Online Sports Betting

A State-by-State Comparison

About this dataset

How prevalent is sports betting across the United States? This dataset provides information on the legal status of sports betting, revenue generated by sports betting, the number of sports betting outlets, and more. Use this dataset to compare the revenue generated by sports betting across different states

How to use the dataset

This dataset can be used to understand the prevalence of sports betting across the United States and to compare the revenue generated by sports betting across states.

Research Ideas

• Understand the prevalence of sports betting across the United States and to compare the revenue generated by sports betting across states
• Understand how the legal status of sports betting affects revenue generated
• Understand how the number of sports betting outlets affects revenue generated

Columns

File: New Jersey.csv | Column name | Description | |:------------------|:--------------------------------------------------------------| | date | The date of the data. (Date) | | New Jersey | The amount of money bet on sports in New Jersey. (Numeric) | | Pennsylvania | The amount of money bet on sports in Pennsylvania. (Numeric) | | Delaware | The amount of money bet on sports in Delaware. (Numeric) | | Mississippi | The amount of money bet on sports in Mississippi. (Numeric) | | Nevada | The amount of money bet on sports in Nevada. (Numeric) | | Rhode Island | The amount of money bet on sports in Rhode Island. (Numeric) | | West Virginia | The amount of money bet on sports in West Virginia. (Numeric) | | Arkansas | The amount of money bet on sports in Arkansas. (Numeric) | | New York | The amount of money bet on sports in New York. (Numeric) | | Iowa | The amount of money bet on sports in Iowa. (Numeric) | | Indiana | The amount of money bet on sports in Indiana. (Numeric) | | Oregon | The amount of money bet on sports in Oregon. (Numeric) | | New Hampshire | The amount of money bet on sports in New Hampshire. (Numeric) | | Michigan | The amount of money bet on sports in Michigan. (Numeric) | | Montana | The amount of money bet on sports in Montana. (Numeric) | | Colorado | The amount of money bet on sports in Colorado. (Numeric) | | Washington DC | The amount of money bet on sports in Washington DC. (Numeric) | | Illinois | The amount of money bet on sports in Illinois. (Numeric) | | Tennessee | The amount of money bet on sports in Tennessee. (Numeric) |

File: PopulationStates.csv | Column name | Description | |:--------------|:----------------------------------------------------| | State | The state in which the data was collected. (String) |

File: homeless.csv | Column name | Description | |:----------------|:----------------------------------------------------| | year | The year the data was collected. (Integer) | | unsheltered | The number of people who are unsheltered. (Integer) |

File: income.csv | Column name | Description | |:------------------|:--------------------------------------------------------------| | Pennsylvania | The amount of money bet on sports in Pennsylvania. (Numeric) | | Delaware | The amount of money bet on sports in Delaware. (Numeric) | | Mississippi | The amount of money bet on sports in Mississippi. (Numeric) | | Nevada | The amount of money bet on sports in Nevada. (Numeric) | | Rhode Island | The amount of money bet on sports in Rhode Island. (Numeric) | | West Virginia | The amount of money bet on sports in West Virginia. (Numeric) | | Arkansas | The amount of money bet on sports in Arkansas. (Numeric) | | New York | The amount of money bet on sports in New York. (Numeric) | | Iowa | The amount of money bet on sports in Iowa. (Numeric) | | Indiana | The amount of money bet on sports in Indiana. (Numeric) | | New Hampshire | The amount of money bet on sports in New Hampshire. (Numeric) | | Michigan | The amount of money bet on sports in Michigan. (Numeric) | | Colorado | The amount of money bet on sports in Colorado. (Numeric) | | Washington DC | The amount of money bet on sports in Washington DC. (Numeric) | | Illinois | The amount of money bet on sports in Illinois. (Nume...

