Informational Differences in NFL Point Spread and Moneyline Markets

Andy Fodor* Ohio University Department

Kevin Krieger University of West Florida Department of Accounting and Finance

David Kirch Ohio University School of Accountancy

Andrew Kreutzer Ohio University Department of Administration

*Contact Author: 234 Copeland Hall Athens, OH 45701 [email protected] 740.818.8683 Introduction

Many past works have examined the efficiency of National Football League (NFL) betting markets (for example Pankoff 1968, Vergin and Scriabin 1978, Gander et al. 1988, Gray and Gray 1997, Levitt 2004, Nichols 2012). Though some papers find chances to profit, very few opportunities to earn statistically significant profits have been documented as such a threshold is quite high. Gandar et al. (1988) describe the scarcity of such findings, and few new examples have emerged in recent years. Burkey (2005) explains how even the findings of statistically insignificant, but profitable, trading rules are often the result of data mining or other concerns.

Typical works examining wagering rely on betting rules which expose improperly set betting lines or . The finding of such an anomaly suggests systematically make mistakes when judging the relative strengths of teams in a contest. In this paper we examine the ability of two NFL markets (point spread and moneyline) to simultaneously incorporate information. To our knowledge, this is the first paper to examine informational differences between the two NFL betting markets. Rather than attempting to judge the correctness of point spreads or moneylines based on external information, we simply employ the reported moneylines as a means to make betting decisions in the point spread . While bookmakers may properly evaluate available information when setting point spreads or odds, we show that the nature of betting markets makes full, simultaneous information incorporation difficult.

Development of Betting Markets Two distinct markets exist for placing bets on National Football League (NFL) games. In one market, point spreads are utilized and the price of betting on each team is typically equal The point spread is subtracted from the score of the favorite team when determining the outcome of the bet. In other words, the favorite team must win by a margin greater than the point spread

(known as “covering the spread”) for the bettors of the favorite to win. For example, if Dallas is favored by 6 points over Washington, and the final score of the game is Dallas winning, 31-20, then Dallas has won by more than the 6 point spread and bets on Dallas would win. If the score were Dallas winning, 31-27, bets on Dallas would lose. We refer to this market throughout the paper as the ‘point spread’ market.

We refer to the other betting market as the ‘moneyline’ market. In this market no adjustments are made to scores. Instead, different prices must be paid to bet on favorite and underdog teams to win the same amount on a correct bet. For example, if a favorite team’s moneyline is -140, $140 must be bet to win $100 on a successful bet. If the underdog’s moneyline is +120, a bet of $100 will pay $120 if successful.

Both markets make adjustments for the relative strength of teams in a game. In the point spread market, adjustments are made to team scores. In the moneyline market, adjustments are made for the probability of the favorite team winning through charging different amounts to make bets on favorite and underdog teams. While the two markets are strongly connected and operate according to the same basic premise, the moneyline market has the ability to more precisely price the relative strength of two teams in a game. In the moneyline market, prices are typically adjusted in increments of $5 or $10 per $100 bet. In the point spread market, adjustments are made in half-point increments. Overall, the result is much finer striation in moneyline markets relative to point spread markets. For example, in our data, point spreads which ranged from -3 to -7 points (9 total prices), saw moneylines which ranged from -140 to -

360. There are 23 available prices within this moneyline range if $10 increments are used and 45 available prices if $5 increments are used.

In this paper we consider the assertion that the information in the moneyline market is more precise than the information in the point spread market. More specifically, we test if data from the finer moneyline market can be used to predict whether favorites will cover the spread in the point spread market. We find that for games with the same point spread, betting on (against) the most (least) favored teams to win (lose), according to the moneyline, results in economically significant profits. Controlling for point spreads, favorite teams in the lowest quintile of moneylines (most favored) cover in 51.1% of games compared to 44.2% of games covered by favorites in the highest quintile (least favored). When examining only games with 3 point spreads, differences are much more pronounced. The percentage of games covered by the most favored teams exceeds the percentage covered by the least favored teams by 15.2%.

