Relationships Between Model-Predicted and Actual Match-Play Exercise-Intensity Performance in Professional Australian Footballers During a Preseason Training Macrocycle

Graham, S.R., Cormack, S., Parfitt, G., Eston, R.

Purpose: To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting preseason variation of match-play exercise intensity (MEI sim/min) using a variable dose-response model.

Methods: Twenty-one professional male AF players completed an 18-week preseason macrocycle. Preseason internal training load was quantified using session RPE (sRPE), and external load from satellite and accelerometer data. Using a training-impulse (TRIMPs) calculation, external load expressed in arbitrary units (a.u.) was represented as TRIMPsDist, TRIMPsHSDist, and TRIMPsPL. Preseason training load and MEI sim/min data were applied to a variable dose-response model, which provided estimates of MEI sim/min. Model estimates of MEI sim/min were correlated with actual measures from each match-play drill performed during the preseason macrocycle. Magnitude-based inferences (effect size ± 90% confidence interval [CI]) were calculated to determine practical differences in the precision of MEI sim/min estimates using each of the internal- and external-load inputs.

Results: Estimates of MEI sim/min demonstrated very large and large associations with actual MEI sim/min with models constructed from external and internal training inputs (r ± 90% CI; TRIMPsDist .73 ± .72–.74, TRIMPsPL .72 ± .71–.73, and sRPESkills .67 ± .56–.78). There were trivial differences in the precision of MEI sim/min estimates between models constructed from TRIMPsDist and TRIMPsPL and between internal input methods.

Conclusions: Variable dose-response models from multiple training-load inputs can predict within-individual variation of MEI sim/min across an entire preseason macrocycle. Models informed by external training inputs (TRIMPsDist and TRIMPsPL) exhibited predictive power comparable to those of sRPESkills models.

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