Looking Back at South Africa: Analyzing and Reviewing the 2010 FIFA World Cup

Deutsch, Roland C.

doi:10.1080/09332480.2011.10739859

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Paul and Mitra (2008) also find that higher ranked FIFA teams have higher probabilities to win, using data of the WC 1994-2006. Binder and Findlay (2012 6 For a systematic approach to evaluate the accuracy of alternative forecasting methods, see Fenton and Constantinou (2010;2011). given by the mean of the distribution.…”

Section: The Elo-rating Methodologymentioning

confidence: 99%

Soccer jersey sponsors and the world cup

Groot

Ferwerda

2015

International Journal of Sports Marketing and Sponsorship

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The market for soccer jerseys is a multibillion market dominated by Adidas, Nike and Puma. This paper investigates whether jersey sponsorship has a non-arbitrary effect on the outcomes of World Cup knockout matches. The results show that in the knockout stages of the last four World Cup tournaments, especially national teams sponsored by Adidas perform significantly better than expected, while teams sponsored by any other company than Adidas, Puma or Nike perform worse. The average advantage per knockout match for the Adidas teams is to raise the probability to win by 10 percent point.

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Section: The Elo-rating Methodologymentioning

confidence: 99%

Soccer jersey sponsors and the world cup

Groot

Ferwerda

2015

International Journal of Sports Marketing and Sponsorship

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“…In this article we follow the retrospective approach and we present a nested generalized Poisson regression model with zero-inflation for the prediction of the scores of single matches, where the model is solely based on the Elo ranking and matches of the participating teams since 2016, where we additionally take the location of matches into account Since the FIFA World Cup 2022 is a complex tournament, involving important effects such as, e.g., group draws (e.g., see Deutsch (2011)) and dependences of the different matches, Monte-Carlo simulations are used to forecast the whole course of the tournament. For a more detailed summary on statistical modeling of major international football events, see, e.g., Groll et al (2015) and references therein.…”

Section: Introductionmentioning

confidence: 99%

Nested Zero Inflated Generalized Poisson Regression for FIFA World Cup 2022

Gilch¹

2022

Preprint

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This article is devoted to the forecast of the FIFA World Cup 2022 via nested zero-inflated generalized Poisson regression. Our regression model incorporates the Elo points of the participating teams, the location of the matches and the of teamspecific skills in attack and defense as covariates. The proposed model allows predictions in terms of probabilities in order to quantify the chances for each team to reach a certain stage of the tournament. We use Monte Carlo simulations for estimating the outcome of each single match of the tournament, from which we are able to simulate the whole tournament itself. The model is fitted on all football games of the participating teams since 2016 weighted by date and importance. Validation with previous tournaments and comparison with other Poisson models are given.

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“…Since the Africa Cup of Nations 2019 is a more complex tournament, involving for instance effects such as group draws, e.g. see (Deutsch, 2011), and dependences of the different matches, Monte-Carlo simulations are used to forecast the whole course of the tournament. For a more detailed summary on statistical modeling of major international football events, see (Groll et al, 2015) and references therein.…”

mentioning

confidence: 99%

Prediction Model for the Africa Cup of Nations 2019 via Nested Poisson Regression

Gilch¹

2019

AJAS

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This article is devoted to the forecast of the Africa Cup of Nations 2019 football tournament. It is based on a Poisson regression model that includes the Elo points of the participating teams as covariates and incorporates differences of team-specific skills. The proposed model allows predictions in terms of probabilities in order to quantify the chances for each team to reach a certain stage of the tournament. Monte Carlo simulations are used to estimate the outcome of each single match of the tournament and hence to simulate the whole tournament itself. The model is fitted on all football games on neutral ground of the participating teams since 2010.

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Looking Back at South Africa: Analyzing and Reviewing the 2010 FIFA World Cup

Cited by 4 publications

References 2 publications

Soccer jersey sponsors and the world cup

Soccer jersey sponsors and the world cup

Nested Zero Inflated Generalized Poisson Regression for FIFA World Cup 2022

Prediction Model for the Africa Cup of Nations 2019 via Nested Poisson Regression

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