Simplifying the complexity of assessing physical performance in professional Australian football

Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach

Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring. The aim of this study was to reduce the dimensionality of the internal and external load measures, by a principal component approach, to describe and explain the resultant equations for TL monitoring during a standard in-season microcycle in sub-elite youth football. Additionally, it is intended to identify the most representative measure for each principal component. A principal component analysis (PCA) was conducted with a Monte Carlo parallel analysis and VariMax rotation to extract baseline characteristics, external TL, heart rate (HR)-based measures and perceived exertion. Training data were collected from sixty sub-elite young football players during a 6-week training period using 18 Hz global positioning system (GPS) with inertial sensors, 1 Hz short-range telemetry system, total quality recovery (TQR) and rating of perceived exertion (RPE). Five principal components accounted for 68.7% of the total variance explained in the training data. Resultant equations from PCA was subdivided into: (1) explosiveness, accelerations and impacts (27.4%); (2) high-speed running (16.2%); (3) HR-based measures (10.0%); (4) baseline characteristics (8.3%); and (5) average running velocity (6.7%). Considering the highest factor in each principal component, decelerations (PCA 1), sprint distance (PCA 2), average HR (PCA 3), chronological age (PCA 4) and maximal speed (PCA 5) are the conditional dimension to be considered in TL monitoring during a standard microcycle in sub-elite youth football players. Current research provides the first composite equations to extract the most representative components during a standard in-season microcycle in sub-elite youth football players. Futures research should expand the resultant equations within training days, by considering other well-being measures, technical-tactical skills and match-related contextual factors.

An assessment of physical and spatiotemporal behaviour during different phases of match play in professional Australian football

Despite advancements in the scale of data available for quantifying the physical and spatiotemporal characteristics of match play, there is an absence of research combining these aspects in professional sport. This study sought to differentiate between phases of play in professional Australian football using novel physical and spatiotemporal metrics. Data was obtained from Australian Football League games to provide new insight into the specific characteristics of each phase of play. A retrospective cross-sectional design was utilised with team's physical and spatiotemporal variables, measured via global navigation satellite system devices. A multinomial logistic regression was conducted to determine which physical and spatiotemporal measures were associated with each phase of play (contested play, defence, offence, set shot, goal reset, umpire stoppage). The addition of the predictors to a model that contained only the intercept significantly improved the fit between the model and data, with the logistic model correctly predicting the phase of play for 63.7% of the cases. This was the first study to concurrently examine differences in physical and spatiotemporal characteristics with respect to phase of play in an Australian football context. Differences in duration, physical and spatiotemporal properties were observed, providing new insight for coaches and subsequently providing direction for conditioning and practice design.