Notes: (1) Monthly payments are due to the State by the 15th of the following month. (2) The operation of the online casino gaming began on October 12, 2021. (3) This amount shall only include coupons and credits issued for use for gaming in the state and redeemed in the State of Connecticut. (4) Per Public Act 21-23, from October 2021 – September 2022 the promotional deduction is limited to the lesser of 25% of online casino gaming Win/Loss, or actual promotional coupons or credits wagered. From October 2022 – September 2023 this limit drops to 20%, and to 15% thereafter. (5) Payment rate is 18% of Gross Gaming Revenue until September 2026; 20% for all subsequent periods. Monthly payment is the greater of the calculated payment or $0 (if calculated payment is negative). (6) MPTN began on-reservation online casino gaming on December 7, 2021, the Mohegan Tribe began on-reservation online casino gaming on May 6, 2022. (7) In July 2024, Mohegan Digital, LLC's online gaming operator identified and reported data latency issues that caused errors in the May 2024 payment calculations submitted to the State of Connecticut. Mohegan Digital, LLC amended their May 2024 return, which resulted in a decrease of $679 in both Wagers and Win/(loss) and a decrease of $104 in Payment. Amended figures are shown above. (8) In June 2025, the MPI Master Wagering License CT, LLC online gaming operator revised an issue with backend changes with how free credits are categorized and amended March and April 2025 returns and reduced Promotional Coupons by $4,973,493 and $5,123,114 respectively. No other originally reported amounts were effected. Corrected amounts are shown here.

Notes: (1) Monthly payments are due to the State by the 15th of the following month. (2) The operation of the online sports wagering began on October 12, 2021. (3) Monthly resettlements are wagers where the original settled wager result is modified due to an error or change in result of the underlying event. (4) Federal Excise Tax is 0.25% of Net Wagers. (5) This amount shall only include coupons and credits issued for use for gaming in the state and redeemed in the State of Connecticut. (6) Per Public Act 21-23, from October 2021 – September 2022 the promotional deduction is limited to the lesser of 25% of sports wagering Win/Loss, or actual promotional coupons or credits wagered. From October 2022 – September 2023 this limit drops to 20%, and to 15% thereafter.. (7) Payment rate is 13.75% of Gross Gaming Revenue. Monthly payment is the greater of the calculated payment or $0 (if calculated payment is negative). (8) In May 2022, MPI Master Wagering License CT, LLC reclassified $2.6M of certain bets from October 2021 - March 2022 originally reported as resettlements, as winnings. This had no impact on Gross Gaming Revenue or payments to the state because winnings and resettlements are treated the same in the GGR calculation. Corrected amounts are reported above. (9) In May 2022, MPI Master Wagering License CT, LLC revised its October 2021 filing to reflect patron winnings that were cashed after the close of operations during the limited hours of Soft Launch. This adjustment reduced the payment due to the State of Connecticut by $31,669; taken as a credit against the May 2022 payment due. (10) In July 2024, Mohegan Digital, LLC's online gaming operator identified and reported data latency issues that caused errors in the May 2024 payment calculations submitted to the State of Connecticut. Mohegan Digital, LLC amended their May 2024 return, which resulted in an increase of $15,952 in both Wagers and Cancelled Wagers, an Increase of $5,375 in Patron Winnings, and a decrease of $5,375 in Monthly Resettlements. Amended figures are shown above.

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The last decade has ushered in a convergence of video gaming and gambling activities. The aim of this thesis was to examine three gambling products related to video games that emerged in the 2010s but are only beginning to be explored in the literature: esports cash betting, esports skin betting, and skin gambling. Esports cash betting involves gambling money on video gaming competitions (esports), typically through regulated online wagering operators or dedicated esports betting providers. In contrast, esports skin betting involves using virtual video game items known as “skins” as opposed to cash to bet on esports; usually facilitated through unregulated online operators. Lastly, these unregulated online operators also often enable skin gambling, where skins are gambled on games of chance, such as roulette, coin-flip, and jackpots (Grove 2016a, 2016b). The main themes explored in this thesis were derived from research on other gambling products. These themes include exposure and accessibility to the three novel gambling activities, motivations for engagement, impacts such as gambling-related harm, and involvement in other forms of monetary gambling. The research entailed three main stages, with earlier stages informing the design of subsequent activities, inclusive of a literature review, qualitative interviews, and a quantitative cross-sectional online survey.

