How an MLS Team Leverages iP: Intelligence Platform to Reduce Player Injury and Save Millions of Dollars Each Season
Across global sport, many professional teams frustratingly face high player injury rates season-over-season and are forced to manage the resulting financial impact tied to player loss. One MLS club has taken this challenge head on by utilizing iP: Intelligence Platform alongside Kitman Labs’ team of performance experts the last few years to better understand the underlying factors of injury and identify methods to eliminate future player loss as much as possible.
An injury risk analysis study conducted by Kitman Labs and this partner over 3 seasons involved statistical analysis to link testing, screening, and load data to injuries.
Periodization strategies were enhanced, and training philosophies were developed for injury prevention, and alarms were set to alert staff to players who were entering ‘At Risk’ zones discovered by analysis.
This optimized approach by the club training staff led to:
- A 28% decrease overall in injuries, with a 31% decrease in In-Game Non-Contact Injuries over a 4 year period since 2019.
- A 33% decrease in Hamstring Injuries and 67% decrease in Knee Injuries over 4 year period.
- Knee Injury Counts Over 4 Year Period – All: 15, 17, 13, 5 => 67% reduction Non-Contact: 8, 10, 6, 3 => 63% reduction
- Knee Injury Rates (per 1000 hours) – All: 6.1, 4.6, 2.7, 0.9 Non-Contact: 3.7, 2.6, 1.2, 0.7 => steady year-on-year decrease from ~1 injury every 2 weeks to ~1 every 5 weeks
- A 50% decrease overall in severe injuries over 3 seasons.
Injury Analysis Result: Year-Over-Year Roster Savings
The Use of iP: Intelligence Platform found that by identifying key injury factors, understanding how to mitigate them, and upgrading overall processes based on the new information, the club’s salary paid out to players unavailable due to injury fell from $2,640,000 in 2020 to $1,830,000 (30% REDUCTION) in a single season.
The above graphs represent each player’s percentage of games missed due to injury multiplied by their salary figure from season 1 (left) to season 3 (right).
