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...

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Gambling Data America can be vital for you to make connections with the gambler. In fact, this database includes information about who gambles. Again, the types of gambling they participate in, and the money affected. Furthermore, everyone can use our valid contacts to expand their betting business in targeted locations. Also, Gambling Data America provides valuable insights. That can support you optimize your betting activities more. As well as it improves profitability. Hence, that helps to stay competitive in the active gambling market. Moreover, Gambling Data America can greatly benefit your betting activities in various ways. This dataset gives you knowledge of what American gamblers like. As a result, that allows you to decide where and how to bet wisely. In other words, this is a very important thigh for targeted marketing. People can use player details and preferences to customize your marketing. Likewise, this helps you reach the right people with your promotions. Indeed, to make it more effective. America Gambling Data is a huge collection of American people. Furthermore, anyone can take advantage of this phone number data by buying this contact list. Similarly, the data we are providing is real and almost 95% latest and active. This website has an expert team that is working regularly to put all the information together. Also, with their effort and our experience, we are creating the best gambling database for our clients. In addition, America Gambling Data has all kinds of information that you need for your business and this will surely grow your business. However, with this library, you can create your own contact directory. Thus, people can build different professions’ contact lists also different states’ lists. So this is a complete package that can be a great asset for any person trying to do marketing.

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.

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 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.

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In Romania there is an issue of excessive gabling and addiction to slots machines. The problem is compounded by the fact that slots and bets places are located in easily accessible places and very frequent, making it easy for people to engage in addictive behavior.

Using data about school locations we want to find out how easily children and teenagers can reach those places.

• Schools locations: schools_location.csv

The schools information was collected from: https://www.siiir.edu.ro/carto/, which is a website developed by the Ministry of Education. The website only offers the name of the schools and their addresses, but not the latitude and longitude, which was obtained in 2 ways, by geocoding the school name and by geocoding the address using Google Geocoding API. If the name and the address coordinates are the same, this ensures the correctness of the data, otherwise there might be some uncertainty to which coordinates are the correct ones.

• Slots locations around schools from google geocoding API: proximity_schools_gambling_sites_location.csv

Using google place API, queries were made looking for bets/casinos/slots around the location of the schools which had a trusted location, meaning that the geocoding of the name and the geocoding of the address were the same or very close.

This survey charted the gambling, social media usage and subjective well-being of young people aged 15-30 years in Finland. The study was conducted as part of the "Problem Gambling and Social Media: Social Psychological Study on Youth Behavior in Online Gaming Communities" research project. The aim of the project was to analyse how young social media users assess, adopt and share gambling-related online content and how online group processes affect their gambling and gambling-related attitudes. This dataset contains additional data collected from popular Finnish social media sites. FSD's holdings also include two other datasets that were collected using a nearly identical questionnaire (FSD3399 and FSD3591). 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). 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 financial circumstances 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, number of inhabitants in municipality of residence, household composition, disposable income, possible financial problems, and economic activity and occupational status.

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https://i.imgur.com/IjWUZfO.gif" alt="Crash Game">

github.com/ccan23/crashminer/

About Game (Official Source From bc.game): "The Crash is a multiplayer game where you can choose between Classic and Trenball modes. In classic mode, you are allowed to cash the money after betting. The amount you can earn will depend on how many players are betting on the round. For the Trenball mode, you will be required to bet on a line color which can be a factor in your winning."

This dataset contains games from game ID 2828375 to 2899999 and all the bets in it.

Important:

If you think you can beat this game please check this link.