The literature review conducted in Stage 1 provides an historical overview of esports betting and skin gambling, and reviews current knowledge. It highlights the growing popularity of these newer forms of gambling, particularly amongst young males. These activities are also advertised heavily to young people. The review discusses concerns that esports betting and skin gambling may contribute to the development of problem gambling, and to underage gambling, as well as industry and government responses to these issues.

The qualitative interviews in Stage 2 were conducted with 30 young male esports bettors and skin gamblers. The interviews explored their experiences of exposure and accessibility to these activities, and the impacts of their engagement. A thematic analysis identified 13 subthemes. Key findings indicated that: 1) gambling with skins provides easily accessible betting options for underage gamblers, 2) skin gambling and esports betting contribute to gambling problems and harm, 3) gambling with skins often precedes engagement in monetary gambling, and 4) esports potentially normalises gambling among youth.

Finally, the quantitative survey in Stage 3 collected data from 737 adult esports bettors and skin gamblers, and the findings were explored in two chapters. The first chapter examined a conceptual model linking video game involvement, video game-related gambling, traditional monetary gambling, and consequent gambling problems and harm. The findings suggested that skin gambling on games of chance was directly linked to gambling problems and harm, while cash betting on esports was only indicative of interest in many forms of potentially harmful gambling. The second chapter explored differences in motivations for engaging in esports cash betting, esports skin betting, and skin gambling on games of chance, and whether these motivations differed by product. It also explored whether the different motivations were associated with gambling frequency, problems, and harm. The results indicated that financial gain and enhancement (e.g., excitement, more enjoyment when watching esports) were the main motivations for all activities, while skin acquisition was an additional motivation for esports skin betting and skin gambling. The competition/challenge motivation for esports skin betting and skin gambling was associated with more frequent gambling, but nevertheless this did not necessarily lead to greater observed gambling problems or harm. Finally, for skin gambling on games of chance, the financial gain motivation was associated with more frequent esports skin betting, and also with greater problem gambling severity and gambling-related harm. A consistent finding for all three activities was that greater motivation for regulating internal states (i.e., to escape, to improve mood) was associated with greater problem gambling severity and experiencing more gambling-related harms regardless of the activity.

This research program contributes to knowledge on the emerging phenomena of esports cash betting, esports skin betting and skin gambling, by documenting their historical development and analysing the experiences, motivations, and potential consequences of participation in these activities. The findings highlight the potential risks and harms associated with these forms of gambling, particularly skin gambling, and the need for more research and regulation to protect vulnerable individuals who engage in these novel forms, such as young people. The findings have the potential to inform education and public health programs, support resources, consumer protection frameworks, and harm minimisation strategies. Skin betting and gambling are often provided outside a strong regulatory framework, and thus these findings are pertinent to an understanding of what, if any, changes in governance should be made.

The similarities and differences in the behaviors of different people have long been of interest, particularly in psychology and other social science fields. Understanding human behavior in particular contexts can help us to make informed decisions. Consider a game of poker - understanding why players raise, call, and fold in various situations can provide a distinct advantage competitively.

Along these lines, we are going to focus on the behavior on online gamblers from a platform called Bustabit. There are a few basic rules for playing a game of Bustabit:

You bet a certain amount of money (in Bits, which is 1 / 1,000,000th of a Bitcoin) and you win if you cash out before the game busts. Your win is calculated by the multiplier value at the moment you cashed out. For example, if you bet 100 and the value was 2.50x at the time you cashed out, you win 250. In addition, a percentage Bonus per game is multiplied with your bet and summed to give your final Profit in a winning game. Assuming a Bonus of 1%, your Profit for this round would be (100 x 2.5) + (100 x .01) - 100 = 151 The multiplier increases as time goes on, but if you wait too long to cash out, you may bust and lose your money. Lastly, the house maintains slight advantages because in 1 out of every 100 games, everyone playing busts.

METADATA Country: Czech Republic Collection mode: Online questionnaire Collection dates: 2023-11-01 to 2023-12-31 Sample size (valid): 1,503 Target population: Adults (18+) with past-month gambling activity Primary measures: GRCS, PGSI-SF, NODS-CLiP, gambling expenditure (% income) File-level notes: anonymised individual-level responses.

