Offline Gambler System V 7 0
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The first data set (ARJEL) was a convenience sample of gamblers recruited through a network of gambling operators. The ARJEL database is maintained by the gambling operator and includes information on account holder and gambling behavior data on individual accounts. The data were retrieved during the period August 2013 to February 2019 and covered the four ARJEL authorized operators (Casino de la Mer, Club 41, Orangebet, and Poker-derby Groupe) and were restricted to French-speaking account holders. We retrieved data for 8,845 account holders. The second data set (FDJ) was a convenience sample from the Fédération des Jeux de Loto data base. Data on individuals' accounts were gathered from various lottery companies authorized to operate in France. The data were gathered during the period August 2006 to December 2018 and covered the following types of lottery games: lotteries based on numerical drawing (16 games), lotteries based on geographical distribution (French territories), lotteries based on number of players, and lotteries based on number of tickets sold. We retrieved data for 2,855,691 account holders.
Our first objective was to confirm whether the French online gambling market can be separated into four types of gambling (poker, horse race betting, sports betting, and lotteries), in agreement with the 2010 law. We then developed a two-step prediction model to predict the type of gambling activity of an online gambler in the future. This model was then used to predict the type of gambling for the gamblers whose account-based data were not used for developing the model. In the first step, using a logistic regression, we explored the influence of five individual-level variables (gender, age group, location, profile, and previous gambling activity) on the type of gambling (4 types of gambling vs. all forms of gambling). The second step of the model was a classification model which predicts the type of gambling for one or several new gambles. It predicts whether a gambler will be involved in sports betting, horse race betting, poker, or lotteries. This part of the model was developed using a multi-class logistic regression model. Because of the peculiarities of the French gambling market, the model was developed separately for poker and for horse race betting, and for sports betting and lotteries.
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