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Int J Sports Physiol Perform. 2017 Jul;12(6):819-824. doi: 10.1123/ijspp.2016-0326. Epub 2016 Dec 5.

Importance of Various Training-Load Measures in Injury Incidence of Professional Rugby League Athletes.

Abstract

PURPOSE:

To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes.

METHODS:

TL and injury data were collected across 3 seasons (2013-2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes' corresponding injury status was marked as "available" or "unavailable." Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted.

RESULTS:

Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes.

CONCLUSIONS:

Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.

KEYWORDS:

GPS; injuries; machine learning; monitoring; random forests; team sport

PMID:
27918659
DOI:
10.1123/ijspp.2016-0326
[Indexed for MEDLINE]

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