16 Oct 2013
In all honesty, I could link to three or four of Chase Stuart's columns per week, so there's a non-trivial amount of discretion going into linking to this one.
If you were to ask me, "What is the one thing that the football analytics community (at least in the public domain) could do right now to advance our cause without requiring more robust data?" I'd answer, "applying more advanced statistical techniques to the data we currently have." Keith Goldner did this when he developed his Markov model of football, but nothing's really been done since in that regard until Stuart applied Bayesian statistics in the linked column. Granted, Bayes Theorem has been around since 1763, and none other than Nate Silver has applied it to baseball, but we seem to be lagging behind. Given my background in measurement methodology, I have ideas for sure, but let's hope Stuart's column is a jumping-off point. The time to move on from correlation and ordinary least squares regression is nigh.
Oh, and since I should probably say something about the column's findings, it turns out that the posterior distribution of this 0-6 Giants team says they're about a 4-12 team in terms of "true" quality, which is slightly worse than the 5-11 that Brian Burke predicts, but better than both their 2-14 Pythagorean expectation and 1-15 estimated wins expectation. We can reconvene in two months to see which of these four prediction methods was right (this time).
31 comments, Last at 18 Oct 2013, 11:10am by TomC
Looking back at FEI's preseason projections, we find that most teams did about what they were supposed to do -- but not in the Big Ten, where things got screwy.