Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz Show
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 n...
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MDPI AG
2025-05-01
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| author | Hana Ivandic Branimir Pervan Josip Knezovic Alan Jovic |
| author_facet | Hana Ivandic Branimir Pervan Josip Knezovic Alan Jovic |
| author_sort | Hana Ivandic |
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| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>73.6</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the selected offer prediction and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>84.6</mn><mo>%</mo></mrow></semantics></math></inline-formula> 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. |
| format | Article |
| id | doaj-art-a0790098837b486eb34b5b4c0b05cf4c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a0790098837b486eb34b5b4c0b05cf4c2025-08-20T01:56:29ZengMDPI AGApplied Sciences2076-34172025-05-011510572210.3390/app15105722Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz ShowHana Ivandic0Branimir Pervan1Josip Knezovic2Alan Jovic3Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaBeat 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>73.6</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the selected offer prediction and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>84.6</mn><mo>%</mo></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/2076-3417/15/10/5722Beat the ChasersTV quiz showdata extractiondata miningmachine learning |
| spellingShingle | Hana Ivandic Branimir Pervan Josip Knezovic Alan Jovic Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz Show Applied Sciences Beat the Chasers TV quiz show data extraction data mining machine learning |
| title | Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz Show |
| title_full | Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz Show |
| title_fullStr | Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz Show |
| title_full_unstemmed | Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz Show |
| title_short | Beat the Offers—A Machine-Learning Approach for Predicting Contestants’ Choices and Games’ Outcomes on a TV Quiz Show |
| title_sort | beat the offers a machine learning approach for predicting contestants choices and games outcomes on a tv quiz show |
| topic | Beat the Chasers TV quiz show data extraction data mining machine learning |
| url | https://www.mdpi.com/2076-3417/15/10/5722 |
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