Türkiye'de Gelecek Yıllar için Toplam Lisanslı Sporcu Sayısının Yapay Sinir Ağları Kullanılarak Analizi
Yıl 2024,
Cilt: 14 Sayı: 4, 2153 - 2171, 15.12.2024
Halil Şenol
,
Halil Çolak
,
Emre Çolak
Öz
Spor terimi, bireylerin fiziksel yeteneklerini, becerilerini ve dayanıklılıklarını geliştirmek için belirli kurallar ve düzenlemeler çerçevesinde gerçekleştirilen rekabetçi veya eğlence amaçlı fiziksel aktiviteler bütünü olarak tanımlanmaktadır. Resmi kayıtlara göre Türkiye'de yaklaşık 6,25 milyon lisanslı sporcu bulunmaktadır (2022 yılı için). Önümüzdeki yıllarda bu sayının tahmin edilmesi, spor politikalarının daha etkili planlanmasına olanak sağlaması açısından önemlidir. Bu bağlamda, 2040 yılına kadar Türkiye'deki toplam sporcu sayısı yapay sinir ağları (YSA) ile tahmin edilmiştir. Sporcu sayısını tahmin etmek için YSA'ların uygulanması, gelecek yıllar için tahminlerin üretilmesini sağlamaktadır. Bu tahminler, sporun yaygınlaşması ve spor ekonomisinin büyüme potansiyeli için kritik bilgiler sağlamaktadır. Çalışmada YSA'nın Levenberg-Marquardt ve Bayes Regülarizasyon algoritmaları kullanılmıştır. 2040 yılına kadar Türkiye'de en az 7,33 milyon sporcu olacağı tahmin edilmektedir. Gelecek çalışmalarda farklı branşlardaki sporcu sayılarının YSA algoritmaları kullanılarak hesaplanıp tartışılması önerilmektedir.
Kaynakça
- Atasoy, M., Dalkılıç, M., & Uğraş, S. (2017). Estımatıon of Lıcensed Sportsman-Woman in Area of Martıal Sports by Artıfıcıal Neural Networks. Kilis 7 December University Journal of Physical Education and Sports Sciences, 1(1), 33-37.
- Bas, E., Egrioglu, E., & Cansu, T. (2024). Robust training of median dendritic artificial neural networks for time series forecasting. Expert Systems with Applications, 238, 122080.
- Başkan, A. H., Özgül, F., Kolukısa, Ş., Çolak, H., & Başkan, A. H. (2020). Giresun University Faculty of Sport Sciences Students Usefulperceptions of theconcept of “Sports Management”. Eurasian Journal of Researches in Social and Economics, 7(10), 58-67.
- Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied computing and informatics, 15(1), 27-33.
- Burden, F., & Winkler, D. (2009). Bayesian regularization of neural networks. Artificial neural networks: methods and applications, 23-42.
- Çolak, H., & Çolak, E. (2024). Estimation of Prevalence Distribution of Pre-obesity by Gender in Türkiye Using Artificial Neural Networks and Time Series Analysis. The Black Sea Journal of Sciences
14(3), 1340-1359.
- Çolak, H., & Şenol, H. (2023). Estimating the Number of Licensed Athletes in Turkey with Artificial Neural Networks until 2030: Academic Evaluations in the Field of Sports Sciences - 7, Duvar Publications.
- da Costa, N. L., de Lima, M. D., & Barbosa, R. (2021). Evaluation of feature selection methods based on artificial neural network weights. Expert Systems with Applications, 168, 114312.
- Dalkılıç, M., Atasoy, M., Yİğİt, Ş., & Mamak, H. (2017). Estimation of Number of Disabled Licensed Sports by Artificial Neural Networks. The Journal of Academic Social Science.
- Dalkılıç, M., Kargün, M., Kızar, O., & Genç, H. (2017). Estimation of Licensed Number of Number of Competitors in the Wrestling of Artificial Neural Networks. Kilis 7 December University Journal of Physical Education and Sports Sciences, 1(1), 15-19.
- Dalkılıç, M., Mamak, H., Atasoy, M., & Mihriay, M. (2017). Forecast of Licensed Number of Living Sports in Water Trophies, Water Ball and Swimming Areas by Artificial Neural Networks.
DrDataStats. (2024). www.drdatastats.com.
- Exel, J., & Dabnichki, P. (2024). Precision Sports Science: What Is Next for Data Analytics for Athlete Performance and Well-Being Optimization? Applied Sciences, 14(8), 3361.
