Universities around the United States are struggling to deliver their “product” in a manner that preserves its educational and social value. By design, universities draw people together as part of the learning process. Covid-19 challenges this objective, requiring students to distance themselves and not gather in large groups. This conflict is forcing universities to rethink their business models, transforming what they do in a manner that adds value while protecting everyone’s health.
My previous Significance article outlined statistical-based forecasts for the 2019 Rugby World Cup (RWC) by Rugby Vision. According to these forecasts, New Zealand were favourites to win the tournament, but the All Blacks were convincingly beaten by England in a semifinal, and South Africa were crowned 2019 RWC champions.
Now that the dust has settled on the 2019 Rugby World Cup, we can look back and analyze the accuracy of the World Rugby rating system. Before each World Cup, a great deal of attention is paid to the numerical ratings and the resulting ordinal ranking positions of the World Rugby system, and the ratings are assumed to provide guidance on what to expect in terms of match outcomes.
Will the New Zealand All Blacks win their third successive Rugby World Cup (RWC) title in Japan in November? Who are their biggest rivals? Which team will be the ‘dark horse’? To answer these questions, we apply statistical models developed by Rugby Vision to estimate each team’s chances of reaching various stages of the competition, such as qualifying for the quarterfinals or winning the tournament.
With the Six Nations rugby championship kicking off this weekend, we have two years of results to add to our understanding of the competition, following my 2017 article, “6 Nations Rugby - who’s the biggest overachiever?”. The six nations involved are England, France, Ireland, Italy, Scotland and Wales. We will look for recent changes in the ratings-based predictability of the teams in general and for recent changes in the distribution of upsets in particular, when ratings-based predictions proved to be wrong.