A few months ago while we were installing Benchmark54 at an English Premier League Club one the medical staff questioned the usefulness of injury audit. He told me that ‘no two ankle injuries are the same’ and that trying to audit injury data was, in his opinion, of limited use, time consuming and unnecessary.
It reminded me to the passage in Ben Goldacre’s Book – Bad Science – where he explains the story of the Cochrane Collaboration Logo . By relying on personal experience solely to guide our treatment choices rather than the published evidence we are condemned to repeat the mistakes of the Obstetricians cited in the Cochrane example.
How else are Football Clubs going to understand injury patterns unless they audit their practice and yet involved with collaborative studies? In Medicine we are well accustomed to these types of study. Multi-center trails are often necessary to give enough power to a study. Yet there is still an air of suspicion surrounding collaborative studies in Sports Medicine and in particular Football. As if, by sharing data, teams will some how lose their competitive advantage. Instead of the rest of the medical community benefiting from the significant investment professional Football Clubs are able provide to their players a lot of what goes on remains hidden from public view.
The result are studies with low numbers of participants (n=49) commonly published. Those of us involved in research will tell you small-sample effects can easily contaminate the design and outcome of a study. A large sample number is especially important to produce a pattern recognition system with high accuracy. This should be kept in mind before ‘buying in’ the results of injury predication/reduction studies that typically follow a single club for two seasons (before and after an intervention).
Looking more closely at ankle injuries: the power of Big Data in Football.
A recent publication from the Football Research Group (@FRGSweden) looked at trends and circumstances surrounding ankle injuries in the UFEA Champions League (CL) over an 11 year period. This paper stated that ankle injuries occurred 1/1000 hours of combined match and training exposure .
The average English Premier League (EPL) team will play 38 games + FA Cup, League Cup – let’s say approx 59 games excluding Champions League or Europa Cup. 59 games x 16.5 hours (11 players x 90mins) =973.5 hours. The average training to match ratio is usually 4:1. This equals 3894 hours training and 973.5 match play. Therefore, in total, each club accumulates 4867.5 hours (or approximately 5000) of player exposure/ per season. This would equate to an average of 5 ankle injures per club per season (assuming that the EPL has a similar injury pattern to the CL). The data tells us that of these about 70% will be joint or ligament injuries. 68% (of the 70%) will be lateral ligament injures. So, in the case of the EPL team they can expect to have between 2 and 3 lateral ligament injures per season with 81% of these taking less than 4 weeks to recover .
Now it is easy to see the importance of maintaining a high quality audit at a Football Club. If, at your club, players are sustaining considerably more than 5 ankle injuries each season then reducing ankle injuries may be the next injury prevention strategy. Of course this is a very simplified example for demonstration purposes, but it highlights how injury analytics have a role to play in Football.
With a wide number of probable ankle injury diagnoses ( Benchmark54 lists 117 ankle injury codes) a small patient group ( n=25 in current EPL teams) and a low prevalence (5 per season) it can seem to an individual club ‘no two ankle injuries are the same’. However, when looking at the bigger picture, Football Clubs are able to benchmark their performance against the published literature through effective auditing of their injures. Benchmark54 helps Football Clubs collect injury data with a extremely high level of accuracy quickly using any mobile device or PC. Careful, accurate data collection identifies problems and defines injury prevention strategies. It can measure outcome data and channel limited resources effectively based on evidence and not opinion.
1: Owen AL, Wong del P, Dellal A, Paul DJ, Orhant E, Collie S. Effect of an injury prevention program on muscle injuries in elite professional soccer. J Strength Cond Res. 2013 Dec;27(12):3275-85. doi: 10.1519/JSC.0b013e318290cb3a. PubMed PMID: 23524368. LINK
2. Raudys, Sarunas J., and Anil K. Jain. “Small sample size effects in statistical pattern recognition: Recommendations for practitioners.” IEEE Transactions on pattern analysis and machine intelligence 13.3 (1991): 252-264
3: Waldén M, Hägglund M, Ekstrand J. Time-trends and circumstances surrounding ankle injuries in men’s professional football: an 11-year follow-up of the UEFA Champions League injury study. Br J Sports Med. 2013 Aug;47(12):748-53. doi: 10.1136/bjsports-2013-092223. Epub 2013 Jun 27. PubMed PMID: 23813486. LINK