Possession of a ball in a ball game is a binary act. You either have it or you don’t. That means that the total value of stats associated with possession is also binary. This is true regardless whether the sport splits the value of a turnover in two or not, and notions of shared blame can cause issues when thinking about football. Football isn’t like other sports. Some of its “turnovers”, the punt especially, aren’t as easily quantifiable in the terms of other sports.
As an example of shared blame, we’ll take on the turnover in basketball. The potential value of the shot in the NBA is one point. This is easy to see, because a shot is worth 2 points and a typical NBA shooting percentage is about 50 percent (or a 3 point shot, with a percentage around 33%). That said, the value of the possession is two points, and the total value of the turnover is also two points.
Wait a minute, you say. The STL stat is generally only valued at 1 point. How can it be two? Well, there are two stats associated with a turnover in basketball. There is the TO stat, and the STL stat. And in metrics like the NBA Efficiency metric, each of these stats is valued at a point. TO + STL = total value of 2 points. The turnover in basketball is worth 2 points, and thus the possession is worth two points. The sum gets hidden because half of it is credited to the thief, and half is debited from the one who lost the ball.
The value of the turnover is the difference in value between the curves.
The classic description of the turnover in football derives from the Hidden Game of Football, and because their equivalent points metric is linear and independent of down and to go measures, the resultant value for the turnover is a constant. This isn’t easy to see in traditional visual depictions, but becomes easy to see when you flip the opposition values upside down.
See how the relative distance between the lines never change? By the way, you can do the same thing for basketball, though the graph is a bit on the trivial side.
This curve probably should have some distance dependence, actually.
These twin plots are a valuable way to think about the game, turnovers, and for that matter, the game of football as a series of transitions between states. For now, by way of example, we’ll use these raw NEP data I calculated for my “states” post. We’ll plot an opposition set of data upside down and show what a state transition walk might look like using these data.
The game of football can be described as a "walk" along a pair of EP curves.
Not that complicated, is it? You could visualize these data two ways: as a kind of “Youtube video” where the specific value for the game changes as plays are executed, and the view remains 2D, or as a 3D stack of planes, each with one graph, each plane representing the game at a single play in the game.
Even in football, though, you could attempt to split the blame for the turnover into two parts: there is the person that lost the ball, and the person that recovers it. So the value for the state transition from one team to the next could be split in two, a la basketball, and credit give to the recovering side and a debit taken from the side losing the ball.
So what about the punt? It has no equivalent in basketball or baseball, and in general, looks just like a single state transition.
The punt, in this depiction, is a single indivisible state transition from one team to the other.
It’s a single whole, and therefore, you can get yourself into logical conundrums when you attempt to split the value of the punt in two.
This whole discussion, by the way, is something of an explanation for Benjamin Morris and folks like him, who saw his live blog on October 9, 2011. It’s not easy getting this point across using his graphics on his site. My point is more fully developed above, and why I was saying the things I did more evident from the graphics above.
Ben, btw, is an awesome analytics blogger. Please don’t take this discussion as any kind of indictment of his work, which is of a very high quality.