Introduction: Representative learning design is a key feature of the theory of ecological dynamics, conceptualising how task constraints can be manipulated in training designs to help athletes selfregulate during their interactions with information-rich performance environments. Implementation of analytical methodologies can support representative designs of practice environments by practitioners recording how interacting constraints influence events, that emerge under performance conditions. To determine key task constraints on kicking skill performance, the extent to which interactions of constraints differ in prevalence and influence on kicking skills was investigated across competition tiers in Australian Football (AF).Method: A data sample of kicks (n = 29,153) was collected during junior, state-level and national league matches. Key task constraints were recorded for each kick, with performance outcome recorded as effective or ineffective. Rules were based on frequency and strength of associations between constraints and kick outcomes, generated using the Apriori algorithm.Results: Univariate analysis revealed that low kicking effectiveness was associated with physical pressure (37%), whereas high efficiency emerged when kicking to an open target (70%). Betweencompetition comparisons showed differences in constraint interactions through seven unique rules and differences in confidence levels in shared rules.Discussion: Results showed how understanding of key constraints interactions, and prevalence during competitive performance, can be used to inform representative learning designs in athlete training programmes. Findings can be used to specify how the competitive performance environment differs between competition tiers, supporting the specification of information in training designs, representative of different performance levels.

Commonly classified as individual, task or environmental, constraints are boundaries which shape the emergence of functional movement solutions. In applied sport, an ongoing challenge is to improve the measurement, analysis and understanding of constraints to key stakeholders. Methodological considerations for furthering these pursuits should be centred around an interdisciplinary approach. This integration of methodology and knowledge from different disciplines also encourages the sharing of encompassing principles, concepts, methods and data to generate new solutions to existing problems. This narrative review discusses how a number of rapidly developing fields are positioned to help guide, support and progress an understanding of sport through constraints. It specifically focuses on examples from the fields of technology, analytics and perceptual science. It discusses how technology is generating large quantities of data which can improve our understanding of how constraints shape the movement solutions of performers in training and competition environments. Analytics can facilitate new insights from numerous and complex data through enhanced non-linear and multivariate analysis techniques. The role of the perceptual sciences is discussed with respect to generating outputs from analytics that are more interpretable for the end-user. Together, these three fields of technology, analytics and perceptual science may enable a more comprehensive understanding of constraints in sports performance.

Representative learning design proposes that a training task should represent informational constraints present within a competitive environment. To assess the level of representativeness of a training task, the frequency and interaction of constraints should be measured. This study compared constraint interactions and their frequencies in training (match simulations and small sided games) with competition environments in elite Australian football. The extent to which constraints influenced kick and handball effectiveness between competition matches, match simulations and small sided games was determined. The constraints of pressure and time in possession were assessed, alongside disposal effectiveness, through an association rule algorithm. These rules were then expanded to determine whether a disposal was influenced by the preceding disposal. Disposal type differed between training and competition environments, with match simulations yielding greater representativeness compared to small sided games. The subsequent disposal was generally more effective in small sided games compared to the match simulations and competition matches. These findings offer insight into the measurement of representative learning designs through the non-linear modelling of constraint interactions. The analytical techniques utilised may assist other practitioners with the design and monitoring of training tasks intended to facilitate skill transfer from preparation to competition.