Abstract
Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods.
Highlights
Forecasting sports events like matches or tournaments has attracted the interest of the scientific community for quite a long time
Sports turn out to be a perfect environment to study the applicability of existing forecasting methods or develop new methods to be transferred to other fields of forecasting
Besides providing accurate forecasts the forecasting models can be valuable in understanding the nature of the underlying processes [2] and, as demonstrated within this study, to gain practical insights to performance analysis in sports
Summary
Forecasting sports events like matches or tournaments has attracted the interest of the scientific community for quite a long time. Sports events like soccer matches take place regularly and generate huge public attention. Extensive data are available and relatively easy to interpret. Due to these factors, sports (and especially soccer) turn out to be a perfect environment to study the applicability of existing forecasting methods or develop new methods to be transferred to other fields of forecasting. Searching for the most accurate sports forecasting methods is both interesting from a scientific view and from an economic view as the huge betting market for soccer (and other sports) is providing the opportunity to win money by forecasting accurately [1]. Besides providing accurate forecasts the forecasting models can be valuable in understanding the nature of the underlying processes [2] and, as demonstrated within this study, to gain practical insights to performance analysis in sports
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