Second Session at the Virtual Poker Table A Contemporary Study of Actual Online Poker Activity

Second Session at the Virtual Poker Table: A Contemporary Study of Actual Online Poker Activity

Technological developments and global television exposure led to a poker boom in the early 2000s, and poker (both live and online) has maintained some of its popularity to the present day. In this study, we investigated online poker play trends using actual online betting record data from 2, 489 subscribers of a major global Internet gaming company from 2015 to 2017. We found that total financial participation (average total expenditure: 439, 7 euros) and time commitment (average number of sessions: 43) were relatively modest over the two-year study period. We identified the top 1% of total expenditures as a subgroup of high-participation players with disproportionately high financial participation (average total expenditure: 272. 581, 4 euros) and time commitment (average number of logged-in sessions: 1149). Our results were similar to those reported in an Internet study by LaPlante et al. (Comput Hum Behav 25(3):711-717, 2009). Poker betting records suggest that despite numerous changes in the online poker environment, players' participation levels are similar to those of 10 years ago. We also analyzed deposit and withdrawal records, and observed similar indicators of moderate gambling behavior in the entire sample (median total deposits over 2 years: 176, 4 euros). Common beliefs

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Avoid common mistakes in manuscripts

Introduction

Gambling involves the risk of gaining something of value in the outcome of events that are determined, at least in part, by chance. Online platforms have increased the opportunities for gambling in a more convenient and easily accessible format (Hojnik, 2018; Lawn et al., 2019). For some, Internet gambling is associated with a number of negative consequences (see, e. g., Håkansson & Widinghoff, 2020). Until now, many researchers have only been able to study gambling and its outcomes through self-report (e. g., questionnaires and surveys) or observation, methods that are vulnerable to potential response bias and low external validity, respectively (e. g., ). Other studies have attempted to overcome these limitations by using actual electronic gaming records provided by Internet gambling operators (Finkenwirth et al., 2020; Gray et al., 2015; Labrie et al., 2008; Luquiens et al., 2016; Tom Et al., 2014). Using this methodological approach, we investigated the behavioral and behavioral outcomes of 2, 489 online poker players. The study examined 12-year behavior.

Poker in Focus

Compared to other gamblers, poker players have been shown to spend more time gambling, gamble more frequently, and score higher on problem gambling indicators (Shead et al., 2008). It has also been suggested that online poker players are different from non-poker players (Barrault & Varescon, 2016). Studies based on self-reported data have shown that the typical online poker player is in his or her 20s (Moreau et al., 2020), plays more frequently (at least once a week) and is more likely to have gambling-related problems (e. g., higher scores on the Problem Gambling Severity Index [PGSI, Ferris and Wynne (2001)] and illusionary control) than the average land-based poker player (Dufour et al. These characteristics and betting patterns of online poker players may increase their risk of gambling addiction, making them a particularly important population to empirically investigate with valid and reliable contemporary data.

Analyzing Online Poker Play Behavior with Actual Betting Records

Rather than using surveys or questionnaires to study Internet poker behavior, some researchers have used summary statistics provided by online poker operators (derived from the operator's database) or databases of hands held by the researchers themselves. For example, Siler (2010) analyzed hand histories of low, medium, and high stakes and observed that players who were more selective about their first hands and bet more aggressively tended to win more often. Potter van Loon et al. (2015) analyzed a database of 12 months of poker hands and found that profitability in the first 6 months predicted profitability in the following 6 months.

In one of the first poker studies to analyze online betting records, Laplante et al., the authors hypothesized that the majority of their sample of online poker players would exhibit moderate gambling behavior, while a minority would exhibit extreme behavior. Using aggregate data provided by BWIN Interactive Entertainment AG (BWIN, a global online gaming operator) (time spent on the site, number of game sessions, etc.), they studied a group of young players from 2005 to 2007 and observed that the top 5% of players (based on chips purchased at the poker table) were different from the rest of the players. Compared to the rest of the players, these "most engaged players" spent significantly more time and money on online poker. However, this study was published 10 years ago, and it is questionable whether the findings are still relevant today.

In particular, the situation of online gambling has changed since 2007. For example, there was a major progress in the spread of poker strategies and knowledge. Professional poker players have written countless books, publish a commentary video for hours, and broadcast streaming content on poker strategies and gameplay for hours. Among the poker players, the proportion of new poker players has been reduced (for example, Weisenthal, 2008, "The Poker Boom Is Dead"), and the current average player is stronger than the average player 10 years ago. It is common to recognize that there is (Negreanu, 2016). The online card room is currently called Fast Fold, and is found on sites such as Full Tilt, Pokerstars, 8888Poker, etc., with new hands and seats with new tables after folding the previous table. ) And Lotary Sit & Amp; Samp? Go's, a single table tournament where the winner gets a random amount (found on sites such as America's Cardroom, SportsBetting. AG, Poker Poker and other sites). In addition, in some states in the United States and countries around the world, players get lost in player pools, which are restricted to competing with other players in specific countries and jurisdictions, and restrict access to wider player pools. Online players will restrict the access to a wide range of online and games by 2018, by 2018, by 2018, by 2018, by by 2018 by 2018 by 2018. 。

Such changes in poker and online poker-addition of skill development resources, new game formats, regulations and legal changes-- The experience of new poker players in recent years is particularly apparent for tables and tournaments, and opposition to it. In relation to the level of power, it may be different from the experience of new players in 2005 and 2006. The indicators of poker activities, such as the time the player continued to work online, the level of the share, financial commitment, and the winning and defeat rate, may reflect these changes. Therefore, the recent investigation of poker newcomers and comparing the results of Laplante and others (2009) 10 years ago, the understanding of the current situation of poker and the potential impact of these changes. It is important to deepen.

