This is the first photograph of a soon-to-be-released version of a device which is changing the world of professional sports.
Even the tiniest improvement in the performance of a professional athlete can mean big dollars.
The business of sport is now so fine-tuned that advances, the ones which don’t involve drugs, are measured in parts of percentage points.
And if you come up with an edge, there’s potential to disrupt the sports business and change way the game is played.
Australian company Catapult Sports, using technology and data manipulation, has quietly found ways to add that little extra advantages to sports team and people, including Australian rules and American football.
Catapult Sports, a spin-off from the Australian Institute of Sport, has just received $3.6 million through private equity house Aura Capital Group, raised from Australian high net worth investors.
The cash will be used to expand offshore sales and marketing where more than 65 per cent of revenues already come from.
Catapult has been around for ten years and works with eight American football teams, a quarter of the league. The technology looks past the forecaster’s tools of past trends, player experience and salaries, to the clinical facts of science.
It’s Google analytics for athletes.
The matchbox-sized device, the soon to be released OptimEye S5 pictured above, is attached to the athletes and beams back data from GPS and inertial sensors, accelerometers, gyroscopes, magnetometers.
The data is collected in a laptop which can be read via a Google-like dashboard in real-time or saved for later analysis.
Catapult works with 17 of the 18 Australian Rules Football teams. From the data it collected, players are now rotated after 8 minutes instead of 12 because Catapult found performance dropped off after the eighth minute.
Some of the insights from gridiron data:
- From an entire season in 2012, Catapult saw 80 per cent of practice time was standing around, somewhat physiologically inefficient and with a static mechanical cost to the athletes.
- In weeks where the same team had a Sunday loss and what would be considered poor offensive output, this correlated to high volume practice sessions on Thursdays
- Catapult noticed specific players with gross movement ‘imbalances’. One player had high intensity movements to his right but could not produce the same to his left. This is either a function of previous position adaptation or possible injury history. The head coach said: “Boy, if this ever gets out, he’s toast.” (Staff are working to correct this.)
- Last season, one team’s offensive lineman covered an average of 2 miles every practice.
- When playing against a no-huddle offense, one team found that their defensive players maintained an average heart rate 25 per cent higher while they were on the field than against a slower tempo squad.
- Consistently across all teams, receivers cover more distance during the week than corners. Does this means that corners don’t work enough or that receivers are overworked? Theoretically, their distances covered should be quite similar, not the 17 per cent difference observed.
- A team found last season that there were benchmarks during their training week which lead to them losing. These included more of the team’s work being at low speeds. This allowed coaches to clearly identify and demonstrate to players that their training was not at the standard required during the week and make an intervention. These interventions included giving an off day, riding the players harder, changing the design of training.
- Teams see huge differences in how hard their individual coaches work their players and are able to work with the coach to either push his group harder or to give him strategies to back off on his players.
- A team had a hyped young receiver and a star veteran receiver. Catapult data was used to show him where he was deficient against the veteran, and where the veteran was outworking him. They also saw differences between the two players that they could game plan for. Such as one being better at decelerating sharply.
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