Lock-in used to be a big thing in software business models. Before lean startups and minimum viable products, before technologists were aware of people’s habits, before Javascript became a full-fledged programming language, before the web browser become an all-purpose interface for computer interaction and software became services, customers looking for vendors to solve a problem and if the problem had some scale there was good reason to worry about getting out of your software deal if things failed to go as planned. Shit sandwiches were unavoidable and potentially expensive.
Fortunately much about commercial computing has evolved, and most of the positive advances revolve around handling the data that fuel the utility of Web-based software services. Things like APIs, cloud computing and improved in-browser data handling with Javascript make the task of moving data around the Internet possible.
There are reasons to worry that time and energy (and data) that go into the cloud can vanish overnight when the company providing the service dissolves, but otherwise, as long as data that goes in can also come out, moving from one software service to another one is now possible, albeit painful.
In Applied Sports Science there firms like SpartaTrac, Fusionetics, EliteForm, Dartfish, Fatigue Science, PUSH and others developing Web software services that involve features of athlete management systems and features of sports sensor data capture applications. These are turnkey services that ask for some initial setup but go on to say what they do and do what they say from there.
Developers often refer to the “stack” an application has been built on: the main programming language, the data base technology, the Internet interface and the operating system. You can see the beginning of a sports science stack building up as the different software services prove their value to teams and athletes and differentiate between each other.
Teams and athletes will choose these services like picking from a menu, and also give themselves opportunities to augment the commercial stack with proprietary, custom applications for athlete performance or game data.
Thinking in terms of a full-service end-to-end stack for athlete performance helps to enable the individual elements in the stack to evolve and transition, providing a mechanism for the functional stack to evolve to meet the needs of the organization. And if there technology elements in the athlete performance stack that lock-in they won’t align with the other stack elements which are evolving with the advances in sports science and technology.
Over the next few years the athlete performance stack is likely to grow large. As the data capture improves and the interfaces become more user-friendly and validation for all the different measurables comes in, the services that combine so that their sum is greater than the indivual parts will be the ones that are absorbed into the stack, something that is likely to change from sport to sport and team to team. The athlete development curve will probably also justify bigger stacks for more elite athletes, progressing from lowest common denominator, broadly applicable technology for youth athletes up to more customizable, more targeted technology for professional athletes.