Accuracy of model for predicting non-contact, soft-tissue injuries
| Actual status | ||||
| Injured | Not injured | |||
| Predicted status | Predicted injury | True positive | False positive | Positive predictive value |
| N=121 | N=20 | 85.8% | ||
| Predicted no injury | False negative | True negative | Negative predictive value | |
| N=18 | N=1589 | 98.9% | ||
| Sensitivity | Specificity | |||
| 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 Positive’—predicted injury but player did not sustain injury; ‘False Negative’—no injury predicted but player sustained injury; ‘True Negative’—no injury predicted and player did not sustain injury. ‘Sensitivity’—proportion of injured players who were predicted to be injured; Specificity—proportion of uninjured players who were predicted to remain injury-free. ‘Likelihood ratio positive’—sensitivity/(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