The Present Study

Laplante et al. (2009) pointed out that online poker was legalized and regulations were enhanced, which has increased the range of use of online poker. In this study, we will provide typical online poker gambling behavior and extreme online poker gambling behavior by evaluating the actual online poker gambling activities of the latest registrants online poker sites. 。 Focusing on the comparison with Laplante and others (2009), (1) the EU countries contained in the datasets of both were very similar, (2) BWIN GVC Holdings PLC (data in this study) I got it from GVC Holdings PLC). We hope that the pool of the entire player is equivalent. Like Laplante's research, we focus on the experience of new subscribers on the online poker site between February 1, 2015 and January 31, 2017 (just 10 years after Laplante and others). Is hitting. We emphasize the group of players with gambling patterns that indicate extreme and potentially excessive commitments (eg, the total amount of cash committed to the pot and/ or tournament). In the new explorable analysis, we will examine the descriptive features of the deposit and withdrawal indicators in the new subscriber on the online drinking site. Laplante et al. (2009) pointed out that online poker was legalized and regulations were enhanced, which has increased the range of use of online poker. In this study, we will provide typical online poker gambling behavior and extreme online poker gambling behavior by evaluating the actual online poker gambling activities of the latest registrants online poker sites. 。 Focusing on the comparison with Laplante and others (2009), (1) the EU countries contained in the datasets of both were very similar, (2) BWIN GVC Holdings PLC (data in this study) I got it from GVC Holdings PLC). We hope that the pool of the entire player is equivalent. Like Laplante's research, we focus on the experience of new subscribers on the online poker site between February 1, 2015 and January 31, 2017 (just 10 years after Laplante and others). Is hitting. We emphasize the group of players with gambling patterns that indicate extreme and potentially excessive commitments (eg, the total amount of cash committed to the pot and/ or tournament). In the new explorable analysis, we will examine the descriptive features of the deposit and withdrawal indicators in the new subscriber on the online drinking site. Laplante et al. (2009) pointed out that online poker was legalized and regulations were enhanced, which has increased the range of use of online poker. In this study, we will provide typical online poker gambling behavior and extreme online poker gambling behavior by evaluating the actual online poker gambling activities of the latest registrants online poker sites. 。 Focusing on the comparison with Laplante and others (2009), (1) the EU countries contained in the datasets of both were very similar, (2) BWIN GVC Holdings PLC (data in this study) I got it from GVC Holdings PLC). We hope that the pool of the entire player is equivalent. Like Laplante's research, we focus on the experience of new subscribers on the online poker site between February 1, 2015 and January 31, 2017 (just 10 years after Laplante and others). Is hitting. We emphasize the group of players with gambling patterns that indicate extreme and potentially excessive commitments (eg, the total amount of cash committed to the pot and/ or tournament). In the new explorable analysis, we will examine the descriptive features of the deposit and withdrawal indicators in the new subscriber on the online drinking site.

Research Questions

Despite online poker operators adding game formats and services, the actual mechanics of online poker games (e. g. cash game and tournament lobby layouts, table and card graphics, button locations, and speed of play) remained essentially unchanged from 2005 to 2015. A quick look at inflation rates assumes that in most countries the value of money has changed by a factor of 1, 25 over the decade (e. g., in Finland statistics, €1, 00 in 2005 is equivalent to €1, 20 in 2015). As poker mechanics and the value of money did not change significantly from 2005 to 2015, we believe there is a limit to how much online poker can change over the years. More specifically, our measurements of poker activity were taken from the Laplante et al.

In this study, we prepared seven measures of poker activity: Five of them (time spent on site, number of poker sessions over 2 years, number of sessions per day, net loss over 2 years, and loss rate) are derived from the Laplante et al. (2010) scale. The other two are slightly different constructs from the respective measures in the previous study. In the present study, we calculate the amount spent on pots and tournament fees, and the average amount spent per session. Laplante et al. (2009) calculate the total amount spent and the average amount spent per session. We consider all seven measures to be accurate or close enough to their corresponding measures to answer Research Question 1: How do the values of the seven measures of poker activity in the present study compare with the corresponding values in Laplante et al. (2009)? Footnote 1

Laplante et al. (2009) tracks a significant correlation between some indicators on poker activities. Some of these significant results are logically guided from the definition of indicators (for example, poker play time and number of poker sessions). However, research has indicated that simple relationships between poker activities and general gambling activities should not be automatically captured or assumed. For example, in the scattering diagram of the winning rate vs. play hand per 100 hand of the n o-limit Holdem Player, Siler (2010) shows a data shape like a conical cloud, or a monotonous relationship (eg, (eg,, or monotonous) It was not clear that the measurement of Pearson correlation and spearman correlation) was appropriate. In another example, a recent study of young players in Daily Fantasy Sport, a different skill game, observed Wiley and others that there was no significant correlation between mistakes and pure losses. The discussion of poker capture methods also questions the value of drawing a simple relationship between poker activities and general gambling activities. The conventional common sense found in books and forums is to determine the gambler's judgment and decisio n-making differences in the individual losing ratio more strongly and more directly.

As a result of analyzing the actual gambler game record, it has been found that the distribution of various gambling activities is generally very biased (for example, Laplante et al., 2009; Tom et al., 2014). If the distribution of the total spending amount (the amount spent on online poker on online poker) is plotted in a percentile, the number of players with a small total spending amount is clearly major and large spending will be a minority (for example, (for example, a player). , 95%/5%or 99%/1%). Laplainte et al. (2009) tracked a significant correlation between several indicators on poker activities. Some of these significant results are logically guided from the definition of indicators (for example, poker play time and number of poker sessions). However, research has indicated that simple relationships between poker activities and general gambling activities should not be automatically captured or assumed. For example, in the scattering diagram of the winning rate vs. play hand per 100 hand of the n o-limit Holdem Player, Siler (2010) shows a data shape like a conical cloud, or a monotonous relationship (eg, (eg,, or monotonous) It was not clear that the measurement of Pearson correlation and spearman correlation) was appropriate. In another example, a recent study of young players in Daily Fantasy Sport, a different skill game, observed Wiley and others that there was no significant correlation between mistakes and pure losses. The discussion of poker capture methods also questions the value of drawing a simple relationship between poker activities and general gambling activities. The conventional common sense found in books and forums is to determine the gambler's judgment and decisio n-making differences in the individual losing ratio more strongly and more directly.

As a result of analyzing the actual gambler game record, it has been found that the distribution of various gambling activities is generally very biased (for example, Laplante et al., 2009; Tom et al., 2014). If the distribution of the total spending amount (the amount spent on online poker on online poker) is plotted in a percentile, the number of players with a small total spending amount is clearly major and large spending will be a minority (for example, (for example, a player). , 95%/5%or 99%/1%). Laplante et al. (2009) tracks a significant correlation between some indicators on poker activities. Some of these significant results are logically guided from the definition of indicators (for example, poker play time and number of poker sessions). However, research has indicated that simple relationships between poker activities and general gambling activities should not be automatically captured or assumed. For example, in the scattering diagram of the winning rate vs. play hand per 100 hand of the n o-limit Holdem Player, Siler (2010) shows a data shape like a conical cloud, or a monotonous relationship (eg, (eg,, or monotonous) It was not clear that the measurement of Pearson correlation and spearman correlation) was appropriate. In another example, a recent study of young players in Daily Fantasy Sport, a different skill game, observed Wiley and others that there was no significant correlation between mistakes and pure losses. The discussion of poker capture methods also questions the value of drawing a simple relationship between poker activities and general gambling activities. The conventional common sense found in books and forums is to determine the gambler's judgment and decisio n-making differences in the individual losing ratio more strongly and more directly.

Methods

Data Acquisition and Participants

Data and Measures

There are various reasons why players have a gambling history and are the minorities that are most enthusiastic about gambling. (1) Started more sessions. (2) It was more active with more hand and pot. (3) A high stage player. Such players are expected to increase the measurement value of poker activity. In indicators such as the number of sessions, cash spent on hand and tournament, and the average amount of cash per session, extreme minority (eg, 5%or 1%) is clearly extreme than the remaining majority. ?

