Are Behavioral Biases Consistent Across the Atlantic? The Over/Under Market for European Soccer

1. Are Behavioral Biases Consistent Across the Atlantic? The Over/Under Market for European Soccer Rodney J. Paul – St. Bonaventure University Andrew P. Weinbach – Coastal Carolina University

2. Introduction • In North American sports betting – Biases demonstrated in totals (over/Under) markets • This study examines European Football (Soccer) Leagues to test for similar bias

3. Betting on Totals • What are Totals? • Over/Under bets • Sportsbooks offer a “total” for total points scored in a game (both teams, combined) • Over – wager that score > total • Under – wager that score < total • Example – final score of 2-1 > 2.5

4. A preference for the “over” • Fans appear to prefer betting on the over (prefer scoring?) • Highest totals appear “too high” (under wins > over wins) • National Football League – Paul and Weinbach (Journal of Sports Economics – 2002) • College Football (NCAA) and Arena Football – Paul and Weinbach (Journal of Economics and Finance – 2005) • National Basketball Association – Paul, Weinbach, Wilson (Quarterly Review of Economics and Finance – 2004) • Similar results found in Major League Baseball, National Hockey League, and Canadian Football League • For Monday Night Football – Better ratings for high scoring games – Paul and Weinbach (Journal of Economics and Business)

5. Source of the bias • All North American Sports exhibit similar results • For games with highest totals • Under wins often enough to reject the null hypothesis of a fair bet (Win% = 50%) • In some cases, under wins enough to reject the null hypothesis of no profits (win % = 52.4%) • Why do totals appear to be inaccurate? • Traditional model of sportsbook behavior – over bettors drive totals “too high” • Levitt (2004) Model of sportsbook behavior – sportsbooks forecast game outcome and fan interest

6. Bias for Over in European Soccer? • European Soccer Leagues • Low-scoring • Huge fan base • Are European Soccer fans different? • No bias, or bias toward “under” may indicate difference • If “over” bias observed, preferences consistent with North American bettors • Attraction of scoring may be universal trait

7. Testing efficiency • Subjective probability = objective probability • Based on sportsbook odds • Do betting odds represent accurate forecasts of true outcomes?

8. Data • www.Football-Data.co.uk • 2 full seasons for 22 leagues • Only leagues with complete data • All bets based on proposition over/under 2.5 goals • Odds adjustments used to adjust prices / rather than moving total • Odds reported as return or payback to the a 1 unit bet on the “over” • For example: a winning over bet with payback of 2.2 will return €2.2 for a €1 wager (1.2 + 1) • Subjective probability calculated based on Gandar , et. al. (2002) methods

9. Table I: Returns and Market Efficiency Tests for European Soccer Totals 2005-2007

14. Results • Full sample - 15,570 observations • “Under” returns -4.770% • “Over” returns -11.64% • Subj. prob (47.7%) • Obj. prob (45.83%) • Z: -4.6620

17. Bettor Biases • Overs vs. Unders • Full Sample - “overs” appear overbet • Favorite-longshot bias • “overs” with longest odds overbet • “unders” with longest odds not overbet • Considering both biases better explains results

18. Conclusion • Over bias similar to North American sports • Favorite longshot bias • Multiple biases present