PREDICTING FANTASY PREMIER LEAGUE POINTS USING CONVOLUTIONAL NEURAL NETWORK AND LONG SHORT TERM MEMORY

Anas Satria Lombu; Irving Vitra Paputungan; Chandra Kusuma  Dewa

doi:10.52436/1.jutif.2024.5.1.1792

Anas Satria Lombu Informatics, Faculty of Industrial Engineering, Universitas Islam Indonesia, Indonesia
Irving Vitra Paputungan Informatics, Faculty of Industrial Engineering, Universitas Islam Indonesia, Indonesia
Chandra Kusuma Dewa Informatics, Faculty of Industrial Engineering, Universitas Islam Indonesia, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.1.1792

Keywords: Convolutional Neural Network, Fantasy Fremier League, Long Short Term Memory

Abstract

Fantasy Premier League is a fantasy sports-based game focused on football, particularly the English Premier League. each manager in this game is given the opportunity to build a virtual team for one season. A virtual team consists of various player positions that will earn FPL points based on their real-word performance. This research aims to implement a deep learning algorithm to predict FPL points generated by players based on their performance in the last 5 matches using a dataset collected from August 14, 2021, to May 21, 2022. The prediction model is designed using a Convolutional Neural Network algorithm consisting of one-dimensional Convolution layers, Max Pooling, and Dense layers. Additionally, a Long Short Term Memory algorithm with LSTM layers and Dense layers totaling 64 units is added as a comparison model to determine the best performing deep learning model in this study. In the first scenario, with a 70:30 data ratio, the average Mean Squared Error values obtained for 4 player positions using CNN are 0.0052 and 0.0027 for LSTM. Meanwhile, in the second scenario with an 80:20 data ratio, the evaluation results are 0.0027 for CNN and 0.0022 for LSTM. the model evaluation results indicate that the LSTM algorithm, utilizing three gates in the model architecture, is superior in recognizing historical data sequences compared to the CNN algorithm.

Downloads

Download data is not yet available.

References

R. Masters, “Annual Report 2021/22,” 2022. [Online]. Available: https://www.premierleague.com/about/publications.

A. T. Nugroho, “Gamifikasi, Pemasaran di Era Digital: Studi pada Pengguna Game Fantasy Premier League di Indonesia,” J. Ris. Komun., vol. 4, no. 2, pp. 261–274, 2021, doi: 10.38194/jurkom.v4i2.376.

M. M. Khamsan and R. Maskat, “Handling Highly Imbalanced Output Class Label: a Case Study on Fantasy Premier League (Fpl) Virtual Player Price Changes Prediction Using Machine Learning,” Malaysian J. Comput., vol. 4, no. 2, pp. 304–316, 2019, [Online]. Available: https://www.calculator.net/standard-deviation-calculator.html.

J. D. O’Brien, J. P. Gleeson, and D. J. P. O’Sullivan, “Identification of skill in an online game: The case of Fantasy Premier League,” PLoS One, vol. 16, no. 3, p. e0246698, Mar. 2021, [Online]. Available: https://doi.org/10.1371/journal.pone.0246698.

M. A. Al-Asadi and S. Tasdemir, “Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques,” IEEE Access, vol. 10, pp. 22631–22645, 2022, doi: 10.1109/ACCESS.2022.3154767.

N. Bonello, J. Beel, S. Lawless, and J. Debattista, “Multi-stream data analytics for enhanced performance prediction in fantasy football,” CEUR Workshop Proc., vol. 2563, pp. 284–292, 2019.

A. Lindberg and D. Söderberg, “Comparison of Machine Learning Approaches Applied to Predicting Football Players Performance,” Chalmers University of Technology, 2020.

M. Bangdiwala, R. Choudhari, A. Hegde, and A. Salunke, “Using ML Models to Predict Points in Fantasy Premier League,” 2022 2nd Asian Conf. Innov. Technol. ASIANCON 2022, pp. 1–6, 2022, doi: 10.1109/ASIANCON55314.2022.9909447.

R. Baboota and H. Kaur, “Predictive analysis and modelling football results using machine learning approach for English Premier League,” Int. J. Forecast., vol. 35, no. 2, pp. 741–755, 2019, doi: 10.1016/j.ijforecast.2018.01.003.

C. Herbinet, “Predicting Football Results Using Machine Learning Techniques,” 2011 Proc. 34th Int. Conv. MIPRO, vol. 48, pp. 1623–1627, 2018, [Online]. Available: https://www.imperial.ac.uk/media/imperial-college/faculty-of-engineering/computing/public/1718-ug-projects/Corentin-Herbinet-Using-Machine-Learning-techniques-to-predict-the-outcome-of-profressional-football-matches.pdf.

D. . Prabowo, “Prediksi hasil pertandingan sepakbola english premier league dengan menggunakan algoritma k-nearest neighbors dan naive bayes classifer,” Universitas Islam Indonesia, 2020.

F. Melky, S. Sendari, and I. A. Elbaith, “Optimization of Heavy Point Position Measurement on Vehicles Using Support Vector Machine,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 3, pp. 673–683, 2023, doi: 10.26555/jiteki.v9i3.26261.

