Research Article
BibTex RIS Cite

Year 2021, Volume: 9 Issue: 1, 1 - 9, 30.06.2021

Yavuz Selim Taşpınar İlkay Çınar Murat Koklu

https://doi.org/10.51354/mjen.802818
Cited By: 2

Abstract

References

  • Bunker, R. and T. Susnjak, The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review. arXiv preprint arXiv:1912.11762, 2019.
  • Zaveri, N., et al., Prediction of Football Match Score and Decision Making Process. International Journal on Recent and Innovation Trends in Computing and Communication, 2018. 6(2): p. 162-165.
  • Chalikias, M., E. Kossieri, and P. Lalou, Football matches: Decision making in betting. Teaching Statistics, 2020.
  • Samba, S., Football Result Prediction by Deep Learning Algorithms. 2019, Tilburg University.
  • Barron, D., et al., Identifying playing talent in professional football using artificial neural networks. Journal of Sports Sciences, 2020: p. 1-10.
  • Joseph, A., N.E. Fenton, and M. Neil, Predicting football results using Bayesian nets and other machine learning techniques. Knowledge-Based Systems, 2006. 19(7): p. 544-553.
  • Owramipur, F., P. Eskandarian, and F.S. Mozneb, Football result prediction with Bayesian network in Spanish League-Barcelona team. International Journal of Computer Theory and Engineering, 2013. 5(5): p. 812.
  • Goddard, J., Regression models for forecasting goals and match results in association football. International Journal of forecasting, 2005. 21(2): p. 331-340.
  • Baboota, R. and H. Kaur, Predictive analysis and modelling football results using machine learning approach for English Premier League. International Journal of Forecasting, 2019. 35(2): p. 741-755.
  • Schumaker, R.P., O.K. Solieman, and H. Chen, Sports data mining. Vol. 26. 2010: Springer Science & Business Media.
  • Chen, M.-Y., T.-H. Chen, and S.-H. Lin, Using Convolutional Neural Networks to Forecast Sporting Event Results, in Deep Learning: Concepts and Architectures. 2020, Springer. p. 269-285.
  • Rudrapal, D., et al., A Deep Learning Approach to Predict Football Match Result, in Computational Intelligence in Data Mining. 2020, Springer. p. 93-99.
  • Reed, D. and P. O’Donoghue, Development and application of computer-based prediction methods. International Journal of Performance Analysis in Sport, 2005. 5(3): p. 12-28.
  • McCabe, A. and J. Trevathan. Artificial intelligence in sports prediction. in Fifth International Conference on Information Technology: New Generations (itng 2008). 2008. IEEE.
  • Danisik, N., P. Lacko, and M. Farkas. Football match prediction using players attributes. in 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA). 2018. IEEE.
  • Huang, K.-Y. and W.-L. Chang. A neural network method for prediction of 2006 world cup football game. in The 2010 international joint conference on neural networks (IJCNN). 2010. IEEE.
  • Odachowski, K. and J. Grekow. Predicting the Final Result of Sporting Events Based on Changes in Bookmaker Odds. in KES. 2012.
  • Hubáček, O., G. Šourek, and F. Železný, Learning to predict soccer results from relational data with gradient boosted trees. Machine Learning, 2019. 108(1): p. 29-47.
  • Constantinou, A.C., Dolores: A model that predicts football match outcomes from all over the world. Machine Learning, 2019. 108(1): p. 49-75.
  • Berrar, D., P. Lopes, and W. Dubitzky, Incorporating domain knowledge in machine learning for soccer outcome prediction. Machine Learning, 2019. 108(1): p. 97-126.
  • Hucaljuk, J. and A. Rakipović. Predicting football scores using machine learning techniques. in 2011 Proceedings of the 34th International Convention MIPRO. 2011. IEEE.
  • Odachowski, K. and J. Grekow. Using bookmaker odds to predict the final result of football matches. in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. 2012. Springer.
  • Prasetio, D. Predicting football match results with logistic regression. in 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA). 2016. IEEE.
  • Bailey, M.J., Predicting sporting outcomes: A statistical approach. 2005, Faculty of Life and Social Sciences, Swinburne University of Technology.
  • Baio, G. and M. Blangiardo, Bayesian hierarchical model for the prediction of football results. Journal of Applied Statistics, 2010. 37(2): p. 253-264.
  • Min, B., et al., A compound framework for sports results prediction: A football case study. Knowledge-Based Systems, 2008. 21(7): p. 551-562.
  • Igiri, C.P. and E.O. Nwachukwu, An improved prediction system for football a match result. IOSR Journal of Engineering (IOSRJEN), 2014. 4(12): p. 12-20.
  • Bolón-Canedo, V., N. Sánchez-Maroño, and A. Alonso-Betanzos, Feature selection for high-dimensional data. 2015: Springer.
  • Mitchell, R., Web scraping with Python: Collecting more data from the modern web. 2018: " O'Reilly Media, Inc.".
  • scoreboard.com. 2020 [cited 2020 17 March]; Available from: www.scoreboard.com.
  • Makris, S. and C. Urgesi, Neural underpinnings of superior action prediction abilities in soccer players. Social cognitive and affective neuroscience, 2015. 10(3): p. 342-351.
  • Cruyff, M.J., et al., A review of regression procedures for randomized response data, including univariate and multivariate logistic regression, the proportional odds model and item response model, and self-protective responses, in Handbook of Statistics. 2016, Elsevier. p. 287-315.
  • Kalantar, B., et al., Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, 2018. 9(1): p. 49-69.
  • Pietraszek, J., et al. The fuzzy approach to assessment of ANOVA results. in International Conference on Computational Collective Intelligence. 2016. Springer.
  • Sokolova, M., N. Japkowicz, and S. Szpakowicz. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. in Australasian joint conference on artificial intelligence. 2006. Springer.
  • Luque, A., et al., The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 2019. 91: p. 216-231.
  • Ozkan, I.A. and M. Koklu, Skin Lesion Classification using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 2017. 5(4): p. 285-289.

Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League

Year 2021, Volume: 9 Issue: 1, 1 - 9, 30.06.2021

Yavuz Selim Taşpınar İlkay Çınar Murat Koklu

https://doi.org/10.51354/mjen.802818
Cited By: 2

Abstract

Football is one of the most popular sports in terms of number of fans in the world. This situation arises from the unpredictable nature of football. People are becoming more and more connected to this sport as it combines emotions such as excitement and joy that it creates in people. Match result prediction is a very challenging problem, and recently the solution to this problem has become very popular. With the result of this unpredictable game the events that occur during the match that affect this result are tried to be predicted by machine learning methods. This study demonstrates our work on finding the most effective features in match result prediction using match statistics from the Italian Serie A League's 2027 pieces match between the 2014-2015 and 2019-2020 seasons and with 54 features for each match. Feature selection testing was conducted to estimate the results of a football match and determine the most important factors. The selection of features was made using the ANOVA method and it was predicted that 28 of the 54 features would be effective in predicting match results. After this stage, fairly high rates classification success was achieved using the logistic regression method. 88.85% as a result of the prediction made with all features and 89.63% success was achieved as a result of the prediction made with 28 selected features. With these results, it is possible to say that process of feature selection increase success in match result prediction.

Keywords

Soccer Football Sport Result Prediction Feature Selection Team Sport

References

  • Bunker, R. and T. Susnjak, The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review. arXiv preprint arXiv:1912.11762, 2019.
  • Zaveri, N., et al., Prediction of Football Match Score and Decision Making Process. International Journal on Recent and Innovation Trends in Computing and Communication, 2018. 6(2): p. 162-165.
  • Chalikias, M., E. Kossieri, and P. Lalou, Football matches: Decision making in betting. Teaching Statistics, 2020.
  • Samba, S., Football Result Prediction by Deep Learning Algorithms. 2019, Tilburg University.
  • Barron, D., et al., Identifying playing talent in professional football using artificial neural networks. Journal of Sports Sciences, 2020: p. 1-10.
  • Joseph, A., N.E. Fenton, and M. Neil, Predicting football results using Bayesian nets and other machine learning techniques. Knowledge-Based Systems, 2006. 19(7): p. 544-553.
  • Owramipur, F., P. Eskandarian, and F.S. Mozneb, Football result prediction with Bayesian network in Spanish League-Barcelona team. International Journal of Computer Theory and Engineering, 2013. 5(5): p. 812.
  • Goddard, J., Regression models for forecasting goals and match results in association football. International Journal of forecasting, 2005. 21(2): p. 331-340.
  • Baboota, R. and H. Kaur, Predictive analysis and modelling football results using machine learning approach for English Premier League. International Journal of Forecasting, 2019. 35(2): p. 741-755.
  • Schumaker, R.P., O.K. Solieman, and H. Chen, Sports data mining. Vol. 26. 2010: Springer Science & Business Media.
  • Chen, M.-Y., T.-H. Chen, and S.-H. Lin, Using Convolutional Neural Networks to Forecast Sporting Event Results, in Deep Learning: Concepts and Architectures. 2020, Springer. p. 269-285.
  • Rudrapal, D., et al., A Deep Learning Approach to Predict Football Match Result, in Computational Intelligence in Data Mining. 2020, Springer. p. 93-99.
  • Reed, D. and P. O’Donoghue, Development and application of computer-based prediction methods. International Journal of Performance Analysis in Sport, 2005. 5(3): p. 12-28.
  • McCabe, A. and J. Trevathan. Artificial intelligence in sports prediction. in Fifth International Conference on Information Technology: New Generations (itng 2008). 2008. IEEE.
  • Danisik, N., P. Lacko, and M. Farkas. Football match prediction using players attributes. in 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA). 2018. IEEE.
  • Huang, K.-Y. and W.-L. Chang. A neural network method for prediction of 2006 world cup football game. in The 2010 international joint conference on neural networks (IJCNN). 2010. IEEE.
  • Odachowski, K. and J. Grekow. Predicting the Final Result of Sporting Events Based on Changes in Bookmaker Odds. in KES. 2012.
  • Hubáček, O., G. Šourek, and F. Železný, Learning to predict soccer results from relational data with gradient boosted trees. Machine Learning, 2019. 108(1): p. 29-47.
  • Constantinou, A.C., Dolores: A model that predicts football match outcomes from all over the world. Machine Learning, 2019. 108(1): p. 49-75.
