An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in Football
A 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 successf...
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MDPI AG
2025-03-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/3/64 |
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| author | Blanca De-la-Cruz-Torres Miguel Navarro-Castro Anselmo Ruiz-de-Alarcón-Quintero |
| author_facet | Blanca De-la-Cruz-Torres Miguel Navarro-Castro Anselmo Ruiz-de-Alarcón-Quintero |
| author_sort | Blanca De-la-Cruz-Torres |
| collection | DOAJ |
| description | A 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. |
| format | Article |
| id | doaj-art-ee5614f2d15440179bb091cfe1fac764 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-ee5614f2d15440179bb091cfe1fac7642025-08-20T03:43:10ZengMDPI AGBig Data and Cognitive Computing2504-22892025-03-01936410.3390/bdcc9030064An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in FootballBlanca De-la-Cruz-Torres0Miguel Navarro-Castro1Anselmo Ruiz-de-Alarcón-Quintero2Department of Physiotherapy, University of Seville, c/Avicena s/n, 41009 Seville, SpainDepartment of Applied Mathematics I, Higher Technical School of Architecture, University of Seville, Avd. Reina Mercedes s/n, 41012 Seville, SpainFootball and Handball Academy, Street nº 12B, Office 6, 41960 Seville, SpainA 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.https://www.mdpi.com/2504-2289/9/3/64generative modelshot on target trajectorygoalkeeper evaluationball positiondata analysis |
| spellingShingle | Blanca De-la-Cruz-Torres Miguel Navarro-Castro Anselmo Ruiz-de-Alarcón-Quintero An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in Football Big Data and Cognitive Computing generative model shot on target trajectory goalkeeper evaluation ball position data analysis |
| title | An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in Football |
| title_full | An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in Football |
| title_fullStr | An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in Football |
| title_full_unstemmed | An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in Football |
| title_short | An Expected Goals On Target (xGOT) Model: Accounting for Goalkeeper Performance in Football |
| title_sort | expected goals on target xgot model accounting for goalkeeper performance in football |
| topic | generative model shot on target trajectory goalkeeper evaluation ball position data analysis |
| url | https://www.mdpi.com/2504-2289/9/3/64 |
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