- Gomis-Gomis, M. J., Pena-Pérez, X., & Pérez-Turpin, J. A. (2023). Sustainability and sports science: A new way for a better future. Sustainability and Sports Science Journal, 1(1), 1-2.
- Hulteen, R. M., Smith, J. J., Morgan, P. J., Barnett, L. M., Hallal, P. C., Colyvas, K., & Lubans, D. R. (2017). Global participation in sport and leisure-time physical activities: A systematic review and meta-analysis. Preventive medicine, 95, 14-25.
- Ishwarya, K., & Nithya, A. A. (2021). Relative analysis and performance of machine learning approaches in sports. Paper presented at the 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA).
- Jauhiainen, S., Kauppi, J.-P., Leppänen, M., Pasanen, K., Parkkari, J., Vasankari, T., . . . Äyrämö, S. (2021). New machine learning approach for detection of injury risk factors in young team sport athletes. International journal of sports medicine, 42(02), 175-182.
- Kasera, M., & Johari, R. (2021). Prediction using machine learning in sports: a case study. Paper presented at the Data Analytics and Management: Proceedings of ICDAM.
- Kelly, S. J., Derrington, S., & Star, S. (2022). Governance challenges in esports: a best practice framework for addressing integrity and wellbeing issues. International Journal of Sport Policy and Politics, 14(1), 151-168.
- Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of applied mathematics, 2(2), 164-168.
- Me, E., & Unold, O. (2011). Machine learning approach to model sport training. Computers in human behavior, 27(5), 1499-1506.
- Mertala, P., & Palsa, L. (2024). Running free: recreational runners’ reasons for non-use of digital sports technology. Sport in Society, 27(3), 329-345.
- 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.
- Qi, Y., Sajadi, S. M., Baghaei, S., Rezaei, R., & Li, W. (2024). Digital technologies in sports: Opportunities, challenges, and strategies for safeguarding athlete wellbeing and competitive integrity in the digital era. Technology in Society, 102496.
- Ryan, L., & Doody, O. (2024). The treatment, outcomes and management of hand, wrist, finger, and thumb injuries in the professional/amateur contact sport athletes: A scoping review. International Journal of Orthopaedic and Trauma Nursing, 101108.
- Seefeldt, V. D., & Ewing, M. E. (1997). Youth Sports in America: An Overview. President's council on physical fitness and sports research digest.
- Suman, S., Singh, S., Mitra, S., & Kumar, M. (2024). Deep neural network model for predicting thermal-hydraulic performance of a solar air heater with artificial roughness: Sensitivity, generalization capacity, and computational efficiency. Process Safety and Environmental Protection.
- Şenol, H. (2021). Methane yield prediction of ultrasonic pretreated sewage sludge by means of an artificial neural network. Energy, 215, 119173.
- Şenol, H., Çolak, E., Elibol, E. A., Hassaan, M. A., & El Nemr, A. (2024). Optimisation of biochar dose in anaerobic co-digestion of green algae and cattle manure using artificial neural networks and response surface methodology. Chemical Engineering Journal, 152750.
- Şenol, H., Çolak, E., & Oda, V. (2024). Forecasting of biogas potential using artificial neural networks and time series models for Türkiye to 2035. Energy, 131949.
- Şenol, H., Dereli, M. A., & Özbilgin, F. (2021). Investigation of the distribution of bovine manure-based biomethane potential using an artificial neural network in Turkey to 2030. Renewable and Sustainable Energy Reviews, 149, 111338.
- TETI. (2024). Türkiye Electricity Transmission Inc. https://www.teias.gov.tr/.
- Tufaner, F., & Avşar, Y. (2016). Effects of co-substrate on biogas production from cattle manure: a review. International journal of environmental science and technology, 13, 2303-2312.
- TurkStats. (2024). www.tuik.gov.tr.
- Wilkens, S. (2021). Sports prediction and betting models in the machine learning age: The case of tennis. Journal of Sports Analytics, 7(2), 99-117.
- Wu, J.-M. (2008). Multilayer potts perceptrons with Levenberg–Marquardt learning. IEEE transactions on neural networks, 19(12), 2032-2043.
- Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks:: The state of the art. International journal of forecasting, 14(1), 35-62.
- Zhang, L., & Li, N. (2022). Material analysis and big data monitoring of sports training equipment based on machine learning algorithm. Neural Computing and Applications, 34(4), 2749-2763.
- Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361.
