“(Extreme) rain stopped play” – assessing the impact of climate change on sport using a combined modelling, statistics, and artificial intelligence approach

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University of Bristol
Start - End
12 Sep
Study Options
Full Time

The weather plays a huge role in almost all outdoor sporting events, often adding excitement and variety for both players and spectators. However, in some cases, the weather can lead to severe disruption. For example, the 2019 Rugby World Cup was seriously affected by Typhoon Hagibis, and Australian bushfires in 2020 impacted the tennis open. Just recently, one qualitative study indicated that more than 60,000 grass- roots football games are postponed or delayed every year in the UK due to adverse weather. In the future, there is potential for these weather impacts to change, possibly leading to catastrophic impacts on sporting events and infrastructure, from the grass roots right up to the international stage. However, very little quantitative work has been done on this topic despite its importance and the potential for engagement with the public in this area. In this project we will apply state-of the art climate, statistical, and flood modelling tools, coupled with artificial intelligence methods, to estimate the future impact of climate change on a variety of sports.

Project Aims and Methods

The overall aim of this project will be to quantify future climate impacts on a variety of sports, at a range of spatial scales from the national to global. Initial approaches will likely involve developing statistical relationships between past weather and impacts on past sporting events, for example the impacts of extreme heat on tennis matches, or the impacts of precipitation on cricket matches. These relationships will then be extrapolated into the future using the recent CMIP6 model results, which can capture the intensity and variability of extreme events, and how they are projected to change in the future, out to 2100 [1], leading to an understanding of how climate change will affect the feasibility of these sporting events. Another exciting possible avenue would be to quantify the impact of climate change on the flooding of grass-roots football pitches (or sports facilities in general) in the UK. This will involve convolving maps of modelled future flood-risk with the locations of facilities derived from satellite data. The flood maps would initially be developed from the Environment Agency coastal extreme level boundaries [2], extending methods that were originally applied by the group at Bristol [3]. Although some databases exist of sporting facilities in the UK, these are not in a format readily suitable for use in flood modelling. Instead, the student will develop methods to map facilities using trained algorithms, in an artificial intelligence framework. Such methods have been applied commercially before on a small scale [4], but here we will develop methods that can be scaled up globally. In any case, there are multiple avenues that this project could go down, and there will be opportunities for the sports and regions investigated, and the methods, to be tailored to the student’s own interests and expertise.

Candidate requirements

The successful candidate should have a quantitative background in the mathematical, physical and/or environmental sciences. Ideally they would have good appreciation of statistical methods. An interest in sport would be an advantage but is not essential! We welcome and encourage student applications from under-represented groups. We value a diverse research environment.

Project partners

The UK Met Office is the national meteorological service for the UK. They provide critical weather services and world-leading climate science, helping people make better decisions to stay safe and thrive. In this project, they will provide expertise on climate impacts, and will build on initial work carried out with BBC Sport and Wimbledon.


The student will gain a wide range of skills in this project, for example in statistical methods, flood modelling, analysis of big data, and artificial intelligence methods. Training in these areas will be provided by a combination of the supervisors, and internal and external training programs.

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