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Intro to Football Analytics: Home Field Advantage in College Football

Taking a look at the effects of playing at home

Mike Zarrilli

This is the third post in our new series at From The Rumble Seat introducing our readers to sports analytics and what we can find out using data. You can view all other Intro to Football Analytics posts in the story stream.

Home Field Advantage is a pretty big deal in college football. Every fan who has cheered their home team on in a huge rivalry game can tell you playing at home matters. Can you imagine having 80,000 people scream at you hoping you fail at your job? No thanks, I'll stay in the stands. But what I want to focus on in this piece is HOW do the home teams gain their advantage? In baseball the home team gets to bat last, which is obviously a huge advantage. But in football there isn't a "get to bat last" equivalent, yet home teams in football still enjoy an advantage. The conventional wisdom is that the home team generally enjoys a 3 point advantage at home. But lets see if we can empirically derive the point advantage.

In order to get an estimate of the home field advantage I looked at all games between 2008 and 2013 and removed any that happened at a neutral location. In those games the home team scored an average of 31.3 points while the away team scored an average of 23.4 points, for an advantage of about 8 points. That seems a little steep, so we need to investigate a little more. The previous analysis included games against FCS Opponents, but we don't really care about how much of a home field advantage we have when we play Elon; we expect to beat them by a lot. So lets exclude those cupcake games. But there are plenty of games against FBS opponents which are going to be blowouts. What I decided to do was only look at inter-conference games between 2008-2013. This way we can hopefully control for the quality of the opponent, since most teams in a conference are at least somewhat competitive with each other. Also up until recently every team had the same amount of home conference games as away conference games, so our data won't be biased by some teams having a greater share of home and away games. In inter-conference games only the home team scored an average of 29.1 points and the away team scored an average of 25.8 points, so a home field advantage of about 3.3 points. This is much more in line with the typical home field advantage cited by most college football fans. The following plot shows the entire distribution of scoring percentages by both the home and away team. The higher the hump the more likely the team scores that many points in a game. This has been smoothed so that scoring 21 and 22 points are almost equally likely, even though it is very rare that a team actually scores 22 points.


This is more for visuals than real analysis, but I think it shows the scoring differences between home and away teams pretty well.

So home teams outscore their opponents by an average of 3-3.5 points. But how do they accomplish this? I looked at a bunch of different statistics and compared the home and away performances in each. It turns out that home teams not only score more points than the away team (again these are all on average) they also gain more yards per play, yards per rush, yards per drop back, fumble less, recover a higher percentage of fumbles, get called for less penalties, kick a higher percentage of touchbacks on kickoffs, convert a higher percentage of third downs, and make a higher percentage of field goals. The only stat I found where the home team performs worse is that they get sacked more often when they drop back to pass. Here are all of the numbers for these. And yes, the margins are small, but this was measured over 2877 games. Combine that with the fact that the home team does better on nearly everything I measured, I think we can make some fair conclusions from this.

Home vs Away Splits

2008 - 2013 Seasons

Statistic Home Away
Points per Game 29.1 25.8
Yards per Play 5.68 5.46
Yards per Carry 4.97 4.74
Yards per Dropback* 6.43 6.20
Run % 51.5% 50.9%
Fumble % 4.1% 4.3%
Fumble Recovery % 49.7% 49.3%
Penalties per Game 5.68 6.09
Sack Rate 6.2% 5.7%
Touchback % 22.0% 20.7%
Third Down % 41.0% 39.0%
Field Goal % 73.8% 71.3%

The most interesting findings to me are the penalties per game and the fumble recovery percentage. The fact that home teams get called for about half a penalty less per game than away teams is pretty clear evidence of home team bias by the refs. What is also interesting is that the home team averages more yards per penalty. So either the penalties the refs aren't calling on the home team are of the 5-yard variety so that the average is higher, or the away team is committing more egregious penalties. We would need to dig deeper with more data to really get at the reason.

We have already proven that fumble recoveries are random, and while I would love to say the effect is real that home teams recover a higher percentage of fumbles than away teams, I don't think the effect is big enough. The difference between 49.7% and 49.3% fumble recovery rates is recovering 1 extra fumble for every 250 fumbles; that seems too small.

With all of these stats leaning towards the home team I'm not sure we can point to a cause of the home field advantage. It seems more likely to me, and this is just my opinion, that something about not having to travel and playing in front of your own fans leads to just playing better, and it is as simple as that.

Take Aways

  • In the 2008-2013 seasons home teams outscored their conference opponents by 3.3 points. This falls in line with the accepted advantage of 3 or 3.5 points.
  • Home teams also outplayed their opponents based on a variety of metrics, like yards per play, field goal percentage, and third down percentage.
  • A possible cause of the home field advantage is bias by the refs.

Future Analysis

  • Which teams have the most home field advantage?
  • Do different factors matter more for home field (rivalry, conference, night games, etc...)
  • What factors may cause home field advantage and can we measure them?
And here is the link to the code I used for this analysis. If you have any questions about it then feel free to ask in the comments.