This article looks at historical trends regarding NFL games and betting. The results are based on historical lines dating back to 1978. The free data source includes available betting lines from Sunshine Forecast. Analysis was performed using a combination of Excel and MATLAB.

In general, we will look largely at probabilities given a line more or less than a certain amount. This is opposed to looking at trends at a given line. Although that would be ideal (how often does one team cover when favored by five and a half points), but that method severely decreases sample sizes and requires extra fitting and smoothing to identify trends.

### Point Spread

The first trend we’ll look at is the probability that a team covers the spread based on how many points they are favored by. Below, we have this probability for home teams determined along with 90% confidence intervals.

One can see that there is a definite trend. In general, the more points that a home team is favored by, the more likely it will not cover the spread. The left hand side of the plot shows essentially how often the home team covers the spread in all situations (favored by more than -20 points). The plot shows that the home team covers about 48% of the time. Similarly, the visiting team covers about 48% of the time, and the rest of the games push.

When the home team is favored by more 10 points or more, they cover the spread only 45% of the time. Around this point, it may prove profitable to start betting the visiting team. Finally, when favored by 15 points, the home team covers 35-40% of the time. Betting the other side is almost certainly profitable in the long term.

You may also notice how the trend jumps around a bit and the confidence intervals increase towards the right side of the plot. This is because the sample size decreases as you hit the extreme cases (large spreads). Something you should notice with later posts as well. That isn’t to say you should ignore the data at the ends, but you should be aware that it is less likely to be accurate than data points with a tight confidence interval.

Another small thing that appears to be significant is a bump at 4 points favor. Interestingly, data shows that the home team is more likely to cover with a 4 or 5 point spread than a 2 or 3 point spread. It is still not profitable to bet long term at those odds, but it is an unexpected trend (that appears significant). The reason may be due to how the game is played in those situations. For example, a team that is down 2 or 3 points will be less aggressive than one that is down by 4 or more (needing a touchdown towards the end).

If we take a look at the same plot but for the visiting team, a few things are different.

Just like the home team, there is a relatively quick decline in the probability the visiting team covers when favored by more than 5 points. What is unusual, however, is an increase in covers when favored above 10 points. This could be due to smaller sample sizes and meaningless. Perhaps, though, visiting teams with huge spreads only happen when they are significantly better than the home team and lead to some blow outs.

### Over/Under Total

Let’s take a look at how your odds change when betting the total. First lets look at betting the over.

In the above plot, we look at the probability of winning an over bet when the total line is less than a particular amount. Along with this, we have 90% confidence intervals that are based on the sample size at each line (all points have at least 50 samples). As one may expect, betting the over appears to be best when the total line is small. For all cases (total line less than 65), the odds of winning by betting the over is about 48%. Looking at the other side of the graph, a total under 40 points has a 50% or higher chance of going over.

Let’s take a slightly different look at the odds of hitting the under.

An interesting point off the bat is the general odds of winning any under bet. At over 49% when including all cases (total greater than 25 points), the under bet has a higher chance of winning than the over shown before. Looking at high total lines, we have a trend that is near profitable. With a total line greater than 45 points, betting the under gives a historical winning percentage of just over 52%, essentially the break even point. On the edge of the plot at total line greater than 50 points we see a sharp decrease in winning percentage. This is likely due to smaller sample sizes, but I would be a bit weary of unusually large totals. In general, a good betting strategy is betting the under, especially on high totals.

### Divisional Games

Here we’ll look at how divisional games affect the total. As you’ll see, taking this into account can make a big difference! First, let’s look at how often divisional games hit the under, opposed to non-divisional.

Excuse the rather brand bar-graph, but the difference is stark. Non-divisional games hit the under about 48.5% of the time, while divisional games hit the under 51.6% of the time. Right of the bat, it appears betting divisional games on the under could be a good strategy. Let’s take a closer look at how this changes with the total line.

