Evaluating the effectiveness by 'how many they got right' can be pretty flawed. For example with Franken/Coleman. How 'wrong' are they when something they proje

Evaluating the effectiveness by 'how many they got right' can be pretty flawed. For example with Franken/Coleman. How 'wrong' are they when something they projected as a 48% chance happens. If you had 10 races that where similarly close you would have to expect to get about 4 or 5 of them 'wrong'. You may actually find that according to their own probabilities they were somewhat 'lucky' in having so many results tip to their most favoured result. Or perhaps they could be 'unlucky' to miss Minnesota. A better way would be to check their results against other 'odds producing' predictive mechanisms. So if a another method has Stevens at 20% and 538 has Stevens at 30% then that actually counts as a win for 538 because they were 'less wrong'. The bigger the difference in their odds the bigger the win. In the real world this would translate to 538 winning money from the other method. I'm sure there are better and more valid statistical techniques for doing this though. I'm hoping that someone gets around to it because I'd be keen to see the results.