In betting models, the price offered by the market is often ignored until the end. However, it seems like the price is a valuable piece of information that cannot be overlooked. Consider a hypothetical 2-horse race that pays double or nothing. Suppose that the market odds (X) and my model predictions (Y) are correlated, as shown in the following table:
+-----------+---------------+
| | X |
| +-------+-------+
| | wrong | right |
+---+-------+-------+-------+
| | wrong | 1/4 | 1/8 |
| Y +-------+-------+-------+
| | right | 1/8 | 1/2 |
+---+-------+-------+-------+
I should bet when my model and the market agree. However, when the models agree, the market's price matches my prediction, and therefore, I don't bet in that case. Instead I only place a bet when my model disagrees with the market. It would be more beneficial to include the market price as an input to my model. I am curious to know whether this approach is widely used or if there is a better way to address this issue in betting models.
