Performance Factors of Foreign Players Contributing to Team Success in Korean Women’s Volleyball

Research on predicting detailed offensive performance indicators in volleyball remains limited. This study empirically analyzed the impact of foreign female volleyball players on team offensive performance and explored the potential for developing data-driven volleyball strategies.

Using data from the 2015–2016 to 2024–2025 seasons, the NeuralProphet time-series model was employed to predict offensive performance indicators at both the team and foreign player levels.

Top-ranking teams achieved an attack efficiency ranging from 27.084% to 32.959% and averaged 2.413 to 3.081 blocks per set. In contrast, bottom-ranking teams recorded an attack efficiency of 22.357% to 26.025% and averaged 1.705 to 2.642 blocks per set, all of which were lower overall than those of the top-ranking teams. Foreign players in top-ranking teams had predicted values of 26.181% to 38.163% for attack efficiency, 20.396% to 28.762% for attack share, and 0.279 to 0.789 average blocks per set. Foreign players in bottom-ranking teams showed predicted values of 22.281% to 28.699% for attack efficiency, 23.216% to 33.694% for attack share, and 0.148 to 0.438 average blocks per set.

Except for the attack share rate, these values were generally higher in top-ranking teams than in bottom-ranking teams. This pattern suggests that offensive efficiency and blocking performance, rather than the proportion of attack share alone, are more strongly associated with higher team rankings.

Professional volleyball clubs can utilize time-series-based predictive information to develop data-driven tactical designs and training feedback systems.