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<p>Football is among the most widely followed sports globally, with professional clubs evolving into complex business entities, and within this context, match ticket pricing strategies play a crucial role in revenue optimization. This paper aims to develop and validate a data-driven framework for optimizing ticket pricing in professional football using ML techniques and advanced optimization models. The proposed framework consists of three phases: first, a survey incorporating choice-based conjoint analysis is administered to fans to assess their preferences regarding key match-related attributes. The resulting insights enable the construction of a tailored variable for predictive modeling. Second, Machine Learning models, trained on historical sales and match data, are employed to predict the likelihood of a specific stadium seat being sold at a given price. Finally, an optimization algorithm determines an optimal pricing strategy that maximizes revenue while preserving accessibility for fans. The framework was empirically validated in collaboration with a top-tier Italian football club during the 2023–24 season and the implementation of the proposed pricing strategy resulted in a substantial increase in ticket sales (+14%) and ticketing revenues (+39%) compared to the previous season, under equivalent external conditions. These findings demonstrate the potential of data-driven pricing strategies in enhancing revenue management in professional sports and provide valuable insights for both academics and industry practitioners.</p>

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Data-driven ticket pricing in football: leveraging customer perceived value for revenue optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Rosa Arboretti,
  • Nicoló Biasetton,
  • Riccardo Ceccato,
  • Alberto Molena,
  • Luigi Salmaso

摘要

Football is among the most widely followed sports globally, with professional clubs evolving into complex business entities, and within this context, match ticket pricing strategies play a crucial role in revenue optimization. This paper aims to develop and validate a data-driven framework for optimizing ticket pricing in professional football using ML techniques and advanced optimization models. The proposed framework consists of three phases: first, a survey incorporating choice-based conjoint analysis is administered to fans to assess their preferences regarding key match-related attributes. The resulting insights enable the construction of a tailored variable for predictive modeling. Second, Machine Learning models, trained on historical sales and match data, are employed to predict the likelihood of a specific stadium seat being sold at a given price. Finally, an optimization algorithm determines an optimal pricing strategy that maximizes revenue while preserving accessibility for fans. The framework was empirically validated in collaboration with a top-tier Italian football club during the 2023–24 season and the implementation of the proposed pricing strategy resulted in a substantial increase in ticket sales (+14%) and ticketing revenues (+39%) compared to the previous season, under equivalent external conditions. These findings demonstrate the potential of data-driven pricing strategies in enhancing revenue management in professional sports and provide valuable insights for both academics and industry practitioners.