iii) For the Jackpot game, the odds are not fixed at the moment when the player wagers.

\[table:wager-distribution\]

![ \[fig:wager\_distribution\] In games (A-G), where players are allowed to choose arbitrary bet values, the wager distribution can be best fitted by log-normal distributions (\[eq:log-normal\]). In game (D), the log-normal distribution is truncated at its maximum bet value, indicated by $^*$. The fitting lines represent the log-normal fittings. Wagers placed under the different maximum allowed bet values are discussed separately, e.g., in game (A), (A$_1$) has a maximum bet value of $500,000$, and (A$_2$) has a maximum bet value of $50,000$. On the other hand, in game (H) where wagers can only be in-game skins, the wager distribution is best described by a pairwise power law with an exponential transition, see Eq. (\[eq:powerlaw\_exp\_powerlaw\]). The red dotted line represents the log-normal fitting and the blue solid line represents the fitting of a pairwise power law with an exponential transition.](figure1.pdf){width="\columnwidth"}

In Table \[table:wager-distribution\] we categorize the 8 datasets based on the above information. At the same time, for each dataset we perform a distribution analysis of wagers at the aggregate level. Within the same dataset wagers placed under different maximum allowed bet values are 