Forecasting World Cup Outcomes with Bookmaker Odds and Tournament Simulation
Summary
The document collects examples of quantitative forecasts for football's World Cup, including reports from financial institutions and a research paper using bookmaker and betting-exchange odds. The described method adjusts quoted odds for bookmakers' profit margins, averages the resulting values on the log-odds scale, and converts them into estimated team-winning probabilities. Tournament simulations then use inferred team strengths to estimate match outcomes and the likelihood of teams reaching each stage.
The research summary reports that this approach had correctly identified the champion in one earlier tournament and three of four semifinalists in another. Those historical examples offer limited evidence of predictive usefulness, but the document does not provide a broader evaluation or a comparison with alternative forecasting methods. Its main relevance to quantitative researchers is as an accessible illustration of probability aggregation, margin adjustment, and simulation applied to a non-financial event.
Key ideas
- Bookmaker odds can be adjusted for profit margins to estimate event probabilities.
- Averaging adjusted odds on a log-odds scale provides a consensus forecast across bookmakers.
- Tournament simulations can translate estimated team strengths into match and progression probabilities.
- The cited approach had some historical successes, but the document gives no broad performance assessment.
- Sports forecasting offers an example of quantitative probability modeling outside financial markets.
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# Quant teams predicting the World Cup
# Quant teams predicting the World Cup
It is a good tradition of the quant teams of the major banks to predict the World Cup. As an example see this new paper from Goldman Sachs:
The World Cup and Economics 2014 (Brazil will win by the way ;-)
They partly do this for fun, for publicity (I just read about this report on Bloomberg) and also for demonstrating typical quant tools in a lighthearted manner (which can be very instructive).
My question Do you know of any other reports of this kind and how to get them?
## Answer by Bob Jansen (score 1, accepted)
https://quant.stackexchange.com/a/12597
I saw this paper by Deutsche through FT Alphaville Markets live.
## Answer by vonjd (score 2)
https://quant.stackexchange.com/a/40052
An interesting variant from Reuters (you can do your own "simulations"):
https://www.breakingviews.com/considered-view/numbers-add-up-to-germany-retaining-world-cup/
Another paper from a renowned source:
Probabilistic forecasts for the 2018 FIFA World Cup based on the bookmaker consensus model Achim Zeileis (achim.zeileis@r-project.org), Christoph Leitner (christoph.leitner@wu.ac.at) and Kurt Hornik (kurt.hornik@wu.ac.at) https://econpapers.repec.org/paper/innwpaper/2018-09.htm
Working Papers from Faculty of Economics and Statistics, University of Innsbruck
Abstract Football fans worldwide anticipate the 2018 FIFA World Cup that will take place in Russia from 14 June to 15 July 2018. 32 of the best teams from 5 confederations compete to determine the new World Champion. Using a consensus model based on quoted odds from 26 bookmakers and betting exchanges a probabilistic forecast for the outcome of the World Cup is obtained. The favorite is Brazil with a forecasted winning probability of 16.6%, closely followed by the defending World Champion and 2017 FIFA Confederations Cup winner Germany with a winning probability of 15.8%. Two other teams also have winning probabilities above 10%: Spain and France with 12.5% and 12.1%, respectively. The results from this bookmaker consensus model are coupled with simulations of the entire tournament to obtain implied abilities for each team. These allow to obtain pairwise probabilities for each possible game along with probabilities for each team to proceed to the various stages of the tournament. This shows that indeed the most likely final is a match of the top favorites Brazil and Germany (with a probability of 5.5%) where Brazil has the chance to compensate the dramatic semifinal in Belo Horizonte, four years ago. However, given that it comes to this final, the chances are almost even (50.6% for Brazil vs. 49.4% for Germany). The most likely semifinals are between the four top teams, i.e., with a probability of 9.4% Brazil and France meet in the first semifinal (with chances slightly in favor of Brazil in such a match, 53.5%) and with 9.2% Germany and Spain play the second semifinal (with chances slightly in favor of Germany with 53.1%). These probabilistic forecasts have been obtained by suitably averaging the quoted winning odds for all teams across bookmakers. More precisely, the odds are first adjusted for the bookmakers' profit margins ("overrounds"), averaged on the log-odds scale, and then transformed back to winning probabilities. Moreover, an "inverse" approach to simulating the tournament yields estimated team abilities (or strengths) from which probabilities for all possible pairwise matches can be derived. This technique (Leitner, Zeileis, and Hornik 2010a) correctly predicted the winner of 2010 FIFA World Cup (Leitner, Zeileis, and Hornik 2010b) and three out of four semifinalists at the 2014 FIFA World Cup (Zeileis, Leitner, and Hornik 2014). Interactive web graphics for this report are available at: https://eeecon.uibk.ac.at/~zeileis/news/fifa2018/Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.