TY - GEN
T1 - Survival Analysis of Offensive Performance in Football Players from South American Leagues
AU - Bernuy Rey, Juan Diego
AU - Granda Zapata, George Patrick
AU - Dios Luna, Jim Bryan
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/11/22
Y1 - 2025/11/22
N2 - Survival analysis consists of a set of techniques aimed at studying the time until the occurrence of a given event. Its application in the sports field is valuable for identifying patterns in player performance evolution and supporting strategic decision-making related to talent management. In this study, a dataset was constructed containing performance statistics of football players from the first divisions of the Argentine, Brazilian, Chilean, and Peruvian leagues between the 2018 and 2024 seasons. An exploratory data analysis was performed, followed by variable selection using Lasso penalization, and the application of different survival analysis models, including Cox Proportional Hazards, Weibull AFT, Random Survival Forest, and DeepSurv. As a result, 96 key variables were selected for survival prediction, and the DeepSurv model achieved the best performance, reaching a concordance index of 0.91 and the lowest integrated brier score, outperforming both traditional and ensemble models in the evaluated metrics.
AB - Survival analysis consists of a set of techniques aimed at studying the time until the occurrence of a given event. Its application in the sports field is valuable for identifying patterns in player performance evolution and supporting strategic decision-making related to talent management. In this study, a dataset was constructed containing performance statistics of football players from the first divisions of the Argentine, Brazilian, Chilean, and Peruvian leagues between the 2018 and 2024 seasons. An exploratory data analysis was performed, followed by variable selection using Lasso penalization, and the application of different survival analysis models, including Cox Proportional Hazards, Weibull AFT, Random Survival Forest, and DeepSurv. As a result, 96 key variables were selected for survival prediction, and the DeepSurv model achieved the best performance, reaching a concordance index of 0.91 and the lowest integrated brier score, outperforming both traditional and ensemble models in the evaluated metrics.
KW - football
KW - Machine Learning
KW - predictive models
KW - sports performance
KW - statistics
KW - survival analysis
UR - https://www.scopus.com/pages/publications/105025352805
U2 - 10.1145/3771678.3771688
DO - 10.1145/3771678.3771688
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:105025352805
T3 - Proceedings of 8th International Conference on Systems Engineering - Cybersecurity and AI: Building a reliable digital future, CIIS 2025
SP - 72
EP - 79
BT - Proceedings of 8th International Conference on Systems Engineering - Cybersecurity and AI
PB - Association for Computing Machinery, Inc
T2 - 8th International Conference on Systems Engineering, CIIS 2025
Y2 - 1 October 2025 through 3 October 2025
ER -