their regular season performance were determined. As a result of the analysis, the participation status of the teams in the Play-off and Play-in tournaments was successfully ranked using regular season defensive and offensive performances with TOPSIS and BORDA counting methods.

Etik Beyan

I declare that my study is among the studies that do not require ethics committee permission because there is no information containing personal data in my study.

Kaynakça

  • Abdi, M., Kargari, M., Alavi, M., & Teimourpour, B. (2025, February). Predicting NBA Playoffs Qualification Using Machine Learning Techniques. In 2025 29th International Computer Conference, Computer Society of Iran (CSICC) (pp. 1-5). IEEE.
  • Akoğul, S., Erisoglu, M., & Erişoğlu, Ü. (2020). Determining the Number of Clusters with the TOPSIS Method in Clustering Based on the Multivariate Mixtures of Normal Distributions. Erciyes University Institute of Science Journal of Science, 36(3), 472-480.
  • Basketball Reference web page, https://www.basketball-reference.com/, Access Date: 17.12.2024
  • 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.
  • Borda, J. C. (1784). Mémoire sur les élections au scrutin, Histoire de l’Académie royale des sciences pour 1781. Paris (English Transl. by Grazia, A. 1953. Isis 44).
  • Bouyssou, D., Marchant, T., Pirlot, M., Tsoukias, A., & Vincke, P. (2006). Evaluation and decision models with multiple criteria: Stepping stones for the analyst (Vol. 86). Springer Science & Business Media.
  • Bozbura, F. T., Beşkese, A., & Kaya, T. S. (2008). Topsis Method on Player Selection in Mba. In 2008 Proceedings of the Twelfth International Research/Expert Conference, Trends in the Development of Machinery and Associated Technology (pp. 401-404).
  • Çakir, S., & Perçin, S. (2013). Performance measurement of logistics firms with multi-criteria decision making methods. Ege Academic Review, 13(4), 449.
  • Çene, E., Özdalyan, F., Parim, C., Mancı, E., & İnan, T. (2024). How do European and non-European players differ: Evidence from EuroLeague basketball with multivariate statistical analysis. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 17543371241242835.
  • 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.
  • Depren, S. K. (2019). The Effectiveness of Different Machine Learning Algorithms on Basketball Players’shooting Performance. Ondokuz Mayıs University Journal of Sports and Performance Researches, 10(3), 256-269.
  • Deshpande, S. K., & Jensen, S. T. (2016). Estimating an NBA player’s impact on his team’s chances of winning. Journal of Quantitative Analysis in Sports, 12(2), 51-72.
  • Dong, Y., Xie, C., & Wang, C. (2015, October). Application of Fuzzy Decision-Making Methods in the NBA Team Ranking. In 2015 3rd International Conference on Mechatronics and Industrial Informatics (ICMII 2015) (pp. 89-92). Atlantis Press.
  • Ertug, G., & Castellucci, F. (2013). Getting what you need: How reputation and status affect team performance, hiring, and salaries in the NBA. Academy of Management Journal, 56(2), 407-431.
  • Fisher, J. D., & Montague, C. (2024). Improving the aggregation and evaluation of NBA mock drafts. Journal of Quantitative Analysis in Sports, (0).
  • Harris, A. R., & Roebber, P. J. (2019). NBA team home advantage: Identifying key factors using an artificial neural network. PLoS One, 14(7), e0220630.
  • HoopsHype web page, https://hoopshype.com/salaries/2023-2024/, Access Date: 18.12.2024.
  • Horvat, T., Job, J., Logozar, R., & Livada, Č. (2023). A data-driven machine learning algorithm for predicting the outcomes of NBA games. Symmetry, 15(4), 798.
  • Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. Multiple attribute decision making: methods and applications a state-of-the-art survey, 58-191.
  • Ji, R. (2020). Research on basketball shooting action based on image feature extraction and machine learning. Ieee Access, 8, 138743-138751.
  • Juravich, M., Salaga, S., & Babiak, K. (2017). Upper echelons in professional sport: The impact of NBA general managers on team performance. Journal of Sport Management, 31(5), 466-479.
  • Kizielewicz, B., & Dobryakova, L. (2020). MCDA based approach to sports players’ evaluation under incomplete knowledge. Procedia Computer Science, 176, 3524-3535.
  • Liu, C. H., Tzeng, G. H., & Lee, M. H. (2012). Improving tourism policy implementation–The use of hybrid MCDM models. Tourism management, 33(2), 413-426.
