Training-load prescription in team-sport athletes is a balance between performance improvement1,2 and injury-risk reduction.3-6 The manipulation of training intensity, duration, and frequency to induce improvements in athletic performance is a fundamental objective of training-plan prescription.7 To inform this process, mathematical models of the relationship between training loads and performance have been proposed for multiple athletic populations.1,7,8 Banister et al1 modeled the response to a training dose using 2 time-decaying functions representing fitness and fatigue. This allowed performance to be projected at a later time by taking the difference between the modeled fitness and fatigue functions. While the accuracy of these models in predicting performance has been limited,7,9 they provide a generalized basis for training prescription. Studies modeling team-sport performance are fewer, possibly due to difficulties in quantifying individual performance in a team environment and mixed training methods. Training load has been identified as a risk factor for injury in recent reviews, with both absolute and relative loads needing to be considered when assessing injury risk.4,6 The acute:chronic workload ratio quantifies an athlete`s relative amount of short-term (acute) to long-term (chronic) training and is an injury-risk factor in a number of team sports.4-6,10 In addition, there is evidence that cumulative absolute workloads can influence injury risk in Australian football.3 Currently, physical-preparation staff are tasked with balancing the training guidelines associated with injury-risk reduction and performance improvement when prescribing training loads. Mathematical optimization is a method that may help in this process, particularly as more data on training-load monitoring become available.5,6,10-12 Optimization is the task of finding a set of values (decision variables) that maximize an objective function (goal) and satisfy a set of constraints. Optimizing training loads has been explored in studies of tapering8 and to generate training plans for performance improvement.13 No study has explored optimization models that incorporate training guidelines for injury prevention based on cumulative loads and workload ratios in team-sport athletes. This study aimed to determine the extent to which current training-load guidelines (for relative and absolute training loads) can be used to generate optimized preseason training plans in Australian football and to investigate the effects of varying optimization targets and load constraints on the computer-generated plans.
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