The phenomenon of informational difference across markets that cover the same ‘events’ can be viewed as similar to relationships between financial markets, specifically stock and option markets. A rich history of literature (a few examples are Black 1975, Manaster and Rendleman

1982, Poteshman 2001, and Cremers and Weinbaum 2010) shows informed traders choose to invest in option markets, rather than equity markets, resulting in faster incorporation of new information in option markets, and changes to profit in equity markets, based on information revealed first in option markets. Moneyline and point spread markets can be thought of similarly in that information is better incorporated in moneyline markets than in point spread markets.

However, there is no evidence to suggest this is the result of sharp bettors preferring moneyline markets, but that informational differences arise simply due to the different working of the two markets. By construction, less stratified point spreads do not have the ability to provide information regarding relative strength of teams to the same degree as do moneylines.

Data, Methodology, and Results

We examine all regular season NFL games over the 2006-2010 seasons. Our moneyline and point spread data used in our main analysis is from footballlocks.com. Team score data and opening and closing line data for line change analysis are from Sports Insight. All moneyline analysis is performed using favorite moneylines.1

Table 1 presents descriptive statistics for favorite team moneylines across all point spreads that occur at least 20 times. As expected, moneylines increase with point spreads. A team with a higher point spread is expected to prevail by a larger margin. It can be inferred that this team is also more likely to win the game. This is reflected in bookmakers requiring that, to bet on the favorite, a higher price must be paid to win a given amount (usually $100 is this base amount).

For all points spreads studied in Table 1 various moneylines are present. This implies that while teams are favored to win by the same number of points, their odds of winning the game are different. While this may seem counterintuitive, it is sensible because the moneyline market generally stratifies relative team strength more finely than does the point spread market.

In Table 1 descriptive statistics are presented for points spreads as low as 1 point and as high as

14 points. Since point spreads are set at half points, there are 27 possible lines over this range.

The corresponding moneylines conservatively range from -130 to -750 (using only the most

(least) extreme money line for wagers with point spreads of -1 (-14)). Since moneylines are

1 Analysis was also performed using underdog moneylines. The nature of our findings was unchanged. most commonly set in increments of 5, there are 125 possible moneylines over this range.2 If bookmakers follow common conventions, there are conservatively four times the number of classifications used over this range of point spreads, in the moneyline market, as are used in the point spread market. The disparity would increase at higher point spreads since at these higher point spreads moneyline ranges are larger.

Table 2 presents breakeven win percentages for moneylines ranging from -110 to -1,000.

These are the percentage of games the favored team must win, given the moneyline, so that the bettor would break even if making a large number of bets on such games. For example, the -500 moneyline means the bettor must wager $500 on the favorite to win $100. Given this is the structure of the bet, the favorite must win 83.3% of games for the bettor to break even.

If we specifically examine the -3 point spread in Table 1, the mean favorite moneyline is

-161.4. Many moneylines occur at this point spreads. The 25th and 75th percentile values, respectively, are -150 and -170, and moneylines are as low as -140 and as high as -200. The mean moneyline of -161.4 roughly corresponds to a breakeven winning percentage of 61.5%.

The 25th and 75th percentile moneylines correspond to breakeven winning percentages of 60.0% and 63.0% respectively. Minimum and maximum moneylines correspond to breakeven winning percentages of 58.3% and 66.7% respectively.

It is clear that bettors in the moneyline market do not judge the relative strength of all teams in games with three-point spreads equally. The same is true of all other point spreads.

With this is mind, we explore whether the more finely stratified information available in the moneyline market has power to predict outcomes in the point spread market. Given a point

2 It can be argued that each market may choose to set prices or bets at increments as fine as they desire. In practice, moneylines are most often set in increments of 5, and prices of making bets in the point spread market are rarely unequal with the exception of games with three-point spreads. When prices are adjusted in the point spread market, they are most often set in increments of 5 as well. spread, we expect favored teams will be more likely to cover the spread when they are more likely to win according the moneyline (i.e. a 1.5 point favorite with a monelyline of -130 is more likely to cover the 1.5 point spread than a 1.5 point favorite with a moneyline of -120).

