Document Type : Research Paper
Authors
1 Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
2 Department of Physical Education and Sport Science, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
Abstract
Clustering players based on their abilities, a new perspective and an important opportunity to meet needs that in the light of traditional talent identification and player science, which is held periodically and there is not enough time for them to appear. Early recognition of these abilities is a factor influencing the success of sports teams. Artificial Neural Network (ANN) is a new method of modelling and prediction. The aim of this study was to cluster basketball players based on their individual abilities. For this purpose, Self-Organizing Map (SOM) Neural Networks (NNs) were used. The data set used by 3000 NBA players for 2011 until 2018 is from the Basketball-Reference[1] site. Each player is assigned 30 attributes to reduce them using the Principal Component Analysis (PCA) method and the features for each player were reduced to 12 samples. In order to implement a SOM of features and functions in MATLAB software 65% of the data were used as the network training phase and the remaining 35% were used to the test phase. 12 players’ features as network input and output 9 clusters resulting from the combination of features. After simulation using SOM, accuracy parameter with the help of this system were obtained above 95%. The result of the study showed that the performance of the SOM in clustering basketball players was higher than the K-Means algorithm. The network implemented in this article has a faster speed in the training process and generalizability than similar cases.
Keywords
- Basketball
- Clustering
- K-Means
- Self-Organizing Map (SOM), Neural Networks (NNs)
- Discriminant analysis
- PCA
Main Subjects
- Lopez, C., Tucker, S., Salameh, T., & Tucker, C. (2018). An unsupervised machine learning method for discovering patient clusters based on genetic signatures. Journal of biomedical informatics, 85, 30-39.
- Roy, A., Pal, A., & Garain, U. (2017). JCLMM: A finite mixture model for clustering of circular-linear data and its application to psoriatic plaque segmentation. Pattern recognition, 66, 160-173.
- Novriansyah, N., Irianto, D. P., Rahmat, Y. N., & Nanda, F. A. (2019). Effect of imagery on free-throw shooting in basketball extracurricular. Jurnal keolahragaan, 7(2), 155-161.
- Ghadiri, S. M. E., & Mazlumi, K. (2020). Adaptive protection scheme for microgrids based on SOM clustering technique. Applied soft computing, 88, 106062. https://doi.org/10.1016/j.asoc.2020.106062
- Abbasvandi, Z., & Nasrabadi, A. M. (2019). A self-organized recurrent neural network for estimating the effective connectivity and its application to EEG data. Computers in biology and medicine, 110, 93-107.
- Gu, J., Pan, Y., & Wang, H. (2015). Research on the improvement of image edge detection algorithm based on artificial neural network. Optik, 126(21), 2974-2978.
- Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
- Chuang, C., Wei, Z., Zhifu, W., & Zhi, L. (2017). The diagnosis method of stator winding faults in PMSMs based on SOM neural networks. Energy procedia, 105, 2295-2301.
- Carbonari, R., Di Maio, R., Piegari, E., D'Auria, L., Esposito, A., & Petrillo, Z. (2018). Filtering of noisy magnetotelluric signals by SOM neural networks. Physics of the earth and planetary interiors, 285, 12-22.
- Nan, F., Li, Y., Jia, X., Dong, L., & Chen, Y. (2019). Application of improved som network in gene data cluster analysis. Measurement, 145, 370-378.
- Dörfler, F., & Bullo, F. (2014). Synchronization in complex networks of phase oscillators: a survey. Automatica, 50(6), 1539-1564.
- Lee, A., Geem, Z. W., & Suh, K. D. (2016). Determination of optimal initial weights of an artificial neural network by using the harmony search algorithm: application to breakwater armor stones. Applied Sciences, 6(6), 164. https://doi.org/10.3390/app6060164
- Kamimura, R. (2019). SOM-based information maximization to improve and interpret multi-layered neural networks: From information reduction to information augmentation approach to create new information. Expert systems with applications, 125, 397-411.
- Dykes, J., MacEachren, A. M., & Kraak, M. J. (2005). Introduction exploring geovisualization. In Exploring geovisualization(pp. 1-19). Elsevier Ltd. https://doi.org/10.1016/B978-008044531-1/50419-X
- Smith, T. H. L., & Alahakoon, D. (2009). Growing self-organizing map for online continuous clustering. In A. Abraham, A. E. Hassanien, & A. Ponce de Leon F de Carvalho (Eds.), Foundations of computational intelligence volume 4: bio-inspired data mining (pp. 49-83). Springer.
- Di Franco, G., & Santurro, M. (2021). Machine learning, artificial neural networks and social research. Quality & quantity, 55(3), 1007-1025.
- Santos-Gago, J. M., Ramos-Merino, M., Vallarades-Rodriguez, S., Álvarez-Sabucedo, L. M., Fernández-Iglesias, M. J., & García-Soidán, J. L. (2019). Innovative use of wrist-worn wearable devices in the sports domain: a systematic review. Electronics, 8(11), 1257. https://doi.org/10.3390/electronics8111257
- Bao, H., & Yao, X. (2021). Dynamic 3D image simulation of basketball movement based on embedded system and computer vision. Microprocessors and microsystems, 81, 103655. https://doi.org/10.1016/j.micpro.2020.103655
- Liu, A. (2021). Simulation of basketball motion image resolution based on FPGA and gaussian mixture model. Microprocessors and microsystems, 82, 103873. https://doi.org/10.1016/j.micpro.2021.103873
- Nanda, F. A., & Dimyati, D. (2019). The psychological skills of basketball athletes: are there any differences based on the playing position? Jurnal keolahragaan, 7(1), 74-82.