Gambling Data Belarus delivers you access to valuable contacts across the gambling industry. In fact, this lead retains player demographics, betting trends, revenue figures, and competitive landscape. Most importantly, we handle these contacts from a dependable and reliable source through verification. Our library about the features of gamblers, includes age, gender, income level, and geographic location. In addition, people can bring this contacts list in an Excel and CSV file from our website. Further, Gambling Data Belarus will give you quick responses from clients and you can increase your business. In Belarus, various types of gambling are popular with diverse interests. Anyway, betting and lotteries are widely famous for their accessibility and possibility for large winnings. However, we will provide you with an up-to-date contact list at a more reasonable price than others. Hence, if you like to succeed in this field, you should count on our Gambling Data Belarus. Belarus gambling data enables targeted marketing efforts, that ensure ads reach the right customer. This contact tool is a fantastic strategy for online marketing campaigns. Even, everybody can grow their products or services very easily through this. You can gain many special offers from our Belarus gambling data. Similarly, by utilizing this gambling dataset you can develop a dynamic industry that brings higher ROI. Also, you can buy a GDPR-verified number database. Our Belarus gambling database offers various advantages for the gambling business. Nevertheless, this supplies insights into customer likes, behavior, and needs. Above all, apprehending popular games and betting platforms allows businesses to match clients’ interests. As such, that increases concentration and dedication and creates an opportunity to win more. Scrutinizing trends in gambling conduct support and inform strategic decisions. To this end, trust us and purchase it now without any time wasted.

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The Gaming and Betting Study: Survey of Loyalty Card Customers, Waves 1 and 2, 2014-2016 was commissioned by the Responsible Gambling Trust (now GambleAware), as part of a programme of research looking at users of machines in bookmakers. This programme aimed to examine whether industry data generated by machines in bookmakers could be used to distinguish between harmful and non-harmful patterns of play.

A baseline survey of people who had a loyalty card for Ladbrokes, William Hill or Paddy Power was conducted in 2014. The survey included questions about gambling behaviour and questions which measured whether someone was a problem gambler or not. Permission was sought to link participant’s survey data with their loyalty card data (this is not included in the deposited dataset). Overall, 4,727 people took part in the survey and 4,001 people agreed that their survey responses and their loyalty card data could be linked. Data was collected either via a web survey or a telephone interview.

A follow-up study was commissioned in 2016 to explore changing patterns of gambling behaviour over time, examine changes in problem gambling behaviour, and identify who is more likely to change problem gambling status. Telephone interviews took place between May and August 2016 and total of 1552 full interviews were obtained.

This study contains the data for all individuals who gave a full interview at both waves.

Gambling Data India can be vital for you to make connections with the gambler. In fact, this library has information about who gambles. However, everyone can use our valid contacts to expand their betting business in targeted locations. Also, Gambling Data India provides valuable insights. That can help you optimize your betting moves more. As well as the contact lead improves profitability. Therefore, that helps to stay competitive in the active gambling need. Moreover, Gambling Data India can greatly benefit your betting activities in various ways. This dataset gives you knowledge of what Indian gamblers like. As a result, this allows you to decide where and how to bet wisely. In other words, it is a very important thigh for targeted marketing. People can utilize player details and preferences to customize your marketing. Likewise, this helps you reach the right people with your advertising. Hence, to make it more effective. India Gambling Data is a huge collection of Indian people. Further, the types of gambling they participate in, and the money affected. Besides, everyone can take advantage of this phone number data by buying this contact list. Similarly, the dataset we are delivering is real and almost 95% latest and active. This website has a talented team that is working regularly to put all the info together. Also, with their step and our experience, we are producing the best gambling database for our clients. In addition, India Gambling Data has all kinds of information that you need for your business and this will surely grow your business. However, through this library, you can create your own contact directory. Thus, our team can make different professions’ contact lists also different states’ lists. Finally, it is a complete package that can be a great asset for any person trying to do marketing.

Can You Predict The Result?

Horse racing is one of the sport which involved many gambling activities. Million of people in the world tried to find their 'winning formula' in order to gain profit from betting. Since there are many factors which could affect the race result, data analysis on horse racing became much interesting.

Hong Kong horse racing is especially interesting due to the follow reasons:
- The handicap system made the race more competitive
- Horse pool is small compared to other countries so that horses will meet their rivalries very often in the races
- Limited number of jockey/trainer
- Data are well managed by the official :)

The Dataset

The dataset contains the race result of 1561 local races throughout Hong Kong racing seasons 2014-16 and more information will be added into the dataset. The dataset is divided into two tables (which can be joined by race_id). Most of the column description can be found below with one extra piece of information:

finishing_position - For special incident, please refer to here

So, can you find any pattern for the winner under some condition? Did you spot out a winning strategy? (FYI, betting on all horse equally will bring a loss of ~17.5% on average) Which jockey/trainer is worth to follow?

Don't wait and start the data analysis! You may find some of the kernels I created useful. Enjoy! And please remember to share your finding with the community!

