Tag Archives: science literacy

why is it so easy to forecast the Presidential election and so hard to forecast the NCAA basketball tournament?

This blog post is inspired by my disappointing NCAA March Madness bracket. I used math modeling to fill my bracket, and I am currently in the 51st percentile on ESPN. On the upside, all of my Final Four picks are still active so I have a chance to win my pool. I am worried that my bracket has caused me to lose all credibility with those who are skeptical of the value of math modeling. After all, guessing can lead to a better bracket. Isn’t Nate Silver a wizard? How come his bracket isn’t crushing the competition? Here, I will make the case that a so-so bracket is not evidence that the math models are bad. To do so, I will discuss why it is so easy to forecast the Presidential election and so hard to forecast the NCAA basketball tournament.

Many models for the Presidential election and the basketball tournament are similar in that they use various inputs to predict the probability of an outcome. I have discussed several models for forecasting the Presidential election [Link] and the basketball tournament [Link].

All models that didn’t solely rely on economic indicators chose Obama to be the favorite, and nearly all predicted 48+ of the states correctly. In other words, even a somewhat simplistic model to forecast the Presidential election could predict the correct outcome 96% of the time. I’m not saying that the forecasting models out there were simplistic – but simply going with poll averages gave good estimates of the election outcomes.

The basketball tournament is another matter. Nate Silver has blogged about how models to predict tournament games using similar math models. Here, we can only predict the correct winner 71-73% of the time [Link]:

Since 2003, the team ranked higher in the A.P. preseason poll (excluding cases where neither team received at least 5 votes) has won 72 percent of tournament games. That’s exactly the same number, 72 percent, as the fraction of games won by the better seed. And it’s a little better than the 71 percent won by teams with the superior Ratings Percentage Index, the statistical formula that the seeding committee prefers. (More sophisticated statistical ratings, like Ken Pomeroy’s, do only a little better, with a 73 percent success rate.)

To do well in your bracket, you would need to make small marginal improvements over using the naive model of always picking the better seed (72% success rate). Here, a 96% success rate would be unrealistic — an improved model that would get 75% of the games correctly would give you a big advantage. The big advantage here means that if you used your improved method in 1000 tournaments, it would do better on average than a naive method. In any particular tournament,  the improved method may still lead to a poor bracket. It’s a small sample.

The idea here is similar to batting averages in baseball. It is not really possible to notice the difference between a 0.250 batter and a 0.300 batter in a single game or even across the games in a single week. The 0.250 hitter may even have a better batting average in any given week of games. Over the course of the season of 162 games, the differences are quite noticeable when looking at the batters’ batting average. The NCAA does not have the advantage of averaging performance over a large number of games — we are asked to predict a small set of outcomes in a single tournament where things will not have a chance to average out (it’s The Law of Small Numbers).

It’s worth noting that actual brackets get fewer than 72% of the games correct because errors are cumulative. If you put Gonzaga in the Elite Eight and they are defeated in the (now) third round and do not make it to the Sweet Sixteen, then one wrong game prediction leads to two wrong games in the bracket.

It’s also worth noting that some games are easier to predict than others. In the (now) second round (what most of us think of as  the first round), no 1 seed has ever lost to a 16 seed, and 2 seeds have only rarely lost to 15 seeds (it’s happened 7 times). Likewise, some states are easy to predict in Presidential elections (e.g., California and Oklahoma). The difference is that there are few easy to predict games in the tournament whereas there are many easy to predict states in a Presidential election. Politico lists 9 swing states for the 2012 election. That is, one could predict the outcome in 82% of the states with a high degree of confidence by using common sense. In contrast, one can confidently predict ~12% of  tournament games in the round of 64 teams using common sense (based on four of the games corresponding to 1 seeds). Therefore, I would argue that there is more parity in college basketball than there is in politics.