  • Berrar, D., P. Lopes, and W. Dubitzky, Incorporating domain knowledge in machine learning for soccer outcome prediction. Machine Learning, 2019. 108(1): p. 97-126.
  • Hucaljuk, J. and A. Rakipović. Predicting football scores using machine learning techniques. in 2011 Proceedings of the 34th International Convention MIPRO. 2011. IEEE.
  • Odachowski, K. and J. Grekow. Using bookmaker odds to predict the final result of football matches. in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. 2012. Springer.
  • Prasetio, D. Predicting football match results with logistic regression. in 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA). 2016. IEEE.
  • Bailey, M.J., Predicting sporting outcomes: A statistical approach. 2005, Faculty of Life and Social Sciences, Swinburne University of Technology.
  • Baio, G. and M. Blangiardo, Bayesian hierarchical model for the prediction of football results. Journal of Applied Statistics, 2010. 37(2): p. 253-264.
  • Min, B., et al., A compound framework for sports results prediction: A football case study. Knowledge-Based Systems, 2008. 21(7): p. 551-562.
  • Igiri, C.P. and E.O. Nwachukwu, An improved prediction system for football a match result. IOSR Journal of Engineering (IOSRJEN), 2014. 4(12): p. 12-20.
  • Bolón-Canedo, V., N. Sánchez-Maroño, and A. Alonso-Betanzos, Feature selection for high-dimensional data. 2015: Springer.
  • Mitchell, R., Web scraping with Python: Collecting more data from the modern web. 2018: " O'Reilly Media, Inc.".
  • scoreboard.com. 2020 [cited 2020 17 March]; Available from: www.scoreboard.com.
  • Makris, S. and C. Urgesi, Neural underpinnings of superior action prediction abilities in soccer players. Social cognitive and affective neuroscience, 2015. 10(3): p. 342-351.
  • Cruyff, M.J., et al., A review of regression procedures for randomized response data, including univariate and multivariate logistic regression, the proportional odds model and item response model, and self-protective responses, in Handbook of Statistics. 2016, Elsevier. p. 287-315.
  • Kalantar, B., et al., Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, 2018. 9(1): p. 49-69.
  • Pietraszek, J., et al. The fuzzy approach to assessment of ANOVA results. in International Conference on Computational Collective Intelligence. 2016. Springer.
  • Sokolova, M., N. Japkowicz, and S. Szpakowicz. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. in Australasian joint conference on artificial intelligence. 2006. Springer.
  • Luque, A., et al., The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 2019. 91: p. 216-231.
  • Ozkan, I.A. and M. Koklu, Skin Lesion Classification using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 2017. 5(4): p. 285-289.
There are 37 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Yavuz Selim Taşpınar 0000-0002-7278-4241

İlkay Çınar 0000-0003-0611-3316

Murat Koklu 0000-0002-2737-2360

Publication Date June 30, 2021
Published in Issue Year 2021 Volume: 9 Issue: 1

Cite

APA Taşpınar, Y. S., Çınar, İ., & Koklu, M. (2021). Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League. MANAS Journal of Engineering, 9(1), 1-9. https://doi.org/10.51354/mjen.802818
AMA Taşpınar YS, Çınar İ, Koklu M. Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League. MJEN. June 2021;9(1):1-9. doi:10.51354/mjen.802818
Chicago Taşpınar, Yavuz Selim, İlkay Çınar, and Murat Koklu. “Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League”. MANAS Journal of Engineering 9, no. 1 (June 2021): 1-9. https://doi.org/10.51354/mjen.802818.
EndNote Taşpınar YS, Çınar İ, Koklu M (June 1, 2021) Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League. MANAS Journal of Engineering 9 1 1–9.
IEEE Y. S. Taşpınar, İ. Çınar, and M. Koklu, “Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League”, MJEN, vol. 9, no. 1, pp. 1–9, 2021, doi: 10.51354/mjen.802818.
ISNAD Taşpınar, Yavuz Selim et al. “Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League”. MANAS Journal of Engineering 9/1 (June 2021), 1-9. https://doi.org/10.51354/mjen.802818.
JAMA Taşpınar YS, Çınar İ, Koklu M. Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League. MJEN. 2021;9:1–9.
MLA Taşpınar, Yavuz Selim et al. “Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League”. MANAS Journal of Engineering, vol. 9, no. 1, 2021, pp. 1-9, doi:10.51354/mjen.802818.
Vancouver Taşpınar YS, Çınar İ, Koklu M. Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League. MJEN. 2021;9(1):1-9.

Cited By

Detection of accident situation by machine learning methods using traffic announcements: the case of metropol Istanbul
International Journal of Applied Mathematics Electronics and Computers
https://doi.org/10.18100/ijamec.1145293
https://doi.org/

Manas Journal of Engineering 

16155