- Zhu, J., Hu, C., Khezri, E., & Ghazali, M. M. M. (2024). Edge intelligence-assisted animation design with large models: a survey. Journal of Cloud Computing, 13(1), 48.
- Zhu, P., & Sun, F. (2020). Sports athletes’ performance prediction model based on machine learning algorithm. Paper presented at the International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019: Applications and Techniques in Cyber Intelligence 7.
Analysis of the Total Number of Licensed Athletes Using Artificial Neural Networks for the Future Years in Türkiye
Yıl 2024,
Cilt: 14 Sayı: 4, 2153 - 2171, 15.12.2024
Halil Şenol
,
Halil Çolak
,
Emre Çolak
Öz
The term sport refers to a collection of competitive or leisure physical activities conducted under certain rules and regulations to enhance individuals' physical capabilities, skills, and endurance. Official figures indicate that there are roughly 6.25 million licensed athletes in Türkiye as of 2022. The projection of this figure in the forthcoming years is crucial for facilitating more efficient sports policy planning. The entire number of athletes in Türkiye till 2040 was projected using artificial neural networks (ANN). The utilization of artificial neural networks to predict the number of athletes facilitates the production of projections for subsequent years. These estimations furnish essential data for the expansion of sports and the growth potential of the sports business. The study utilized the Levenberg-Marquardt and Bayesian Regularization techniques of ANN. By the year 2040, it is projected that Türkiye would have a minimum of 7.33 million athletes. Future research should quantify and analyze the number of athletes across various disciplines utilizing ANN algorithms.
Kaynakça
- Atasoy, M., Dalkılıç, M., & Uğraş, S. (2017). Estımatıon of Lıcensed Sportsman-Woman in Area of Martıal Sports by Artıfıcıal Neural Networks. Kilis 7 December University Journal of Physical Education and Sports Sciences, 1(1), 33-37.
- Bas, E., Egrioglu, E., & Cansu, T. (2024). Robust training of median dendritic artificial neural networks for time series forecasting. Expert Systems with Applications, 238, 122080.
- Başkan, A. H., Özgül, F., Kolukısa, Ş., Çolak, H., & Başkan, A. H. (2020). Giresun University Faculty of Sport Sciences Students Usefulperceptions of theconcept of “Sports Management”. Eurasian Journal of Researches in Social and Economics, 7(10), 58-67.
- Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied computing and informatics, 15(1), 27-33.
- Burden, F., & Winkler, D. (2009). Bayesian regularization of neural networks. Artificial neural networks: methods and applications, 23-42.
- Çolak, H., & Çolak, E. (2024). Estimation of Prevalence Distribution of Pre-obesity by Gender in Türkiye Using Artificial Neural Networks and Time Series Analysis. The Black Sea Journal of Sciences
14(3), 1340-1359.
- Çolak, H., & Şenol, H. (2023). Estimating the Number of Licensed Athletes in Turkey with Artificial Neural Networks until 2030: Academic Evaluations in the Field of Sports Sciences - 7, Duvar Publications.
- da Costa, N. L., de Lima, M. D., & Barbosa, R. (2021). Evaluation of feature selection methods based on artificial neural network weights. Expert Systems with Applications, 168, 114312.
- Dalkılıç, M., Atasoy, M., Yİğİt, Ş., & Mamak, H. (2017). Estimation of Number of Disabled Licensed Sports by Artificial Neural Networks. The Journal of Academic Social Science.
- Dalkılıç, M., Kargün, M., Kızar, O., & Genç, H. (2017). Estimation of Licensed Number of Number of Competitors in the Wrestling of Artificial Neural Networks. Kilis 7 December University Journal of Physical Education and Sports Sciences, 1(1), 15-19.
- Dalkılıç, M., Mamak, H., Atasoy, M., & Mihriay, M. (2017). Forecast of Licensed Number of Living Sports in Water Trophies, Water Ball and Swimming Areas by Artificial Neural Networks.
DrDataStats. (2024). www.drdatastats.com.
- Exel, J., & Dabnichki, P. (2024). Precision Sports Science: What Is Next for Data Analytics for Athlete Performance and Well-Being Optimization? Applied Sciences, 14(8), 3361.
- Gomis-Gomis, M. J., Pena-Pérez, X., & Pérez-Turpin, J. A. (2023). Sustainability and sports science: A new way for a better future. Sustainability and Sports Science Journal, 1(1), 1-2.