  • Liu, Y., Yang, Y., Liu, Y., & Tzeng, G. H. (2019). Improving sustainable mobile health care promotion: a novel hybrid MCDM method. Sustainability, 11(3), 752.
  • Liu, Z., & Guo, J. (2025). Research on NBA Event Prediction Based on Hybrid Machine Learning Model. International Journal of High Speed Electronics and Systems, 34(01), 2540171.
  • Martinez, J. A., & Caudill, S. B. (2013). Does Midseason Change of Coach Improve Team Performance? Evidence From the NBA. Journal of Sport Management, 27(2).
  • Mertz, J., Hoover, L. D., Burke, J. M., Bellar, D., Jones, M. L., Leitzelar, B., & Judge, W. L. (2016). Ranking the greatest NBA players: A sport metrics analysis. International Journal of Performance Analysis in Sport, 16(3), 737-759.
  • Nguyen, N. H., Nguyen, D. T. A., Ma, B., & Hu, J. (2022). The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity. Journal of Information and Telecommunication, 6(2), 217-235.
  • Nutting, A. W., & Price, J. (2017). Time zones, game start times, and team performance: evidence from the NBA. Journal of Sports Economics, 18(5), 471-478.
  • Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445-455.
  • Ouyang, Y., Li, X., Zhou, W., Hong, W., Zheng, W., Qi, F., & Peng, L. (2024). Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology. Plos one, 19(7), e0307478.
  • Özkir, V., & Değirmenci, A. (2023). A novel multiple criteria ranking approach for determining the Most Valuable Player (MVP) of a sport season: A numerical study from NBA league. Journal of Soft Computing and Decision Analytics, 1(1), 265-272.
  • Pradhan, S., & Abdourazakou, Y. (2020). “Power ranking” professional circuit eSports teams using multi-criteria decision-making (MCDM). Journal of Sports Analytics, 6(1), 61-73.
  • Pradhan, S., & Chachad, R. (2021). Re-ranking regular seasons in the National Basketball Association’s modern era: A replication and extension of Pradhan (2018). Journal of Statistics and Management Systems, 24(7), 1503-1522.
  • Price, J., Soebbing, B. P., Berri, D., & Humphreys, B. R. (2010). Tournament incentives, league policy, and NBA team performance revisited. Journal of Sports Economics, 11(2), 117-135.
  • Sarlis, V., Papageorgiou, G., & Tjortjis, C. (2024). Injury patterns and impact on performance in the NBA League Using Sports Analytics. Computation, 12(2), 36.
  • Sonatha, Y., Azmi, M., & Rahmayuni, I. (2021). Group Decision Support System Using AHP, Topsis and Borda Methods for Loan Determination in Cooperatives. JOIV: International Journal on Informatics Visualization, 5(4), 372-379.
  • Sportac web page, https://www.spotrac.com/nba/tax/_/year/2024/sort/tax_total, Access Date: 20.12.2024
  • Temizel, F., & Bayçelebi, B. E. (2016). The Relationship Between TOPSIS Orders of Financial Ratios and Annual Yield Orders: An Application on Textile Manufacturing Sector. Anadolu University Journal of Social Sciences, 16(2), 159-170.
  • Terzopoulou, Z., & Endriss, U. (2021). The Borda class: An axiomatic study of the Borda rule on top-truncated preferences. Journal of Mathematical Economics, 92, 31-40.
  • Thabtah, F., Zhang, L., & Abdelhamid, N. (2019). NBA game result prediction using feature analysis and machine learning. Annals of Data Science, 6(1), 103-116.
  • Ulas, E. (2021). Examination of National Basketball Association (NBA) team values based on dynamic linear mixed models. PLoS one, 16(6), e0253179.
  • Vaz de Melo, P. O., Almeida, V. A., & Loureiro, A. A. (2008, August). Can complex network metrics predict the behavior of nba teams?. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 695-703).
  • Weiss, J. B., & Sommers, P. M. (2009). Does team racial composition affect team performance in the NBA?. Atlantic Economic Journal, 37, 119-120.
  • Xu, K., Lin, H. L., & Qiu, J. (2024). Constructing an evaluation model for the comprehensive level of sustainable development of provincial competitive sports in China based on DPSIR and MCDM. Plos one, 19(4), e0301411.
  • Yang, M., Wei, Y., Liang, L., Ding, J., & Wang, X. (2021). Performance evaluation of NBA teams: A non-homogeneous DEA approach. Journal of the Operational Research Society, 72(6), 1403-1414.
  • Yeung, M. (2025). Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction. In ITM Web of Conferences (Vol. 70, p. 04024). EDP Sciences.
  • Yilmaz, M. R., & Chatterjee, S. (2000). Patterns of NBA team performance from 1950 to 1998. Journal of Applied Statistics, 27(5), 555-566.
  • Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and economic development of economy, 17(2), 397-427.

