The study delves into FIFA’s role as the global regulatory authority for football, managing the sport’s development and major events like the FIFA World Cup. FIFA’s influence extends to economic goals, impacting football clubs globally as they invest in skilled players. The market valuation of players is crucial, guiding budget allocation for transfers. Using data from the FIFA 19 video game and real-world statistics, the study employs Decision Tree Regression (DTR) and Random Forest Regression (RFR) models, addressing multicollinearity with Variance Inflation Factor (VIF). The Rhizostoma Optimization Algorithm (ROA) and Dwarf Mongoose Optimizer (DMO) optimize models. Results show RFR-based models, particularly RFRO, outperform DTR-based ones, achieving over 99% R2 value and 12% error relative to mean market values. Ensemble models RFRD and DTRD provide a reliable prediction capability of around 98%, aiding real-world decision-making in the football transfer market for club managers, coaches, and stakeholders across different leagues.

[1] Agushaka J., Ezugwu A., and Abualigah L., “Dwarf Mongoose Optimization Algorithm,” Computer Methods in Applied Mechanics and Engineering, vol. 391, pp. 114570, 2022. https://doi.org/10.1016/j.cma.2022.114570

[2] Ahmad A., Farooq F., Niewiadomski P., Ostrowski K., Akbar A., Aslam F., and Alyousef R., “Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm,” Materials, vol. 14, no. 4, pp. 1-21, 2021. https://doi.org/10.3390/ma14040794

[3] Akinola O., Ezugwu A., Oyelade O., and Agushaka J., “A Hybrid Binary Dwarf Mongoose Optimization Algorithm with Simulated 738 The International Arab Journal of Information Technology, Vol. 21, No. 4, July 2024 Annealing for Feature Selection on High Dimensional Multi-Class Datasets,” Scientific Reports, vol. 12, no. 1, pp. 1-22, 2022. https://www.nature.com/articles/s41598-022- 18993-0

[4] Akinwande M., Dikko H., and Samson A., “Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable (s) in Regression Analysis,” Open Journal of Statistics, vol. 5, no. 7, pp. 754-767, 2015. DOI:10.4236/ojs.2015.57075

[5] Al-Asadi M. and Tasdemır S., “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

[6] Angel B. and Gasparetto T., Routledge Handbook of Football Business and Management, Routledge, 2018. https://doi.org/10.4324/9781351262804

[7] Baldi R., They Always Score: The Unforgettable, Improbable, Iconic Story of Manchester United’s Treble Winners, Birlinn Ltd, 2023. https://www.amazon.co.uk/They-Always-Score- Unforgettable-Improbable/dp/191353895

[8] Basirat M., Khajeheian D., and Arbatani T., “A Theoretical Model for Identifying Media Value of Football Players in Iranian Professional League,” Sport Management Studies, vol. 11, no. 57, pp. 121-140, 2019. https://doi.org/10.22089/smrj.2019.7317.2550

[9] Behravan I. and Razavi S., “A Novel Machine Learning Method for Estimating Football Players’ Value in the Transfer Market,” Soft Computing, vol. 25, no. 3, pp. 2499-2511, 2021. https://doi.org/10.1007/s00500-020-05319-3

[10] Breiman L., “Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001. https://doi.org/10.1023/A:1010933404324

[11] Breiman L., Friedman J., Olshen R., and Stone C., Classification and Regression Trees, CRC Press, 1984. https://www.academia.edu/5867603/Classificatio n_and_Regression_Trees

[12] Carmichael F., Rossi G., and Thomas D., “Production, Efficiency, and Corruption in Italian Serie A Football,” Journal of Sports Economics, vol. 18, no. 1, pp. 34-57, 2017. https://doi.org/10.1177/1527002514551802

[13] Coates D. and Parshakov P., “The Wisdom of Crowds and Transfer Market Values,” European Journal of Operational Research, vol. 301, no. 2, pp. 523-534, 2022. https://doi.org/10.1016/j.ejor.2021.10.046

[14] Elliott R., The English Premier League: A Socio- Cultural Analysis, Taylor and Francis, 2017. https://doi.org/10.4324/9781315636696

[15] Erdal H., “Two-Level and Hybrid Ensembles of Decision Trees for High Performance Concrete Compressive Strength Prediction,” Engineering Applications of Artificial Intelligence, vol. 26, no. 7, pp. 1689-1697, 2013. https://doi.org/10.1016/j.engappai.2013.03.014

[16] Felipe J., Fernandez-Luna A., Burillo P., De la Riva L., Sanchez-Sanchez J., and Garcia-Unanue J., “Money Talks: Team Variables and Player Positions that most Influence the Market Value of Professional male Footballers in Europe,” Sustainability, vol. 12, no. 9, pp. 1-8, 2020. https://doi.org/10.3390/su12093709

[17] Fieldsend D., The European Game: The Secrets of European Football Success, Arena Sport, 2017. https://www.casemateipm.com/9781909715486/t he-european-game/

