For today’s blog we’re returning to analyse this Google document from James Spencer, which covers the entire history of Andrew O’Keefe’s reign as host from late 2015 to mid-2021, with the intention of understanding what, if anything the name gender composition of a team tells us about their likely performance.

For today’s blog, we’re going to move away from analysing contestant data and instead analyse Chaser data using:

• For Australia: this Google document from James Spencer, which covers the entire history of Andrew O’Keefe’s reign as host from late 2015 to mid-2021.

• For the UK: data from this website, which covers the entire history of up until 26 November 2021.

In the last blog we build models for The Chase game show to estimate a team’s chances of winning at various points during an episode. We did that using data from this Google document from James Spencer, which covers the entire history of Andrew O’Keefe’s reign as host from late 2015 to mid-2021.

Those models suggested that there was something special about Seat 1 in that the fate of the contestant in that Seat seemed to have particular significance for the team’s eventual fortune. Put succinctly, teams that included the contestant from Seat 1 in the final won money more often than otherwise equivalent teams without the contestant from Seat 1.

After analysing the performance data for Seat 1 contestants and finding little evidence that they were, on average, notably stronger contestants in terms of Cash Builder, Contribution, and FInal Target statistics, I hypothesised that, perhaps

• The presence of Seat 1 in the Final Chase is enough to slightly put off the Chaser (or is a signal that he or she is not quite on his or her game in that episode)

• Contestants in Seat 1 are, on average, better at taking advantage of pushback opportunities

Today we’ll use the Pushback data to explore these hypotheses.

In the last few blogs we’ve built models and created tables to explore various aspects of The Chase game show using data from this Google document from James Spencer, which covers the entire history of Andrew O’Keefe’s reign as host from late 2015 to mid-2021.

Today I want to look at a new aspect: how best to estimate the chances of a team ultimately winning whilst an episode is in progress. In particular, I want to estimate it at five specific points in the contest:

In the last blog we looked at how precisely we could forecast the High and Low Chaser offers using data from this Google document from James Spencer, which covers the entire history of Andrew O’Keefe’s reign as host from late 2015 to mid-2021.

There are other interesting questions that we can investigate using this data, and today we’ll analyse how a contestant’s estimated probability of getting home depends on how well he or she did in the Cash Builder, which offer he or she chose, and other features of the episode.

Previously, we’ve investigated the relative performance of the Chasers on the Australian and UK versions of the show using data from this Google document from James Spencer for the Australian data, and from this website for the UK data.

James, to celebrate the 1,000th episode of The Chase Australia, has recently added contestant-by-contestant data to his Google document, which allows us to investigate another topic I’ve long been interested in: how predictable are the Chasers’ high and low offers?