Pinnacle golf odds
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Pinnacle Golf Odds: A Guide to Golf Betting Strategies
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Because Pinnacle and Betcris apply differential margins to their odds, it will be easiest to focus on the margin-free probabilities for this exercise. Thus Pinnacle's opening price was The closing margin-free price for Betcris was Therefore Betcris' price, from opening to close, moved We repeat this calculation for all bets, focusing only on the odds offered for the first listed player in each matchup to avoid double counting.
In these bets, Pinnacle's price on average moved Therefore part of Pinnacle's price movement towards Betcris could be occurring before Betcris actually posts their odds [3]. In any case, the takeaway here is that Pinnacle's closing prices are influenced by Betcris' prices to a considerable degree. Also, given that in the previous section we argued that Pinnacle's closing lines are the most accurate in our database, Betcris' closing prices should move towards Pinnacle's more than they currently do if their only concern when setting odds was accuracy, which it likely is not [4].
The next table summarizes the results of this exercise for other pairs of books. If a bettor can add predictive value to the bookmaker's odds in any way they stand a chance of making money; at the very least they will not lose as much money as they would if betting randomly.
A successful bettor does not require a model that fits the data better than the bookmaker's odds, but rather just one that can improve upon those odds enough to overcome the margin they are up against. In comparing our model's odds to those of bookmakers, I've developed a greater appreciation for the quality of their predictions. Even a "soft" book like Bet has margin-free probabilities that fit the data pretty well; since , their matchup odds have performed similar to ours.
That is, while we can make money betting against Bet evidence to come , they could also make money betting against us if we added some reasonable margin to our odds and started bookmaking. As alluded to in the previous paragraph, if you have a model that fits the data as well as the model you are betting against e. Let's take a detailed look at how the Data Golf model performed over the last 2 years.
Recall that in our model did not include course-specific adjustments, while in our PGA Tour model incorporated both course fit and course history adjustments and our European Tour model incorporated course history. 
The table below shows our overall betting performance across all books since early for various expected value thresholds as before, using a 1-unit stake for all bets.
Rather, we need to account for the fact that if the match is tied your bet is returned. The effect here is minor; using the second method will move your expected value closer to zero. Intuitively, the difference is simply that I think you should be including the void bets in the calculation of your overall rate of return and expected value.
We have a worked example here. That is, they price the longshot too short and the favourite too long. The practical point here is that the returns from blindly betting on all outcomes will only be zero if the bookmaker's odds are well-calibrated i. This doesn't mean their prices are perfect as in this example , just that they are roughly calibrated.
The fact that after we remove the margin the average return to blind betting is close to zero at all books indicate that our margin-free odds estimates are reasonable. There is a philosophical point here about why I am assuming that if we remove the margin "correctly" the odds will be well-calibrated. If that bothers you, you can alternatively just think of this exercise as producing the most accurate margin-free probabilities possible from the offered margin-included odds.