[18] Franceschi M., Brocard J., Follert F., and Gouguet J., “Football Players in Light of Economic Value Theory: Critical Review and Conceptualisation,” Managerial and Decision Economics, vol. 45, no. 2, pp. 896-920, 2023. https://doi.org/10.1002/mde.4039

[19] González-Rodenas J., Moreno-Pérez V., López- Del Campo R., Resta R., and Del Coso J., “Evolution of Tactics in Professional Soccer: An Analysis of Team Formations from 2012 to 2021 in the Spanish LaLiga,” Journal Human Kinetics, vol. 87, pp. 207-216, 2023. DOI:10.5114/jhk/167468

[20] Goswami S. and Chakrabarti A., “Feature Selection: A Practitioner View,” International Journal of Information Technology and Computer Science, vol. 6, no. 11, pp. 66-77, 2014. DOI:10.5815/ijitcs.2014.11.10

[21] Hamil S., “The German Football Bundesliga,” Birkbeck College, pp. 1-17, 2014. https://www.academia.edu/10298953/THE_GER MAN_FOOTBALL_BUNDESLIGA

[22] He M., Cachucho R., and Knobbe A., “Football Player’s Performance and Market Value,” in Proceedings of the Machine Learning and Data Mining for Sports Analytics ECML/PKDD Workshop, Porto, pp. 87-95, 2015. https://api.semanticscholar.org/CorpusID:39624891

[23] Herm S., Callsen-Bracker H., and Kreis H., “When the Crowd Evaluates Soccer Players’ Market Values: Accuracy and Evaluation Attributes of an Online Community,” Sport Management Review, vol. 17, no. 4, pp. 484-492, 2014. https://doi.org/10.1016/j.smr.2013.12.006

[24] Horn C., An Exploratory Study into Select Technical Key Performance Indicators and Estimated Transfer Fees in the 2nd Division of the German Bundesliga, Master’s Thesis, Stellenbosch University, 2023. https://scholar.sun.ac.za/server/api/core/bitstreams/c 52a0036-07f6-45e0-8a24-0c26bda7a3b2/content

[25] Isikdemir E., Ozkurkcu S., and Ozer S., Prediction of Football Players’ Value in the Transfer Market of Well-known European ... 739 “Technical Analysis of Goals Scored in 3 Different European Leagues in the 2020-2021 Football Season,” Journal of Sport Sciences Researches, vol. 8, no. 3, pp. 458-472, 2023.

[26] Juuri S., Predicting the Results of NFL Games Using Machine Learning, Master’s Thesis, Aalto University, 2023. https://aaltodoc.aalto.fi/server/api/core/bitstreams/80b6 e0d0-f5d1-4c19-abd3-667ee40d9c93/content

[27] Karbassi A., Mohebi B., Rezaee S., and Lestuzzi P., “Damage Prediction for Regular Reinforced Concrete Buildings Using the Decision Tree Algorithm,” Computers and Structures, vol. 130, pp. 46-56, 2014. https://doi.org/10.1016/j.compstruc.2013.10.006

[28] Lepschy H., Wasche H., and Woll A., “Success Factors in Football: An Analysis of the German Bundesliga,” International Journal of Performance Analysis in Sport, vol. 20, no. 2, pp. 150-164, 2020. https://www.tandfonline.com/doi/full/10.1080/24 748668.2020.1726157

[29] Liaw A. and Wiener M., “Classification and Regression by RandomForest,” R News, vol. 2, no. 3, pp. 18-22, 2002. https://journal.r- project.org/articles/RN-2002-022/RN-2002-022.pdf

[30] Lowe S., Fear and loathing in La Liga: Barcelona, Real Madrid, and the World’s Greatest Sports Rivalry, Bold Type Books, 2014. https://www.amazon.com/Fear-Loathing-Liga- Barcelona-Greatest/dp/1568584504

[31] Mahareek E., Cifci M., El-Zohni H., and Desuky A., “Rhizostoma Optimization Algorithm and its Application in Different Real-World Optimization Problems,” International Journal of Electrical and Computer Engineering, vol. 13, no. 4, pp. 4317-4338, 2023. DOI:10.11591/ijece.v13i4.pp4317-4338

[32] Majewski S., “Identification of Factors Determining Market Value of the most Valuable Football Players,” Central European Management Journal, vol. 24, no. 3, pp. 91-104, 2016. DOI:10.7206/jmba.ce.2450-7814.177

[33] Matheson V., European Football: A Survey of the Literature, Williams College, Department of Economics Williamstown, 2003.

