Analytics and the market for lemons

George Akerlof shared the 2001 Nobel prize in economics for his work on asymmetric information, which describes the situation when buyers don’t know as much about a product as sellers do. His most famous example of this situation is the market for used cars. Sellers know a lot about their used cars, but potential buyers don’t. So it’s hard for buyers to tell a good car from a bad one, popularly known as a “lemon”. As long as there’s some chance that a used car will turn out to be a lemon, a buyer will never pay full value for…
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Beyond shots: OptaPro Forum 2015

Today I had the pleasure of presenting at the OptaPro Forum in London alongside some of my favorite analysts, including Simon Gleave and Garry Gelade, and in front of many others: Paul Riley, Mark Taylor, Colin Trainor, Michael Cox, and Omar Chaudhuri, to name just a few. I spoke about creating metrics for players and teams using expected goals – but without basing the models on shots. (Click here for the video.) The first part of the talk outlined my work to date with shots-based models, summarized here. Next I recapped the “danger zone entries” model posted here in December….
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Expected goals from situations

There’s a shift taking place in the soccer analytics landscape, and it’s probably overdue. For the past year or so, expected goals models based on shots have been very much in vogue. But recently, several analysts and commentators – notably Max Odenheimer, Richard Whittall, and myself – have pointed out that every situation on the field has some chance of turning into a goal. The big questions are whether these situations can be measured and, if so, whether the resulting models are superior to existing alternatives. The answer to the first question depends in large part on the quality of…
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