Table 2

Accuracy of model for predicting non-contact, soft-tissue injuries

Actual status
InjuredNot injured
Predicted statusPredicted injuryTrue positiveFalse positivePositive predictive value
Predicted no injuryFalse negativeTrue negativeNegative predictive value
87.1 (80.5 to 91.7)%98.8 (98.1 to 99.2)%
Likelihood ratio positive
70.0 (45.1 to 108.8)
Likelihood ratio negative
0.1 (0.1 to 0.2)
  • True Positive’—predicted injury and player sustained injury; ‘False Positivepredicted injury but player did not sustain injury; ‘False Negative’—no injury predicted but player sustained injury; ‘True Negativeno injury predicted and player did not sustain injury. ‘Sensitivityproportion of injured players who were predicted to be injured; Specificity—proportion of uninjured players who were predicted to remain injury-free. ‘Likelihood ratio positivesensitivity/(1−specificity); ‘Likelihood ratio negative(1−sensitivity)/specificity.

  • While there were 91 players in the sample, injury predictions based on the training loads performed by individual players were made on a weekly basis, so that within the total cohort, there was a total number of true positive and negative predictions, and a total number of false positive and negative predictions. Sensitivity and specificity data, and positive and negative likelihood ratios are expressed as rates (and 95% CIs).

  • Reproduced from Gabbett.42