NBA Takım Performanslarının TOPSIS ve BORDA Sayım Yöntemleri İle Değerlendirilmesi

Yıl 2025, Cilt: 15 Sayı: 3, 911 - 930, 30.09.2025

Öz

NBA'de hücum ve savunma performansları, takımların ve oyuncuların başarısında kritik rol oynamaktadır. Bir takımın kolektif performansı şampiyonluk yolunda belirleyici olmaktadır. NBA'de takım performansı, bireysel yeteneklerin ötesinde bir öneme sahiptir. Bu bağlamda çalışmada NBA’de Doğu ve Batı konferanslarındaki tüm takımların 2023/2024 sezonunda normal sezonda oynanan maçlar göz önüne alınmıştır. NBA’de Doğu ve Batı konferanslarındaki takımların bir sezonluk hücum ve savunma performansları, çok kriterli karar verme (ÇKKV) yöntemlerinden TOPSIS ve BORDA sayım yöntemleri kullanılarak analiz edilmesi amaçlanmıştır. Bu doğrultuda normal sezon performansı ile Play-off ve Play-in turnuvaları ile yoluna devam edecek takımlar belirlenmiştir. Yapılan analizler sonucunda TOPSIS ve BORDA sayım yöntemleri ile normal sezon savunma ve hücum performansları kullanılarak takımların Play-off ve Play-in turnuvalarına katılıp katılamama durumları başarılı bir şekilde sıralanmıştır.

Etik Beyan

Çalışmamda kişisel veri içeren herhangi bir bilgi bulunmadığından dolayı çalışmamın etik kurul izni gerektirmeyen çalışmalar arasında yer aldığını beyan ederim.

