It’s forecasting, not a prediction. If the weather forecast said there was a 28% chance of rain tomorrow and then tomorrow it rained would you say the forecast was wrong? You could say that if you want, but the point isn’t to give a definitive prediction of the outcome (because that’s not possible) it’s to give you an idea of what to expect.
If there’s a 28% chance of rain, it doesn’t mean it’s not going to rain, it actually means you might want to consider taking an umbrella with you because there’s a significant probability it will rain. If a batter with a .280 batting average comes to the plate with 2 outs at the bottom of the ninth, that doesn’t mean the game is over. If a politician has a 28% probability of winning an election, it’s not a statement that the politician will definitely lose the election.
If the weather forecast said there was a 28% chance of rain tomorrow and then tomorrow it rained would you say the forecast was wrong?
Is it possible for the forecast to be wrong?
I think so. If you look at all the times the forecast predicts a 28% chance of rain, then it should rain on 28% of those days. If it rained, say, on half the days that the forecast gave a 28% chance of rain then the forecast would be wrong.
With Silver, the same principle applies. Clinton should win at least 50% of the 2016 elections where she has at least a 50% chance of winning. She didn’t.
If Silver kept the same model over multiple elections, then we could look at his probabilities in finer detail. But he doesn’t.