We examine this conjecture in Table 3. For each point spread, games are divided into quintiles according to the favorite moneylines. The high (low) quintile games are those where the favorites are least (most) heavily favored relative to other games with the same point spread. All games are then aggregated according to moneyline quintile. Table 3 presents the outcomes of games against the spread for each moneyline quintile. As expected, a higher percentage of low quintile favorites cover the spread (51.1%) than do high quintile favorites (44.2%). While the relationship is not perfectly monotonic, the percentage of favorites that cover decreases going from the low to high quintile. This clearly shows that the probability a team will win the game inferred from the more precise moneyline market has power to predict outcomes in the less precise point spread market.

The profitability of betting with and against favorite teams in each quintile is also presented in Table 3. It is profitable to bet against favorites in the highest three moneyline quintiles, and, as would be intuitive, it is most profitable to bet against favorites in the high quintile. It is never profitable to bet on favorites, but the smallest losses occur in the lowest two quintiles. These findings can be at least partially attributed to the favorite covering the spread in only 48% of games in our sample.

Returning to Table 1, the three-point spread is unique for a number of reasons. First, 232 games this point spread. This is more than double any other point spread in our sample.

Second, the standard deviation of the favorite moneyline is high relative to neighboring point spreads. And last, the range of favorite moneylines is also relatively high. This is not surprising as bookmakers are hesitant to move lines away from exactly three points, allowing the relative strengths of teams in these games to have greater disparities. Figure 1 shows the percentage of lines that move for each starting point spread. In support of previous literature, lines starting at three points are much less likely to change than those starting at other point spreads. Only

56.2% of three-point lines move compared to 77.6% for other games examined.

Given the unique characteristics of the three-point spread we repeat the analysis of Table

3 for the subsample of games with three-point spreads. The results, presented in Table 4, are the same in nature as those in Table 3, though much more pronounced. Low quintile favorites cover in 51.2% of games while high quintile favorites cover in only 36.0% of games. It is clear that for the three-point spreads, moneylines are very informative concerning outcomes in the point spread markets. This can be attributed to the wider variety of relative strengths of teams playing one another. Bettors in moneyline markets recognize these differences and bet accordingly, causing moneylines to move to reflect the market’s opinion of the favorite (and simultaneously underdog) team. While similar pressure may exist in the point spread market the line may not be moved by bookmakers.

Profitability numbers are similar to those in Table 3. While betting against high quintile favorite teams is profitable, betting with low quintile favorite teams is not. Betting against high quintile favorite teams provides a profit of 19.8%. Profits decrease in lower quintiles but exceed

10% in all but the lowest quintile. This can be largely attributed to a smaller than expected percentage (42.1%) of favorites covering three-point spreads in our sample. This proportion is significantly different from .5 at the 5% level. Whether this is a true bias related to the three- point spread or an anomaly will be left later research.

Prices in the Point Spread Market

Books will adjust the relative prices of making bets in the point spread market. This is common for games with three-point spreads as books are hesitant to move from this number

(e.g., the point spread of a game may be adjusted so that the favorite is -3 (-125) while the underdog is +3 (+105), combining a moneyline-type adjustment to the point spread). Though we do not have data to test the degree to which prices in the point spread market are adjusted to account for different relative team strength within given point spread categories, anecdotal evidence from observing posted point spreads and prices of these bets suggests this practice is not so common that the point spread market nears the precision in price setting of the moneyline market.

In Table 5 we present the profitability of betting against the favorite team within each quintile at various prices for making bets. While it may be argued that we are selecting teams with higher prices to make bets, or that we benefit from an unusually low number of favorites covering three-point spreads, the price of placing a bet must reach -180 before the strategy of betting against high-quintile favorites becomes unprofitable. This demonstrates the hesitancy of those setting point spreads to move from a three-point line or properly adjust the price of bets.