The dataset comprises responses from a study designed to explore the relationship between gaming addiction and motivational factors among Polish gamers. It includes various data points gathered from a diverse sample of participants.

1. Instruments and Validation: Gaming Addiction Test (GAT): This dataset includes scores from an adapted version of the Internet Addiction Test (IAT) by K. Young, tailored specifically for gaming addiction. The adaptation aligns with Griffiths' Gaming Addiction Criteria, ICD-11 Criteria, and DSM-V criteria. The reliability of this test was assessed using Cronbach's Alpha and McDonald's Omega indicators, ensuring its robustness and consistency in measuring gaming addiction. Motivation to Play Online Games Test: The dataset also contains scores from a modified version of Nick Yee's (2007) Motivation to Play Online Games test. This adaptation measures various motivational factors for gaming, such as escapism, social interaction, and immersion. The reliability of this test was similarly confirmed using Cronbach's Alpha and McDonald's Omega indicators.

2. Participants: Sample Size: The dataset includes data from 520 Polish participants. Data Collection Method: Data was collected using an online surveying tool. The survey was distributed through community channels of popular online games, including World of Warcraft, League of Legends, Dota 2, CS:GO, Apex Legends, and Diablo III and IV.

3. Data Points: Scores: The dataset includes the sums of raw scores and standardized scores (Standard Ten and Z-value) for both the Gaming Addiction Test and the Motivation to Play Online Games test. Descriptive Statistics: Required demographic information is provided, including participants' age, gender, and gaming habits (online vs. offline gaming). Gender was self-identified within four categories: man, woman, other (specify), and prefer not to tell. However, for this study, the descriptors were consolidated to Male and Female, as none of the 520 participants selected 'other' or 'prefer not to tell'.

4. Reliability Indicators: Cronbach's Alpha and McDonald's Omega: These indicators are included in the dataset to validate the internal consistency and reliability of the adapted psychometric tests.

5. Survey Distribution: Community Channels: The survey was disseminated through various online platforms and community channels associated with popular games.

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This dataset offers a comprehensive and dynamic overview of the evolving landscape of online sports betting within the state.

Notes: (1) Monthly payments are due to the State by the 15th of the following month. (2) The operation of the online sports wagering began on October 12, 2021. (3) Monthly resettlements are wagers where the original settled wager result is modified due to an error or change in result of the underlying event. (4) Federal Excise Tax is 0.25% of Net Wagers. (5) This amount shall only include coupons and credits issued for use for gaming in the state and redeemed in the State of Connecticut. (6) Per Public Act 21-23, from October 2021 – September 2022 the promotional deduction is limited to the lesser of 25% of sports wagering Win/Loss, or actual promotional coupons or credits wagered. From October 2022 – September 2023 this limit drops to 20%, and to 15% thereafter.. (7) Payment rate is 13.75% of Gross Gaming Revenue. Monthly payment is the greater of the calculated payment or $0 (if calculated payment is negative). (8) In May 2022, MPI Master Wagering License CT, LLC reclassified $2.6M of certain bets from October 2021 - March 2022 originally reported as resettlements, as winnings. This had no impact on Gross Gaming Revenue or payments to the state because winnings and resettlements are treated the same in the GGR calculation. Corrected amounts are reported above. (9) In May 2022, MPI Master Wagering License CT, LLC revised its October 2021 filing to reflect patron winnings that were cashed after the close of operations during the limited hours of Soft Launch. This adjustment reduced the payment due to the State of Connecticut by $31,669; taken as a credit against the May 2022 payment due.

This Report describes online gambling participation on the British Columbia Lottery Corporation’s PlayNow.com platform, by BC residents in June 2015. Data were provided to the Centre for Gambling Research at UBC (PI: Dr Luke Clark) by BCLC, in a de-identified format and under a confidentiality agreement, in a project that was funded by the BC Ministry of Finance. In this Report, we describe data for five game categories: lottery, eCasino, sports betting, social games (including Keno), and bingo. We focus on behavioural variables reflecting gambling intensity (e.g. total number of bets, total monthly spend). Analyses include an examination of how participation rates vary across the five game categories, and the distribution in engagement across different game categories, including Pareto analyses that describe the proportion of overall gambling activity attributable to the top 20% most engaged gamblers.