Entain PLC (ATTAN, former GVC Holdings PLC) uses a safe data transfer protocol to identify the daily tabulation of sports betting, online casinos, and poker activities (both cash game tables and tournaments). He provided the dataset. With 494 people, they were first recorded in February 2015, respectively. ATHAIN also provided persona l-level population statistical data and transaction data that describes the attempts and completion of cash deposit and withdrawal from the player's gambling account. Of the 72. 494, 4667 poker activity data were distributed. In order to set a tw o-year research period, the start and end date of the research period, like Laplante et al. (2009), were set on February 1, 2015 and January 31, 2017, respectively. In accordance with their methods, they excluded 2160 players who played poker-cash, tournaments, or both on a different day during the survey period. Of the 2517 players with the remaining 2 footnotes, the first day was the first day (that is, the player who was active only in the last month of the survey period). The remaining 2504 players excluded eight players who played only the free roll tournament (that is, players who worked only in the last month of the survey period). I did not use it. players have a history of gambling and are the most absorbed in gambling. (1) Started more sessions. (2) It was more active with more hand and pot. (3) A high stage player. Such players are expected to increase the measurement value of poker activity. In indicators such as the number of sessions, cash spent on hand and tournament, and the average amount of cash per session, extreme minority (eg, 5%or 1%) is clearly extreme than the remaining majority. ?

Entain PLC (ATTAN, former GVC Holdings PLC) uses a safe data transfer protocol to identify the daily tabulation of sports betting, online casinos, and poker activities (both cash game tables and tournaments). He provided the dataset. With 494 people, they were first recorded in February 2015, respectively. ATHAIN also provided persona l-level population statistical data and transaction data that describes the attempts and completion of cash deposit and withdrawal from the player's gambling account. Of the 72. 494, 4667 poker activity data were distributed. In order to set a tw o-year research period, the start and end date of the research period, like Laplante et al. (2009), were set on February 1, 2015 and January 31, 2017, respectively. In accordance with their methods, they excluded 2160 players who played poker-cash, tournaments, or both on a different day during the survey period. Of the 2517 players with the remaining 2 footnotes, the first day was the first day (that is, the player who was active only in the last month of the survey period). The remaining 2504 players excluded eight players who played only the free roll tournament (that is, players who worked only in the last month of the survey period). I did not use it. There are various reasons why players have a gambling history and are the minorities that are most enthusiastic about gambling. (1) Started more sessions. (2) It was more active with more hand and pot. (3) A high stage player. Such players are expected to increase the measurement value of poker activity. In indicators such as the number of sessions, cash spent on hand and tournament, and the average amount of cash per session, extreme minority (eg, 5%or 1%) is clearly extreme than the remaining majority. ?

Analytic Procedures

We obtained five data sets for these 2, 489 players, including (1) player demographics, (2) daily cash game activity summaries, (3) daily tournament activity summaries, (4) deposit records, and (5) withdrawal records (see Appendix Tables 1A-1J for a detailed description of each metric derived from these data sets). The Cambridge Health Alliance (CHA) IRB reviewed our study design and confirmed that the study did not qualify as human subjects research because all data had been deleted. Data were transferred via secure FTP from ATHAIN to the CHA Addiction Section.

Table 1. Metrics of poker activity, deposit activity, and withdrawal activity used in the analysis

We used the distributed game and tournament data to create clusters and other summary statistics covering the entire study period. We used these summary statistics to create seven metrics of poker activity similar to those used by Laplante et al. (2009). These metrics include duration, number of sessions, total spending, sessions per day, average spending per session, net loss, and loss rate. Table 1 provides a final list of all metrics included in our analysis and observations. For more details on the differences in measures between the two studies, see Supplementary Appendix A & B. Of particular note is the difference between Laplante et al.'s (2009) total bet (i. e., cash transferred from the player's account to the stack or to the seat at the table) and total spending, which includes real game chips that contributed to the pot as calls, bets, and raises (i. e., cash transferred from the player's stack to the center of the table). Players tended to bet the same chips multiple times in the same session. For example, a player may be all-in on one side, not call, and return the chips that were raised.

We used the distributed cash-in and cash-out data to create metrics that summarize the economic activity of players. These metrics and their definitions are in the bottom two-thirds of Table 1. Additional details are provided in the last third of Appendix Table 1.

We performed four analysis. Each indicator of poker activity and deposit and withdrawal was calculated for each of the 0th place (minimum), 25th, 50th (median), 75th, 95th, 99th, and 100th (maximum). Five of these are the standards of five standard numbers. The remaining second, 95th and 99th, include in detail how some indicators are distorted, based on the proposal of the reviewer. Since multiple statistical tests were conducted for four blocks, we selected a significant level α = 0, 001 in consideration of multiple comparisons. In the following U-test of Man Whitney, the relevant statistical w given by the Wilcox. test () function of R is reported.

Results

Demographics

The first block is population statistics. We created distribution of gender and gender country countries. We have calculated average, standard deviation, and fiv e-age summary.

The first block is a poker activity. We have seven poker activity indicators throughout the sample (that is, the number of sessions, the number of sessions per day, total spending, average spending per session, total net loss, total net loss) I checked the distribution. We have acquired average, standard deviation, and seven percentile, conducted a regular Colmogorov Sumirnov certification, and investigated which variables have distorted distributions. From the experience of gambling data so far, it was expected that the distribution was distorted. Therefore, the no n-parametric correlation test was selected. The spearman correlation between the measured values of the poker activity was calculated, and the statistical significance for the pair of each measured value was tested. As an unexpected explorable analysis, the Mann-Whitney U test was compared with the men and women of the sample with seven indicators. Spearman correlations and Man Whitney's U-test can be seen in the online appendix. We performed four analysis. Each indicator of poker activity and deposit and withdrawal was calculated for each of the 0th place (minimum), 25th, 50th (median), 75th, 95th, 99th, and 100th (maximum). Five of these are the standards of five standard numbers. The remaining second, 95th and 99th, include in detail how some indicators are distorted, based on the proposal of the reviewer. Since multiple statistical tests were conducted for four blocks, we selected a significant level α = 0, 001 in consideration of multiple comparisons. In the following U-test of Man Whitney, the relevant statistical w given by the Wilcox. test () function of R is reported.

Poker Activity

The first block is population statistics. We created distribution of gender and gender country countries. We have calculated average, standard deviation, and fiv e-age summary.

The first block is a poker activity. We have seven poker activity indicators throughout the sample (that is, the number of sessions, the number of sessions per day, total spending, average spending per session, total net loss, total net loss) I checked the distribution. We have acquired average, standard deviation, and seven percentile, conducted a regular Colmogorov Sumirnov certification, and investigated which variables have distorted distributions. From the experience of gambling data so far, it was expected that the distribution was distorted. Therefore, the no n-parametric correlation test was selected. The spearman correlation between the measured values of the poker activity was calculated, and the statistical significance for the pair of each measured value was tested. As an unexpected explorable analysis, the Mann-Whitney U test was compared with the men and women of the sample with seven indicators. Spearman correlations and Man Whitney's U-test can be seen in the online appendix. We performed four analysis. Each indicator of poker activity and deposit and withdrawal was calculated for each of the 0th place (minimum), 25th, 50th (median), 75th, 95th, 99th, and 100th (maximum). Five of these are the standards of five standard numbers. The remaining second, 95th and 99th, include in detail how some indicators are distorted, based on the proposal of the reviewer. Since multiple statistical tests were conducted for four blocks, we selected a significant level α = 0, 001 in consideration of multiple comparisons. In the following U-test of Man Whitney, the relevant statistical w given by the Wilcox. test () function of R is reported.