Kaynakça

  • Abdi, M., Kargari, M., Alavi, M., & Teimourpour, B. (2025, February). Predicting NBA Playoffs Qualification Using Machine Learning Techniques. In 2025 29th International Computer Conference, Computer Society of Iran (CSICC) (pp. 1-5). IEEE.
  • Akoğul, S., Erisoglu, M., & Erişoğlu, Ü. (2020). Determining the Number of Clusters with the TOPSIS Method in Clustering Based on the Multivariate Mixtures of Normal Distributions. Erciyes University Institute of Science Journal of Science, 36(3), 472-480.
  • Basketball Reference web page, https://www.basketball-reference.com/, Access Date: 17.12.2024
  • 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.
  • Borda, J. C. (1784). Mémoire sur les élections au scrutin, Histoire de l’Académie royale des sciences pour 1781. Paris (English Transl. by Grazia, A. 1953. Isis 44).
  • Bouyssou, D., Marchant, T., Pirlot, M., Tsoukias, A., & Vincke, P. (2006). Evaluation and decision models with multiple criteria: Stepping stones for the analyst (Vol. 86). Springer Science & Business Media.
  • Bozbura, F. T., Beşkese, A., & Kaya, T. S. (2008). Topsis Method on Player Selection in Mba. In 2008 Proceedings of the Twelfth International Research/Expert Conference, Trends in the Development of Machinery and Associated Technology (pp. 401-404).
  • Çakir, S., & Perçin, S. (2013). Performance measurement of logistics firms with multi-criteria decision making methods. Ege Academic Review, 13(4), 449.
  • Çene, E., Özdalyan, F., Parim, C., Mancı, E., & İnan, T. (2024). How do European and non-European players differ: Evidence from EuroLeague basketball with multivariate statistical analysis. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 17543371241242835.
  • 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.
  • Depren, S. K. (2019). The Effectiveness of Different Machine Learning Algorithms on Basketball Players’shooting Performance. Ondokuz Mayıs University Journal of Sports and Performance Researches, 10(3), 256-269.
  • Deshpande, S. K., & Jensen, S. T. (2016). Estimating an NBA player’s impact on his team’s chances of winning. Journal of Quantitative Analysis in Sports, 12(2), 51-72.
  • Dong, Y., Xie, C., & Wang, C. (2015, October). Application of Fuzzy Decision-Making Methods in the NBA Team Ranking. In 2015 3rd International Conference on Mechatronics and Industrial Informatics (ICMII 2015) (pp. 89-92). Atlantis Press.
  • Ertug, G., & Castellucci, F. (2013). Getting what you need: How reputation and status affect team performance, hiring, and salaries in the NBA. Academy of Management Journal, 56(2), 407-431.
  • Fisher, J. D., & Montague, C. (2024). Improving the aggregation and evaluation of NBA mock drafts. Journal of Quantitative Analysis in Sports, (0).
  • Harris, A. R., & Roebber, P. J. (2019). NBA team home advantage: Identifying key factors using an artificial neural network. PLoS One, 14(7), e0220630.
  • HoopsHype web page, https://hoopshype.com/salaries/2023-2024/, Access Date: 18.12.2024.
  • Horvat, T., Job, J., Logozar, R., & Livada, Č. (2023). A data-driven machine learning algorithm for predicting the outcomes of NBA games. Symmetry, 15(4), 798.
  • Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. Multiple attribute decision making: methods and applications a state-of-the-art survey, 58-191.
  • Ji, R. (2020). Research on basketball shooting action based on image feature extraction and machine learning. Ieee Access, 8, 138743-138751.
  • Juravich, M., Salaga, S., & Babiak, K. (2017). Upper echelons in professional sport: The impact of NBA general managers on team performance. Journal of Sport Management, 31(5), 466-479.
  • Kizielewicz, B., & Dobryakova, L. (2020). MCDA based approach to sports players’ evaluation under incomplete knowledge. Procedia Computer Science, 176, 3524-3535.
  • Liu, C. H., Tzeng, G. H., & Lee, M. H. (2012). Improving tourism policy implementation–The use of hybrid MCDM models. Tourism management, 33(2), 413-426.
  • Liu, Y., Yang, Y., Liu, Y., & Tzeng, G. H. (2019). Improving sustainable mobile health care promotion: a novel hybrid MCDM method. Sustainability, 11(3), 752.
  • Liu, Z., & Guo, J. (2025). Research on NBA Event Prediction Based on Hybrid Machine Learning Model. International Journal of High Speed Electronics and Systems, 34(01), 2540171.
  • Martinez, J. A., & Caudill, S. B. (2013). Does Midseason Change of Coach Improve Team Performance? Evidence From the NBA. Journal of Sport Management, 27(2).
  • Mertz, J., Hoover, L. D., Burke, J. M., Bellar, D., Jones, M. L., Leitzelar, B., & Judge, W. L. (2016). Ranking the greatest NBA players: A sport metrics analysis. International Journal of Performance Analysis in Sport, 16(3), 737-759.
  • Nguyen, N. H., Nguyen, D. T. A., Ma, B., & Hu, J. (2022). The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity. Journal of Information and Telecommunication, 6(2), 217-235.
  • Nutting, A. W., & Price, J. (2017). Time zones, game start times, and team performance: evidence from the NBA. Journal of Sports Economics, 18(5), 471-478.
  • Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445-455.
  • Ouyang, Y., Li, X., Zhou, W., Hong, W., Zheng, W., Qi, F., & Peng, L. (2024). Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology. Plos one, 19(7), e0307478.
  • Özkir, V., & Değirmenci, A. (2023). A novel multiple criteria ranking approach for determining the Most Valuable Player (MVP) of a sport season: A numerical study from NBA league. Journal of Soft Computing and Decision Analytics, 1(1), 265-272.
  • Pradhan, S., & Abdourazakou, Y. (2020). “Power ranking” professional circuit eSports teams using multi-criteria decision-making (MCDM). Journal of Sports Analytics, 6(1), 61-73.
  • Pradhan, S., & Chachad, R. (2021). Re-ranking regular seasons in the National Basketball Association’s modern era: A replication and extension of Pradhan (2018). Journal of Statistics and Management Systems, 24(7), 1503-1522.
  • Price, J., Soebbing, B. P., Berri, D., & Humphreys, B. R. (2010). Tournament incentives, league policy, and NBA team performance revisited. Journal of Sports Economics, 11(2), 117-135.
  • Sarlis, V., Papageorgiou, G., & Tjortjis, C. (2024). Injury patterns and impact on performance in the NBA League Using Sports Analytics. Computation, 12(2), 36.
  • Sonatha, Y., Azmi, M., & Rahmayuni, I. (2021). Group Decision Support System Using AHP, Topsis and Borda Methods for Loan Determination in Cooperatives. JOIV: International Journal on Informatics Visualization, 5(4), 372-379.
  • Sportac web page, https://www.spotrac.com/nba/tax/_/year/2024/sort/tax_total, Access Date: 20.12.2024
  • Temizel, F., & Bayçelebi, B. E. (2016). The Relationship Between TOPSIS Orders of Financial Ratios and Annual Yield Orders: An Application on Textile Manufacturing Sector. Anadolu University Journal of Social Sciences, 16(2), 159-170.
  • Terzopoulou, Z., & Endriss, U. (2021). The Borda class: An axiomatic study of the Borda rule on top-truncated preferences. Journal of Mathematical Economics, 92, 31-40.
  • Thabtah, F., Zhang, L., & Abdelhamid, N. (2019). NBA game result prediction using feature analysis and machine learning. Annals of Data Science, 6(1), 103-116.
  • Ulas, E. (2021). Examination of National Basketball Association (NBA) team values based on dynamic linear mixed models. PLoS one, 16(6), e0253179.
  • Vaz de Melo, P. O., Almeida, V. A., & Loureiro, A. A. (2008, August). Can complex network metrics predict the behavior of nba teams?. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 695-703).
  • Weiss, J. B., & Sommers, P. M. (2009). Does team racial composition affect team performance in the NBA?. Atlantic Economic Journal, 37, 119-120.
  • Xu, K., Lin, H. L., & Qiu, J. (2024). Constructing an evaluation model for the comprehensive level of sustainable development of provincial competitive sports in China based on DPSIR and MCDM. Plos one, 19(4), e0301411.
  • Yang, M., Wei, Y., Liang, L., Ding, J., & Wang, X. (2021). Performance evaluation of NBA teams: A non-homogeneous DEA approach. Journal of the Operational Research Society, 72(6), 1403-1414.
  • Yeung, M. (2025). Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction. In ITM Web of Conferences (Vol. 70, p. 04024). EDP Sciences.
  • Yilmaz, M. R., & Chatterjee, S. (2000). Patterns of NBA team performance from 1950 to 1998. Journal of Applied Statistics, 27(5), 555-566.
  • Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and economic development of economy, 17(2), 397-427.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Nicel Karar Yöntemleri
Bölüm Araştırma Makalesi
Yazarlar