This survey charted the gambling, social media usage and subjective well-being of young people aged 15-25 years in the United States. The study was conducted as part of the "Problem Gambling and Social Media: Social Psychological Study on Youth Behaviour in Online Gaming Communities" research project. The aim of the project was to analyse how young social media users evaluate, adopt and share gambling-related online content and how online group processes affect their gambling and gambling-related attitudes. FSD's holdings also include two other datasets that were collected using a nearly identical questionnaire (FSD3399 and FSD3400). Data for the research project have been collected in Finland, the United States, Spain, and South Korea. First, the respondents were asked which social media services they used (e.g. Facebook, YouTube, Instagram, discussion forums, online casinos) and how often. Topics that the respondents discussed on gambling-related social media were charted more closely, and they were asked, for example, whether the discussion usually related to instructions or tips on gambling or to problem gambling and recovering from problem gambling. Some questions on the respondents' social media activity were also presented, for instance, how often they saw gambling-related advertising online, how often they changed their most important social media passwords, and how often they uploaded pictures of themselves on social media. The respondents were asked whether they had ever been harassed online or had been the victim of a crime on the Internet in the past three years (e.g. defamation, identity theft, fraud, sexual harassment). The respondents' identity bubbles on social media were surveyed by using the IBR scale (Identity Bubble Reinforcement Scale). The respondents were asked, for instance, whether they thought they could be themselves on social media and whether they only interacted with people similar to them on social media. Additionally, the CIUS scale (Compulsive Internet Use) was used to examine problems related to Internet use. Questions focused on, for example, whether the respondents found it difficult to stop using the Internet when they were online, whether people close to them said they should use the Internet less, and whether they felt restless, frustrated or irritated when they couldn't use the Internet. In the next section of the questionnaire, the respondents were randomly assigned to two groups for a vignette experiment. Respondents in the test group were told they belong to Group C because they had answered the earlier questions in a similar manner to others in the group. Those in the control group were given no information on the group. The respondents were presented with different gambling-related social media scenarios, and they were asked to evaluate the contents of the gambling-related messages by "liking" or "disliking" the message or by not reacting to it at all. Each respondent was shown four different gambling messages with different contents. Three factors were manipulated in the scenarios (2x2x2 design): expressed stance of the message on gambling (positive or negative), narrative perspective of the message (experience-driven first-person narration or fact-driven third-person narration) and majority opinion of other respondents on the message (positively or negatively biased distribution of likes or dislikes). For Group C, the majority opinion was seemingly provided by other Group C members, whereas for the control group the majority opinion was seemingly provided by other respondents. Additionally, the respondents' attitudes towards the message were surveyed with statements regarding, for instance, how likely they would find the message interesting or share it on social media. Next, the respondents' attitudes towards gambling were charted by using the ATGS scale (Attitudes Towards Gambling Scale). They were asked, for example, whether people should have the right to gamble whenever they want, whether most people who gamble do so sensibly and whether it would be better if gambling was banned altogether. The respondents' gambling habits were examined by using the SOGS scale (South Oaks Gambling Screen), and they were asked, for instance, which types of gambling they had done in the past 12 months (played slot machines, visited an online casino, bet on lotteries etc.), whether the people close to them had gambling problems, and whether they had borrowed money to gamble or to pay gambling debts. In addition, the respondents' alcohol consumption was surveyed with a few questions from the AUDITC scale (The Alcohol Use Disorders Identification Test), and they were asked whether they had used various drugs for recreational purposes (e.g. cannabis, LSD, amphetamine, opioids) and which online resources they had used to purchases these drugs (e.g. Facebook, Instagram, Craigslist). The respondents' subjective well-being and social relationships were examined next. The respondents were asked how happy they were in general and how satisfied they were with their economic situation and life in general. They were also asked how well the single statement "I have high self-esteem" from the SISE scale (Single-item Self-esteem Scale) described them. The three statements on lacking companionship, feeling left out and feeling isolated from the LONE scale (Three-item Loneliness Scale) were also included in the survey. Feelings of belonging to different groups or communities (e.g. family, friends, neighbourhood, parish/religious community) were charted, and the 12-item GHQ scale (General Health Questionnaire) was used to survey the respondents' recent mental health. Questions included, for example, whether the respondents had been able to concentrate on what they were doing, had felt they couldn't overcome their difficulties, and had been losing confidence in themselves. Finally, the respondents' sense of control over the events in their lives was examined with the MASTERY scale (Sense of Mastery Scale), with questions focusing on, for instance, whether they thought they had little control over the things that happen to them and whether they often felt helpless in dealing with the problems of life. The respondents' impulsivity was surveyed by using the EIS scale (Eysenck Impulsivity Scale) and their willingness to delay gratification was surveyed with the GRATIF scale (Delay of Gratification). Background variables included the respondent's gender, age, country of birth (own and parents') level of education, type of municipality of residence, household composition, disposable income, possible financial problems, and economic activity and occupational status.

