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A Survey on Football Player Performance and Value Estimation Using Machine Learning Techniques

Year 2022, Volume: 5 Issue: 2, 57 - 62, 31.12.2022

Abstract

The popularity of games like FIFA and Football Manager has attracted millions of players. Data plays an increasingly prominent role in these games. In other words, these games contain data from all soccer players worldwide, which can be used to simulate soccer games. Therefore, a lot of experts and a lot of tools are involved in producing football data. It is not only the production of this data that has attracted researchers, but also the analysis of it since useful and consistent information can be extracted from it to design real-world football applications. In this study, we introduce a survey of studies which focus on the prediction of player value/performance using machine learning techniques. As far as we know, there is no survey in the literature that specifically addresses this topic.

References

  • Herm S., Callsen-Bracker H.M., Kreis H. (2014). When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community, Sport Management Review, Vol 17, No:4, p.484-492, DOI: 10.1016/j.smr.2013.12.006
  • Singh P., Lamba P. 2019). Influence of crowdsourcing, popularity and previous year statistics in market value estimation of football players. Journal of Discrete Mathematical Sciences and Cryptography, Vol 22, No 2, p. 113-126, DOI: 10.1080/09720529.2019.1576333
  • Kirschstein T., Liebscher S. (2019). Assessing the market values of soccer players – a robust analysis of data from German 1. and 2. Bundesliga, Journal of Applied Statistics, Vol. 46, No 7, p. 1336-1349, DOI: 10.1016/j.ejor.2017.05.005
  • Müller O., Simons A., Weinmann M.. (2017). Beyond crowd judgments: Data-driven estimation of market value in association football. European Journal of Operation Research, Vol. 263, No:2, p.611-624, DOI: 10.1016/j.ejor.2017.05.005
  • Hanso L., Tama B.A., Cha M. (2022). Prediction of Football Player Value using Bayesian Ensemble Approach. Preprint to be submitted. DOI:10.48550/arXiv.2206.13246
  • Chavan A., Dondio P. (2019). Recruitment of Suitable Football Player by using Machine Learning Techniques. Msc. Research Project
  • Al Asadi M.A., Taşdemir Ş. (2021). Empirical Comparisons for Combining Balancing and Feature Selection Strategies for Characterizing Football Players Using FIFA Video Game System. IEEE ACCESS, Vol 9, p. 149266-149286, DOI: 10.1109/ACCESS.2021.3124931
  • Al Asadi M.A., Taşdemir Ş. (2021). Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques. IEEE ACCESS, Vol 10, p. 22631-22645, DOI: 10.1109/ACCESS.2022.3154767
  • Yaldo L., Shamir L. (2017). Computational Estimation of Football Player Wages. International Journal of Computer Science in Sport, Vol 16, No 1, p. 18-38, DOI: 10.48550/arXiv.2206.13246
  • Parath R., Shah S., Surve A., Mittal J. (2018). Player Performance Prediction in Football Game, 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA2018), p. 1148-1153, DOI: 10.1109/ICECA.2018.8474750
  • Yiğit A.T., Samak B., Kaya T. (2020). Football Player Value Assessment Using Machine Learning Techniques, Springer Nature Switzerland, p. 289-297, DOI: 10.1007/978-3-030-23756-1_36
  • Apostolou K., Tjortjis C. (2019). Sports Analytics algorithms for performance prediction 10th International Conference on Information, Intelligence, Systems and Applications (IISA), DOI:10.1109/IISA.2019.8900754
  • Rajesh P., Alam M., Tahernezhadi M., (2020). A Data Science Approach to Football Team Player Selection. IEEE International Conference on Electro Information Technology (EIT), p. 175-183, DOI:10.1109/EIT48999.2020.9208331
  • He M., Cachucho R., Knobbe A. (2015). Football Player’s Performance and Market Value. Proceedings of the 2nd workshop of sports analytics, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
  • Stanojevic R., Gyarmati L. (2016). Towards data-driven football player assessment. IEEE 16th International Conference on Data Mining Workshops, p. 167-172, DOI: 10.1109/ICDMW.2016.0031
  • Rao V., Shrivastava A. (2017). Team Strategizing using a Machine Learning Approach. International Conference on Inventive Computing and Informatics (ICICI 2017), p. 1032-1035, DOI: 10.1109/ICICI.2017.8365296
  • Uzochukwu O. C., Enyindah P. (2015). A Machine Learning Application for Football Players’ Selection. International Journal of Engineering Research & Technology (IJERT). Vol. 4, No 10, p.459-465, DOI : 10.17577/IJERTV4IS100323
  • Behravan I., Razavi S.M. (2020). A novel machine learning method for estimating football players’ value in the transfer market. Soft Computing, Vol. 25, p 2499-2511,DOI:10.1007/s00500-020-05319-3
  • Cotta L., Benevenuto F., Vaz de Melo P., Loureiro A. (2016). Using FIFA Soccer video game data for soccer analytics, Workshop on large scale sports analytics, DOI: 10.1145/1235
Year 2022, Volume: 5 Issue: 2, 57 - 62, 31.12.2022

