> It's hard to quantify just how close he was. It is hard to do with the final, "chance of winning" numbers, but it is much easier with his share of the vote p

> It's hard to quantify just how close he was. It is hard to do with the final, "chance of winning" numbers, but it is much easier with his share of the vote predictions, and you can check those predictions for all states that way, even the ones where the winner was certain. Say Nate predicts that candidate A will get 80% of the votes in a particular state, with a margin of error of 2%. Then, after the results are out, if that candidate got 81% of the votes, then it was a good prediction, if they got 90% of the votes, it was a bad prediction. To quantify that, you want to compare the actual error with the predicted error. There are better ways than this, but I'll make one up on the spot: score = 1 / (actual error percentage points * predicted error percentage points). In the first case, the score would be 1 / (1 * 2) = 0.5. In the second, 1 / (10 * 2) = 0.05. The bigger the score, the better the prediction. (This isn't a great model, since it rates a prediction with a large margin of error which happened to be bang on higher than a roughly right prediction with a narrow predicted error, but this is the general idea.)