Statistics v Analytics

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Anyone who is half interested in sport will know about the analytics revolution and / or terms like sabermetrics. These basic concepts explain philosophical movements and various techniques that have in many cases fundamentally changed the way we think about sport (e.g. Moneyball) and play sport at the professional level (e.g. use of the 3 point shot in the NBA). I feel like volleyball lags a long way behind in this ‘new world’ and terms like statistics and analytics are fundamentally misunderstood. I have written about some of the differences between between statistics and analytics and I want to take it one step further.

The analytics movement starts with the premise that the common statistics used to describe the game do not give a true reflection of what really happens. The example that drives the narrative of Moneyball is batting average in baseball. This is one of the most traditional statistics in baseball and turned out not to have nearly as much impact on winning as on-base percentage. From this simple idea you start to build new and better structures of seeing, measuring and most importantly, understanding.


Which brings us to volleyball and analytics. There is a lot more study being done around the place on volleyball that I see, and obviously stuff that I don’t see. However, most of what I see is some form of rehashing existing statistics; attack efficiency, blocks per set, digs etc. The lesson of the analytics movement is that traditional statistics are precisely what restricts improved understanding. New understanding only follows from developing new tools.

The lesson is, if you are using the old statistics you are not doing analytics, no matter big the data set. The Science Untangled suite of apps has a lot of different statistics, most of which haven’t been tested but seem like a good start. You can test them all on your own data sets. Others are also looking at new methods and measures, let’s call it real analytics, can be found here and here. Viva la revolución!

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