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Football clubs invest in the implementation of scientific insights that improve the quality of youth academies. In the long run, clubs expect their youth academy investments to result in better trained players. The purpose of this paper is to quantify the impact of the attended youth academies' quality on the future market value of a player.

A dataset containing 94 players trained in 13 different academies has been constructed. The dataset contains characteristics of the players and information on the quality of their attended academies. The impact of the quality of the attended academies on players' future market values was estimated empirically through multiple regression analysis.

The quality of a youth academy has a significant positive impact on a player's market value, which in turn is correlated with higher future wages for players and transfer fees for clubs.

Clubs are advised to pay sufficient attention to investments in their youth academy. This will eventually lead to better trained players and higher revenues. Players in turn should strive to be part of the best academies that provide good training and the opportunity to become a top-earning player. For policymakers, such as football federations, the results imply that stimulating club investments in academies can lead to better national team performances.

The impact of the quality of a youth academy on an individual professional football player's career has never been quantified in the literature before. To this end, a new variable has been constructed using scientific assessments of youth academies.

Ticket sales are an essential source of income for football clubs and federations. Analyzing the determinants of fans' willingness-to-pay for tickets is therefore an important exercise. By knowing the match- and fan-related characteristics that influence how much a fan wants to pay for a ticket, as well as to what extent, football clubs and federations can modify their ticket offering and targeting in order to optimize this revenue stream.

Using a detailed discrete choice experiment, based on McFadden's random utility theory, this paper formulates a Bayesian hierarchical multinomial logit model. Such models are very common in the discrete choice modeling literature. The analysis identifies to what extent match and personal attributes influence fans' willingness-to-pay for games of the Belgian men's and women's football national teams.

The results show that the strength of the opponent, the type of competition, the location of the seats in the stadium, the day and kick-off time of the match and the ticket price exert an influence on the choice of the respondent. Fans are attracted most by competitive games against strong opponents. They prefer to sit along the sideline, and they have clear preferences for specific kick-off days and times. The authors also find substantial variation between socio-demographic groups, defined in terms of factors such as age, gender and family composition.

The authors use the results to estimate the willingness-to-pay for match tickets for different socio-demographic groups. Their findings are useful for football clubs and federations interested in optimizing the prices of their match tickets.

To the best of the authors' knowledge, no stated preference methods, such as discrete choice analysis, have been used to analyze the willingness-to-pay of sports fans. The advantage of discrete choice analysis is that options and variations in tickets that are not yet available in practice can be studied, allowing football organizations to increase revenues from new ticketing instruments.

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