## 2 Related Work  
In this section, we provide a brief review of the field in terms of data mining and knowledge extraction on quiz-based games and shows. To the best of our knowledge, there has been no published scientific work on either The Chase or Beat the Chasers quizzes. We thus focus on other cases of knowledge extraction in a quizzing context. Because of the limited scientific literature specifically targeting quizzes, we also consider not only the adjacent fields of students' academic performance and the prediction of sports' outcomes but also the application of artificial intelligence in consumer behavior analysis, marketing, and gamification. First, we consider various TV quiz shows. For a show named _The Price is Right_, [PERSON] [9] considered various strategies to maximize the winning probability based on the players' bidding order. They considered the preceding bids of the other players and finally analyzed how confidence can affect the winning probability of a player. [PERSON] and [PERSON] analyzed the determinants of successful participation on a popular TV quiz show _Who Wants to Be a Millionaire_? [10]. The analysis was based on the German instance of the quiz. The authors tested the assumed advantage of social capital in terms of personal networks and human capital in terms of participants' education. The same TV quiz show was the topic of research of [PERSON] et al. [11,12]. They developed a virtual player for the game, which used various natural language-processing techniques to rank the answers according to several lexical, syntactical, and semantic criteria. These sources also implicitly affirm that the application of the techniques available under the artificial intelligence umbrella to TV quizzes has existed for a not-so-brief period. On the other hand, the authors in [13] explored the use of crowdsourcing and lightweight machine-learning techniques to create a player for the same game. Their system collected answers from a crowd of mobile users and aggregated these responses using various algorithms, including majority voting and confidence-weighted voting. The results demonstrated that by effectively combining crowdsourced data with machine learning, even difficult questions could be answered with a high degree of accuracy, suggesting the feasibility for building a \"super player\" for question answering. To sum up, research on TV quiz shows so far has primarily focused either on analyzing factors that contributed to or have led to successful participation or on building virtual players, whereas our study concentrates on predicting both in in-game decision making and the final games' outcomes. One of the most complete approaches we found involving a contestant's profile was related not to the TV quiz show but to a reality show. In [PERSON] et al. (2014), [PERSON] et al. trained three machine-learning models on demographic and other profile-related data extracted from aired episodes, trying to predict the winner of _The Bachelor_. The authors stated that they found clear consistency across all three models, which enabled them to pinpoint the specific demographic (e.g., exact age, race, or home region) and in-show achievement parameters that influence the probability of progressing far in the show. The approach taken by the authors in that study to some extent resembles the approach we took in this paper, mainly in terms of the game's outcome prediction using contestants' profiles. Significant work has been conducted within the context of predicting students' performance. [PERSON] et al. (2015), in their literature-based review of machine-learning algorithms for the prediction of students' performance, stated contentious results with respect to the selection of a model that best predicts performance. They stated that varying prediction levels may be a result of differences in socioeconomic status or other types of students' backgrounds, which were not considered when assessing the accuracy of the models. [PERSON] et al.