Abstract

References

  • Herm S., Callsen-Bracker H.M., Kreis H. (2014). When the crowd evaluates soccer players’ market values: Accuracy and evaluation attributes of an online community, Sport Management Review, Vol 17, No:4, p.484-492, DOI: 10.1016/j.smr.2013.12.006
  • Singh P., Lamba P. 2019). Influence of crowdsourcing, popularity and previous year statistics in market value estimation of football players. Journal of Discrete Mathematical Sciences and Cryptography, Vol 22, No 2, p. 113-126, DOI: 10.1080/09720529.2019.1576333
  • Kirschstein T., Liebscher S. (2019). Assessing the market values of soccer players – a robust analysis of data from German 1. and 2. Bundesliga, Journal of Applied Statistics, Vol. 46, No 7, p. 1336-1349, DOI: 10.1016/j.ejor.2017.05.005
  • Müller O., Simons A., Weinmann M.. (2017). Beyond crowd judgments: Data-driven estimation of market value in association football. European Journal of Operation Research, Vol. 263, No:2, p.611-624, DOI: 10.1016/j.ejor.2017.05.005
  • Hanso L., Tama B.A., Cha M. (2022). Prediction of Football Player Value using Bayesian Ensemble Approach. Preprint to be submitted. DOI:10.48550/arXiv.2206.13246
  • Chavan A., Dondio P. (2019). Recruitment of Suitable Football Player by using Machine Learning Techniques. Msc. Research Project
  • Al Asadi M.A., Taşdemir Ş. (2021). Empirical Comparisons for Combining Balancing and Feature Selection Strategies for Characterizing Football Players Using FIFA Video Game System. IEEE ACCESS, Vol 9, p. 149266-149286, DOI: 10.1109/ACCESS.2021.3124931
  • Al Asadi M.A., Taşdemir Ş. (2021). Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques. IEEE ACCESS, Vol 10, p. 22631-22645, DOI: 10.1109/ACCESS.2022.3154767
  • Yaldo L., Shamir L. (2017). Computational Estimation of Football Player Wages. International Journal of Computer Science in Sport, Vol 16, No 1, p. 18-38, DOI: 10.48550/arXiv.2206.13246
  • Parath R., Shah S., Surve A., Mittal J. (2018). Player Performance Prediction in Football Game, 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA2018), p. 1148-1153, DOI: 10.1109/ICECA.2018.8474750
  • Yiğit A.T., Samak B., Kaya T. (2020). Football Player Value Assessment Using Machine Learning Techniques, Springer Nature Switzerland, p. 289-297, DOI: 10.1007/978-3-030-23756-1_36
  • Apostolou K., Tjortjis C. (2019). Sports Analytics algorithms for performance prediction 10th International Conference on Information, Intelligence, Systems and Applications (IISA), DOI:10.1109/IISA.2019.8900754
  • Rajesh P., Alam M., Tahernezhadi M., (2020). A Data Science Approach to Football Team Player Selection. IEEE International Conference on Electro Information Technology (EIT), p. 175-183, DOI:10.1109/EIT48999.2020.9208331
  • He M., Cachucho R., Knobbe A. (2015). Football Player’s Performance and Market Value. Proceedings of the 2nd workshop of sports analytics, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
  • Stanojevic R., Gyarmati L. (2016). Towards data-driven football player assessment. IEEE 16th International Conference on Data Mining Workshops, p. 167-172, DOI: 10.1109/ICDMW.2016.0031
  • Rao V., Shrivastava A. (2017). Team Strategizing using a Machine Learning Approach. International Conference on Inventive Computing and Informatics (ICICI 2017), p. 1032-1035, DOI: 10.1109/ICICI.2017.8365296
  • Uzochukwu O. C., Enyindah P. (2015). A Machine Learning Application for Football Players’ Selection. International Journal of Engineering Research & Technology (IJERT). Vol. 4, No 10, p.459-465, DOI : 10.17577/IJERTV4IS100323
  • Behravan I., Razavi S.M. (2020). A novel machine learning method for estimating football players’ value in the transfer market. Soft Computing, Vol. 25, p 2499-2511,DOI:10.1007/s00500-020-05319-3
  • Cotta L., Benevenuto F., Vaz de Melo P., Loureiro A. (2016). Using FIFA Soccer video game data for soccer analytics, Workshop on large scale sports analytics, DOI: 10.1145/1235
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Review Articles
Authors

Vehbi Hakan Sayan 0000-0002-5892-3954

Emrah Hançer 0000-0002-3213-5191

Publication Date December 31, 2022
Acceptance Date December 30, 2022
Published in Issue Year 2022 Volume: 5 Issue: 2

Cite

APA Sayan, V. H., & Hançer, E. (2022). A Survey on Football Player Performance and Value Estimation Using Machine Learning Techniques. Scientific Journal of Mehmet Akif Ersoy University, 5(2), 57-62.