The AGRI National Project (ANP; https://research.ucalgary.ca/alberta-gambling-research-institute/research/national-gambling-study) provided an unparalleled opportunity for the investigation of how gambling in Canada had been impacted by COVID. While the ANP Online Panel was intended to capture Canada-wide gambling and problem gambling, while accounting for the inter-provincial variation in legal gambling provision, the ANP COVID Online Panel was designed to extend this examination to include the impact of the COVID-19 pandemic social and economic restrictions on gambling and problem gambling. To examine the impact of the pandemic on gambling in Canada, we extended the ANP Online Panel Study, conducting two additional data collection waves. This two-wave panel study was administered by Leger and re-recruited AGRI National Project Online Panel participants. As such, the ANP online panel follow-up survey data became the baseline data for this study. For the first wave of data collection, COVID Wave 1, recruitment from ANP Online Panel follow-up participants (n = 4707), was conducted expeditiously (May 14th – June 1st, 2020). Data collection began one month after the nation-wide ‘lockdown’ began and concluded while all of the provinces were still enforcing these widespread social and economic restrictions. A total of n = 3449 participants completed the COVID Wave 1 survey. Six-months later, the COVID Wave 2 recruitment began, and participants who had completed the COVID Wave 1 survey were invited to participate. The COVID Wave 2 data collection period took place between the 1st and 20th of December 2020, after the easement of nation-wide COVID restrictions. During this Wave 2 data collection period however, while the nation-wide lockdown was repealed, many provinces were still instituting some restrictions. Gambling venues for example, were open but reduced capacity to adhere to social distancing space requirements. Nonetheless, the COVID Wave 2 data collection was designed to determine what pandemic lockdown related changes were enduring. Furthermore, together with the ANP Online Panel follow-up data as a baseline, this study becomes an ABA design, with a large and stratified sample. COVID Wave 2 data, as an added benefit, provides the AGRI National Project with an appropriately timed third annual data collection. Additional information on sampling, retention, study variables, and survey questionnaires can be located in the accompanying user manual and codebooks. The manual and codebooks were created by Rokelle T. Shaw and Carrie A. Shaw.

Problem gambling is a major public health concern and is associated with profound psychological distress and economic problems. There are numerous gambling communities on the internet where users exchange information about games, gambling tactics, as well as gambling-related problems. Individuals exhibiting higher levels of problem gambling engage more in such communities. Online gambling communities may provide insights into problem-gambling behaviour. Using data scraped from a major German gambling discussion board, we fine-tuned a large language model, specifically a Bidirectional Encoder Representations from Transformers (BERT) model, to predict signs of problem-gambling from forum posts. Training data were generated by manual annotation and by taking into account diagnostic criteria and gambling-related cognitive distortions. Using cross-validation, our models achieved a precision of 0.95 and F1 score of 0.71, demonstrating that satisfactory classification performance can be achieved by generating high-quality training material through manual annotation based on diagnostic criteria. The current study confirms that a BERT-based model can be reliably used on small data sets and to detect signatures of problem gambling in online communication data. Such computational approaches may have potential for the detection of changes in problem-gambling prevalence among online users.