The first block is population statistics. We created distribution of gender and gender country countries. We have calculated average, standard deviation, and fiv e-age summary.

Financial Activity

The third block is economic activity. The exploring analysis of the distribution was completed on the indicator of deposit and withdrawal activities reported in Table 1. When investigating the indicators of payment activities, the data of 2489 players in the analysis sample was used. When considering the index of withdrawal activities, the data of 876 players who tried to withdraw at least once during the survey period was used. First, the seven percentile, average value, and standard deviation of 18 economic indicators were calculated. Next, we calculated the two spearmen correlation queues, calculated the 10 scale of the stock activity, and the other for the eight scale of the withdrawal activity, and verified whether both correlations were significant. 。 As a result, it turned out that 43 players canceled only the withdrawal (did not complete the withdrawal), so this 43 people were excluded from the withdrawal group and the spearman correlated as an unexpected sensitivity analysis. I guessed it again. Here are the singl e-rate statistics of 2489 applicants and all 876 people who have tried to withdraw. The three spearmen correlation queues (that is, one is about deposit, two drawers) are available in an electronic appendix.

The fourth and last blocks are the most involved players. We have identified the most involved players based on the total expenditure. We created a centimeter plot showing the distribution of total expenditures, and used the numbers in the plot to determine the size of the most involved minority. Centile Plot analysis method used in previous gambling records (for example, Labrie et al, the seven scale of poker activities reported in the second block, the most involved players and the remaining most involved For unpolked players, the average, standard deviation, and the median test are calculated, and the most involved players are the most involved players. Identified.

The sample was 2238 men (89, 9 %), 251 women (10, 1 %), the average age was 29, 6 years old, and the standard deviation was 9, 0 years. 75 % or more of the sample were under 35 years (summary of five numbers: 18-23-27-34-69). The third block is economic activity. The exploring analysis of the distribution was completed on the indicator of deposit and withdrawal activities reported in Table 1. When investigating the indicators of payment activities, the data of 2489 players in the analysis sample was used. When considering the index of withdrawal activities, the data of 876 players who tried to withdraw at least once during the survey period was used. First, the seven percentile, average value, and standard deviation of 18 economic indicators were calculated. Next, we calculated the two spearmen correlation queues, calculated the 10 scale of the stock activity, and the other for the eight scale of the withdrawal activity, and verified whether both correlations were significant. 。 As a result, it turned out that 43 players canceled only the withdrawal (did not complete the withdrawal), so this 43 people were excluded from the withdrawal group and the spearman correlated as an unexpected sensitivity analysis. I guessed it again. Here are the singl e-rate statistics of 2489 applicants and all 876 people who have tried to withdraw. The three spearmen correlation queues (that is, one is about deposit, two drawers) are available in an electronic appendix.

Most Involved Players

The sample was 2238 men (89, 9 %), 251 women (10, 1 %), the average age was 29, 6 years old, and the standard deviation was 9, 0 years. 75 % or more of the sample were under 35 years (summary of five numbers: 18-23-27-34-69). The third block is economic activity. The exploring analysis of the distribution was completed on the indicator of deposit and withdrawal activities reported in Table 1. When investigating the indicators of payment activities, the data of 2489 players in the analysis sample was used. When considering the index of withdrawal activities, the data of 876 players who tried to withdraw at least once during the survey period was used. First, the seven percentile, average value, and standard deviation of 18 economic indicators were calculated. Next, we calculated the two spearmen correlation queues, calculated the 10 scale of the stock activity, and the other for the eight scale of the withdrawal activity, and verified whether both correlations were significant. 。 As a result, it turned out that 43 players canceled only the withdrawal (did not complete the withdrawal), so this 43 people were excluded from the withdrawal group and the spearman correlated as an unexpected sensitivity analysis. I guessed it again. Here are the singl e-rate statistics of 2489 applicants and all 876 people who have tried to withdraw. The three spearmen correlation queues (that is, one is about deposit, two drawers) are available in an electronic appendix.

Of the 2489 players, data was available for the country of residence of 2485. Of these 2485, France (n = 691, 27. 8%) and Germany (n = 662, 26. 6%) were the most represented countries. All other countries had percentages below 6. 0%.

Discussion

Means, standard deviations, and seventh percentiles were calculated for each measure of poker activity (see Table 2). Kolmogorov-Smirnov tests showed that none of the seven variables were normally distributed (all p values were below 0. 001).

Demographics

Table 2 Summary statistics for poker activity measures for the full sample (N = 2489)

Based on the means, the typical duration of play for poker players over the two-year study period was between 2 and 3 months. The average Net Loss was 52, 3€, which is less than 3€ per month during the two-year study period, or less than 30€ per month if we assume that the participants only played poker for two or three months based on the median period. For comparison, Table 2 presents the corresponding medians from LaPlante et al. (2009) and the paper (Division on Addiction, 2021, retrieved 2021-05-14) that presents the values corrected from Table 1 in this manuscript. The medians in this study are smaller than those in other studies. For example, the total total expenditure, the average expenditure per connection period, and the median net loss are about half of the corresponding medians in the previous study. However, from a practical point of view, the results of this study are considered to be of a similar size and magnitude.

Poker Activity

Table 3 presents descriptive statistics on deposit activity. The majority of players made at least one failed deposit (footnote 3), and the majority completed at least 80% of their attempted deposits. The median deposit amount was less than 240 euros and the median number of days to deposit was 6, 0 days, which corresponds to an average of less than 40 euros per day for 4 months. Of the 2489 players, 1449 (58, 2%) deposited using only one method. More than half of the players (1570 out of 2489, 63, 1%) did not use a credit card to deposit.

Table 3 Summary statistics for measures of deposit activity for the full sample (N = 2. 489)

Table 4 presents summary statistics regarding withdrawal behavior. Of the 876 players (35. 2% of the sample) who attempted at least one withdrawal during the study period, 591 (67. 5%) had no reverse withdrawals and 43 (4. 9%) had only reverse withdrawals. The remaining 242 players (27, 6%) had at least one completed withdrawal and at least one reverse withdrawal.