Acknowledgement

The data are extracted from the website of The Hong Kong Jockey Club

How to get started?

In case you are not familiar with Hong Kong horse racing, please see this notebook as a get started tutorial.

Over the past 15 years, the turnover from off course horse race betting in the United Kingdom has been on a general trend of decline. As of March 2024, the figure totaled 3.08 billion British pounds. This was both a decrease over the previous year and 46 percent lower than it had been as of March 2009.

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The Finnish Drinking Habits Survey 2016 charted alcohol use, use of other substances, drinking occasions, consumed quantities of alcohol, consequences of alcohol use and attitudes toward alcohol in Finland. Some questions also covered gambling. The Finnish Drinking Habits Surveys have been conducted in intervals of eight years beginning from 1968; FSD's holdings include datasets from 2008 and 2016. The data comprise three parts: the 'main data' collected with face-to-face interviews, background variables obtained from registers (gender and year of birth), and data collected with a self-administered drop-off questionnaire. Of the 2016 study, FSD's holdings also include separate datasets concerning drinking occasions and abstaining occasions (FSD3313 and FSD3314). First, the respondents' alcohol use and attitudes toward alcohol were charted with attitudinal statements regarding responsible alcohol use, getting intoxicated, and using alcohol in the presence of children. It was also charted which, if any, types of alcoholic drinks the respondents had consumed within the previous 12 months, at which age they consumed an alcoholic beverage for the first time, and at which age they were intoxicated for the first time. The data include a constructed variable b27 which defines the respondents as either alcohol users or non-drinkers. Alcohol consumption and motives for using alcohol were examined in detail. The respondents were asked to estimate how often they had consumed different quantities of alcohol within a period of one day during the previous 12 months. Questions also covered the type of drinks consumed, e.g. beer, cider, wine, and spirits. It was also charted how often the respondents drank beer or wine with meals, and how often they consumed enough alcohol to feel inebriated. Consequences of consuming alcohol were examined with questions pertaining to drinking problems, controlling consumption, reactions of the people around, and the respondents' own relationship with alcohol use. The relationship of non-drinkers with alcohol was also charted, as well as their reasons for not consuming alcohol. Attitudinal statements presented to the respondents concerned religious beliefs, health, principles, fears, wellbeing, and spending time and money. Questions were also asked regarding other people's attitudes toward the respondents' abstinence, and whether they had previously used alcohol. All respondents were asked whether they had brought or ordered alcoholic beverages from abroad during the previous 12 months, and how many liters. They were also asked questions regarding risks caused by alcohol use on health and other aspects of life as well as where they got their information on the health effects of alcohol. Consequences of other people's alcohol use were charted with regard to how often during the previous 12 months the respondents had been harassed or physically attacked by intoxicated people or otherwise felt threatened in public or private spaces. In addition, the respondents were asked to estimate whether any of their close relatives or acquaintances had problems with alcohol and to what extent their alcohol use had affected the respondents' lives during the previous year. One question group also covered gambling, gambling frequency and possible problems caused by gambling. With regard to health, the respondents were enquired about their state of health, loneliness, overall satisfaction with life, and close relationships with other people. The respondents' weight was also charted as well as cigarette, e-cigarette and snus use. The interview data also contain information regarding the duration, location and reliability of the responses. The additional questionnaire included the Alcohol Use Disorders Identification Test (AUDIT) as well as questions regarding the use of narcotic substances. The AUDIT measured, for instance, how often the respondents consumed alcoholic beverages and how many drinks, how often they could not stop drinking once they had started, how often they had failed to do what was normally expected because of drinking, or had feelings of guilt or remorse after drinking. It was also asked whether relatives or friends, doctors or other health workers had been concerned about their drinking or suggested cutting down on drinking. Questions were also asked concerning using medicinal drugs for non-medicinal purposes, poly drug use, and use of narcotic substances such as cannabis, amphetamine, methamphetamine, heroin, cocaine and ecstasy. The data also contain respondent-level aggregate variables on alcohol use concerning drinking frequency and annual consumption, such as Quantity-Frequency (QF) and Graduated Frequency (GF) measures. Background variables included gender, year of birth, marital status, education, economic activity and occupational status, socio-economic status, and household composition.