The German Sports-Betting Market: Uncertainty and Chaos for Private Providers Like Bwin

Hambach, Wulf; Schöttle, Hendrik

doi:10.1089/glr.2006.10.521

Cited by 1 publication

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“…To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from share of this amount [Hambach and Schottle 2006;Weinberg 2003]. Moreover, there are also those who say that sports betting and predictions can save the world [Lightman 2010], that is, predict hurricanes, markets, wars, and even climate change.…”

Section: Introductionmentioning

confidence: 99%

Forecasting in the NBA and other team sports

Melo

Almeida

Loureiro

et al. 2012

ACM Trans. Knowl. Discov. Data

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The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can correctly guess the outcome of a game or a league. In this work, our approach is to move away from the traditional way of predicting sports events, and instead to model sports leagues as networks of players and teams where the only information available is the work relationships among them. We propose two network-based models to predict the behavior of teams in sports leagues. These models are parameter-free, that is, they do not have a single parameter, and moreover are sport-agnostic: they can be applied directly to any team sports league. First, we view a sports league as a network in evolution, and we infer the implicit feedback behind network changes and properties over the years. Then, we use this knowledge to construct the network-based prediction models, which can, with a significantly high probability, indicate how well a team will perform over a season. We compare our proposed models with other prediction models in two of the most popular sports leagues: the National Basketball Association (NBA) and the Major League Baseball (MLB). Our model shows consistently good results in comparison with the other models and, relying upon the network properties of the teams, we achieved a ≈ 14% rank prediction accuracy improvement over our best competitor.

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Section: Introductionmentioning

confidence: 99%