Table 4 Summary Statistics for withdrawal activities (n = 876)

Financial Activity

We have ranked 2489 players in total spending and divided the score to 100 Sentenar Subguis. Table 5 shows the total value, maximum value of the total spending of the top 6 percentile group, and the total amount of total spending. The total spending of 2, 489 players is about 24 million yen, and the top 25 players have spent more than 15 million yen. Next, the total expenditure of a player with a large total expenditure (that is, the 9 9-percentile group player, which has the highest total expenditure) was over 150. From these results, it was not appropriate to combine 99 cm and 100 cm groups to create the "most involved" parcel. The players were divided into a group of 25 people ("1 % of the highest involvement") and the remaining 2464 groups ("99 % of the remaining").

Table 5 Data of the total spending amount of the top 6 groups

Most Involved Players

Table 6 shows the average, standard deviation, and median of the seven scale of 1 % and the remaining 99 % poker activity involved in the poker. The distribution of 1%and 99%for six out of the seven indicators was significantly different (Man n-WHITNEY U test, P value is 0, 001 or less). The most enthusiastic 1 % played more and worked on the site longer. The average and average of the number of sessions, total spending, and the average of the average dose per session was more than one digit than the remaining 99%of the most eager 1%(eg, average per session. Opening amount, average: 540, 9 euros vs. 31, 2 euros?

Table 6 The most involved in poker 1%and the remaining 99%poker activity indicators.

However, the overall total expenditure difference did not lead to an increase in loss. There was no significant difference between the most involved 1 % and the remaining 99 % net loss. The exception of this is the most involved 1 % performance heterogeneity. In particular, the composition of the two groups is in detail, and the 1 % group has six players (the largest winner) with the largest pure loss and four players with the largest net loss. In other words, it contained the most lost during the survey period.

In this study, we tried to recreate the observation of Laplante and other online poker (2009) for online poker using a modern sample betting record consisting of 2, 489 new members of the Internet gambling service. Before the analysis, the situation of the online gambling is constantly changing, but as expected, Laplaine et Al. It was similar to 2009), but there was a slight difference. In this online poker cohort, the majority of the players played at a moderate percentage on the entain site (for example, the loss rate per month is about 3 euros, the consumption per session is 10 euros or less). Reproducing this important discovery in this study is important in understanding modern poker gambling. In the following, the results of this study are summarized and the background and interpretation of the observed results are shown. We will also consider the potential impact of our observation results on public health.

Limitations

Despite the same standards, the population statistical features of the sample of this study are different from the population statistical characteristics of Laplante et al. (2009). However, the two samples are not completely separated. First, the analysis sample of this study contained 2, 489 players, and the analysis sample of Laplante et al. (2009) contained 3, 445 players. It is not clear whether the decrease in this new subscriber indicates the tendency of the entire online poker ecosystem (for example, population statistics, mindset, online poker ecosystem). Tendency of operations specializing in poker of ENTAIN, such as population statistics, ideas, mathematical sense, etc. of those who may become a new online poker player (decrease in media exposure, decrease in marketing efforts, secure gaming licenses, etc.) I don't know if it is due to poker participants random monthly fluctuations. The average age of two analysis samples is similar, but the recent samples are slightly higher (27. 9 years old in 2009 surveys, 29. 1 years old in this survey). This age configuration is about the same as other online poker players (Duffourt et al., 2015; Hopley et al., 2012; Palomäki et al., 2016), and young players first participate in the online poker. It suggests that the age is likely to be almost the same as 10 years ago.

The distribution of residence in the analysis sample of this study was much more concentrated in several selected countries, rather than the distribution corresponding to the research in 2009. As with the difference in the number of new players, this may be due to changes in the law (eg, a country that surrounds players) or marketing programs (eg, poker celebrities in different countries have a different online card room with different online card rooms). You can do it.

Future Research Directions

Laplante et al., (2009, p. 715) states that their results support the idea of "most people bet on the Internet". Ten years later, the modern analysis of the actual poker activity gained further consequences of this idea. First, the median play period of this group was two and a half months. In contrast, Laplante et al. (2009) had a median play time for six and a half months. The published books (such as WeisenThal, 2008) and poker strategies (Hull, 2013, etc.) suggest that poker competition has become more intense than in 2005.

Second, the abstract statistics for average spending per connection period also show more modest actions. The average spending per session was 7. 9 euros. In the context, a 7. 9 euro session expenditure is that a cash game player may play 2 hands in one session and al l-in for about 4 euros each time on each hand (for example, € 0, 02/€ 0. At a table without restrictions on 05). Based on the definition of total spending and general gambling, if the behavior is equivalent between research, the median spending around the login session is expected to be higher than Laplante et al. (2009). However, it was actually the opposite. This pattern may indicate that Microstake's games tend to increase, or that the number of stakes that can be used on ENTAIN servers is decreasing. Footnote 4 The distribution of residence in the analysis sample of this study was much more concentrated in several selected countries, rather than the distribution corresponding to the 2009 research. As with the difference in the number of new players, this may be due to changes in the law (eg, a country that surrounds players) or marketing programs (eg, poker celebrities in different countries have a different online card room with different online card rooms). You can do it.

Second, the abstract statistics for average spending per connection period also show more modest actions. The average spending per session was 7. 9 euros. In the context, a 7. 9 euro session expenditure is that a cash game player may play 2 hands in one session and al l-in for about 4 euros each time on each hand (for example, € 0, 02/€ 0. At a table without restrictions on 05). Based on the definition of total spending and general gambling, if the behavior is equivalent between research, the median spending around the login session is expected to be higher than Laplante et al. (2009). However, it was actually the opposite. This pattern may indicate that Microstake's games tend to increase, or that the number of stakes that can be used on ENTAIN servers is decreasing. The distribution of residence in the analysis sample of the fou r-footed studies has concentrated considerably in several selected countries, rather than the distribution corresponding to the 2009 research. As with the difference in the number of new players, this may be due to changes in the law (eg, a country that surrounds players) or marketing programs (eg, poker celebrities in different countries have a different online card room with different online card rooms). You can do it.

Conclusion

Laplante et al., (2009, p. 715) states that their results support the idea of "most of the people who bet on the Internet". Ten years later, the modern analysis of the actual poker activity gained further consequences of this idea. First, the median play period of this group was two and a half months. In contrast, Laplante et al. (2009) had a median play time for six and a half months. The published books (such as WeisenThal, 2008) and poker strategies (Hull, 2013, etc.) suggest that poker competition has become more intense than in 2005.

