In the previous blog post, I mentioned in passing that the new UK Chase data source I’d kindly been given included occupation, but said no more about it there.

That was largely because I knew that occupations were sufficiently diversely described that nothing meaningful was likely to be obtained by analysing using raw occupations alone - we’d have maybe thousands of unique occupations. What I knew I needed was some way of clustering those occupations, or grouping them based on some agreed schema.

In the most recent blog post here, we analysed the performance of contestants on the UK Chase based on inferring their gender from their first names.

It turns out that the incredibly diligent website owner of OneQuestionShootout has the actual gender data for over 9,000 contestants, as well as their age and occupation. He’s been kind enough to give me access to that treasure trove of data, and today we’ll take our first look at it to investigate two main questions:

1. How does contestant performance and behaviour vary based on a contestant’s age and gender? We’ll look at performance based on the average Cash Builder amount accrued, the likelihood of getting home, the average amount contributed to the Prize Fund, and the average Contestant Winnings. We’ll also look at the propensity to take the Low, Middle, or High offer, which obviously has a big influence on how Cash Builder amounts are converted into team and individual winnings

2. What is the optimal mix of ages across the four people in a team?

Some time ago, I looked at the performance of Australian contestants based on the gender implied by their first names, but it turns our that I never got around to doing the same for UK contestants.

Today, we close that gap.

Firstly, a quick reminder that we are again using Derek Howard’s Gender by Name database to attach implied genders to names, and we are using the player-by-player data on OneQuestionShootout for our raw data.

Derek’s database attaches a probability of being male to a large number of first names, and we deem a contestant name to have been appropriately name-gendered if that probability is under 25% or over 75%. For the UK data that leaves only about 6% of the 9,260 contestants with an unknown name gender.

Back in 2020 I wrote a piece here about the comparative performance of UK and Australian Chasers, and I would refer you back to that piece if you want to know more about the data set I’m using for this new article. This time, I’ll only be providing an update on the relative performances of the UK Chasers alone, using data from the first ever UK Chase show back on 29 June 2009, all the way through to 24 October 2025.

The adequacy of any decision should never be assessed purely on its outcome, but instead on how well it reflected the information available at the time. In wagering, that means profitability is often a poor reflection of the ability of the wagerer, although it becomes a fairer measure the longer the time span of wagers we are reviewing.

But there’s another, perhaps better, metric that we can use, and that is the frequency with which the wagerer creates what called Closing Line Value (CLV). Put simply, a bet has positive CLV if, at the line or price at which the market closes, the bet has a positive expected return assuming that the bookmaker’s final line or price provides an accurate estimate of the team’s true outcome probabilities.