- Hulteen, R. M., Smith, J. J., Morgan, P. J., Barnett, L. M., Hallal, P. C., Colyvas, K., & Lubans, D. R. (2017). Global participation in sport and leisure-time physical activities: A systematic review and meta-analysis. Preventive medicine, 95, 14-25.
- Ishwarya, K., & Nithya, A. A. (2021). Relative analysis and performance of machine learning approaches in sports. Paper presented at the 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA).
- Jauhiainen, S., Kauppi, J.-P., Leppänen, M., Pasanen, K., Parkkari, J., Vasankari, T., . . . Äyrämö, S. (2021). New machine learning approach for detection of injury risk factors in young team sport athletes. International journal of sports medicine, 42(02), 175-182.
- Kasera, M., & Johari, R. (2021). Prediction using machine learning in sports: a case study. Paper presented at the Data Analytics and Management: Proceedings of ICDAM.
- Kelly, S. J., Derrington, S., & Star, S. (2022). Governance challenges in esports: a best practice framework for addressing integrity and wellbeing issues. International Journal of Sport Policy and Politics, 14(1), 151-168.
- Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of applied mathematics, 2(2), 164-168.
- Me, E., & Unold, O. (2011). Machine learning approach to model sport training. Computers in human behavior, 27(5), 1499-1506.
- Mertala, P., & Palsa, L. (2024). Running free: recreational runners’ reasons for non-use of digital sports technology. Sport in Society, 27(3), 329-345.
- 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.
- Qi, Y., Sajadi, S. M., Baghaei, S., Rezaei, R., & Li, W. (2024). Digital technologies in sports: Opportunities, challenges, and strategies for safeguarding athlete wellbeing and competitive integrity in the digital era. Technology in Society, 102496.
- Ryan, L., & Doody, O. (2024). The treatment, outcomes and management of hand, wrist, finger, and thumb injuries in the professional/amateur contact sport athletes: A scoping review. International Journal of Orthopaedic and Trauma Nursing, 101108.
- Seefeldt, V. D., & Ewing, M. E. (1997). Youth Sports in America: An Overview. President's council on physical fitness and sports research digest.
- Suman, S., Singh, S., Mitra, S., & Kumar, M. (2024). Deep neural network model for predicting thermal-hydraulic performance of a solar air heater with artificial roughness: Sensitivity, generalization capacity, and computational efficiency. Process Safety and Environmental Protection.
- Şenol, H. (2021). Methane yield prediction of ultrasonic pretreated sewage sludge by means of an artificial neural network. Energy, 215, 119173.
- Şenol, H., Çolak, E., Elibol, E. A., Hassaan, M. A., & El Nemr, A. (2024). Optimisation of biochar dose in anaerobic co-digestion of green algae and cattle manure using artificial neural networks and response surface methodology. Chemical Engineering Journal, 152750.
- Şenol, H., Çolak, E., & Oda, V. (2024). Forecasting of biogas potential using artificial neural networks and time series models for Türkiye to 2035. Energy, 131949.
- Şenol, H., Dereli, M. A., & Özbilgin, F. (2021). Investigation of the distribution of bovine manure-based biomethane potential using an artificial neural network in Turkey to 2030. Renewable and Sustainable Energy Reviews, 149, 111338.
- TETI. (2024). Türkiye Electricity Transmission Inc. https://www.teias.gov.tr/.
- Tufaner, F., & Avşar, Y. (2016). Effects of co-substrate on biogas production from cattle manure: a review. International journal of environmental science and technology, 13, 2303-2312.
- TurkStats. (2024). www.tuik.gov.tr.
- Wilkens, S. (2021). Sports prediction and betting models in the machine learning age: The case of tennis. Journal of Sports Analytics, 7(2), 99-117.
- Wu, J.-M. (2008). Multilayer potts perceptrons with Levenberg–Marquardt learning. IEEE transactions on neural networks, 19(12), 2032-2043.
- Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks:: The state of the art. International journal of forecasting, 14(1), 35-62.
- Zhang, L., & Li, N. (2022). Material analysis and big data monitoring of sports training equipment based on machine learning algorithm. Neural Computing and Applications, 34(4), 2749-2763.
- Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361.
- Zhu, J., Hu, C., Khezri, E., & Ghazali, M. M. M. (2024). Edge intelligence-assisted animation design with large models: a survey. Journal of Cloud Computing, 13(1), 48.
- Zhu, P., & Sun, F. (2020). Sports athletes’ performance prediction model based on machine learning algorithm. Paper presented at the International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019: Applications and Techniques in Cyber Intelligence 7.