It seems like you are confusing predictions about this election with overall model accuracy.
The state by state predictions are the primary method by which you can judge the accuracy of his model. If his model forecasts a state as 60% for one candidate, you can assess that level of accuracy by looking at all 60% predictions.
Let's take some examples from todays date on FiveThirtyEight.
* Florida 54.8% chance of Romney win
* Virginia 67.0% chance of Obama win
* Nevada 67.9% chance of Obama win
* North Carolina 79.6% chance of Romney win
* New Hampshire 80.4% chance of Obama win
* Iowa 80.7% chance of Obama win
* Nevada 88.7% chance of Obama win
Other states like Texas, Utah, Idaho, Wyoming are projected at 100% for Romney and New York, California, Oregon, and Illinois at 100% for Obama.
If any of the 100% states go to the opposite candidate, that is a model problem. If some of the ones specified with extremely high percentages 95% go against his predictions with a high margin of victory, again that is a model problem.
Finally, some of those close races should go against the models prediction.
Let's take the 7 states listed above. 5 of the 7 are projected for Obama and 2 for Romney. However there is only about a 37.68% chance of that exact distribution happening. I break it down as follows:
Obama-Romney
* 0-7 0.02%
* 1-7 0.14%
* 2-5 2.26%
* 3-4 12.14%
* 4-3 31.48%
* 5-2 37.68%
* 6-1 16.00%
* 7-0 0.28%
Each of these numbers are probabilistic statements about the likelihood of the overall event occurring based on the probabilities. They are from a single simulation of the 7 state probabilities run 10,000 times. Each of them has it's own distribution, eg 5-2 was 37.68% in the first run, 37.38% in the next, then 38.03%, then 36.85%, etc.
This is a very simple model prediction, but by taking all 50 states into account you can get a very clear assessment of how well his model is actually predicting the outcome of the election. Complicating matters is the time series nature of the predictions.
This type of model is precisely the way you get away from "Fooled by Randomness and all that". His model is clearly articulating the amount of uncertainty in the forecast.
The state by state predictions are the primary method by which you can judge the accuracy of his model. If his model forecasts a state as 60% for one candidate, you can assess that level of accuracy by looking at all 60% predictions.
Let's take some examples from todays date on FiveThirtyEight.
* Florida 54.8% chance of Romney win
* Virginia 67.0% chance of Obama win
* Nevada 67.9% chance of Obama win
* North Carolina 79.6% chance of Romney win
* New Hampshire 80.4% chance of Obama win
* Iowa 80.7% chance of Obama win
* Nevada 88.7% chance of Obama win
Other states like Texas, Utah, Idaho, Wyoming are projected at 100% for Romney and New York, California, Oregon, and Illinois at 100% for Obama.
If any of the 100% states go to the opposite candidate, that is a model problem. If some of the ones specified with extremely high percentages 95% go against his predictions with a high margin of victory, again that is a model problem.
Finally, some of those close races should go against the models prediction.
Let's take the 7 states listed above. 5 of the 7 are projected for Obama and 2 for Romney. However there is only about a 37.68% chance of that exact distribution happening. I break it down as follows:
Obama-Romney * 0-7 0.02%
* 1-7 0.14%
* 2-5 2.26%
* 3-4 12.14%
* 4-3 31.48%
* 5-2 37.68%
* 6-1 16.00%
* 7-0 0.28%
Each of these numbers are probabilistic statements about the likelihood of the overall event occurring based on the probabilities. They are from a single simulation of the 7 state probabilities run 10,000 times. Each of them has it's own distribution, eg 5-2 was 37.68% in the first run, 37.38% in the next, then 38.03%, then 36.85%, etc.
This is a very simple model prediction, but by taking all 50 states into account you can get a very clear assessment of how well his model is actually predicting the outcome of the election. Complicating matters is the time series nature of the predictions.
This type of model is precisely the way you get away from "Fooled by Randomness and all that". His model is clearly articulating the amount of uncertainty in the forecast.