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
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.
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You can toggle between the Default View and Visualised Analysis on desktop.
Default view
Within the default view simple estimations are shown for various data sources (see the data sources section to understand which sources are surfaced when). They are structured as shown in the images 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
Mobile
Desktop
You can see the severity scale in full by clicking “See Severity Scale”.
Also within this section is a tracker that tells you 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 included.
//need pic of visualised analysis here
- 10th Percentile – This value is higher than only 10% of the dataset or range
- 25th Percentile – This value is higher than only 25% of the dataset or range
- Median – The number that is in the middle of the entire range in an ordered group.
- Mean – The average number within the group of data
- 75th Percentile – This value is higher than 75% of the dataset or range
- 90th – Only 10% of values are higher than this point
Statistical guide – Understanding data output
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 mens elite club injury study: This surfaces data from paper published by UEFA (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.
Circumstances where you will not see estimated recovery injury data:
- You have not added an OSIICS code
- There is not enough data related to this OSIICS code to surface a useful estimation.
- There is not an injury group that matches this OSIICS code.
- The injury was closed without an OSIICS code or before the release of this feature.
Data Overview
Your Data
Good data practices: Accuracy in playing and training statuses will mean greater confidence in the data you are shown. Additionally, ‘signing off’ injuries once fully returned to play will also ensure accuracy and should be encouraged by all clinicians.
ScribePro Data
Data included in the ‘ScribePro Squad’ are 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 represented. This allows quick assessment for strength of reliability of the surfaced data allowing you to understand the information in more detail as it is presented.
The ‘ScribePro Team Database’ includes fully anonymised injury data from active users across all sporting contexts. This means injuries in the groups will feature the same injuries 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 aim to include upper limb specific injury groupings.
UEFA Elite Club Injury Study Data UEFA have conducted the Elite Club Injury Study (ECIS) from inception in 2001 and continues to do so with those competing at the highest level of club competition. The collection of the same injury detail from equivalent women’s clubs began in 2018 and continues to be recorded (Hallen et al., 2024).
Injury epidemiology is recorded and published to the sports medicine community regularly, with specific insights published that look at subsets of the injury database. One of which 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). The authors listed the most frequent injury types across 16 consecutive seasons, with a dataset of 22942 injuries. The injury recording processes are consistent with the larger study publications and are in accordance with the consensus statement on injury definitions (Fuller et al., 2006). 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. 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 working with alternative injury datasets.
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.
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 as of 2023, previously containing a scale of 4 ranges, now containing 6 ranges.
- Minor 1-3 days lost
- Mild 4-7 days lost
- Moderate 8-28 days lost
- Severe >28 days lost
The International Olympic Committee updated Severity(Walden et al., 2023) to capture more granular details in cases of severe injury.
- 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
Severity has more recently been combined with injury incidence to create an Injury Burden; a metric that can provide more insight into how an injury can impact on a squad.
Desktop
Mobile
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.
Glossary
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.
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.
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.