Data availability

Notes

The results of the total spending amount can be compared with the Currie et al. (2017) and recently Louderback and others (2021). Currie and colleagues suggested that the "safest" limit of the bet was 75 Canada dollar (about 53 euros). Footnote 5 Louderback et al. (2021) set a bet on a specific online amount of 167 and 97 euros per month. Based on the idea that "al l-in" is the highest amount that players bet on the hand, the total expenditure here is the al l-i n-han d-han d-han d-hande d-han d-han d-han d-hande d-han d-han d-handed hulistic. Assuming that it is almost twice as large as the amount considered to be. CURRIE et al The total expenditure limit is € 8059 (that is, € 167, 9x2 per month for 24 months). On the other hand, in our samples, the total expenditure 75 % was € 1936, 60. If the hypothesis of the total spending and betting amount is correct, more than 75 % of this analysis sample will be gambling within a more secure gambling range. Gambling that exceeds this restriction is worth noting that it is gambling addiction. The results related to the total expenditure can be compared with the "safer gambling values" proposed by Currie (2017) and recently LouderBack and others (2021). Currie and colleagues suggested that the "safest" limit of the bet was 75 Canada dollar (about 53 euros). Footnote 5 Louderback et al. (2021) set a bet on a specific online amount of 167 and 97 euros per month. Based on the idea that "al l-in" is the highest amount that players bet on the hand, the total expenditure here is the al l-i n-han d-han d-han d-hande d-han d-han d-han d-hande d-han d-han d-handed hulistic. Assuming that it is almost twice as large as the amount considered to be. CURRIE et al The total expenditure limit is € 8059 (that is, € 167, 9x2 per month for 24 months). On the other hand, in our samples, the total expenditure 75 % was € 1936, 60. If the hypothesis of the total spending and betting amount is correct, more than 75 % of this analysis sample will be gambling within a more secure gambling range. Gambling that exceeds this restriction is worth noting that it is gambling addiction. The results of the total spending amount can be compared with the Currie et al. (2017) and recently Louderback and others (2021). Currie and colleagues suggested that the "safest" limit of the bet was 75 Canada dollar (about 53 euros). Footnote 5 Louderback et al. (2021) set a bet on a specific online amount of 167 and 97 euros per month. Based on the idea that "al l-in" is the highest amount that players bet on the hand, the total expenditure here is the al l-i n-han d-han d-han d-hande d-han d-han d-han d-hande d-han d-han d-handed hulistic. Assuming that it is almost twice as large as the amount considered to be. CURRIE et al The total expenditure limit is € 8059 (that is, € 167, 9x2 per month for 24 months). On the other hand, in our samples, the total expenditure 75 % was € 1936, 60. If the hypothesis of the total expenditure amount and the bet is correct, more than 75 % of this analysis sample will be gambling within a more secure gambling range. Gambling that exceeds this restriction is worth noting that it is gambling addiction.

Observing the trends also reported by Laplante et al. (2009) (overall moderation, poker sessions less than once a day, etc.) may suggest something. In general, game landscapes and environments tend to be resistant to extreme changes, especially when most of the fundamental rules of the game (such as basic no-limit hold'em rules) remain unchanged. Nelson et al. (2021) point out that there are similarities between sports betting behavior in 2015 and sports betting behavior about 10 years earlier on the websites of the same major operators. A more theoretical example of behavior that does not fundamentally change is the casino game of blackjack. Casinos may make minor modifications to the rules (e. g., blackjack payouts are 6 to 5, dealers stand on soft 17). However, the underlying theme of strategy (e. g., hoping the dealer will bust by playing a small card, hitting to total 17 or more vs. cards 9 or face) remains the same (Wong, 1994).

Analysis of the deposit and withdrawal data showed moderate behavior for most, but not all, players (see next section for most players involved). By comparison, a typical starting stack (e. g., buy-in to start one session of live poker) at a low-stakes table in a live poker room is €200. A typical buy-in to start one session of live poker game is €200 to €300. A live low-stakes player will deposit more cash to the cashier or dealer in one night than more than half of our cohort deposited during the two-year study period. We also found that most poker players in our sample did not use credit cards to make deposits. This suggests that most online poker players play with their own money, rather than cash or borrowed funds. Also, because credit card companies charge high fees for processing deposits, some players may have suffered their first loss and started their session on a negative note before even playing a single hand.

Finally, almost two-thirds of players (64. 8%) did not attempt to withdraw funds from their account. Poker players appear to be inclined to leave money in their accounts (e. g., for the next session). This may be especially true for profitable players, who consider a growing account balance or "bankroll" to be part of their pride and identity as a poker player (Johnson, 2017).

The majority of our sample online poker players made a limited and limited financial investment as described above, but a small number of players show high involvement and financial investment levels. Ta. When we ploted the distribution of the total expenditure, we evaluated whether players with smaller values clearly occupy the majority, and whether the players with larger values accounted for a minority. The distribution of the total expenditure of this study is the same shape as other distributions related to gambling and similar activities (for example, in 2021, TOM, 2014, Wiley, etc.). I am. Nevertheless, it was a prerequisite for the subsequent comparison to make sure that the group could be divided into majority and minority.

