In 2007, the Crayola crayon company encountered a problem. Labels were coming off of their crayons. Up to that point, Crayola had done little to implement data-driven methodology into the process of manufacturing their crayons. But that was about to change. An elementary data analysis showed that the adhesive didn’t consistently set properly when the labels were dry. Misting crayons as they went through the labeling machines solved the problem, and that project’s success prompted Crayola to expand the use of statistical methods. The following year, the company’s initial wave of Six Sigma projects saved more than $1.5 million, and Crayola now relies on a data-driven culture of continuous improvement to enhance the quality of their crayons.
But statistical success stories don’t have to be confined to the business world. Baseball has already proven that the advancement of statistical analyses can revolutionize a sport. Basketball and hockey teams are also starting to look into how analytics can improve the quality of their team. The only sport that seems to be lagging behind is football. But all it will take is one team to have success implementing statistics into their game plan, and others will surely follow.
Are you listening, Virginia Tech?
The Hokies are playing the defending National Champion Ohio State Buckeyes on Monday night. Ohio State is a double digit favorite in the game, and a good part of the reason is because their offense is great.
Just how great? I’m glad you asked.
Ohio State’s Offense the Previous Two Seasons
Recently, I created a regression model that can calculate the number of points a football team is expected to score based on their field position and whether they are playing at home or on the road. The data comes from every Big Ten conference game the last two seasons. To no surprise, the farther you are from the end zone, the fewer points you’re expected to score. And you’re expected to score fewer points on the road than at home.
Since Ohio State is playing at Virginia Tech, let’s focus on teams playing on the road. I took the data from my previous analysis, removed drives by the home team, and I removed Ohio State. Here is a fitted line plot of the data:
This is exactly what we would expect. The farther you are from the end zone, the fewer points you’re expected to score. Now let’s make the same plot for all of Ohio State’s road drives the previous two seasons.
You can start Ohio State anywhere on the field, and odds are they are going to score you on before you score on them. Start Ohio State on their own 1 yard line, and the model says their expected points are still 2.6 (compared to a value of -1.8 for the other Big Ten teams). But the most impressive part is that the data included 20 drives that Ohio State started inside their own 20 yard line. Here is a bar chart of the next score:
Even backed up in their own territory and playing on the road, Ohio State was the next team to score 75% of the time, with almost all of those scores being touchdowns. With an offense that good, it really begs the question.
Why even give them the ball?
The Onside Kick
The onside kick is mainly used at the end of games when a losing team is desperate to get the ball back before the clock runs out. But there is no rule saying you can’t do an onside kick early in the game. Or even do an onside kick every time you have a kickoff.
Would it actually benefit Virginia Tech to attempt an onside kick every time? Let’s calculate the percentage of kicks they would need to recover to make it worth it. If you kick the ball deep, most drives will start from the 25 yard line. So we’ll use that for Ohio State’s starting position on a deep kick. The model above shows that we can expect Ohio State to score about 3.4 points starting from their own 25 yard line.
An onside kick needs to travel at least 10 yards in order for the kicking team to legally recover it. Kickoffs are from the 35 yard line, so if Virginia Tech recovers they will be 55 yards from the end zone, and if Ohio State recovers they will be 45 yards from the end zone. This gives Virginia Tech an expected point value of 2.5 points (calculated using this regression model) and Ohio State would have an expected point value of 4.5 points. Now we can use algebra to calculate the break-even success rate, where p is the probability that Virginia Tech recovers the onside kick.
-3.4 = 2.5*p – 4.5*(1-p)
-3.4 = 2.5*p – 4.5 + 4.5*p
1.1 = 7*p
p = 1.1/7 = .157 = 16%
So if Virginia Tech can recover the onside kick about 16% of the time, their total expected points will be the same as if they were to kick deep. If they can recover a higher percentage, then they should be attempting an onside kick every time.
I couldn’t find any good data on college onside kicks, but in the NFL, non-surprise onside kick recovery rates are approximately 20%. The success rate in college football should be pretty similar. And hey, 20% > 17%, so onside kick every time, right?
Not so fast.
Anytime you perform a data analysis, it’s important to know where your data came from. In this case, our expected points for Virginia Tech came from data from all Big Ten teams. So really, it’s what we would expect an average Big Ten offense to score against an average Big Ten defense. Last year, according to Football Outsiders S&P+ ratings, Virginia Tech ranked 85th in offense, and Ohio State ranked 11th in defense. So when Virginia Tech has the ball, it will really be closer to a below average Big Ten offense going up against an above average Big Ten defense. This means our estimate for Virginia Tech’s expected points after a successful onside kick is probably a little too high.
Additionally, Virginia Tech had the #10 ranked defense last year and almost everybody returns from that defense this year. Our model for Ohio State's expected points is based off of an average Big Ten defense. So we should lower Ohio State’s expected points for both a deep kickoff and an unsuccessful onside kick. But how much we should decrease these values by is hard to quantify. So let's look at different values and see how it affects the break-even success rate. In the previous equation, I decreased both Ohio State's expected points and Virginia Tech's expected points by the values in the following table.
|Decrease Expected Points By||New Break-Even Success Rate|
We see that the larger the effect of the better than average defenses and Virginia Tech's poor offense, the more the statistics side with not attempting an onside kick every time.
To Onside Kick or Not to Onside Kick?
With the uncertainty of how much the defenses and Virginia Tech's offense affect the numbers, we can’t definitively say that Virginia Tech should onside kick every single time. However, this data analysis has shown enough that we can definitely say one thing.
Virginia Tech should attempt an onside kick……..at least once.
The 20% value we used for onside kick recoveries was for non-surprise onside kicks. However, in the NFL surprise onside kicks succeed close to 60% of the time. And the first onside kick Virginia Tech attempts will certainly take Ohio State by surprise. And the second one probably will too. Maybe even a third. But eventually Ohio State would adjust their formation of their kick return team and the success rate would drop to that 20% value.
So if Virginia Tech wants to maximize their chances of winning, they really should attempt at least one onside kick. Until Ohio State adjusts their kick return team, anytime Virginia Tech kicks the ball deep they’re just giving away free points.
Ohio State at Home
I’d be remiss if I didn’t share one last thing about the Ohio State offense. The previous data for them only included Big Ten games played on the road. Impressive as it was, their offense gets even better when playing at home. How much better? Well, it doesn’t matter where they start with the football
Like, at all.
Virginia Tech can be thankful they’re playing Ohio State at home. If it was at Columbus, this data analysis not only would have concluded that they should onside kick every time, but it would have said never to punt either!
Before Crayola fully embraced the widespread use of statistical analyses, the vice president of manufacturing said he saw people spend more time trying to figure out how to come up with data that supported their thesis rather than letting the data reveal where they needed to go. It’s that kind of thinking that is too prevalent in football today. If you were to never punt against Ohio State, the first time the Buckeyes scored a touchdown after you failed on 4th down, people would point to that as proof that your strategy doesn’t work. But the numbers speak for themselves. Had you punted, Ohio State probably would have scored a touchdown anyway.
So take note Hawaii, Northern Illinois, Western Michigan, Maryland, Penn State, Minnesota, and Michigan State. If you go into Columbus and willingly give Ohio State possession of the football, you’re doing nothing but hurting your football team. Go ahead and ignore the data if you want. Just know that if you do, you might end up with some defective crayons.