- Sagoe, D., & Pallesen, S. (2018). Androgen abuse epidemiology. Current opinion in endocrinology & diabetes and obesity, 25(3), 185-194.
- Travassos, B., Araujo, D., Davids, K., O'hara, K., Leitão, J., & Cortinhas, A. (2013). Expertise effects on decision-making in sport are constrained by requisite response behaviours–a meta-analysis. Psychology of sport and exercise, 14(2), 211-219.
- Li, X., Huang, L., Li, B., Wang, H., & Han, C. (2020). Time for a true display of skill: top players in league of legends have better executive control. Acta psychologica, 204, 103007. https://doi.org/10.1016/j.actpsy.2020.103007
- Murgia, M., Sors, F., Muroni, A. F., Santoro, I., Prpic, V., Galmonte, A., & Agostini, T. (2014). Using perceptual home-training to improve anticipation skills of soccer goalkeepers. Psychology of sport and exercise, 15(6), 642-648.
- Tunç, U. Z. L. U., & Şaykol, E. (2020). Evaluating a player’s network class in a multiplayer game with fuzzy logic. Gümüşhane üniversitesi fen bilimleri enstitüsü dergisi, 10(1), 163-173. https://doi.org/10.17714/gumusfenbil.518689
- Zhuang, Z. Y., Su, C. R., & Chang, S. C. (2019). The effectiveness of IF-MADM (intuitionistic-fuzzy multi-attribute decision-making) for group decisions: methods and an empirical assessment for the selection of a senior centre. Technological and economic development of economy, 25(2), 322-364.
- Sarlis, V., & Tjortjis, C. (2020). Sports analytics—Evaluation of basketball players and team performance. Information systems, 93, 101562. https://doi.org/10.1016/j.is.2020.101562
- Wedding, C., Woods, C. T., Sinclair, W. H., Gomez, M. A., & Leicht, A. S. (2020). Examining the evolution and classification of player position using performance indicators in the National Rugby League during the 2015–2019 seasons. Journal of science and medicine in sport, 23(9), 891-896.
- Cui, Y., Gómez, M. Á., Gonçalves, B., & Sampaio, J. (2019). Clustering tennis players’ anthropometric and individual features helps to reveal performance fingerprints. European journal of sport science, 19(8), 1032-1044.
- Cui, Y., Gómez, M. Á., Gonçalves, B., & Sampaio, J. (2018). Performance profiles of professional female tennis players in grand slams. PloS one, 13(7), e0200591.
https://doi.org/10.1371/journal.pone.0200591 - Van de Ven, E., & Alfons, A. (2018). Clustering soccer players to find the drivers of soccer team performance (Master Thesis, Erasmus University Rotterdam). Retrieved from https://thesis.eur.nl/pub/43857/Ven-van-de.pdf
- Kim, J., Wi, J., Jang, S., & Kim, Y. (2020). Sequential recommendations on board-game platforms. Symmetry, 12(2), 210. https://doi.org/10.3390/sym12020210
- Taghva, M., Bamakan, S., & Toufani, S. (2011). A data mining method for service marketing: a case study of banking industry. Management science letters, 1(3), 253-262.
- Bonyadi Naeini, A., Yousef, S., & Faezirad, M. (2016). Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM). Industrial management studies, 14(40), 165-187. (In Persian). DOI: 22054/jims.2016.3914
- Höner, O., Leyhr, D., & Kelava, A. (2017). The influence of speed abilities and technical skills in early adolescence on adult success in soccer: a long-term prospective analysis using ANOVA and SEM approaches. PloS one, 12(8), e0182211.
https://doi.org/10.1371/journal.pone.0182211 - Hussain, H. (2019). Using K-Means clustering algorithm to redefine NBA positions and explore roster construction. https://towardsdatascience.com/using-k-means-clustering-algorithm-to-redefine-nba-positions-and-explore-roster-construction-8cd0f9a96dbb
- Han, Q., Liu, J., Shen, Z., Liu, J., & Gong, F. (2020). Vector partitioning quantization utilizing K-means clustering for physical layer secret key generation. Information sciences, 512, 137-160.
- Yamaghani, M., & Zargari, F. (2017). Classification and retrieval of radiology images in H. 264/AVC compressed domain. Signal, image and video processing, 11(3), 573-580.
- Zhan, Y., Lu, S., Xiang, T., & Wei, T. (2021). Application of convolutional neural network in random structural damage identification. Structures, 29, 570-576. https://doi.org/10.1016/j.istruc.2020.11.056
- Yamaghani, M., & Zargari, F. (2018). Compression and retrieval of radiology images by using HEVC standard. Journal of machine vision and image processing, 5(1), 129-139.
- Zhang, S., Lorenzo, A., Gómez, M. A., Mateus, N., Gonçalves, B., & Sampaio, J. (2018). Clustering performances in the NBA according to players’ anthropometric attributes and playing experience. Journal of sports sciences, 36(22), 2511-2520.
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