S. An, M. Lee, S. Park, H. Yang, and J. So, “An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition,” 2020, [Online]. Available: http://arxiv.org/abs/2008.10400.

D. Bhatt et al., “Cnn variants for computer vision: History, architecture, application, challenges and future scope,” Electron., vol. 10, no. 20, pp. 1–28, 2021, doi: 10.3390/electronics10202470.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018.

D. Ramdas, “Using Convolution Neural Networks to Predict the Performance of Footballers in the Fantasy Premier League,” University of South Africa, 2022.

S. Bhatt, K. Chen, V. L. Shalin, A. P. Sheth, and B. Minnery, “Who should be the captain this week? leveraging inferred diversity-enhanced crowd wisdom for a fantasy premier league captain prediction,” Proc. 13th Int. Conf. Web Soc. Media, ICWSM 2019, no. Icwsm, pp. 103–113, 2019, doi: 10.1609/icwsm.v13i01.3213.

N. J. L. Marfu’ah and A. Kurniawardhani, “Comparison of CNN and SVM for Ship Detection in Satellite Imagery,” Automata, vol. 1, no. 1, 2020, [Online]. Available: https://journal.uii.ac.id/AUTOMATA/article/view/13973.

P. Pokharel, A. Timalsina, S. Panday, and B. Acharya, “Proceedings of 12 th IOE Graduate Conference Fantasy Premier League-Performance Prediction,” vol. 8914, pp. 1862–1869, 2022.

A. E. Karrar, “The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values,” Indones. J. Electr. Eng. Informatics, vol. 10, no. 2, pp. 375–384, 2022, doi: 10.52549/ijeei.v10i2.3730.

Y. Zhang and P. J. Thorburn, “Handling missing data in near real-time environmental monitoring: A system and a review of selected methods,” Futur. Gener. Comput. Syst., vol. 128, pp. 63–72, 2022, doi: 10.1016/j.future.2021.09.033.

U. M. Khaire and R. Dhanalakshmi, “Stability of feature selection algorithm: A review,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 4, pp. 1060–1073, 2022, doi: 10.1016/j.jksuci.2019.06.012.

A. Iparragirre, T. Lumley, I. Barrio, and I. Arostegui, “Variable selection with LASSO regression for complex survey data,” Stat, vol. 12, no. 1, 2023, doi: 10.1002/sta4.578.

H. Henderi, “Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer,” IJIIS Int. J. Informatics Inf. Syst., vol. 4, no. 1, pp. 13–20, 2021, doi: 10.47738/ijiis.v4i1.73.

L. Grinsztajn, E. Oyallon, and G. Varoquaux, “Why do tree-based models still outperform deep learning on typical tabular data?,” Adv. Neural Inf. Process. Syst., vol. 35, no. NeurIPS, 2022.

R. P. Alonso and M. B. Babac, “Machine learning approach to predicting a basketball game outcome,” Int. J. Data Sci., vol. 7, no. 1, p. 60, 2022, doi: 10.1504/ijds.2022.124356.

H. Van Eetvelde, L. D. Mendonça, C. Ley, R. Seil, and T. Tischer, “Machine learning methods in sport injury prediction and prevention: a systematic review,” J. Exp. Orthop., vol. 8, no. 1, 2021, doi: 10.1186/s40634-021-00346-x.

D. Mualfah, W. Fadila, and R. Firdaus, “Teknik SMOTE untuk Mengatasi Imbalance Data pada Deteksi Penyakit Stroke Menggunakan Algoritma Random Forest,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 2, pp. 107–113, 2022, doi: 10.37859/coscitech.v3i2.3912.

I. Walsh et al., “DOME: recommendations for supervised machine learning validation in biology,” Nat. Methods, vol. 18, no. 10, pp. 1122–1127, 2021, doi: 10.1038/s41592-021-01205-4.

Q. Li, Y. Chen, and Y. Zeng, “Transformer with Transfer CNN for Remote-Sensing-Image Object Detection,” Remote Sens., vol. 14, no. 4, 2022, doi: 10.3390/rs14040984.

E. Oktafanda, “Klasifikasi Citra Kualitas Bibit dalam Meningkatkan Produksi Kelapa Sawit Menggunakan Metode Convolutional Neural Network (CNN),” J. Inform. Ekon. Bisnis, vol. 4, no. 3, pp. 72–77, 2022, doi: 10.37034/infeb.v4i3.143.

F. Isdaryani, N. Cholis Basjaruddin, and A. Lugina, “Masked Face Recognition and Temperature Monitoring Systems for Airplane Passenger Using Sensor Fusion,” J. Nas. Tek. Elektro dan Teknol. Inf. |, vol. 11, no. 2, pp. 140–147, 2022.

S. Mahjoub, L. Chrifi-Alaoui, B. Marhic, and L. Delahoche, “Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks,” Sensors, vol. 22, no. 11, pp. 1–20, 2022, doi: 10.3390/s22114062.

W. T. Handoko and A. N. Handayani, “Forecasting Solar Irradiation on Solar Tubes Using the LSTM Method and Exponential Smoothing,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 3, pp. 649–660, 2023, doi: 10.26555/jiteki.v9i3.26395.