  The statistical concept of Gambler's Ruin suggests that gambling has a large
amount of risk. Nevertheless, gambling at casinos and gambling on the Internet
are both hugely popular activities. In recent years, both prospect theory and
lab-controlled experiments have been used to improve our understanding of risk
attitudes associated with gambling. Despite theoretical progress, collecting
real-life gambling data, which is essential to validate predictions and
experimental findings, remains a challenge. To address this issue, we collect
publicly available betting data from a \emph{DApp} (decentralized application)
on the Ethereum Blockchain, which instantly publishes the outcome of every
single bet (consisting of each bet's timestamp, wager, probability of winning,
userID, and profit). This online casino is a simple dice game that allows
gamblers to tune their own winning probabilities. Thus the dataset is well
suited for studying gambling strategies and the complex dynamic of risk
attitudes involved in betting decisions. We analyze the dataset through the
lens of current probability-theoretic models and discover empirical examples of
gambling systems. Our results shed light on understanding the role of risk
preferences in human financial behavior and decision-makings beyond gambling.
