2025-05-142025-05-142025-03-10Cruz Torres, B.d.l., Navarro Castro, M. y Ruiz de Alarcón Quintero, A. (2025). An expected goals on target (xGOT) model: accounting for goalkeeper performance in football. Big Data and Cognitive Computing, 9 (3), 64. https://doi.org/10.3390/bdcc9030064.2504-2289https://hdl.handle.net/11441/172718A key challenge in utilizing the expected goals on target (xGOT) metric is the limited public access to detailed football event and positional data, alongside other advanced metrics. This study aims to develop an xGOT model to evaluate goalkeeper (GK) performance based on the probability of successful actions, considering not only the outcomes (saves or goals conceded) but also the difficulty of each shot faced. Formal definitions were established for the following: (i) the initial distance between the ball and the GK at the moment of the shot, (ii) the distance between the ball and the GK over time post-shot, and (iii) the distance between the GK’s initial position and the goal, with respect to the y-coordinate. An xGOT model incorporating geometric parameters was designed to optimize performance based on the ball position, trajectory, and GK positioning. The model was tested using shots on target from the 2022 FIFA World Cup. Statistical evaluation using k-fold cross-validation yielded an AUC-ROC score of 0.67 and an 85% accuracy, confirming the model’s ability to differentiate successful GK performances. This approach enables a more precise evaluation of GK decision-making by analyzing a representative dataset of shots to estimate the probability of success.application/pdf13 p.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Generative modelShot on target trajectoryGoalkeeper evaluationBall positionData analysisinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.3390/bdcc9030064