Economic, population and political determinants of the 2014 World Cup match results

Abstract

In this study, it is found that economic, population and political variables are significant determinants of the goal differences in the 2014 football World Cup matches after taking into consideration the football strength of the two teams on the field. It is shown that the strength difference of the two teams had a strong positive non-linear impact on goal differences and that the goal difference increased with strength but at a decreasing rate. Non-linear impact was also evident with regard to the political conditions of one country relative to the rival country. Furthermore, per capita income and population have a positive linear impact. A number of dummy variables were also found to have a non-significant impact on the game results. It was noted that only the variable that relates to the continent of the football teams had a positive impact.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. See Conchas, ‘Research Possibilities’.

2. See Dyte and Clarke, ‘A Ratings Based Poisson Model’; Suzuki et al., ‘A Bayesian Approach for Predicting Match Outcomes’; Leitner et al., ‘Forecasting Sports Tournaments by Ratings of (Prob)abilities’; and Leitner et al., ‘Bookmaker Consensus and Agreement’ for an example of such studies.

3. For example, using this approach, Zeileis et al., ‘Home Victory for Brazil’ foretold that Brazil, Argentina and Germany will win the World Cup with 22.5, 15.8 and 13.4% probabilities, respectively.

4. Goldman-Sachs Global Investment Research, The World Cup and Economics 2014 have developed a regression model to predict match results and most importantly the winner of the 2014 World Cup. Similarly, Groll et al., Who will Win the Trophy?, have developed a regression model to predict the winner of the 2014 World Cup.

5. FIFA provides a ranking of countries using a weighted average of the last four years taking into consideration (a) the result of the match, (b) the importance of the match, (c) the strength of the opponent and (d) the strength of the confederation. FIFA reports rankings since 1993. June 2014 was the last FIFA ranking report before the World Cup in Brazil. For example, Spain with 1485 points was in first position just before the World Cup begun. The most recent ranking report is available on http://www.fifa.com/worldranking/rankingtable/index.html. See also FIFA's method on http://www.fifa.com/worldranking/procedureandschedule/menprocedure/index.html. In general, it is very difficult to make predictions in football and particularly games that are played in World Cup finals. For example, Ramírez and Cardona, ‘Which Team will Win the 2014 FIFA World Cup?’ developed a Bayesian approach to predict the teams which will qualify for the next rounds. From the 8 matches of the round of 16, their model predicted only one (12.5%) match that of Argentina vs. Switzerland. Furthermore, from their 16 teams, 6 did not make it to the actual round of 16 (37.5%).

6. For example, see Hoffman et al., ‘The Socioeconomic Determinants’; Houston and Wilson, ‘Income, Leisure and Proficiency’; and Macmillan-Smith, ‘Explaining International Soccer Rankings’.

7. See Torgler, ‘The Economics of the Football FIFA World Cup’.

8. See Karlis and Ntzoufras, ‘Bayesian Modelling of Football Outcomes’.

9. To be fair with the bookmakers and statisticians, the Brazil–Germany match result may not have been the same if two of the best Brazilian players were able to play. For example, in the Goldman-Sachs Global Investment Research, The World Cup and Economics 2014 is stated that ‘… if a key player who was responsible for a team’s recent successes is injured, this will have no bearing on our predictions’. Neymar, a world class midfielder, and Brazil’s captain, Thiago Silva, a world class defence player, could not play in this particular game.

10. See Szymanski and Smith, ‘The English Football Industry’.

11. For an example of studies that look at this relationship with explicit reference to Brazil see Couto, ‘Football, Control and Resistance’ and de Melo and Drumond, ‘Globo, the Brazilian Military Dictatorship’.

12. Alternative to FIFA rankings are the Elo rankings and the bookmakers rankings. All these rankings are highly correlated. Interestingly, the Elo ranking was favoured in Brazil for the 2014 World Cup Games as this was admitted by the Goldman-Sachs Global Investment Research, The World Cup and Economics 2014 study. This is because the Elo ranking gives more emphasis on the most recent game results relative to FIFA rankings.

13. Karlis and Ntzoufras, ‘Bayesian Modelling of Football Outcomes’, 135.

14. More rigorous tests of empirical distribution tests such as the Anderson–Darling test reject the hypothesis of normal distribution of goal differences.