You Might Want to Moneyline an Underdog

The team you want to bet on is 3.5 point underdogs. You have two choices, you can take the points and bet on the spread or you can take your underdog to win outright and bet on the moneyline. Typically taking the points will require you to wager $110 to win $100 (110/100) depending on the vig. The moneyline bet will give you odds, paying $150 for risk $100 (100/150).

What should you do? Take the moneyline bet every time.

Why? Because the expected value of the moneyline bet is significantly higher than the spread bet. Since we’ve already decided to bet on the underdog, what we need to know is what the percentage of the time that the 3.5 point spread will come into play, that is the underdog will lose the game by 3 points or fewer, resulting in the underdog covering the spread but losing the game. Then we need to determine whether the moneyline risk reward compensates us enough to offset the additional risk.

A few statistics from our basketball data set, specifically the 1,025 games with a 3.5 point spread. Side note – a 3.5 point spread is one of the most common type of spreads in college basketball.

  • The underdog won 409 games (39.9%)
  • The underdog covered the spread in 528 games (51.5%)
  • The underdog covered the spread but lost the game in 119 games (11.6%)

Using these facts, the expected outcome of each bet is calculated below.

  • Underdog Spread = -$1.74
    • (.515*100)+(.484*-110)
  • Underdog Moneyline = -$.85
    • (.393*150)+(.607*-100)

As you can see, the expected payout is for both bets is negative. Sports books wouldn’t be in business over the long run if they set positive payout scenarios. But more importantly, the moneyline expected payout is $.89 higher. While this doesn’t seem like much, it’s worth noting that card counting at Blackjack raises the payout per $100 wagered from -$1 loss to a $1 game or a difference of $2. This same strategy which takes no work increases your expected payout by 45% of the advantage card counting gets you. You may also note that the moneyline odds could be +155 or +160 for this line which would increase your payout.

A few notes

  • The spread and the moneyline, while related, do not always move in lockstep. For the above example, I could find instances where a 3.5 point spread was listed for moneyline odds at +145 to +165 and this plays an important part in the analysis with the greater the payout increasing the appeal of moneyline.
  • A different and less formulaic way to think about this problem is to think about the percentage of time you will lose a moneyline bet but would have won the spread bet. In this instance, we know these odds – 11.6%. You are risking around 50% more betting spreads instead of moneyline to win 11.6% more wagers.
  • This same logic holds for betting on a favorite with the moneyline in this example. However, analysis has shown that this rule is much less likely to hold at other odds. View this post for more details.
    • Favorite Spread = -$8.25
      • (.484*100)+(.515*-110)
    • Favorite Moneyline = -$6.11
      • (.393*-170)+(.607*100)

How Likely Is The Favorite to Win?

Your team is favored by 8 points. How likely does this make them to win?

In college basketball, there is a 77% chance a team favored by 8 points will win. In fact, each point in the spread increases the chance of winning by about 3.5%. A good rule of thumb is that 50%+(3.5 x spread) will get you within 1-2% of the chance of the favorite winning. However, this rule only applies up to about 11 point spreads at which point each additional point added to the spread is worth significantly less than 3.5% additional chance of winning.

Using 22,215 college basketball games played from November 2012 to January 2018, the actual data is displayed below in table format.

Spread Games Percentage of Games Won by Favorite
1 703 52.7%
1.5 913 54.7%
2 1,010 55.3%
2.5 1,117 56.9%
3 1,002 60.6%
3.5 1,025 60.0%
4 995 62.3%
4.5 999 66.4%
5 892 66.8%
5.5 960 71.1%
6 848 69.8%
6.5 860 70.3%
7 781 76.6%
7.5 804 77.1%
8 710 77.0%
8.5 678 80.3%
9 577 81.1%
9.5 599 84.8%
10 540 82.2%
10.5 499 84.9%
11 452 85.8%
11.5 416 89.6%
12 378 89.1%
12.5 399 83.9%
13 362 89.7%
13.5 330 90%
14 301 92.3%
14.5 279 92.4%
15 258 95.7%
15.5 242 96.6%
16 193 94.8%
16.5 191 95.2%
17 191 93.1%
17.5 128 96.8%
18 153 98.0%
18.5 123 95.1%
19 117 98.2%
19.5 101 99.0%
20 or more 1,089 98.5%

Here is the table data in chart form. As you can see, a relatively straight line until 20 points at which point the n-size per spread drops below 100 and the chance of winning stays relatively flat at 98%-99%.

View the table data in chart form.

Notes

  • The highest spread with an underdog winner was 25.5. Interestingly, 3 teams won with that spread.
  • Spreads above 20 are rare, accounting for less than 5% of all games with a spread.

How Accurate Are Vegas Lines?

I’ve heard it many times…Vegas knows! But I wondered. How good are the lines?

I was unhappy with what I could find on the internet so I decided to look into it. To start, I defined what I wanted to know. How accurate are Vegas sports betting lines? To be more specific, I wanted to know the average difference between the line Vegas set for a game and the actual outcome. For example, New England was 4.5 point favorites over Philadelphia in Super Bowl LII. Philadelphia won the game 41-33. So in this example Vegas was 12.5 points off on setting the spread (Philadelphia was expected to lose by 4.5 points and won by 8 points).

But is this typical? In Super Bowl LI New England was 3 point favorites over Atlanta and won the game by 6 points. So Vegas was off by only 3 points in this example and pretty accurately predicted the outcome of the game.

In this instance, we need to look at a larger sample of games. For this analysis I choose college basketball for a few reasons.

  • College basketball is a mainstream sport which means many people want to place bets on it which is one check on accuracy.
  • College basketball has a lot of games, which means larger populations to analyze. There are 256 NFL games a year (32 teams play 16 games) while in college basketball there may be 4,000 games in a season with a spread. Larger data sets allow for different types of analysis.

For purposes of analyzing the data, the metric Absolute Difference from Spread (ADS) was calculated for every game in the data set. For 22,215 college basketball games in the population, the median ADS was 7 and the mean ADS was 8.6. To state this another way, half of college basketball games are within 7 points of the spread and half are greater than 7 points. 

View the data in chart form.

Is this good? We will explore this in a later post.

Data

  • The data used for this analysis is 22,215 college basketball games played between November 9, 2012 and January 31, 2018.
  • Absolute Difference from Spread (ADS) is the difference between the predicted outcome of the game and the actual outcome of the game measured using spread. For example, if Team A is favored by 15 and wins by 13, the ADS is 2. However, if Team A is favored by 5 and loses by 2 the ADS is 7.
  • The team with the largest ADS was Pittsburgh which on January 24, 2017 was 5 point underdogs to Louisville yet lost the game by 55 points. By the ADS measure, this was the most lopsided game in the last five years.