Bayesian forecasting of UEFA Champions League under alternative seeding regimes |
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Institution: | 1. Instituto Nacional de Estadística y Geografía, Mexico;2. University of Liverpool Management School;3. Università di Sassari and CRENoS, Italy;4. Instituto Flores de Lemus and Department of Statistics, Universidad Carlos III de Madrid, Spain;1. The University of Tokyo, Department of Mathematical Informatics, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan;2. RIKEN, Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan |
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Abstract: | The evaluation of seeding rules requires the use of probabilistic forecasting models both for individual matches and for the tournament. Prior papers have employed a match-level forecasting model and then used a Monte Carlo simulation of the tournament for estimating outcome probabilities, thus allowing an outcome uncertainty measure to be attached to each proposed seeding regime, for example. However, this approach does not take into account the uncertainty that may surround parameter estimates in the underlying match-level forecasting model. We propose a Bayesian approach for addressing this problem, and illustrate it by simulating the UEFA Champions League under alternative seeding regimes. We find that changes in 2015 tended to increase the uncertainty over progression to the knock-out stage, but made limited difference to which clubs would contest the final. |
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Keywords: | OR in sports Seeding Football Monte Carlo simulation Bayesian |
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