In team-sport, physical and skilled output is often described via aggregate parameters including total distance and number of skilled involvements. However, the degree to which these output change throughout a team-sport match, as a function of time, is relatively unknown. This study aimed to identify and describe segments of physical and skilled output in team-sport matches with an example in Australian Football. The relationship between the number of change points and level of similarity was also quantified. A binary segmentation algorithm was applied to the velocity time series, collected via wearable sensors, of 37 Australian football players. A change point quotient of between 1 and 15 was used. For these quotients, descriptive statistics, spectral features and a sum of skilled involvements were extracted. Segment similarity for each quotient was evaluated using a random forest model. The strongest classification features in the model were spectral entropy and skewness. Offensive and defensive involvements were the weakest features for classification, suggesting skilled output is dependent on match circumstances. The methodology presented may have application in comparing the specificity of training to matches and designing match rotation strategies.