Estimated Recovery Overview

Our new feature, Estimated Recovery, intelligently collates and intuitively presents injury-specific recovery timelines. It enables quick access to objective data that would otherwise require extensive, time-consuming searching.

This guide provides the methodology behind the numbers and allows scientific transparency for what is presented to you within the ScribePro Team system

Overview

Click the links to jump to any section below:

Understanding the Output – Default views and visual analysis
Data and Trust – The data behind the tool
Clinical Application – How to use clinically
Glossary – Key terms and references

Quick Start guide:

Understanding the Output

Default view vs Visual analysis

“Default View” refers to the default format we use to surface the Estimated Recovery data. It’s available on both desktop and mobile whereas “Visualise Analysis” is only available on desktop. “Visual Analysis” refers to a visualised format used to display the Estimated Recovery data.

🖥️ You can toggle between the Default View and Visual Analysis on desktop.

Default view

The default view shows simple estimations for each data source available, the images below show how this information is displayed. To learn more about which sources are included, see the Data Sources section below For each data source the mean, 10th and 90th percentiles are shown as well as how severe the estimation is. The number of injuries that each estimation is based on is also shown. They’re structured as follows

This section also includes a tracker showing which day of injury it is, providing extra context in relation to the estimated recovery.

Visual analysis

The Visualised Analysis uses box plot and whisker graphs to display the Estimated Recovery data. Here’s a quick guide of what point shows.

10th Percentile – Only 10% of the data is lower than this value.
25th Percentile – 25% of the data is lower than this value.
Median – The middle value when all data points are ordered.
Mean – The average value
75th Percentile – 75% of the data is lower than this value.
90th – Only 10% of the data is higher than this value.

Data and Trust

Data sources: when they appear and when they won’t

There are 3 possible estimations that are surfaced within an injury. These are:

Your own squad: This includes any closed injuries within your squad that have an OSIICS code and fall into the same OSIICS group as your current injury. Where there is no OSIICS group the comparison will be done to injuries with the exact same code only.

UEFA’s elite club injury study: This surfaces data from paper published by UEFA on injuries in men’s football (Ekstrand et al., 2019) where your current injury has an OSIICS code that falls into the same group as an injury type from the paper. For mapping see here [source]

ScribePro: This surfaces anonymised aggregate data from across ScribePro which has been analysed by our in house clinically lead data analysis team. The OSIICS code grouping for this follow the grouping in the other data sources.

You may not see an estimated recovery source if:

You have not added an OSIICS code
There is not enough data related to this OSIICS code within the data set.
The injury was closed without an OSIICS code or before the release of this feature.

Data Overview

Your Data

Good data practices improve the quality of the insights you see. To get the most out of your data, clinician’s should be encouraged to ensure that playing and training statuses are recorded correctly. Additionally, ‘signing off’ injuries once fully returned to play will ensure recovery estimations remain accurate to the time of injury and do not change with additional injuries added to your squad.

ScribePro Data

Data included in the ‘ScribePro Team’ data set is collated based solely on the OSIICS codes and the subsequent group that the code corresponds with. Outliers in the data sets are assessed internally to validate whether they are accurate and reliable to include within the analysis. As part of our data transparency process, the feature clearly indicates the number of injuries used in the analysis. This allows quick assessment for strength of reliability of the surfaced data.

The ScribePro Team data includes fully anonymised injuries from active users across all sporting contexts. This means the groups will include injuries from across different sports. The result is greater reliability in the data displayed as the emphasis is on the recovery of the injury, rather than the sporting context. Future iterations will expand on the current data set to include a wider range of injuries including upper limb specific injury groupings.

UEFA Elite Club Injury Study Data
UEFA have conducted the Elite Club Injury Study (ECIS) since 2001 and continues to do so with those competing at the highest level of club competition. Injury epidemiology is recorded and published by UEFA to the sports medicine community regularly, with specific insights published that look at subsets of the injury database.

One of these studies was the return to play time for the 30 most common injuries recorded over the then 16-year data collection period (Ekstrand et al., 2019) which has been used for this feature. The authors listed the most frequent injury types across 16 consecutive seasons, with a dataset of 22942 injuries. The injury recording processes are in accordance with the consensus statement on injury definitions (Fuller et al., 2006). Results are presented detailing frequency, mean, median, percentile ranges and re-injury rates per injury outlined. The clarity of this presentation allows for direct comparison metrics for clinicians with their own squad data.

Data presented in the research article are easily accessible and worth examining in closer detail. As a summary, the most common injuries do significantly group in the lower limb with concussions and low back pain the only exceptions in index injuries suffered.

