The Secret of Sports Analysis
I've spent my entire adult life working in jobs which involve high level analysis of high performance sport (whatever that means). Throughout this time the most consistent thing I've noticed is that there are people who don't like statistics in general (or don't like a particular type of statistic) and also people who think data is the only way to properly assess athletes and performance.
In my experience:
- people who don't like statistics either don't understand them or don't like the way others use them to discount the human component of sport.
- people who believe data will provide all the answers don't understand the way human elements impact sport, or don't like the way some people believe that is the only important part of sport and that it is some sort of 'secret'.
Context 1 - Data v KnowledgeThe Knowledge Hierarchy is a simple way of looking at how you use the data you collect. Basically, you need to work to make it useful, and then you need to work hard some more for it to provide insights.
A simple example is when people criticise data systems. It is certainly not unreasonable to do this, all have flaws. But the system itself is just a system to collect data, problems only come when assumptions are made which go beyond the sensitivity of the data that is collected. In situations like this, people are looking at data or information, but they are expecting it to be knowledge. The knowledge comes when you look at the information in Context, and process it according to your own experiences, needs and understanding. Looking at it critically and recognising its flaws is part of this process. The outcome of this process may well be to decide that this system will not give you the opportunity to discover what you want to about that skill/system/trait. This doesn't mean the system is outright wrong, just that it is not right for your context. This is the way it is supposed to work.
Context 2 - Personal NeedsAs the level of competition gets higher, the relative ability of teams and athletes becomes closer. That is, athletes become better and more consistent. Therefore when using data to analyse performance, the sensitivity of certain data moves to a point that it doesn't really distinguish anything useful anymore.
For example in volleyball, detailed analysis of hitting tendencies is critical to develop comprehensive match plans at the highest level. As a consequence, more basic analysis is irrelevant. However at a lower level the hitter most likely has only one tendency and any variation is due to poor contact on the ball. As a consequence more advanced analysis is irrelevant.
The key here is not whether the analysis system is not right or wrong, but whether it is right or wrong for the context within which you are working.