An understanding of the effect contextual data may have on key match-play technical performance indicators in the Australian Football League Women’s (AFLW) competition is warranted due to its rapid evolution. To address this, predictive models were fit to determine which technical match-play data, including new contextual information, more accurately predict AFLW match outcomes (win/loss, margin), and what are the most important contexts and technical predictors of team performance? Thirteen random forest models were fit, each with greater data contextual interaction including relative to opposition and harder-to-attain match-play variables, field location, and individual player contributions. Models were assessed by prediction performance on match outcome in a holdout sample and variable importance through Mean Decrease in Gini Index. Effective kicks and entries into attacking locations were important in models. Territory gained, contexts of relative performance to the opposition, and locational information around actions improved prediction. This methodology represents the most in-depth analysis of women’s Australian football technical match-play performance to date. Commentary presented surrounded issues of using aggregated datasets, prediction with match-play success as a dependent variable, and that detailed, process-oriented approaches are needed in future to avoid large assumptions.