In this paper, data mining techniques are used to analyze data gathered from online poker. The study focuses on short-handed Texas Hold’em, and the data sets used contain thousands of human players, each having played more than 1000 hands. The study has two, complementary, goals. First, building predictive models capable of categorizing players into good and bad players, i.e., winners and losers. Second, producing clear and accurate descriptions of what constitutes the difference between winning and losing in poker. In the experimentation, neural network ensembles are shown to be very accurate when categorizing player profiles into winners and losers. Furthermore, decision trees and decision lists used to acquire concept descriptions are shown to be quite comprehensible, and still fairly accurate. Finally, an analysis of obtained concept descriptions discovered several rather unexpected rules, indicating that the suggested approach is potentially valuable for the poker domain.