Misbehaving: The Making of Behavioral Economics (42 page)

In the 1999 draft, Ditka decided that the only thing stopping the Saints from winning a championship soon was the acquisition of one player, a running back named Ricky Williams. The Saints owned the number twelve pick, and Ditka was worried that Williams would be snapped up before their turn came, so he announced publicly that he would be willing to trade away all of his picks if he could get Williams (not the smartest negotiation strategy). When it was the Washington Redskins’ turn at the fifth pick and Ricky Williams was still available, the Saints were able to complete the trade Ditka wanted, although at a very steep price. Specifically, to move from the twelfth pick to the fifth pick, the Saints gave up all the picks they had in the current draft plus their first- and third-round picks the following year. Those latter picks turned out to be particularly costly to give away, because the Saints ended up as the second worst team in the league in 1999, meaning they gave away the second pick in the entire draft in 2000. Clearly, snagging Williams was not enough to turn the team around, and Ditka was fired. Williams played four years for the Saints and was a very good but not transformative player, and the team could have used the help of all the players they might have acquired with the draft picks they traded away. Cade and I wondered: why would anyone make such a trade?

The Saints’ trade was just an extreme example of the general behavior we thought we would find, namely overvaluing the right to pick early. Five findings from the psychology of decision-making supported our hypothesis that early picks will be too expensive:

1.
People are overconfident
. They are likely to think their ability to discriminate between the ability of two players is greater than it is.

2.
People make forecasts that are too extreme
. In this case, the people whose job it is to assess the quality of prospective players—scouts—are too willing to say that a particular player is likely to be a superstar, when by definition superstars do not come along very often.

3.
The winner’s curse
. When many bidders compete for the same object, the winner of the auction is often the bidder who most overvalues the object being sold. The same will be true for players, especially the highly touted players picked early in the first round. The winner’s curse says that those players will be good, but not as good as the teams picking them think. Most teams thought that Ricky Williams was an excellent prospect, but no one loved him as much as Mike Ditka.

4.
The false consensus effect
. Put basically, people tend to think that other people share their preferences. For instance, when the iPhone was new I asked the students in my class two anonymous questions: do you own an iPhone, and what percentage of the class do you think owns an iPhone? Those who owned an iPhone thought that a majority of their classmates did as well, while those who didn’t thought iPhone ownership uncommon. Likewise in the draft, when a team falls in love with a certain player they are just
sure
that every other team shares their view. They try to jump to the head of the line before another team steals their guy.

5.
Present bias
. Team owners, coaches, and general managers all want to win
now
. For the players selected at the top of the draft, there is always the possibility, often illusory, as in the case of Ricky Williams, that the player will immediately turn a losing team into a winner or a winning team into a Super Bowl champion. Teams want to win now!

So our basic hypothesis was that early picks were overvalued, meaning that the market for draft picks did not satisfy the efficient market hypothesis. Fortunately, we were able to get all the data we needed to rigorously test this hypothesis.

The first step in our analysis was just to estimate the market value of picks. Since picks are often traded, we could use the historical trade data to estimate the relative value of picks. If you want to get the fifth pick and you have the twelfth pick, as Ditka did, how much do you normally have to throw in to make that trade? The outcome of that analysis is shown in figure 18 below. The dots are specific trades that we used to estimate the curve. There are two things that jump out from this figure. The first is that it is very steep: the first pick is worth about five times as much as the thirty-third pick, the first one taken in the second round. In principle, a team with the first pick could make a series of trades and end up with five early picks in the second round.

The other thing to notice about this figure is how well the curve fits the data. The individual trades, represented by the dots, lie very close to the estimated line. In empirical work you almost never get such orderly data. How could this happen? It turns out the data line up so well because everyone relies on something called the Chart, a table that lists the relative value of picks. Mike McCoy, a minority owner of the Dallas Cowboys who was an engineer by training, originally estimated the Chart. The coach at the time, Jimmy Johnson, had asked him for help in deciding how to value potential trades, and McCoy eyeballed the historical trade data and came up with the Chart. Although the Chart was originally proprietary information only known by the Cowboys, eventually it spread around the league, and now everyone uses it. Figure 19 shows how highly the chart values first-round picks.

