Objectives: To evaluate the relationships between the athlete distribution of team performance indicators and quarter outcome in elite women’s Australian Rules football matches.D esign: Retrospective longitudinal cohort analysis.Methods :Thirteen performance indicators were obtained from 56 matches across the 2017 and 2018 Australian Football League Women’s (AFLW) seasons. Absolute and relative values of 13 performance indicators were obtained for each athlete, in each quarter of all matches. Eleven features were further extracted for each performance indicator, resulting in a total of 169 features. Generalised estimating equations (GEE) and regression decision trees were run across the different feature sets and dependent variables, resulting in 22 separate models.Results: The GEE algorithm produced slightly lower mean absolute errors across all dependent variables and feature sets comparative to the regression decision tree models. Quarter outcome was more accurately explained when considered as total points scored comparative to quarter score margin. Team differential and the 75th percentile of individual athlete Inside 50s were the strongest features included in the models.Conclusions: Modelling performance statistics by quarter outcomes provides specific practical information for in-game tactics and coaching in relation to athlete performances each quarter. Within the current elite women’s Australian Rules football competition, key high performing individual athletes’ skilled performances within matches contribute more to success rather than a collective team effort.