This is again evidence of the inability, or unwillingness, of a traditionally managed Point Spread market to incorporate all available information.

Conclusion

In this paper we examine the ability of two NFL markets (point spread and moneyline) to incorporate information. Presumably bookmakers employ the same information when setting both point spreads and moneylines, and we expect bookmakers take the same care to set both measures efficiently. While bookmakers may properly evaluate all available information when setting point spreads or moneylines, the nature of point spread markets makes full information incorporation difficult when compared to moneyline markets.

In this paper we show that the information in the moneyline market is more precise than the information in the point spread market. Specifically, we provide evidence that profits can be made in point spread markets by using moneylines that convey information regarding the relative strength of teams in a contest. Controlling for point spread, we show teams that are greater favorites according to the moneyline are more likely to cover point spreads, while lesser favorites are less likely to cover point spreads. This can be explained by the relative lack of stratification in point spread markets, where lines move in half point increments, as opposed to moneyline markets where odds move in increments of $5 or $10 per $100 bet. There is no evidence to suggest better information, or distinct information, is available for setting moneylines; thus, we can only attribute the ability of moneylines to predict outcomes in point spread markets to operational differences across the two markets.

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Table 1: Descriptive Statistics

Table 1 presents descriptive statistics for moneylines for the favorite team. Games are first divided according to point spread. The number of games at each point spread, as well as the mean, standard deviation, minimum, maximum, 25th percentile value, median, and 75th percentile value are presented. If fewer than 20 games had a given point spread, all games with this point spread were excluded. The sample includes all National Football League games in the 2006-2010 regular seasons.

Standard 25th 75th Line N Mean Deviation Minimum Maximum Percentile Median Percentile -1 43 -119.5 3.05 -130 -115 -120 -120 -120 -1.5 34 -125.4 2.85 -135 -120 -125 -125 -125 -2 56 -133.7 3.75 -145 -130 -135 -135 -130 -2.5 54 -141.1 4.52 -150 -135 -145 -140 -140 -3 232 -161.4 11.8 -200 -140 -170 -160 -150 -3.5 98 -190.2 9.43 -210 -170 -200 -190 -185 -4 53 -207.0 10.2 -240 -175 -210 -210 -200 -4.5 52 -222.8 9.42 -240 -200 -230 -222.5 -220 -5 38 -236.3 11.6 -260 -210 -245 -235 -230 -5.5 53 -247.6 11.0 -280 -200 -250 -250 -245 -6 51 -268.7 12.6 -300 -245 -275 -270 -260 -6.5 66 -294.4 14.7 -330 -270 -300 -290 -280 -7 79 -320.9 17.3 -360 -285 -330 -320 -310 -7.5 37 -357.7 19.9 -410 -330 -370 -355 -340 -8 32 -370.2 22.9 -410 -310 -380 -372.5 -355 -8.5 25 -401.4 38.7 -500 -350 -410 -400 -370 -9 34 -439.0 33.5 -550 -400 -460 -427.5 -420 -9.5 32 -499.2 54.6 -600 -400 -550 -492.5 -450 -10 39 -535.6 45.9 -625 -420 -575 -530 -500 -10.5 21 -584.5 51.5 -725 -475 -600 -600 -550 -11 20 -623.8 44.8 -700 -550 -650 -612.5 -600 -14 21 -971.4 137 -1250 -750 -1100 -1000 -850

Table 2: Moneyline Breakeven Win Percentages

Table 2 presents the percentage of games the favored team must win, given the moneyline, such that the bettor would break even if making a large number of bets on such games.

