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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.

A collaborative design environment is needed for multidisciplinary design optimization (MDO) process, based on all the modules those for different design/analysis disciplines, and a systematic coupling should be made to carry out aerodynamic shape optimization (ASO), which is an important part of MDO.

Computerized environment for aircraft synthesis and integrated optimization methods (CEASIOM)-ASO is developed based on loosely coupling all the existing modules of CEASIOM by MATLAB scripts. The optimization problem is broken down into small sub-problems, which is called “sequential design approach”, allowing the engineer in the loop.

CEASIOM-ASO shows excellent design abilities on the test case of designing a blended wing body flying in transonic speed, with around 45 per cent drag reduction and all the constraints fulfilled.

Authors built a complete and systematic technique for aerodynamic wing shape optimization based on the existing computational design framework CEASIOM, from geometry parametrization, meshing to optimization.

CEASIOM-ASO provides an optimization technique with loosely coupled modules in CEASIOM design framework, allowing engineer in the loop to follow the “sequential approach” of the design, which is less “myopic” than sticking to gradient-based optimization for the whole process. Meanwhile, it is easily to be parallelized.