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The blue bar displays the response time, which is better when smaller.

If no bar is displayed for a specific time it means that the service was down and the site was offline.

For each simulation, we take the mean of the absolute errors.

The resulting distribution (above) is the standard error of the mean, in this case giving us a 95% confidence interval of 0.34 /- 0.2.

They're projecting Kelvin Benjamin will score 18.4 points in my league. “ That was something I casually said at work one day before week 9.

He's only scored over 18 points twice, and one of those times was 18.2 points. At Datascope, these kind of statements (and others) spark fierce debate and quickly escalate into bets.

However, one could argue that the near-miss on Forte’s total is more impressive.

The chart above shows a bootstrapped estimate of the mean absolute error.

With bootstrapping, we create many simulations of our observed data by randomly sampling from it (with replacement).

The first chart shows the absolute error distribution for fantasy relevant players, while the second shows the standard error of the mean for that distribution (again using bootstrapping). One issue with this approach is that it doesn’t take the underlying projection into account.

We can see that the standard error of the mean shifts further to the right, this time with a 95% confidence interval of 1.40 /- 0.25. For instance, imagine Matt Forte was projected to score 20 points while Ka’Deem Carey (Forte’s backup) was projected to score one point.