Breakeven Breakeven Moneyline Win % Moneyline Win % -1000 90.9% -200 66.7% -900 90.0% -190 65.5% -800 88.9% -180 64.3% -700 87.5% -170 63.0% -600 85.7% -160 61.5% -500 83.3% -150 60.0% -400 80.0% -140 58.3% -350 77.8% -130 56.5% -300 75.0% -120 54.5% -250 71.4% -110 52.4%

Table 3: Betting Results by Moneyline Quintiles

Table 3 presents the number of games when the favorite covers the spread, the game pushes, and the favorite fails to cover the spread, after dividing games based on moneylines. The percentage of games where the favorite covered (excluding games that pushed), and the percentage profit from betting on favorites to cover or fail to cover the spread are also presented. For each point spread, games are divided into quintiles according to the favorite’s moneyline. Games are then aggregated such that all the high moneyline games for each given point spread are in the same group, all low moneyline games are in the same group etc. Games in the high (low) quintile have favorites who are least (most) likely to win according the moneyline within their point spread groups. If fewer than 20 games had a given point spread, all games with this point spread were excluded. The sample includes all National Football League games in the 2006-2010 regular seasons.

Favorite Moneyline No Cover Quintile N Cover Push No Cover Cover % Cover Profit Profit Low 228 112 9 107 51.1 -2.3 -6.5 2 255 131 4 120 52.2 -0.4 -8.6 3 311 141 6 164 46.2 -11.5 2.6 4 283 129 7 147 46.7 -10.5 1.6 High 203 88 4 111 44.2 -15.3 6.4 High-Low 6.9 13.0 -12.8

Table 4: Betting Results by Moneyline Quintiles for 3 Point Spreads

Table 4 presents the number of games when the favorite covers the spread, the game pushes, and the favorite fails to cover the spread after dividing games based on moneylines. The sample includes all National Football League games in the 2006-2010 regular seasons with point spreads of exactly three points. The percentage of games where the favorite covered (excluding games that pushed), and the percentage profit from betting on favorites to cover or fail to cover the spread are also presented. Games are divided into quintiles according the favorite’s moneyline. Games in the high (low) quintile have favorites who are least (most) likely to win according the moneyline within their point spread groups.

Favorite Moneyline No Cover Quintile N Cover Push No Cover Cover % Cover Profit Profit Low 47 21 6 20 51.2 -1.9 -6.0 2 58 23 3 32 41.8 -19.1 10.5 3 32 12 2 18 40.0 -22.2 13.6 4 67 25 4 38 39.7 -22.8 14.2 High 28 9 3 16 36.0 -27.9 19.8 High-Low 15.2 26.0 -25.8 All 232 90 18 124 42.1 -18.2 9.8

Table 5: Betting Results by Price

Table 5 presents the percentage profit from betting on favorites to fail to cover the spread. The sample includes all National Football League games in the 2006-2010 regular seasons with point spreads of exactly three points. Games are divided into quintiles according to the favorite’s moneyline. Games in the high (low) quintile have favorites who are least (most) likely to win, according the moneyline, within their point spread groups. The Price of Bet is the amount that must be wagered to win 100 units.

Price of Bet Favorite Moneyline Quintile -110 -120 -130 -140 -150 -160 -170 -180 Low -6.0 -9.2 -11.9 -14.3 -16.3 -18.1 -19.6 -21.0 2 10.5 6.3 2.8 -0.2 -2.9 -5.2 -7.2 -9.0 3 13.6 9.4 5.8 2.7 0.0 -2.3 -4.4 -6.3 4 14.2 10.0 6.3 3.2 0.5 -1.9 -4.0 -5.8 High 19.8 15.5 11.8 8.7 6.0 3.6 1.5 -0.4 All 9.8 5.7 2.3 -0.6 -3.2 -5.4 -7.4 -9.1

Figure 1: Line Movements

Figure 1 presents the percentage of games in which the opening line moved based on the initial point spread. If fewer than 20 games had a given point spread, all games with this point spread were excluded. The sample includes all National Football League games in the 2006-2010 regular seasons.

100%

90%

80%

70% % of Lines Lines of % Moved

60%

50%

6.5 9.0 1.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 7.0 7.5 9.5

10.0 10.5 11.0 13.0 14.0 Opening Points Spread