Randomness of O/U: Expectations vs. outcomes of game Total

Totals are more random (harder to predict) than spreads:

Interesting side note, that the O/U error is not centered at the mode, and there is a longer tail of overs.

The above histogram of errors shows that the O/U curve is "fatter". This would also be explained by the lower correlation coefficient between betting lines and outcomes (above 0.4 for spreads; closer to 0.3 for totals). A more scattered scatterplot.

Anecdotally, I've found it's easy to look stupid when making O/U calls: you can bet the under with "high probability"... and see a game explode for 20 points over. And vice-versa.

Strategy: exploit the more significant discrepancies

Taking all the above into consideration, I take the same strategy as I last analyzed in my post about Spreads: do not to bet on small differences or "key numbers". It is to look for more significant discrepancies between my model output and Vegas. Partially related to the inaccuracy of O/U betting lines, there are likely to be more opportunities to exploit.

There is wide variation in the success of this approach, from week to week. But I still believe my method is sound (you can read about in my profile, regarding fantasy football models).

Comparing my model to Vegas lines and actual score results (on past results):

• The Results are of course much more spread out, and giving a pattern with "key numbers"

• There is little difference in shape between my model and the Vegas lines

• This means the model can only succeed by identifying particular kinds of deviations from betting lines.

/Subvertadown