Analyzing College Football Overtimes

Kevin Rudy 25 October, 2013

Allen RobinsonTwo weeks ago Penn State and Michigan played in a quadruple-overtime thriller that almost went into a 5th overtime. Had Penn State coach Bill O’Brien kicked a field goal in the 4th overtime instead of going for it on 4th and 1, the game would have continued. But the Nittany Lions converted the 4th down (which, by the way, wasn’t a gamble) and went on to score the game winning touchdown in the 4th overtime.

Watching this game got me asking a bunch of questions. How many college football overtime games go into 4 overtimes? Did Penn State still have home-field advantage since they were playing at home? How often is there an overtime period where neither team scores? (This happened twice in the Penn State-Michigan game.) If a team scores to tie the game in the closing minute (Penn State tied the game with 27 seconds left) do they have momentum on their side, and are thus more likely to win in overtime?

What better way to answer these questions than to conduct a data analysis using Minitab Statistical Software? Let's get to it!

The Data

With all the detail I wanted to get into, I wasn’t able to find any satisfactory overtime data online. So I had to collect all the data I wanted manually. It was slow going, but I was able to record every overtime game since 2008 (including bowl games). This gives us a sample of 156 overtime games. Obviously I would have liked to include every overtime game since 1996 (when college football started having overtime), but there are only so many hours in a day. So for now, our 156 games will have to suffice. If you want to follow along, you can get the data here.

Now that the boring part is over, let’s get to the statistics!

How many overtimes do games last?

Penn State and Michigan played 4 overtimes. A few weeks earlier Buffalo and Stony Brook played a 5-overtime game. Are these results typical when a game goes to overtime, or does it usually take only one extra session?

Bar Chart

Over the last 5 years, over 71% of games took only 1 overtime to produce a winner. And there are only a handful of games that make it past 2 overtimes. So if you hear somebody complaining that the college football overtimes rules are bad because it makes games last forever, know that most of the time, that’s not actually the case. 

Of course, there will always be outliers, as the Penn State-Michigan game showed us. But don't worry, Nittany Lion and Wolverine fans—odds are you won't have to go through 4 extra periods of heart-attack city again anytime soon.

Is there home field advantage in overtime?

For each game, I recorded whether the home team or away team won. There were also 13 bowl games, so I simply marked those games as “Neutral.”

Bar Chart

Of the 143 games played at one of the teams' home field, the home team won 80 of them, which comes out to 56%. After playing 4 quarters of football and having a tie score, I'm going to assume the teams are about equal. So if there was no home-field advantage, we would expect the home team to win about 50% of the time. We can perform a 1 proportion test to see if our sample proportion of 56% is significantly greater than 50%. If it is, we can conclude that home-field advantage does still exist even when a game goes to overtime.

One Proportion Test

At the α = 0.10 level, we can conclude that our sample proportion is significantly greater than 50%. The p-value is 0.09, which means if the true proportion of the home team winning was actually 50%, the probability that we would have seen a sample proportion of .56 or higher is only 9%.

I think this is enough evidence to conclude that home field advantage still exists in overtime.

Typically, an α of 0.05 is used, and we would reach a different conclusion if we use 0.05 for α. But I believe the reason the test is not significant at the 0.05 level is because we do not have enough power. In hypothesis testing, power is the likelihood that you will find a significant difference when one truly exists. We can use Minitab’s Power and Sample Size analysis to determine the power of our test when α is 0.05.

Power and Sample Size Analysis

This tells us that even if home teams did in fact have a 56% chance of winning in overtime (as opposed to 50%), we would only have a 41.6% chance of detecting a difference with our sample size. That's not very high. To increase the power of the test, we would need to collect a larger sample. But in the meantime, concluding home field advantage exists at the α = 0.10 level works for me.

When Penn State scored against Michigan with 27 seconds left, Bill O’Brien thought about going for a 2-point conversion instead of tying the game with an extra point. However, he decided on the latter. Seeing as Penn State was playing at home, this was likely the correct decision, as the odds were slightly in their favor to win in overtime (whereas, on average, teams convert 2-point conversions less than 50% of the time).

Does the team that plays offense 2nd win more often?

We just saw that home teams have a slight advantage in overtime. The consensus thought is that teams that start overtime on defense also have an advantage. That way you get to play offense 2nd, and you know exactly what you need to score to either win or tie the game. But will the statistics show that the team that gets the ball 2nd actually wins more often?

Bar Chart

When a team was victorious in overtime, they started on offense 2nd 61.5% of the time. This appears to be a huge advantage. But is it significantly different form 50% (the percentage we would expect if there were no advantage)?

One Proportion Test

With a p-value of 0.002, we can conclude that a team that starts on offense 2nd has a greater than 50% chance of winning the game. Coaches are clearly making the correct decision in overtime periods when they choose to start on offense 2nd.

And this brings us back to Bill O’Brien’s 4th and 1 call in the 4th overtime. Had Penn State kicked the field goal, they would have gone to a 5th overtime where Michigan would have been on offense 2nd. Tying the game would have actually given the advantage back to the Wolverines. By trying to win the game right there, O’Brien was making the correct decision. And the outcome worked out pretty well for him too, as Penn State went on to score the game winning touchdown on that drive.

Notice how I separated the decision from the outcome in the previous paragraph. It's always important to consider coaching decisions independently from the outcome. Even if Penn State had failed on the 4th-and-1 play, it was still the correct decision. The only difference is that outcome would have been poor. Coaches can't tell the future, so they have to make decisions without knowing what the outcome will be. Keep that in mind the next time somebody is using hindsight to judge a coaching decision.

Double Overtime?

What would a blog post on college football overtime be if it didn't extend into part 2?!?! There is so much more I want to get to! What is the most common combined score by both teams in overtime? How often is there an overtime period where neither team scores? Do teams that score to tie the game carry that momentum into the extra session for an overtime victory? It’s all going to have to wait for another day. Check back next week where I’ll (hopefully) finish off this blog post in double overtime!