Assessing team performance in association football (commonly known as football or soccer) is challenging due to the sport’s low-scoring nature and inherent unpredictability. While evaluating strategies based on space control and the creation of open spaces has been explored in the literature, the temporal aspect of space availability for the ball carrier remains under-explored. This work introduces a novel time-series performance evaluation metric, Ball Carrier Open Space (BCOS), which focuses on the temporal dynamics of space available to the ball carrier to assess team performance. Additionally, it presents a novel approach to quantify open space for the ball carrier using player data extracted from television footage. This work discuss on BCOS in defensive third, central third and attacking third and a machine learning model is developed to evaluate their significance and temporal patterns. Trained model achieved 80.7% accuracy in classifying match-winning performances, underscoring the significance of BCOS. Correlation analysis between temporal features and match outcomes further reveals that BCOS in central third and attacking third are more important for match winning outcomes, while first-half performance plays a more critical role in determining match results than second-half performance. Based on the results of the correlation analysis, this study proposes a weighted ball carrier open space (wBCOS) metric to assess team performance, assigning weights to BCOS in attacking third, central third and defensive third based on their contributions to positive match outcomes. A machine learning model trained using wBCOS achieved an 82.5% accuracy in classifying match-winning performances, surpassing the performance of any previously published match-winner classification model.
