8 injurieswhere the nationalrate predicts≈25.
Female U15-U19 soccer players, 2020-2024, using the original app (SoccerPulse). An independent Bayesian analysis commissioned by PlayerPulse, compared against a national high-school benchmark. Observational.
An ACL tear means surgery, months of rehab, and a real chance the injury reshapes a teenager's season, her scholarship, her relationship with the game. The question was simple: how did the ACL injury rate of players using SoccerPulse compare with the national rate?
25 predicted.
8 recorded.
At the national injury rate, a cohort of this size, 203,112 exposures, would be expected to record about 25 ACL injuries. The SoccerPulse cohort recorded 8. That gap is the finding. It is a comparison of rates, and it does not say what caused the difference.
Female U15-U19 soccer players, 2020-2024, the original app (SoccerPulse). An independent Bayesian analysis commissioned by PlayerPulse, against a national high-school benchmark. Observational.
The raw rate.
ACL injuries per 100,000 player-exposures. Lower is better.
Two distributions that barely touch.
A Bayesian analysis gives a full distribution of plausible injury rates for each group, rather than a single number. The two curves sit almost entirely apart.
Curves drawn from the posterior summaries (Beta posteriors, normal approximation), per 100,000 exposures.
Female U15-U19 soccer players, 2020-2024, the original app (SoccerPulse). An independent Bayesian analysis commissioned by PlayerPulse, against a national high-school benchmark. Observational.
Even the most conservative end of the 95% interval still means a 50% reduction in ACL injuries.
probability the cohort's rate is lower
Female U15-U19 soccer players, 2020-2024, the original app (SoccerPulse). An independent Bayesian analysis commissioned by PlayerPulse, against a national high-school benchmark. Observational.
The method.
ACL injuries are rare events, so the analysis used Bayesian inference with a Jeffreys prior and 100,000 posterior samples. The benchmark is the girls' high-school soccer row of a published study hosted by the NIH (PMC3867093).
It is an observational comparison of raw rates against that national benchmark. It is not a trial, and it does not show that the app caused the difference.
Full PDF report ↗The benchmark has 786,293 exposures. Together the two groups total 989,405.
Every player, every morning.
Every player generates signals every day. The app turns them into the one question a coach can act on: is this athlete ready to train hard today?
The cohort used the original app, SoccerPulse. These are today's PlayerPulse player screens, with sample data.
Soreness is holding you back after Saturday's match. Great sleep is keeping you fresh.
This week is in line with your last four weeks.
Wellness report
Each morning, players take 15 seconds to report soreness, energy, stress, sleep and mood.
Post-session report
A quick 1-10 effort rating (RPE) turns "that felt brutal" into data: the load each body carried.
Training load
Those ratings roll into this week against the last four, so a sharp jump shows before the next session.
Readiness score
Coaches see who is primed and who is running on empty, and adjust the plan. Know when to push. Know when to rest.
The analysis compares rates. It does not isolate which part of the routine, if any, made the difference.
Better calls for everyone.
Coaches
Modify practice in real time: ease off the player who is over-loaded, push the one with room to give. The right session for each body, that day.
Parents
Know when the extra training helps and when it quietly undoes the work at practice — when to push, and when to pause.
Players
Understand their own body and share it. One quick daily check-in keeps every coach and parent on the same page.
It comes down to availability.
Availability is how many players are healthy and on the field, measured across the squad and player by player.
32% reduction in ACL injuries among female athletes in programs using PlayerPulse.
Comparative analysis of sixteen high school teams over a four-year period.
A different study from the SoccerPulse cohort above. The cohort's numbers do not apply to it, and its number does not apply to the cohort.
Know when to push. Know when to rest.
Players take 15 seconds a morning. Coaches see who is ready.
Independent Bayesian analysis commissioned by PlayerPulse · Female U15-U19 soccer players, 2020-2024, the original app (SoccerPulse) · Observational · Benchmark: NIH PMC3867093 · Read the full analysis