how to pick a winning bracket using analytics

I’ve written about the NCAA basketball tournament many times before – click on my March Madness tag for my past posts. This time, I am going to summarize the different ways to select the winning teams in your bracket. There are many ways to choose a bracket, but using math models and analytics techniques seem to work best. Plus, it’s more fun than just guessing.

As I see it, there are two ways to pick a bracket: you can look at the team matchups and choose or you can look at the seed number and choose. I lean toward team level matchups when creating my own brackets, but I use the seed numbers, too. Some seed matchups (7/10 seeds for example) have historically high rates of producing upsets.

There are several tools that rank teams, and these ranking tools provide a way to see which team is “better” – the one with the higher ranking. One traditional tool is the RPI (access the RPI rankings here), but it’s not considered to be very good.

There are a number of more sophisticated ranking tools that use math modeling.

• LRMC – the standard LRMC and LRMC with no margin of victory [methodology]
• Ken Pomoroy’s rankings [Methodology]
• Sagarin rankings [methodology is proprietary]
• Massey Ratings [some methodology]
• ESPN’s BPI rankings [methodology]

These ranking tools are great and do well at predicting individual games, and they do extremely well on average. This means that these methods would do the best when averaged over, say, 1000 basketball tournaments. We don’t have 1000 tournaments – we have just one. Keep that in mind. These rankings also do not necessarily give insight into a matchup in a specific game.

Two models consider individual matchups when computing how far each team will make it in the tournament.

• Wayne Winston posts tournament odds on his blog based on his math models. He picks Louisville as the favorite followed by Florida (a 3 seed) and Indiana.
• Nate Silver has his tournament odds on fivethirtyeight. He uses a different model than Wayne Winston but finds similar results. Silver’s model picks Louisville, Indiana, then Florida [methodology].

The other way to pick rankings is to look at the seeds in the matchups. This is useful when a weak team from a major conference plays a top mid-major team. See this Business Week article on Sheldon’s advice for picking a good bracket. There is one tool developed by Sheldon Jacobson and his collaborators that focuses on seeds:

• BracketOdds [methodology here and here and more info here]

Here is one last thing to keep in mind:

• Preseason rankings matter: teams that are in the top 25 before the season starts are likely to go far in the conference despite their seeds, and likewise, top 25 teams at the end of the season who were unranked at the beginning of the season are likely to go home early.

There are other articles out there on how to pick a winning bracket. Here is what I recommend reading:

• Carl Bialik (the Numbers Guy at the WSJ) wrote an article about various tools available for picking a winning bracket.

Good luck with your bracket!