Estimated Recovery
How to use
Clinical guide – Good data input
We understand the importance of injury analysis for clinical teams in sport – particularly gauging themes and spotting trends surrounding the likes of injury recovery across your squad. By fully understanding these, a clinician becomes informed, allowing for greater discussion about what strategies to put in place with the ultimate goal of reducing injuries where possible.
Such insights are upheld by the accuracy and relevance of the data used to produce objective information. This means that it’s imperative to have a high level of quality control over the data included; poor data entered for analysis will only result in poor outputs.
Good data accurately represents the clinical and situational context. The quality of this data is reinforced by a consistent upload schedule which frames the data clearly.
Beginning with diagnosis, utilising the intuitive OSIICS search function will ensure assigning a diagnostic code is as simple as possible. It is these OSIICSn codes that form the basis of the analysis.
Statistical guide – Understanding data output
Glossary
Box plot: A visual representation of the data, utilising a box with different markers which indicate varying key aspects of the dataset.
Mean: The average number within the group of data.
Median: The number that is in the middle of the entire range in a group.
Percentile: Splits date into 100 equal-sized groups allowing comparison to the group.
Injury severity: Time based groupings based the duration of days lost from full training and playing.
Injury Incidence: Summarises injuries based upon exposure to the sport, often described as number of injuries sustained per 1000 exposure hours.
Return to play: Defined as the date of the first session that a player returns to full, unrestricted training with the team.