nep-spo New Economics Papers
on Sports and Economics
Issue of 2009‒06‒03
two papers chosen by
Joao Carlos Correia Leitao
Technical University of Lisbon

  1. Contests with doping By Dmitry Ryvkin
  2. Measuring Consensus in Binary Forecasts: NFL Game Predictions By ChiUng Song; Bryan L. Boulier; Herman O. Stekler

  1. By: Dmitry Ryvkin (Department of Economics, Florida State University)
    Abstract: Doping, or the use of illegal performance-enhancing drugs, is an epidemic problem in sports ranging from the Olympics to high school athletics. Sports organizations have been trying numerous approaches to discouraging these activities. This paper presents a theoretical model of doping in a winner-take-all contest environment to help investigate the efficiency of anti-doping enforcement policies. We show that, under fairly general conditions, the optimal frequency of random testing increases in the number of tournament participants. We also find that the presence of even a very small penalty, in addition to expulsion from the contest, makes random testing more effective, especially in large tournaments. Additionally, we find that for a given testing frequency, the optimal level of the penalty can be nonmonotonic in the number of players.
    Keywords: doping, contest, entry
    JEL: D02 D74 K42 M54
    Date: 2009–06
  2. By: ChiUng Song (Science and Technology Policy Institute); Bryan L. Boulier (Department of Economics The George Washington University); Herman O. Stekler (Department of Economics The George Washington University)
    Abstract: Previous research on defining and measuring consensus (agreement) among forecasters has been concerned with evaluation of forecasts of continuous variables. This previous work is not relevant when the forecasts involve binary decisions: up-down or win-lose. In this paper we use Cohen¡¯s kappa coefficient, a measure of inter-rater agreement involving binary choices, to evaluate forecasts of National Football League games. This statistic is applied to the forecasts of 74 experts and 31 statistical systems that predicted the outcomes of games during two NFL seasons. We conclude that the forecasters, particularly the systems, displayed significant levels of agreement and that levels of agreement in picking game winners were higher than in picking against the betting line. There is greater agreement among statistical systems in picking game winners or picking winners against the line as the season progresses, but no change in levels of agreement among experts. High levels of consensus among forecasters are associated with greater accuracy in picking game winners, but not in picking against the line.
    Keywords: binary forecasts, NFL, agreement, consensus, kappa coefficient
    Date: 2008–07

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