Bayesian methods are becoming increasingly popular in sports analytics. Identified advantages of the Bayesian approach include the ability to model complex problems, obtain probabilistic estimates and predictions that account for uncertainty, combine information sources and update learning as new data become available. The volume and variety of data produced in sports activities over recent years and the availability of software packages for Bayesian computation have contributed significantly to this growth. This comprehensive survey reviews and characterizes the latest advances in Bayesian statistics in sports, including methods and applications. We found that a large proportion of these articles focus on modeling/predicting the outcome of sports games and on the development of statistics that provides a better picture of athletes’ performance. We provide a description of some of the advances in basketball, football and baseball. We also summarise the sources of data used for the analysis and the most commonly used software for Bayesian computation. We found a similar number of publications between 2013 and 2018 as compared to those published in the three previous decades, which is an indication of the growing adoption rate of Bayesian methods in sports.

Bayesian techniques are being quickly adopted in sports settings. The volume and variety of data produced in sports activities over the past years and the availability of software packages for Bayesian computation have contributed positively to this growth. This article provides a brief review of the latest advances in Bayesian statistics in sports, covering methods and applications. In the literature surveyed, we found a similar number of publications between 2013 and 2017 and those published in the three previous decades, which is an indication of the rapid adoption rate of Bayesian methods in sports.

Two Sustainable Development Goals are focused directly on combating the impacts of climate change on coral reef communities. These are: providing cost-effective and timely information to reef managers (SDG 13 - climate action) and sustaining healthy coral reefs in the future (SDG 14 - life below water). Citizen science (CS) features prominently in a range of programs that have been developed to address these goals. One such program is Virtual Reef Diver, which is designed to help monitor the health of the Great Barrier Reef in Australia. This program engages citizen scientists in two ways. Scuba-divers are asked to take geo-coded underwater images of the reef and upload them to an online virtual reef. Members of the public across the world are then asked to classify these images with respect to key reef indicators such as coral. Through the lens of a Virtual Reef Diver event held as part of 2021 Australia’s National Science Week, we describe important features of this program that positively address common concerns about CS data, including the scientific trustworthiness of the data, the ability to incorporate these data with other more traditional data sources, and the quantifiable improvement in information about reef health using these data for management decisions. This demonstrates the important role that citizen science can play in achieving SDGs.