Similar to the under percentage plot looked at in Part 2, the odds of hitting the under increase as the total line increases. For divisional games, however, it is quite a bit higher. With a total line of at least 35 for divisional games, betting the under appears profitable. Somewhere around a total of 47-50, betting the under give us nearly a 60% win rate! If that holds, it is an outstanding rate of return.

This begs the question, why do divisional games hit the under so much? My best guess is because the teams play each other often, the defenses are more prepared than they are against non-divisional opponents. The coaches are more aware of the schemes presented by their opponent as well. This, combined with the books getting even bets on both sides, may result in a large trend like this.

### Streaks

Now we’re let’s examine how winning and losing streaks affect a teams chance to cover. First lets look at how the home team does with a losing streak.

In the above plot, we are technically looking at winning streaks, but negative win streaks are losing streaks. Here we can see that home teams are more likely to cover with a losing streak, at least to a point. There is a significant increase in cover probability for teams with a losing streak of 5 or more games, at 52%. It appears that teams on losing streaks are undervalued, and it may be beneficial to bet them to cover. In the few cases with teams that had longer losing streaks, say over 8 games, the trend jumps around. Once again, be aware of the small sample size.

Considering our previous articles, teams with losing streaks may have large spreads as well. Always remember that a bad NFL team is still full of professionals. Let’s flip the graph and take a look at streaks greater than a certain amount. Now we are essentially looking at win streaks opposed to losing streaks.

Here, we see that betting on a winning home team is an okay strategy but only to a point. Teams with a winning streak of at least 3-4 cover about 52% of the time, which isn’t bad. However, when looking at only win streaks greater than that result in a significantly lower probability of covering. Let’s see if this holds for visiting teams. First lets look at visiting teams with losing streaks.

Here we have one of the strongest trends out there. Losing teams do very well on the road against the spread. That isn’t to say they are winning these games, but road teams on a losing streak are severely undervalued. Visiting teams with a 5+ game losing streak cover over 52% of the time, and teams on a 7+ game losing streak cover over 60% of the time! Bet on that!

Finally, lets take a look at visiting teams on a win streak.

It gets a little messy after 5 games, but the initial trend is clear. Visiting teams on a win streak do not cover as often. With a win streak of at least 4 or 5 games, visiting teams only cover 46% of the time, compared to about 48% in general. After 5 games, the trend appears to disappear and reappear, so I would take it with a grain of salt.

### Rest

We are now going to look at how rest affects a teams odds against the spread. In the graph below, we look at the rest differential plotted against the odds the visiting team covers. Rest differential is how much more time off one team has over another.

As we work our way towards the right side of the graph, the visiting team has more rest than the home team. Here there is a clear trend! When a visiting team has more rest than the home team, that is days since their last game, they are more likely to cover the spread. One would think that Vegas would take rest into account when setting their lines, but clearly visiting teams with rest are undervalued.

A good thing to note is how the trend is clear regardless of the amount of extra rest. It doesn’t just apply to teams coming off a bye week. A team that played Monday night or Thursday night, for example, could setup a situation where there is a rest differential of a few days.

Let’s take a look at the same situation but with the home team.

For some reason, the same trend does not hold in this case. With a couple extra days of rest, the home team has a higher chance to cover, but with 4 or more it’s about equal to the full population. It’s hard to say exactly why this is the case, but it appears Vegas does not correctly value home rest.

### Homesick

Finally, we’ll see how teams may be under or overvalued based on their time since their last home or away game. To start things off, let’s look at how often a home team covers based on their time away.

Not too much to say here, but it appears that teams on a long home stretch may benefit against the spread. Teams that go 3+ weeks without an away game improve their odds of covering by a few percentage points. If we switch to looking at the visiting team, something a little clearer shows up.

Visiting teams that have played another away game in the last week or two improve their odds of covering the spread. At about 51%, these trends are not ideal/profitable long term, but they are interesting to point out. It is likely the case that teams playing away games perform poorly, making them undervalued for the next week or two.

There are many different angles to take when it comes to sports and football betting. Here we only looked at a few. Professional betters certainly analyze these sorts of trends and many others. Good luck betting, and thanks for reading!