A believer in the hot hand would do the opposite. To date, there is little research on real gambling. Our research (1) demonstrates the existence of a hot hand, (2) investigates gamblers’ beliefs in a hot hand and the gamblers’ fallacy, and (3) explores the causal relationship between a hot hand and the gamblers’ fallacy. We used a large online gambling database. First, we counted all the sports betting results to see whether winning was more likely after a streak of winning bets or after a streak of losing

ones. Second, we examined the record of those gamblers who has long streaks of wins to see whether they had higher returns; this could be a sign of real skill. Third, we used the odds and the stake size to predict the probability of winning. The complete gambling history of 776 gamblers between 1 January 2010 and 31 December 2010 was obtained from an online gambling company. In total, 565,915 bets were placed by these gamblers during the click here year. Characteristics of the samples are shown in Table 1. Each gambling record included the following information: game type (e.g., horse racing, football, and cricket), game name (e.g. Huddersfield v West Bromwich), Selumetinib in vitro time,

stake, type of bet, odds, result, and payoff. Each person was identified by a unique account number. All the bets they placed in the year were arranged in chronological order by the time of settlement, which was precise to the minute. The time when the stake was placed was not available but, according to the gambling house, there is no reason to think that stakes are placed long before the time of settlement. Each account used one currency, which was chosen when the account was opened; no change of currency was allowed during the year. If there is a hot hand, then, after a winning bet, the probability of winning the next bet should go up. We compared the probability of winning after different run lengths of previous wins (Fig. 1). If the gamblers’ fallacy is not a fallacy, the probability of winning should go up after losing several

bets. We also compared the probability of winning in this situation. To produce the top panel of Fig. 1, we first counted all the bets in GBP; there were 178,947 bets won and 192,359 bets lost. The probability of winning was 0.48. Second, we took all the 178,947winning bets and counted the why number of bets that won again; there were 88,036 bets won. The probability of winning was 0.49. In comparison, following the 192,359 lost bets, the probability of winning was 0.47. The probability of winning in these two situations was significantly different (Z = 12.10, p < .0001). Third, we took all the 88,036 bets, which had already won twice and examined the results of bets that followed these bets. There were 50,300 bets won. The probability of winning rose to 0.57. In contrast, the probability of winning did not rise after gambles that did not show a winning streak: it was 0.45.