Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz Show

Abstract

Beat the Chasers is a popular UK-originating TV quiz show that premiered in Croatia in 2023. On the show, a contestant challenges a team of up to five chasers with respect to the offers provided by the production. Each offer balances risk and reward, varying in prize money, time advantage, and the number of chasers. In this paper, we first present the dataset obtained by extracting data from the publicly broadcast episodes of Beat the Chasers in Croatia. We then apply various machine-learning models with the goals of predicting (1) which offer a contestant is most likely to select and (2) the game’s outcome. The best-case results suggest that we can successfully do both by reaching an F1-score of 73.6% for the selected offer prediction and 84.6% for the game’s outcome prediction. Regarding the feature importance analysis, we identified the contestant’s hometown size, NUTS 2 region, age group, and gender as the most relevant features in the case of the selected offer prediction. As for the outcome prediction, the game-specific features emerged as the most important, namely, the cash builder result, the selected number of chasers, and the chasers’ time in the selected offer.

Keywords

• Beat the Chasers
• TV quiz show
• data extraction
• data mining
• machine learning