The lines almost immediate improved the Thunder’s breakeven win % from ~50% to ~65%. Without giving a chance for the lines to reach a new equilibrium, another bombshell was dropped at 1:54pm ET: Anthony Davis was questionable. The lines continued to move in the Thunder’s direction for the next hour or so before seemingly reaching an equilibrium a little after 3pm ET.
When it was finally announced that AD was downgraded to Out around 45 minutes before tip, the line began to further trend toward OKC.
So how should we judge these movements? Did the market immediately factor in new information?
Although the market reacted fairly well, there was still some opportunity to get a bet in before the market reached a new equilibrium, particularly with regard to the AD news. The market may not have fully reacted immediately, but this isn’t enough evidence to disprove the EMH.
0 for 1.
2. Important Information is Freely Available to All Participants
Does everyone have access to the same information? Certainly not everyone would agree with me, but I generally believe that most sports information is freely available these days. The barrier to information is lower than it’s ever been. People use information in different ways to give them certain edges, but information asymmetry by itself isn't a reason to disprove EMH.
Geez. 0 for 2 (really need to start providing some evidence here, huh?).
3. A Large Number of Rational, Profit Maximizing Market Participants
I think we can all agree that the drunk guy parlaying the Gatorade color and coin flip at the Super Bowl might not be rational or profit maximizing.
And judging by a few Reddit comments there are plenty of sports bettors who aren’t strictly profit maximizers:
“I'm not going to be dealing with 7 different bookies just to raise my ROI by .1 or .5 or even 1%.”
“I tend to gamble more when I’m bored.”
“I was drunk and wanted to bet so I threw down 5 units on an Australian women's basketball game on a blind tip from the Nitrogen chat room.”
The vast majority of sports bettors aren’t profit maximizers, but utility maximizers. Sports betting offers a form of exhilaration and entertainment that can’t be found in other places. A lot of that excitement manifests itself in poor-EV-yet-thrilling wagers (such as parlays, teasers and futures) that sportsbooks happily offer you.
Just how much are non-profit maximizing behaviors costing sports bettors? To answer that, let’s take a peak at the Nevada’s annual sports betting report. In 2019, sportsbooks in Nevada took $5.3 billion in wagers and held $329 million, representing a hold of 6.2%. We've discussed elsewhere how standard -110 odds gave sportsbooks a hold of 4.5%, which we could chisel away at pretty easily with some basic line shopping. Thus, if market participants were truly profit maximizers, we’d expect a hold significantly less than 6.2%.
OK – so finally we have some evidence that the EMH might not hold. Let’s test it with some data.
Testing Weak Form Efficiency
The three forms of market efficiency are Strong Form, Semi-Strong Form, and Weak Form. The Strong Form assumes that all information (private and public) is baked into the market. The Semi-Strong Form assumes that all public information is baked into the market price of an asset. The Weak Form states that historical prices cannot be used to predict future prices.
If we show that the weakest form of the EMH can be disproved, we can largely disregard the EMH.
Straight from Morningstar: “The weak form of EMH assumes that current stock prices fully reflect all currently available security market information. It contends that past price and volume data have no relationship with the future direction of security prices. It concludes that excess returns cannot be achieved using technical analysis.”
MLB Moneyline Movements
Let’s go ahead and use MLB Moneyline data from the 2015-2018 seasons to see if we can predict the direction of the closing line, and therefore generate theoretical value (CLV) by beating the closing line.
We gathered the Closing Line as well as the line 2-hours prior to first pitch (T-2) to see if we can recognize any patterns. We can then test the statistical significance of those patterns to give us a sense of whether they have any merit.
The traditional school of thought is that if you’re betting favorites, it’s best to bet them early. If a dog, wait until close to game time. Does this theory hold? If it does, we could use it to disprove Weak Form EMH.
The first thing we can do is review the parity of the teams playing at two different points in time. If we're expecting a close game two hours prior to first pitch, are we expecting the same thing right before the game starts?
Below we assess the magnitude of the average deviation of prices from a "50/50 game" over the 9,813 games in our sample. Two hours prior to first pitch, the average favorite was -142, or a 42 cent deviation from -100 (or +100). At game time, the average favorite was -144, or a 44 cent deviation from -100 (or +100). If we look at the distribution, we see that there are more games with an average deviation greater of 100 or more at game time than at T-2.