Clinical Application

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.

OSIICS Codes

OSIICS (v15) (Orchard, 2024) Groupings were created for each of the top 30 injuries in the referenced UEFA research paper. Further groups were created to increase the scope of the feature and allow for maximum recovery estimated to be calculated. The grouping process tries where possible to account for injury severity, so to differentiate ruptures from standard sprains or strains. Mapping can be downloaded here

Estimated Recovery can remain flexible over time, meaning groups can be adapted and created in response to new research and clinical feedback. Therefore, the feature will be able to be adapted over time, allowing greater levels of injury context and detail to be included in future iterations.

Context note – best practices

Once an injury is signed off, you will be able to add a note to provide extra context to the injury recovery.. This could be helpful for colleagues or for when reviewing injuries at a later date, for example to explain why recovery took longer or shorter than expected. See image for example (could include an image of the one we used with the hamstring early on).

Severity

The Severity Scale offers a simple and fast way to understand how serious an injury is and can be used as a differentiator. . For example, two lateral ankle sprains can be quickly assessed with an understanding of days lost; one totalling only 5 days lost would obviously be much less severe than one taking 8 weeks to recover.

The categorisation of Severity has been updated by the International Olympic Committee (Walden et al., 2023) as of 2023 to to capture more granular details in cases of severe injury. Previously containing a scale of four ranges, it now contains six.

Very minor 1-3 days lost
Minor 4-7 days lost
Mild 8-28 days lost
Moderate 29-90 days lost
Significant 91-180 days lost
Severe >180 days lost

When an injury’s signed off, severity will merge with the ‘day of injury’ counter, displaying a final total days of injury and the correlating severity – in the example above, 32 days were lost due to this injury, resulting with a “moderate” severity measure.
Signing off an injury will also capture a snapshot of estimated recovery as it is at the time of sign off so that even if estimations change over time you can still see the data that was available to clinicians while the injury was ongoing.

Glossary and Definitions

Box plot: A visual representation of the distribution of data using 5 key markers: The lower (Q1) and upper (Q3) quartiles indicated by the box with the median (Q2) indicated with the line in the box and the 10th and 90th percentiles shown with the whiskers that extend from the box.

Q1: the 25th percentile indicating the data value below which 25% of the observations fall when the data is ordered from smallest to largest.
Q2: also known as the median, which is the value in the middle of the grouped data range.
Q3: the 75th percentile indicating the data value below which 75% of the observations fall when the data is ordered from smallest to largest.
10th Percentile: the data value below which 10% of the observations fall when the data is ordered from smallest to largest.
90th Percentile: the data value below which 90% of the observations fall when the data is ordered from smallest to largest.

Mean: The average number within the group of data, calculated by summing all the data points and dividing it by the total number of points.

Injury severity: Time based groupings based the duration of days lost from full training and playing, defined by the football-specific extension of the IOC consensus statement as 0 days, 1-3 days, 4-7 days, 8-28 days, 29-90 days, 91-190 days and >180 days.

Injury Incidence: The number of injuries that occur within a specific group (eg. athletes) over a defined period of time (eg. per 1000 hours of playing and training)

Return to play: Defined as the date of the first session that a player returns to full, unrestricted training with the team.

Injury: An injury begins once an athlete is unable to complete unrestricted training or compete in matchplay. The day the injury occurs is classed as ‘day 0’ and the following day will be ‘day 1’ of the injury.

Closed Injury: An injury is counted as closed when training is set to fully or it has been signed off

References:

Ekstrand, J., Krutsch, W., Spreco, A. et al. Time before return to play for the most common injuries in professional football: a 16-year follow up of the UEFA Elite Club Injury Study. Br J Sports Med. 2019;54: 421-426.

Fuller, C.W., Ekstrand, J., Junge, A. et al. Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Br J Sports Med. 2006;40(3): 193-201.

Hallen, A., Tomas, R., Ekstrand, J. et al. UEFA Women’s Elite Club Injury Study: a prospective study on 1527 injuries over four consecutive seasons 2018/2019 to 2021/2022 reveals thigh muscle injuries to be most common and ACL injuries most burdensome. Br J Sports Med. 2024;58(3):128-135.

Orchard JW. Orchard Sports Injury and Illness Classification System (OSIICS) Version 15. La Trobe University. 2024.

Walden, M., Mountjoy, M., McCall, A. et al. Football-specific extension of the IOC consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020. Br J Sports Med. 2023;57:1341-1350.