[34] McHale I. and Holmes B., “Estimating Transfer Fees of Professional Footballers Using Advanced Performance Metrics and Machine Learning,” European Journal of Operational Research, vol. 306, no. 1, pp. 389-399, 2023, https://doi.org/10.1016/j.ejor.2022.06.033

[35] Metelski A., “Factors Affecting the Value of Football Players in the Transfer Market,” Journal of Physical Education and Sport, vol. 21, no. 2, pp. 1150-1155, 2021. https://efsupit.ro/images/stories/aprilie2021/Art% 20145.pdf

[36] Peters J., De Baets B., Verhoest N., Samson R., Degroeve S., De Becker P., and Huybrechts W., “Random Forests as a Tool for Ecohydrological Distribution Modelling,” Ecological Modelling, vol. 207, no. 2-4, pp. 304-318, 2007. https://doi.org/10.1016/j.ecolmodel.2007.05.011

[37] Podzemsky L., Analysis of Investments and Market Value of Football Clubs, Bachelor’s Thesis, Charles University 2022. https://dspace.cuni.cz/bitstream/handle/20.500.11956/1 75470/130344478.pdf?sequence=1&isAllowed=y

[38] Prinz A. and Thiem S., “Value‐Maximizing Football Clubs,” Scottish Journal Political Economy, vol. 68, no. 5, pp. 605-622, 2021. https://doi.org/10.1111/sjpe.12282

[39] Putra M., Dewi D., Putri W., Hendrowati R., and Kurniawan T., “Contributed Factors in Predicting Market Values of Loaned out Players of English Premier League Clubs,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 9, pp. 359-365, 2023. DOI:10.14569/IJACSA.2023.0140939

[40] Rodriguez-Galiano V., Sanchez-Castillo M., Chica-Olmo M., and Chica-Rivas M., “Machine Learning Predictive Models for Mineral Prospectivity: An Evaluation of Neural Networks, Random Forest, Regression Trees and Support Vector Machines,” Ore Geology Reviews, vol. 71, pp. 804-818, 2015. https://doi.org/10.1016/j.oregeorev.2015.01.001

[41] Rossi G., Tanna G., and Addesa F., “Production, Efficiency and Corruption in Italian Serie A: A DEA Analysis,” Birkbeck, University of London, vol. 9, no. 1, pp. 1-24, 2016. https://eprints.bbk.ac.uk/id/eprint/18395/

[42] Sadoun A., Najjar I., Alsoruji G., Wagih A., and Abd Elaziz M., “Utilizing a Long Short-Term Memory Algorithm Modified by Dwarf Mongoose Optimization to Predict Thermal Expansion of Cu-Al2O3 Nanocomposites,” Mathematics, vol. 10, no. 7, pp. 1-17, 2022. https://www.mdpi.com/2227-7390/10/7/1050

[43] Sener I. and Karapolatgil A., “Rules of the Game: Strategy in Football Industry,” Procedia-Social and Behavioral Sciences, vol. 207, pp. 10-19, 2015. https://doi.org/10.1016/j.sbspro.2015.10.143

[44] Sengupta S., “Understanding La Liga: Are Match Performances and Player Market Value Related?,” International Research Journal of Nature Science and Technology, vol. 2, no. 6, pp. 1-11, 2020. https://scienceresearchjournals.org/IRJNST/2020 /volume-2%20issue-6/irjnst-v2i6p101.pdf

[45] Singh G. and Panda R., “Daily Sediment Yield Modeling with Artificial Neural Network Using 10-fold Cross Validation Method: A Small Agricultural Watershed, Kapgari, India,” International Journal of Earth Sciences and 740 The International Arab Journal of Information Technology, Vol. 21, No. 4, July 2024 Engineering, vol. 4, no. 6, pp. 443-450, 2011. file:///C:/Users/user/Downloads/Daily_Sediment _Yield_Modeling_with_Artificial_Neur.pdf

[46] Swanepoel M. and Swanepoel J., “The Correlation between Player Valuation and the Bargaining Position of Clubs in the English Premier League (EPL),” International Journal of Economics and Finance Studies, vol. 8, no. 1, pp. 209-225, 2016. https://www.sobiad.org/eJOURNALS/journal_IJ EF/archieves/IJEFS2016_1/Paper74_Swanepoel_ Swanepoel.pdf

[47] Tomlinson A. and Young C., German Football: History, Culture, Society, Routledge, 2006. http://ndl.ethernet.edu.et/bitstream/123456789/24 867/1/Alan_Tomlinson_2006.pdf

[48] Wagner F., Preuss H., and Könecke T., “A Central Element of Europe’s Football Ecosystem: Competitive Intensity in the ‘Big Five,” Sustainability, vol. 13, no. 6, pp. 3097, 2021. https://doi.org/10.3390/su13063097

[49] Wang Y., Tarakci H., and Prybutok V., “Model Comparison of Regression, Neural Networks, and XGBoost as Applied to the English Premier League Transfer Market,” International Journal of Sport Management and Marketing, vol. 23, no. 6, pp. 543-559, 2023. https://doi.org/10.1504/IJSMM.2023.133786

[50] Zaib R. and Ourabah O., “Large Scale Data Using K-Means,” Mesopotamian Journal of Big Data, vol. 2023, pp. 36-45, 2023. https://doi.org/10.58496/MJBD/2023/006