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You’re on the bus. It’s early in the morning and difficult to remember what number comes after one. (Twelfty, isn’t it?). There seem to be some other people on the bus, people with coats, and hair, and bags, and mobile phones. When the early morning coffee starts to kick in, you start counting the colours of the coats and the hair and the bags. “Wow!” you think, “That’s amazing! There are seventeen people on this bus with blonde coats! That’s millions!” “So what?” says the bus driver when you try to tell him about your exciting discovery. Counting the things you see is relatively easy. Descriptive statistics is about giving some context to your observations, and starting you on the way to a genuine discovery. Raw meat There is a trend in sports analytics to count everything that moves. The Six Nations (that’s the rugby tournament that marks the start of Spring) has an official analytics partner. The “analytics” involves lots of counting, and helpful numbers pop up in the television coverage on “statistics” such as lineouts won on own throw. We can assume that they’re selling something more sophisticated to the teams but what’s presented is probably better described as “metrics”. The closest they get to statistics is percentages of play in action areas, but even then we’re not told what percentages one would ordinarily expect. The real break-through in sports analytics has been in measurement and coding and there are piles of raw numbers generated every week. Players wear GPS trackers that measure distance covered and the intensity of impacts. We now know, for example, that scrums are about 6G and tackles can be over 30G (thanks, the42.ie!). Video analysis has also progressed to classifying the contribution of each player arriving, but that’s data transformation, not analysis. Read the full article: http://www.statsjobs.com/thats-a-surprisingly-big-number-descriptive-statistics/ You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.