References

In particular, the sample of this online poker player was much less enthusiastic (or the top 1%) than Laplante et al. (2009) 10 years ago. One of the possible explanations is that the difference between the total betting money and the total expenditure is that the larger the number of hand and the size of the share (that is, as the number of more active players increases). This means that the difference between the hig h-level teams in the neighborhood means that the total expenditure is larger than the total bet, and the total expenditure is more flat than the total bet, and then suddenly. You will have a curved curve. This situation creates a qualitative difference observed between 95 and 99 million cohort groups of two studies.
Also, the difference in the size of the most involved group may have been due to random fluctuations. In any case, many of the problems in the 2009 paper remain. The most involved group caused more controversy, had more sessions, had a larger total in pots and tables, and was lower than each of the majority. In the case of the total number of sessions/ sessions, total expenditures, and total amount, the average of the most participating groups, both of which, were larger than the average of the corresponding majority. Most of our sample online poker players made a limited and limited financial investment as described above, but a small number of players are disproportionately involved and financial investment. Indicated the level. When we ploted the distribution of the total expenditure, we evaluated whether players with smaller values clearly occupy the majority, and whether the players with larger values accounted for a minority. The distribution of the total expenditure of this study is the same shape as other distributions related to gambling and similar activities (for example, in 2021, TOM, 2014, Wiley, etc.). I am. Nevertheless, it was a prerequisite for the subsequent comparison to make sure that the group could be divided into majority and minority.
In particular, the sample of this online poker player was much less enthusiastic (or the top 1%) than Laplante et al. (2009) 10 years ago. One of the possible explanations is that the difference between the total betting money and the total expenditure is that the larger the number of hand and the size of the share (that is, as the number of more active players increases). This means that the difference between the hig h-level teams in the neighborhood means that the total expenditure is larger than the total bet, and the total expenditure is more flat than the total bet, and then suddenly. You will have a curved curve. This situation creates a qualitative difference observed between 95 and 99 million cohort groups of two studies.
Also, the difference in the size of the most involved group may have been due to random fluctuations. In any case, many of the problems in the 2009 paper remain. The most involved group caused more controversy, had more sessions, had a larger total in pots and tables, and was lower than each of the majority. In the case of the total number of sessions/ sessions, total expenditures, and the total amount, the average of the most participating groups, both of which, were one more than one digit than the average of the corresponding majority. The majority of our sample online poker players made a limited and limited financial investment as described above, but a small number of players show high involvement and financial investment levels. Ta. When we ploted the distribution of the total expenditure, we evaluated whether players with smaller values clearly occupy the majority, and whether the players with larger values accounted for a minority. The distribution of the total expenditure of this study is the same shape as other distributions related to gambling and similar activities (for example, in 2021, TOM, 2014, Wiley, etc.). I am. Nevertheless, it was a prerequisite for the subsequent comparison to make sure that the group could be divided into majority and minority.
In particular, the sample of this online poker player was much less enthusiastic (or the top 1%) than Laplante et al. (2009) 10 years ago. One of the possible explanations is that the difference between the total betting money and the total expenditure is that the larger the size of the hand and the size of the share (that is, as the number of active players increases). This means that the difference between the hig h-level teams in the neighborhood means that the total expenditure is larger than the total bet, and the total expenditure is more flat than the total bet, and then suddenly. You will have a curved curve. This situation creates a qualitative difference observed between 95 and 99 million cohort groups of two studies.
Also, the difference in the size of the most involved group may have been due to random fluctuations. In any case, many of the problems in the 2009 paper remain. The most involved group caused more controversy, had more sessions, had a larger total in pots and tables, and was lower than each of the majority. In the case of the total number of sessions/ sessions, total expenditures, and the total amount, the average of the most participating groups, both of which, were one more than one digit than the average of the corresponding majority.
The contrast of the results of the two papers is net loss. Laplante et al. (2009), which was the most involved, lost as a group (average 2888 euros), while the 1%of this study was more than 33. 000 euros (average of 1300 euros or more. The profits of a certain player decreased by more than 20. 000 euros, and another player decreased by more than 50. 000 euros). It is not clear whether this is a statistical random fluctuation or the configuration of the most involved su b-group in the poker player pool has actually changed. Aside from the common and differences between the groups that are most obsessed with poker in two studies, there are significant differences between the players who are very enthusiastic about poker and other players, so they are poker. Very enthusiastic players are signs of disability associated with poker, or more benign (for example, very high income, abundant financial power, inherited wealth, poker as a profession) It is suggested that it is. The contrast of the two papers is net loss. Laplante et al. (2009), which was the most involved, lost as a group (average 2888 euros), while the 1%of this study was more than 33. 000 euros (average of 1300 euros or more. The profits of a certain player decreased by more than 20. 000 euros, and another player decreased by more than 50. 000 euros). It is not clear whether this is a statistical random fluctuation or the configuration of the most involved su b-group in the poker player pool has actually changed. Aside from the common and differences between the groups that are most obsessed with poker in two studies, there are significant differences between the players who are very enthusiastic about poker and other players, so they are poker. Very enthusiastic players are signs of disability associated with poker, or more benign (for example, very high income, abundant financial power, inherited wealth, poker as a profession) It is suggested that it is. The contrast of the results of the two papers is net loss. Laplante et al. (2009), which was the most involved, lost as a group (average 2888 euros), while the 1%of this study was more than 33. 000 euros (average of 1300 euros or more. The profits of a certain player decreased by more than 20. 000 euros, and another player decreased by more than 50. 000 euros). It is not clear whether this is a statistical random fluctuation or the configuration of the most involved su b-group in the poker player pool has actually changed. Aside from the common and differences between the groups that are most obsessed with poker in two studies, there are significant differences between the players who are very enthusiastic about poker and other players, so they are poker. Very enthusiastic players are signs of disability associated with poker, or more benign (for example, very high income, abundant financial power, inherited wealth, poker as a profession) It is suggested that it is.
This study is not without limits. First of all, the online poker play is limited to one major online gambling company. This study does not evaluate gambling behavior by other gambling operators. Therefore, the survey result may not include all of the individual online poker behavior, including both the regulated sites and illegal sites. At a higher macro level, the comparison of the results of this study and the results of Laplante et al. (2009) can reflect the aging of Entain Poker player pools, not the evolution of the entire group that plays online poker. There is sex. Second, laws and regulations that manage online poker may continue to change. The tendency described here may not be generalized, and it may not be possible to compare it directly to the trend of poker player survey in the future. Furthermore, the research period is COVID-19 or older (for example, Hodgins & Amp; AMP; Stevens, 2021) reported a major change in pandemic gambling behavior (for example, casinos). There is. Nevertheless, this result is not confused by the influence of pandemic, so if a future researcher wants to compare gambling before and after the bell of No. 19 bells, at least the role as a recent anchor point. Can be fulfilled. Third, and most importantly, the measurement based on the total cash (that is, total expenditure) is new in this literature. See E-Fill). As far as we know, no one has studied the relationship between the cash in the pot and the other indicators related to the participation of poker, and there is no criterion for how much dollar and euro amount will be regarded as excess or problem.
This is not a limit within the scope of research, but these data have problems with screamers (for example, short biological economic gambling screens, Tom et al., 2014) and gambling control. There are markers and proxies (for example, voluntary sel f-exclusion, Nelson et al. There are also overlapping parts, but there is a problem (or harmful) poker gambling with excessive poker gambling. On the other hand It is possible without the case, and the same is true of 1%/99%of the total expenditure should not be accepted as a problematic poker play rate. 。
Based on this result, we need to consider various research paths in the future. First, it is important to analyze the results of this survey to which types of poker games are associated with the increase in participants. For example, you can create the most participating group based on only cash pokers and tournament poker, and see how much the most participated in this study is 1%. In the poker world, the standard way is to not combine cash games and tournaments. This is because the variety of results, banking management, and even strategies in the game are very different. Similarly, if players are categorized mainly in cash games, intermediate tournaments, and tournaments, the period and net income of each group can be compared.
Woodside and Zhang (2012) have divided a group of casinos frequently using the income level and the number of visits visits. In future research, it may be possible to divide the balance of player groups, player pools, or both, using variables such as average tournament size, number of cash game tables, and poker activity days. Further, if there is a dataset that contains both poker activity data and gambling severity, the potential relationship between the measured values of T can be examined. Based on this result, it is necessary to consider various research paths in the future. First, it is important to analyze the results of this survey to which types of poker games are associated with the increase in participants. For example, you can create the most participating group based on only cash pokers and tournament poker, and see how much the most participated in this study is 1%. In the poker world, the standard way is to not combine cash games and tournaments. This is because the variety of results, banking management, and even strategies in the game are very different. Similarly, if players are categorized mainly in cash games, intermediate tournaments, and tournaments, the period and net income of each group can be compared.
Woodside and Zhang (2012) have divided a group of casinos frequently using the income level and the number of visits visits. In future research, it may be possible to divide the balance of player groups, player pools, or both, using variables such as average tournament size, number of cash game tables, and poker activity days. Further, if there is a dataset that contains both poker activity data and gambling severity, the potential relationship between the measured values of T can be examined. Based on this result, we need to consider various research paths in the future. First, it is important to analyze the results of this survey to which types of poker games are associated with the increase in participants. For example, you can create the most participating group based on only cash pokers and tournament poker, and see how much the most participated in this study is 1%. In the poker world, the standard way is to not combine cash games and tournaments. This is because the variety of results, banking management, and even strategies in the game are very different. Similarly, if players are categorized mainly in cash games, intermediate tournaments, and tournaments, the period and net income of each group can be compared.
Woodside and Zhang (2012) have divided a group of casinos frequently using the income level and the number of visits visits. In future research, it may be possible to divide the balance of player groups, player pools, or both, using variables such as average tournament size, number of cash game tables, and poker activity days. Further, if there is a dataset that contains both poker activity data and gambling severity, the potential relationship between the measured values of T can be examined.
Second, in future studies that include actual hand histories (handwritten records including cards dealt and betting actions), especially for players who participate heavily in cash games, researchers may be able to identify certain tendencies or strategies (e. g., increased semi-bluffing, large 3-bet ranges) associated with higher values of poker activity indicators (e. g., total spending). Such strategy quirks may be signs of risk of poker-related losses or may be signs of rational decision-making or a more sophisticated understanding of poker. Such studies may lead to new approaches to reducing poker-related harms: examine one's gambling tendencies, identify the parts of the game that negatively affect mental state and outcomes, and then target those harmful parts with treatment or education. The number of sessions in the most heavily participated group (mean > 1, 800, median > 1, 100) suggests that the sample size is large enough that such examinations (both at the individual level and at the level of the more heavily participated subgroups) are feasible. Third, although we have compared our results with those of LaPlante et al. (2009) narratively (e. g., median age, median number of daily sessions) and included some statistical tests in the supplementary appendix, we refrain from stating that our results are definitively different or better (or worse). Equivalence tests (e. g., Lakens, 2017) may be a possible approach to test whether the distribution of poker measures changed between the two study years in the two studies. However, for each measure, a judgement would be required about the magnitude of change or shift that would warrant being classified as significant. This is not in statistical terms of context-free frequency, but in practical terms based on the units and values of the measures. For example, deriving what constitutes a significant change in net losses would require proposing a formula for factors such as inflation and cost of living differences. Such aggregation is beyond the scope of this study. However, given how skewed the distributions of poker variables are, constructing appropriate statistical tests may be interesting both as a topic in gambling research and as a theoretical problem in statistics more broadly.
Our study aimed to replicate and extend the findings of Laplante et al. (2009). We investigated real online poker behaviour, in the form of electronic betting records, over a two-year period in a group of 2, 489 new subscribers to a major gaming operator's platform. By examining their poker and financial activities, we compared their behavior to that observed in the pre-online poker era 10 years ago (i. e., Laplante et al., 2014). Although the online poker landscape may have changed over the years, our results suggest that many facts about the online poker landscape 10 years ago remain valid today. Our analysis provided two dominant profiles of online gambling behavior: the “insignificant majority” (e. g., 99% of online poker players) and the “significant minority” (e. g., the top 1% of the most avid online poker players), to borrow Juran’s (1954) terminology from the Pareto principle. The insignificant majority do not spend much money, do not lose, and appear to be satisfied with low-stakes games. However, the significant minority have significantly different gambling behavior and are more likely to gamble in games with significantly higher stakes. In line with previous studies (e. g., Deng et al., 2021; Tom et al., 2014), we found that the majority of online poker players are more likely to gamble in games with significantly higher stakes than the majority of online poker players. 2014), problem gambling behaviors appear to be more prevalent among the most avid poker players than the majority of other players, highlighting the importance of responsible gambling tools and player safety measures for this subset of the most avid players.
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
For details of the a priori assumptions related to these research questions, see Study Management.
This exclusion is rather large, but it is intended to distinguish between more regular poker players (who are of more interest to us from a public health perspective) and players who are merely experimenting with Entain's online poker platform and do not intend to use the service in the long term. Without this exclusion, these online poker "experimenters" would have significantly influenced our results.
Various deposits include a reason for benign reasons such as incorrect input of credit card / debit card information, and a reason to be concerned, such as deposits that exceed the limit of the law or state regulations. There is a possibility of "failure" for the reason. However, the reader advises to interpret the indicators carefully because they could not access the information about why the deposit failed.
If you do not access all history of the hand, you can only guess the average average consumption of 7. 9 euros per session based on the rules of experience. For example, an average No Limit Holdem Cash Game Player has two or three important pots per session (for example, a pr e-flop, three times the big blinds, and the remaining three streets, each of them. If you think that you are participating in 3), the cache throwing into a single session is about € 8, meaning the hand in which the player puts about 2, 5 or 3, 0 euros in the pot. do. This is likely to occur on a Microstakes table of blinds for € 0, 01 €/0, 02 € 0, € 0, 02 €/0, 05 €. If all of these assumptions and estimates are correct, the results will be the same as Feedler (2011), which most of the online poker is played in Small Stakes.
The 53 euro is based on the Canadian dollar vs. euro exchange rate (0, 7073631730) on January 1, 2017.
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Acknowledgements

