Business Professor Develops Model for Drafting Fantasy Football Team
NFL training camps start gearing up in the coming weeks, and for millions of fans that means one thing: it's time to start thinking about drafting their fantasy football teams. Jeffrey Ohlmann's research might be able to help. Ohlmann, an assistant professor of management sciences in the University of Iowa's Tippie College of Business, is one of the first academics to study sports league drafts from a scholarly perspective. His first effort, "A Player Selection Heuristic for a Sports League Draft," was recently published in the Journal of Quantitative Analysis in Sports. In the paper, Ohlmann and his co-authors describe a decision-making model that utilizes mathematical techniques to maximize the value of the players a team drafts. At its heart, the model tries to help fantasy drafters overcome the fundamental handicap of not knowing what players will be available to draft in future rounds. Ohlmann's quantitative analysis model suggests drafters estimate the draft strategies of opposing teams to develop projections of which players will be available. Ohlmann said that although individual guesses regarding opposing teams' selections may be wrong, the errors often cancel each other out to create a sufficiently accurate forecast of the draft as a whole. The result, he said, is a model that produces a robust draft strategy that, on average, dominates alternative drafting rules-of-thumb. Does the model always result in the best team possible? "There are scenarios in which the model will under-perform alternative approaches," Ohlmann explains. "However, in a majority of the cases, our model performs favorably." Ohlmann and his colleagues, Michael J. Fry and Andrew W. Lundberg of the University of Cincinnati, implemented the model in a computer spreadsheet. Using player statistics from the 2005 NFL season, they tested five scenarios with differing levels of information about opposing teams' strategies. The tests suggest that the model requires relatively little draft forecast accuracy to out-perform alternative drafting rules-of-thumb. While Ohlmann and his colleagues demonstrate their model with a fantasy football league, he thinks a similar approach could be applied to building a real sports team. However, he said a crucial step in developing a useful draft-day tool that a professional sports team could use is determining the best way to measure a player's abilities. Ohlmann said this could be accomplished by combining quantitative methods with scouts' expertise. "Quantitative analysis is not a crystal ball and will not be 100 percent accurate in identifying which players are going to be successful, but it will help minimize the error that results when the front office personnel are 'fooled by their eyes' and let emotions affect their decisions," he said. Ohlmann said the underlying theme of his research has uses beyond fantasy sports drafts. The work is related to the more general problem of how an individual makes choices when those choices may be altered based on the choices other people make. Ohlmann also has discovered that sports provide an excellent teaching tool for helping students understand quantitative methods. "Many students are sports fans, and even if they aren't, they can relate to the examples." Ohlmann's research mirrors the trend in sports management at the top professional levels, where the use of quantitative analysis to guide decision-making is becoming more accepted. The most well-known proponent of quantitative analysis within the professional sports ranks might be Billy Beane, who, as general manager, has built the Oakland A's into a consistent winner despite low player salary budgets by relying on statistical analysis to draft and sign players. Beane's methods, outlined in the best-selling 2003 book, "Moneyball," are based on the ideas of Bill James, a writer who, in the 1970s, pioneered the analysis of baseball players' performance by a thorough examination of his statistics and spawned the field of sabermetrics. Recently, one of Beane's protégés, Boston Red Sox general manager Theo Epstein, built the Red Sox into a World Series winner by combining quantitative analysis and a $100 million-plus salary budget. Coincidentally, Ohlmann notes, the Red Sox are owned by John Henry, who made his fortune applying quantitative analysis to futures markets. In recent years, other sports have been catching up to the sabermetrics-obsessed sport of baseball. Today, numerous teams in the NBA and the NFL employ "quant" experts to perform quantitative analysis to guide personnel decisions and in-game strategy. Recognizing the abundance of decision-making problems in the sports industry and the growing availability of data to analyze these problems, scholarly attention on sports problems is growing. The Institute for Operations Research and the Management Sciences recently formed a sub-section that focuses on the promotion of sports-based research, in which Ohlmann is an officer. Ohlmann said he thinks his paper might be an indicator of this growing interest of both academics and practitioners. In the first two months after publication, his paper had been downloaded from the journal's web site more than 100 times. The article can be accessed online at www.bepress.com/jqas/vol3/iss2/5.
Contact: Tom Snee, UI News Services, 319-384-0010