After looking at the difference in mean WATS (Wins Against The Spread) between home and away teams, and discovering a statistically significant discrepancy, I decided my next analysis would look to see if there was a similar discrepancy between the mean WATS of favorites and underdogs.
What would a sizable difference in mean WATS signify here? If favorites were able to beat the spread more frequently than underdogs, it would mean that bettors typically underestimate good teams and overestimate bad ones. In the opposite case, it would mean that bettors overestimate good teams while underestimating bad ones.
Going into this analysis, I thought there was a slight chance I would discover that favorites covered spread less frequently than underdogs. The reason for this prediction is that there is evidence indicating that the betting populace has a tendency to prefer betting on favorites even if the spread favors the underdog (a theory on the reason for this is interesting: It seems that when bettors first hear about a game, they make a lightning quick calculation of who they think should win, and then subsequently have a hard time incorporating new information about the spread into their decision . This is a classic example of the “fast-thinking” parts of the brain overriding the more logical “slow-thinking” parts.) Knowing this, casinos would naturally adjust the line to give underdogs a better chance of beating the spread than they would have given an unbiased bettor pool.
Although it is generally agreed upon that bettors tend to prefer favorites, there have been both previous analyses finding that underdogs and favorites beat the spread an equal amount of the time, and previous analyses finding that underdogs have the upper hand. To test this hypothesis, I ran a paired samples t-test to see whether there was a significant difference between teams records against the spread as favorites and teams records against the spread as underdogs.
Results: I found that from 2009-2014, favorites covered the spread 3112 times for a 49.35% winning percentage, and underdogs covered the spread 3119 times for a 50.42% winning percentage. This difference was not large enough to be significant (.224), and the null hypothesis was confirmed.
Analysis: The results indicate that home teams and away teams have nearly identical records against the spread. I am not exactly shocked by these results, as I mentioned above that previous analyses have found similar results. In any case, I feel this is a good place to discuss the various ways in which bookies can theoretically turn a profit. As I’ve mentioned in other places, one way is to ensure even action on both sides of a given contest and simply collect the vig, being completely indifferent to the outcome.
Another seemingly more risky strategy would be to ensure that the line you have set guarantees a 50/50 chance of either team covering, and to be indifferent to the amount of action on both sides. This strategy would pay off in the long run, as losses incurred when teams with the heavier action wins are offset by gains when the team with the heavier action loses. I say that this strategy is riskier because it is much more difficult to measure and therefore to make adjustments to: If you are concerned only about an equal amount of action on both sides of a game, you can easily move the line if there is an imbalance, whereas in setting a line to ensure a 50/50 chance of covering for either side, you do not have this quantitative feedback.
Of course there is also a third (more profitable) option that becomes available if action is extremely one-sided on a particular event, whereby the oddsmakers can set a line that ensures there is more money placed on a team that has a less than 50% chance of beating the spread. All that being said, it is impossible to know what type of strategy the oddsmakers in our data set are employing without having access to the amount of money wagered on each event.