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Funding

LANGHAM, E. THORNE, H., BROWNE, M., ROSE, J., & amp; amp; amp; amp; amp; Rockloff, M. J. Proposal of classification. https: // doi. Org/10. 1186/s12889-016-2747-0. Papers

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Sitting on a virtual poker table: positive epidemiological research on Internet poker gambling behavior in the real world. Computers in Human BEHAVIOR, 25 (3), 711-717. Https: // doi. Org/10. 1016/j. chb.
Lawn, S., Oster, C., Riley, SMITH, SMITH, D., BAIGENT, M., & Amp; Rahamathulla, M. (2020). https: // doi. Org/10. 3390/IJERPH17030744. ArticlePubmedpubmed centralGoogle scholar

Louderback, E. R., Laplante, D. A., Currie, S. R., & amp; amp; Nelson, S. E. (2021). Development and validation of low-risk gambling thresholds using actual bettor data from a major online gambling operator. Psychology of addictive behaviors. 35 (8), 921-938 https://doi. org/10. 1037/adb0000628. ArticlePubMedGoogle Scholar

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Last modified: 27.08.2024

• Returning to the virtual casino: A contemporary study • Second session at the virtual poker table: A contemporary study of actual online poker activity. Second Session at the Virtual Poker Table: A Contemporary Study of Actual Online Poker Activity two independent Canadian studies: Low-risk gambling limits. Second Session at the Virtual Poker Table: A Contemporary Study of Actual Online Poker Activity. Matthew A. Tom; Timothy C. Edson; Debi A. LaPlante. Original.

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Second Session at the Virtual Poker Table A Contemporary Study of Actual Online Poker Activity

Second Session at the Virtual Poker Table: A Contemporary Study of Actual Online Poker Activity

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Introduction

Poker in Focus

Analyzing Online Poker Play Behavior with Actual Betting Records

The Present Study

Research Questions

Methods

Data Acquisition and Participants

Data and Measures

Analytic Procedures

Results

Demographics

Poker Activity

Financial Activity

Most Involved Players

Discussion

Demographics

Poker Activity

Financial Activity

Most Involved Players

Limitations

Future Research Directions

Conclusion

Data availability

Notes

References

Acknowledgements

Funding

Author information

Authors and Affiliations