Enes Filiz 0000-0002-8006-9467

Yayımlanma Tarihi 30 Eylül 2025
Gönderilme Tarihi 14 Mart 2025
Kabul Tarihi 10 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 3

Kaynak Göster

APA Filiz, E. (2025). Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 911-930. https://doi.org/10.18074/ckuiibfd.1657759
AMA Filiz E. Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. Eylül 2025;15(3):911-930. doi:10.18074/ckuiibfd.1657759
Chicago Filiz, Enes. “Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods”. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 15, sy. 3 (Eylül 2025): 911-30. https://doi.org/10.18074/ckuiibfd.1657759.
EndNote Filiz E (01 Eylül 2025) Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 15 3 911–930.
IEEE E. Filiz, “Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods”, Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, c. 15, sy. 3, ss. 911–930, 2025, doi: 10.18074/ckuiibfd.1657759.
ISNAD Filiz, Enes. “Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods”. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 15/3 (Eylül2025), 911-930. https://doi.org/10.18074/ckuiibfd.1657759.
JAMA Filiz E. Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2025;15:911–930.
MLA Filiz, Enes. “Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods”. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, c. 15, sy. 3, 2025, ss. 911-30, doi:10.18074/ckuiibfd.1657759.
Vancouver Filiz E. Evaluation Of NBA Team Performances with TOPSIS and BORDA Counting Methods. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2025;15(3):911-30.