When Cade and I tracked down Mr. McCoy, we had a nice conversation with him about the history of this exercise. McCoy stressed that it was never his intention to say what value picks
should
have, only the value that teams had used based on prior trades. Our analysis had a different purpose. We wanted to ask whether the prices implied by the chart were “right,” in the efficient market hypothesis sense of the term. Should a rational team be willing to give up that many picks in order to get one of the very high ones?

Two more steps were required to establish our case that teams valued early picks too highly. The first of these was easy: determine how much players cost. Fortunately, we were able to get data on player compensation. Before delving into those salaries, it is important to understand another peculiar feature of the National Football League labor market for players. The league has adopted a salary cap, meaning an upper limit on how much a team can pay its players. This is quite different from many other sports, for example Major League Baseball and European soccer, where rich owners can pay as much as they want to acquire star players.

The salary cap is what makes our study possible. Its existence means that each team has to live within the same budget. In order to win regularly, teams are forced to be economical. If a Russian oligarch wants to spend hundreds of millions of dollars to buy a soccer superstar, one can always rationalize the decision by saying that he is getting utility from watching that player, as with buying an expensive piece of art. But in the National Football League, acquiring an expensive player, or giving away lots of picks to get a star like Ricky Williams, involves explicit opportunity costs for the team, such as the other players that could have been hired with that money or drafted with those picks. This binding budget constraint means that the only way to build a winning team is to find players that provide more value than they cost.

The league also has rules related to rookie salaries. The compensation of first-year players, by draft order, is shown in figure 20. The figures we use here are the official “cap charge” that the team is charged, which includes the player’s salary plus an amortization of any signing bonus paid up front. Figure 20 shares many features of figure 18. First of all, the curve is quite steep. High picks are paid much more than lower-round picks. And again, the estimated line is a very good fit for the data because the league pretty much dictates how much players are paid in their initial contracts.

So high picks end up being expensive in two ways. First, teams have to give up a lot of picks to use one (either by paying to trade up, or in opportunity cost, by declining to trade down). And second, high-round picks get paid a lot of money. The obvious question is: are they worth it?

Another way of asking this question is: what would have to be true to make the price of early picks rational, and is it in fact true? The price says that, on average, the first player taken in the draft is five times better than the thirty-third player. That fact alone does not tell us anything, since players’ values can vary by much more than a 5:1 ratio. Some players are perennial all-stars who can transform a team. Others are complete busts that cost the team a lot of money and provide little in return. In fact, high-profile busts actually hurt performance because the teams are unable to ignore sunk costs. If a team is paying a high draft pick a lot of money, it feels under a lot of pressure to put him in the game, regardless of how well he is playing.

The key appears to be how good a team’s managers are at distinguishing between stars and busts. Here is a simple thought experiment. Suppose you rank all the players taken at a given position (quarterback, wide receiver, etc.) by the order in which they were picked. Now take two players drafted consecutively, such as the third running back and the fourth. What is the chance that the player taken earlier is better by some objective measure? If the teams were perfect forecasters, then the player taken first would be better 100% of the time. If the teams have no ability, then the earlier pick will be better half the time, like flipping a coin. Take a guess at how good teams are at this task.

In reality, across the entire draft, the chance that the earlier player will be better is only 52%. In the first round it is a bit higher, 56%.
†
Keep that thought in mind, both as you read the rest of this chapter and the next time you want to hire someone and are “sure” you have found the perfect candidate.

Although this result gives a strong hint of how our analysis would come out, it is worthwhile to provide an outline of our more thorough evaluation. We followed the performance of each player drafted during our study period for the duration of his initial contract. Then, for each player-year, we assigned an economic value to the performance of that player; in other words, we estimated the value the player provided to the team that year. We did so by looking at how much it would cost to hire an equivalent player (by position and quality) who was in the sixth, seventh, or eighth year of his contract, and was thus being paid the market rate, because after his initial contract ran out he became a free agent. A player’s performance value to the team that drafted him is then the sum of the yearly values for each year he stays with the team until his initial contract runs out. (After that, to retain him, they will have to pay the market price or he can jump to another team.)

In figure 21, we plotted this total “performance value” for each player, sorted by draft order, as well as the compensation curve shown in figure 20. Notice that the performance value curve is downward-sloping, meaning that teams do have some ability to rate players. Players who are taken earlier in the draft are indeed better, but by how much? If you subtract the compensation from the performance value, you obtain the “surplus value” to the team, that is, how much more (or less) performance value the team gets compared to how much it has to pay the player. You can think of it like the profit a team gets from the player over the length of his initial contract.