Know When to Fold'Em: a Supervised Machine Learning Approach to Tilt Detection in Online Poker

Abstract

Tilt, a psychological state where players lose emotional control, significantly affects decision-making and financial outcomes in poker. This paper addresses the challenge of detecting tilt in online poker by leveraging supervised machine learning on a publicly available repository of online hand history data. We first pre-process the data by converting the hands into chronological sequences played by each player on a given table and extract key features related to player behavior in accordance with existing research. We then propose and validate the Composite Tilt Indicator (CTI), intended to represent the likelihood that a player was tilted in a given sequence, in order to label the dataset. We then train and evaluate a supervised machine learning model to detect tilt, achieving high performance on key metrics such as precision and recall. This work contributes to poker research by providing a systematic statistical framework to approach tilt detection where previous methods have relied on subjective player testimony or facial recognition.