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Exploring Stochastic Efficiency Analysis for Expected Goals in Football: Assessing Offensive Efficiency Across Europes’s Major Football Leagues
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Orientador(es)
Resumo(s)
This thesis explores how football analytics have changed with the use of advanced
metrics, especially focusing on expected goals (xG) to measure player and team
performance. The goal is to understand how xG relates to some of the football statistics and
metrics that are used a lot, like number of shots, age, and market value of the players in the
top five European football leagues from the season 2014-2015 to the season 2022-2023 .
Using data from reliable football analytics websites, this study uses Stochastic Frontier
Analysis (SFA) to evaluate offensive inefficiency, using the xG metric as the output of the
equation and market value, age, shots, and player position were the chosen inputs. The
results demonstrate that defenders and goalkeepers showed that they have less impact on
offensive efficiency than the forwards and the midfielders, who showed that they have a
significant impact on the game. Even with certain data limitations, the conclusions offer
football teams useful information to improve player efficiency and guide strategic
investment choices.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Expected Goals (xG) Stochastic Frontier Analysis (SFA) Offensive Efficiency Performance Metrics Football Analytics SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure