Research Article
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Year 2021, Volume 9, Issue 1, 1 - 9, 30.06.2021
https://doi.org/10.51354/mjen.802818

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
https://doi.org/10.51354/mjen.802818

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.

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.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Yavuz Selim TAŞPINAR (Primary Author)
SELÇUK ÜNİVERSİTESİ, DOĞANHİSAR MESLEK YÜKSEKOKULU
0000-0002-7278-4241
Türkiye


İlkay ÇINAR
SELÇUK ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ
0000-0003-0611-3316
Türkiye


Murat KOKLU
SELÇUK ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ
0000-0002-2737-2360
Türkiye

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

Cite

Bibtex @research article { mjen802818, journal = {MANAS Journal of Engineering}, issn = {1694-7398}, eissn = {1694-7398}, address = {}, publisher = {Kyrgyz-Turkish Manas University}, year = {2021}, volume = {9}, pages = {1 - 9}, doi = {10.51354/mjen.802818}, title = {Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League}, key = {cite}, author = {Taşpınar, Yavuz Selim and Çınar, İlkay and Koklu, Murat} }
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 . DOI: 10.51354/mjen.802818
MLA Taşpınar, Y. S. , Çınar, İ. , Koklu, M. "Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League" . MANAS Journal of Engineering 9 (2021 ): 1-9 <https://dergipark.org.tr/en/pub/mjen/issue/64132/802818>
Chicago Taşpınar, Y. S. , Çınar, İ. , Koklu, M. "Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League". MANAS Journal of Engineering 9 (2021 ): 1-9
RIS TY - JOUR T1 - Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League AU - Yavuz Selim Taşpınar , İlkay Çınar , Murat Koklu Y1 - 2021 PY - 2021 N1 - doi: 10.51354/mjen.802818 DO - 10.51354/mjen.802818 T2 - MANAS Journal of Engineering JF - Journal JO - JOR SP - 1 EP - 9 VL - 9 IS - 1 SN - 1694-7398-1694-7398 M3 - doi: 10.51354/mjen.802818 UR - https://doi.org/10.51354/mjen.802818 Y2 - 2021 ER -
EndNote %0 MANAS Journal of Engineering Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League %A Yavuz Selim Taşpınar , İlkay Çınar , Murat Koklu %T Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League %D 2021 %J MANAS Journal of Engineering %P 1694-7398-1694-7398 %V 9 %N 1 %R doi: 10.51354/mjen.802818 %U 10.51354/mjen.802818
ISNAD Taşpınar, Yavuz Selim , Çınar, İlkay , Koklu, Murat . "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
AMA Taşpınar Y. S. , 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.
Vancouver Taşpınar Y. S. , Çınar İ. , Koklu M. Improvement of Football Match Score Prediction by Selecting Effective Features for Italy Serie A League. MANAS Journal of Engineering. 2021; 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", MANAS Journal of Engineering, vol. 9, no. 1, pp. 1-9, Jun. 2021, doi:10.51354/mjen.802818

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