for NCAA Tournament Pools

The annual NCAA men’s basketball tournament inspires many individuals to wager money in office and online pools that require entrants to predict the outcome of every game prior to the tournament’s onset. Coupled with the haphazard team selection behavior of many casual players, office pools ’ complexity suggests the possible existence of well-informed strategies that are profitable in the long run. Previous work in this area has focused on development of strategies that attempt to maximize the expected score of a set of selections. Unfortunately, the vast majority of pools use simple scoring schemes that do not reward the correct picking of upsets, meaning that an entry sheet that maximizes expected points will feature mostly favorites. This in turn means the sheet will have too much in common with many other players ’ sheets to be profitable. In this article, we seek to identify strategies that are contrarian in the sense that they favor teams that have a high probability of winning, yet are likely to be underbet by our opponents relative to other teams in the pool. Using 2003-2005 data from a medium-sized ongoing Chicago-based office pool, we show that such strategies can outperform the maximum expected score strategy in terms of expected payoff. We also developed “predicted contrarian” approaches that tackle the more difficult case where we assume opponent betting behavior is unknown, but may be estimated using web-downloadable data on the teams in the tournament