<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The utilization of metrics such as expected goals (xG) has the potential to provide teams with a competitive edge. By incorporating xG into their analysis and decision-making processes, teams can gain valuable insights. This study proposes a new approach to football xG modeling using Kos Angle which represents the shooting angle, from which we substract the angles occupied by players inside the shot angle. The objective of this study is to evaluate the impact of the Kos Angle feature on the performance of football xG models. After developing the mathematical formula of the Kos Angle, we selected additional features and built different xG models. Subsequently, the impact of the Kos Angle feature on the models’ performances was evaluated, revealing an increase in Recall and Precision and a decrease in Brier score and RMSE. We also found that the Kos Angle accounted for a significant portion of the models’ predictive power. By providing a more realistic representation of shot situations, the addition of the Kos Angle feature allows the improvement of xG models performances, which can give a more valuable insights to football professionals who rely on xG metrics and their variations. </p>
</abstract>

The utilization of metrics such as expected goals (xG) has the potential to provide teams with a competitive edge. By incorporating xG into their analysis and decision-making processes, teams can gain valuable insights. This study proposes a new approach to football xG modeling using Kos Angle which represents the shooting angle, from which we substract the angles occupied by players inside the shot angle. The objective of this study is to evaluate the impact of the Kos Angle feature on the performance of football xG models. After developing the mathematical formula of the Kos Angle, we selected additional features and built different xG models. Subsequently, the impact of the Kos Angle feature on the models’ performances was evaluated, revealing an increase in Recall and Precision and a decrease in Brier score and RMSE. We also found that the Kos Angle accounted for a significant portion of the models’ predictive power. By providing a more realistic representation of shot situations, the addition of the Kos Angle feature allows the improvement of xG models performances, which can give a more valuable insights to football professionals who rely on xG metrics and their variations.

The Kos Angle, an optimizing parameter for football expected goals (xG) models

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"The Kos Angle, an optimizing parameter for football expected goals (xG) models"}

The utilization of metrics such as expected goals (xG) has the potential to provide teams with a competitive edge. By incorporating xG into their analysis and...

The Kos Angle, an optimizing parameter for football expected goals (xG) models

Publié en ligne: 13 sept. 2023

Pages: 49 - 61

DOI: https://doi.org/10.2478/ijcss-2023-0010

Mots clés
FOOTBALL, EXPECTED GOALS, MACHINE LEARNING, FEATURE IMPORTANCE, CLOSED ANGLES

© 2023 Hassani Karim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The Kos Angle, an optimizing parameter for football expected goals (xG) models

Publié en ligne: 13 sept. 2023

Pages: 49 - 61

DOI: https://doi.org/10.2478/ijcss-2023-0010

Mots clésFOOTBALL, EXPECTED GOALS, MACHINE LEARNING, FEATURE IMPORTANCE, CLOSED ANGLES

© 2023 Hassani Karim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
FOOTBALL, EXPECTED GOALS, MACHINE LEARNING, FEATURE IMPORTANCE, CLOSED ANGLES