Tag: football
Probabilistic forecasting for the FIFA Women's World Cup 2023
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Winning probabilities for all teams in the FIFA Women's World Cup are obtained using a consensus model based on quoted bookmakers' odds. The favorite is defending World Champion United States, followed by European Champion England, and Spain. Read more ›
Machine learning of a 2022 FIFA World Cup multiverse
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Probabilistic forecasts for the 2022 FIFA World Cup are obtained by using a hybrid model that combines data from three advanced statistical models through random forests. The favorite is Brazil, followed by Argentina, Netherlands, Germany, and France. Read more ›
Probabilistic forecasting for the UEFA Women's Euro 2022
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Using a consensus model based on quoted bookmakers' odds winning probabilities for all competing teams in the UEFA Women's Euro are obtained: The favorite is Spain, followed by host England, France, and the Netherlands as the defending champion. Read more ›
distributions3 @ useR! 2022
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Conference presentation about the 'distributions3' package for S3 probability distributions (and 'topmodels' for graphical model assessment) at useR! 2022: Slides, video, replication code, and vignette. Read more ›
The Poisson distribution: From basic probability theory to regression models
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Brief introduction to the Poisson distribution for modeling count data using the distributions3 package. The distribution is illustrated using the number of goals scored at the 2018 FIFA World Cup, suitable for self-study or as a classroom exercise. Read more ›
Updated forecasts for the UEFA Euro 2020 knockout stage
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After all group stage matches at the UEFA Euro 2020 we have updated the knockout stage forecasts by re-training our hybrid random forest model on the extended data. This shows that England profits most from the realized tournament draw. Read more ›
Evaluation of the UEFA Euro 2020 group stage forecast
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A look back on the group stage of the UEFA Euro 2020 to check whether our hybrid machine learning forecasts based were any good... Read more ›
Working paper for the UEFA Euro 2020 forecast
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A working paper describing the data and methods used for our probabilistic UEFA Euro 2020 forecast, published earlier this week, is available now. Additionally, details on the predicted performance of all teams during the group stage are provided. Read more ›
Hybrid machine learning forecasts for the UEFA Euro 2020
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Probabilistic forecasts for the UEFA Euro 2020 are obtained by using a hybrid model that combines data from four advanced statistical models through random forests. The favorite is France, followed by England and Spain. Read more ›
Hybrid machine learning forecasts for the 2019 FIFA Women's World Cup
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Using a random forest ensemble learner we obtain probabilistic forecasts for the FIFA Women's World Cup in France: The clear favorite is defending champion USA followed, with some margin, by host France, England, and Germany. Read more ›