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NFL Betting: Is the Market Efficient?

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NFL Betting: Is the Market Efficient? Skidmore College Creative Matter Economics Student Theses and Capstone Projects Economics 2017 NFL Betting: Is the Market Efficient? Mathew Marino Skidmore College Follow this and additional works at: https://creativematter.skidmore.edu/econ_studt_schol Part of the Other Economics Commons Recommended Citation Marino, Mathew, "NFL Betting: Is the Market Efficient?" (2017). Economics Student Theses and Capstone Projects. 27. https://creativematter.skidmore.edu/econ_studt_schol/27 This Thesis is brought to you for free and open access by the Economics at Creative Matter. It has been accepted for inclusion in Economics Student Theses and Capstone Projects by an authorized administrator of Creative Matter. For more information, please contact [email protected]. NFL Betting: Is the Market Efficient? By Mathew Marino A Thesis Submitted to Department of Economics Skidmore College In Partial Fulfillment of the Requirement for the B.A Degree Thesis Advisor: Qi Ge May 2, 2017 1 Abstract The Purpose of this research paper is to analyze the NFL point spread and Over/Under betting market and determine if it follows the efficient market hypothesis. This research paper uses data from armchairanalysis.com for the 2000 NFL season through the 2015 NFL season including playoffs. I use OLS and probit regressions in order to determine NFL betting market efficiency for the point spread betting market and the Over/Under betting market. The results indicate, that as a whole, the NFL betting market appears to be efficient. However, there may be behavioral tendencies that can be taken advantage of in order to make consistent betting profits. 2 I. Introduction According to Statista.com (2015), the sports gambling market has been estimated to be worth between $700 billion and $1 trillion, but the exact size is difficult to measure because it is not legal everywhere. The most popular sport to bet on is professional American football, which is known as the National Football League (NFL). The increase in the amount of money being wagered and popularity has led financial economists to become more interested in the sports industry and study the efficiency of the market (Borghesi, 2006). The Efficient Market Hypothesis (EMH), as formalized by Fama (1970), is a theory that states financial markets are efficient if the prices of stocks contain all publicly available information. That is, stocks are always traded at their fair value and it is impossible to buy undervalued stocks for profit or sell overpriced stocks for profit in the long run. In more basic terms, it should be impossible for an individual to “beat the market” because the price of a good should include all publicly available information. In recent years, the efficient market hypothesis has been applied to sports betting markets because testing for efficiency in the financial markets is very difficult, if not impossible. The main difficulty with testing the efficient market hypothesis in the financial markets is that there is no direct test for market efficiency because the true value of the investment is never revealed (Fama, 1970, Gray & Gray, 1997). A long period of time exists between the purchase of a stock and the sale of that stock, making it very difficult to measure the efficiency. Due to this inability to accurately test the efficiency of the financial markets, many economists have turned to sports betting to test for market efficiency. The sports betting market provides a perfect testing ground for market efficiency because there are many similarities between the two markets. There are many participants in both markets, large sums of money are exchanged, and the betting lines are formed based on publicly available information, similar to stock prices. Bookmakers act in a 3 similar fashion as stock brokers; both charge fees for matching buyers and sellers (Borghesi, 2006). Other analogous variables exist in both the stock market and the NFL betting market. Gray and Gray (1997) cite that a team’s recent record is similar to the recent performance of the stock market performance. Both stocks and teams that have been performing well as of late, attract more attention and more people would like to bet or invest on that team or stock. In addition, according to the efficient market hypothesis, past performance of stocks should not help investors predict the future performance. In terms of football, if Team A won their game last week, that should have no effect on whether they win their game this week. The essence of the efficient market hypothesis is to determine if an investor could earn abnormal returns compared to the market returns by looking at a stock’s recent performance or other related variables. In football betting, could a bettor earn profits based on the recent performance of the team or other related variables that could affect the game outcome such as weather or injuries. By studying the 16 NFL seasons from 2000 through 2015, this study contributes to the discussion on NFL betting and market efficiency. This paper examines the point spread betting market that was first analyzed by Pankoff (1968) by using performance measures (Gray & Gray, 1997, Zuber et al. 1985, 1988) as well as exogenous factors such as stadium characteristics (Boulier et al. 2007) and weather (Borghesi, 2006). My paper will also investigate the Over/Under NFL betting market using similar variables that were used to test the point spread market. To my knowledge, through reading related literature, no paper has touched upon this area, and this is certainly a feature of NFL betting that is interesting to analyze. My findings for the point spread betting market are consistent with those of previous literature. By testing the NFL point spread market for 16 seasons, I was unable to find variables that would allow a bettor to earn abnormal returns and profit in the long run, leading me to the 4 conclusion that the NFL betting market is indeed efficient. My paper does find a statistical inefficiency in the Over/Under betting market that may allow a bettor to earn abnormal returns. The ����%& variable appears to increase the likelihood of the score of the game going over the Over/Under betting line. However, this single variable may not be enough to base a betting strategy on. The rest of this paper is organized as follows. Section 2 discusses the NFL betting market and explains the nuances that exist. Section 3 examines existing literature that is relevant to this topic. Section 4 describes the data that will be used in this study. Section 5 introduces four econometric models, two standard OLS regressions and two probit regressions. This section will also explain the variables used in the models. Section 6 discusses the results from the two econometric models. Section 7 offers my concluding remarks. II. The NFL Betting Market The betting market contains jargon that must be known to fully understand the rest of this paper and interpret the results. In NFL betting, there are two types of bets, the money line and the point spread. A bet on the money line is when you bet that Team A will beat Team B straight up. You expect Team A to beat Team B, or vice versa, there is no point spread involved. A bet on the point spread is when you “give” a team points in an effort to level the playing field, to make bets for either team equally attractive. A point spread is produced by bookmakers when one team is considered better than another team, in order to equalize the betting volume on each team. The point spread is negative for the favorite and positive for the underdog. The favorite has to win by at least the spread margin and the underdog has to win outright or lose by less than the margin. For example, Team A is the favorite with a spread of -3 and Team B is the underdog with a spread of 5 +3. Team A has to win the game by more than 3 points in order to win against the spread, also called covering the spread. Inversely, Team B has to lose the game by 3 points or less or win the game outright. If the difference in the score of the game is exactly 3 points, the result is a push and all money is returned to the bettors. In an effort to avoid ‘pushes’, the spread is usually set as a half number, for example the spread would be -2.5 for Team A or +2.5 for Team B. The point spread and the Over/Under line are set at the beginning of the week, known as the opening line. The line may adjust throughout the week based on new information, such as injury reports or weather reports, until right before the game starts, known as the closing line. The same holds true for the Over/Under betting line. One important aspect of football betting is that once you place a bet, your bet is locked in at that spread. Unlike horse betting, where no matter when you placed your bet, your bet is based on the closing odds. The closing line is the important betting line, in the sense that it should be the most unbiased predictor of the game outcome, and is the betting line that I use in the paper. Bets under the spread follow the “Bet $11 to win $10” rule. You must risk $11 to win $10 in the point spread betting market (Humphreys, 2011). This 10% commission is called vigorish and is the commission the bookmaker collects on all bets. In order for a bettor to break-even in the betting market, they must win 52.4% of their bets.
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