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  • Writer's pictureBrandon Huggard

Machine learning: Changing the landscape of sports business

By Brandon Huggard

Jan. 28, 2022

Photo Credit: Associated Press

‘Dizzying’ would only begin to capture the magnitude of money that soccer clubs around the world spend on bringing in the best possible talent. Regularly upwards of nine figures, the deals that teams make with players must extract value if the clubs want to remain successful.

This is why, through the use of cutting edge technology and data processing, some of the largest clubs in the world are beginning to implement machine learning into their transfer market strategy. Using artificial intelligence, specifically neural networks, in combination with robust statistics that more effectively communicate a given player’s impact, next-generation research has revolutionized the transfer market process.

The emergence of statistics like expected goals (xG) and expected assists (xA), which both include key defensive parameters in order to remain usable across any position, have allowed for teams to consider inputs that remain relevant across almost any league in the world.

With the establishment of a robust and naturally adjusting set of statistics that can measure a player’s impact in an objective sense, teams can configure inputs for transfer market valuations in an AI based neural network and be left with an output relaying “true” value. Rebellion Research pointed out a relevant case study in the case of Coutinho to Barcelona, stating that:

“Coutinho was an elite player at Liverpool. But never truly adapted to Barcelona and has yet to live up to his price tag as the third-largest transfer of all time [$148.5 Million]. If used to analyze the transfer, machine learning would have been able to ascertain that Coutinho’s ability was not worth anything close to the amount of money Barcelona spent, leading them [to] cancel the purchase.”

Comparatively, a different set of parameters could have seen Coutinho’s recent move to Aston Villa in an entirely different light. Barcelona was anxious to do away with the $148.5 million Brazilian real (about 27.46 million USD) as his value had tanked given recent form and struggles with consistent playing time. Aston Villa, now led by Coutinho’s former teammate Steven Gerrard, were able to capitalize on that drastic reduction in value and secure a player with an incredibly high ceiling.

With more and more clubs becoming hesitant to open their checkbooks on account of the ongoing pandemic, the injection of machine learning and sports science into the realm of transfer market analytics will have a lasting impact on how the world’s biggest soccer clubs go about bringing in new players.

One is left to wonder: is soccer only the beginning? We often see massive contracts in domestic sports leagues like the NFL, NBA, and MLB. How long until data analytics and machine learning perfect processes previously susceptible to human error?


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