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...

NBA Betting Lines

NBA Moneyline, Spread and Total Bets

By [source]

About this dataset

This extensive dataset is an absolute must-have for bettors, sports enthusiasts, and data scientists eager to gain insight into the inner workings of professional basketball. It comprises over 100 data points, offering a wealth of information on betting lines from December 2nd 2021 to December 11th 2022, including moneyline, spread, and total bets along with game date, period and team information; plus 1st, 2nd, 3rd and 4th quarter scores; and final scores. This powerful dataset can not only be used to inform bettors on the best betting opportunities available at any given point in time—through uncovering patterns or relationships between outcomes—but also be utilized by research professionals or statisticians to demonstrate how certain events or circumstances impact sporting events across the board. Get ready for a one-of-a-kind experience as you unlock invaluable knowledge about sports betting trends!

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How to use the dataset

This dataset offers an extensive collection of NBA betting trends from December 2nd 2021 to December 11th 2022. With information related to moneyline, spread, and total bets, game date, period, and team information, and scores for each quarter as well as the final score of each game – this data set is valuable tool in your sports betting arsenal.

Analyzing Trends To uncover hidden trends in today’s NBA games – you can use this dataset to compare data points among teams over a given amount of time. Data points such as games won/lost, points scored per game/per period etc... can be compared between two or more teams that are competing against one another during the same time frame. This comparison can then be used to analyze which team should be favored when it comes to making a bet on a particular sporting event or match-up.

Evaluating Odds You can also use this dataset to evaluate the odds which are typically set by bookmakers before any given sporting event takes place. By utilizing data related to money line bets, spreads and totals – you gain something called “value” which describes whether or not there is any chance that you might earn more money if all the factors surrounding a particular bet come up with the expected results . If the value of your bet is greater than what bookmakers expected it would be - then there is an opportunity for profit making if all goes according plan when placing your wager!

Making Effective Wagers 

Using this data set will help you make informed decisions when it comes placing wagers on professional basketball matches. Be sure to analyze available upcoming lines carefully when tilting towards certain players/teams... Taking into consideration how their performance has been within past weeks rather than months could mean find yourself on top with some positive returns already made! Don't forget: Always check out statistical averages prior making bets so that way they give proper decision weighting possibilities (in terms of odds)

Research Ideas

• Discovery of insights into player performance and the factors that affect it, such as playing conditions, fatigue, injuries and opponent strength: by analyzing betting lines before and after key events (e.g. trades, acquisitions) researchers can assess the impact of these events on player performance and team success/failure
• For trend tracking across seasons: by analyzing betting lines over multiple seasons users can identify changes in the market that create favorable plays or unfavorable ones over time
• To better understand line movements between different sports books: this dataset provides an avenue to compare and contrast betting lines from a variety of sports books in order to gain deeper insight into fluctuations in odds between them

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The dataset is NBA statistical data, each column is average statistic number for past ten games. we combine it with bet365s handicap data. Last two columns are response variables.

Firstly, we have to understand how sportsbooks earn the money. Generally speaking, most time they earn their profit by collecting commissions on bets. For instance, if you wager a 100 dollars bet and odd is 0.97, you will get 197 dollars where sportsbook gets 3 dollars profit. If there is a Basketball game Team A versus Team B, they will try to do is make mounts of betting money are balanced paced on Team A versus team B, which will maximize their profit; otherwise they may face a significant risk.

How sportsbooks do to balance the betting money is very simple, which is handicap. For example, they will turn the A versus B in A needs to win B by 3 points handicap. If bettors keep on betting the same side, sportsbooks will keep increasing the Handicap until bets balancing on both sides.

Although it seems like sportsbooks have a hundred percent chance of winning, many of them ended up bankrupt every year. This is due to the reasons below. Since the sports betting market is fairly competitive, a sportsbook would not change their odds obviously different from others. Otherwise, they will lose their customers overnight. The only option they have is to change the handicap of games to balance their bets. However, if one sportsbook's handicap is significantly different from its competitors, it would be another problem which would make them bankrupt; Furthermore, big sports leagues like NFL, NBA and MLB, their information is public and real-time spread. It cannot be manipulated like the stock market.

Marcus melo15best@hotmail.com

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Context

As part of a research that I made at the University about trends of sports betting agencies (bookmakers), I collected data about winning odds for 2 years. The data collected from a global bookmaker and it includes winning odds of football matches around the world.

The goal of the research was to learn and identify trends in matches (lines) that is possible to bet, in order to make a smart decision (bet).

Many gamblers see the odds in the moment they bet (between the first opportunity to bet and match start date) but they don't know the history of the changes in winning odds. The bookmakers change the winning odds according to several scenarios: players injury, weather at the time of the match, rumors about players, opening players, the wisdom of the crowd, stadium match and many more factors. The gamblers don’t have this kind of information or the ability to analyze it to the right odd.

A change in the winning odds due to these factors can help the gambler make a better bet (for example, a team that was a favorite at the beginning of the line but few minutes before the start match the team is underdog - has a high probability to lose).

Content

The data set has been collected for 2 years from a global bookmaker and includes all the winning odds of football matches from all over the world. The data set contains more than 35,000 matches, in which more than 200,000 changes in the winning odds that were made up to the beginning of the match. The matches from all over the world – from an amateur league to a professional’s leagues.

The database is divided into two tables (CSV files):

Matches_Odds - Describes the odd's change for a winning (and the time that it happened) by the bookmaker for every match till the start match date.

Matches_Results - Describes the result for every match.

Inspiration

Is it possible to predict the result of a match and get higher accuracy from the betting agency? If we want to predict tie – Yes or No, how the model will be change?

Is it possible to achieve high accuracy in matches that have reverse line movement?

Does the date of odd’s change and the start date of the match affect the result of the match ?

Develop and check which independent variables affect the match result prediction.

What is the optimal time a gambler should bet on a match?

Which leagues/teams can achieve a higher accuracy thanks to the winning odds? Should we make a cluster to the leagues/teams?

Is the expected value according to the bookmaker is positive?

Relevant Links

The basics of reverse line movement

Why do betting odds change?

Beating the bookies with their own numbers

My Research

In my research, I developed independent variables and examined their impact on matches that were reversed movement of winning odds. In addition, a statistical model was adjusted to predict the result of a match, when the final accuracy was more than 5.43% of the accuracy of the betting agency (bookmaker).

The development environment was R Studio.

I hope this data set will help further studies :)

Elad Silvas

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Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates much more events and it is very important and interesting to take into account the context in which those events were generated. This dataset should keep sports analytics enthusiasts awake for long hours as the number of questions that can be asked is huge.

Content

This dataset is a result of a very tiresome effort of webscraping and integrating different data sources. The central element is the text commentary. All the events were derived by reverse engineering the text commentary, using regex. Using this, I was able to derive 11 types of events, as well as the main player and secondary player involved in those events and many other statistics. In case I've missed extracting some useful information, you are gladly invited to do so and share your findings. The dataset provides a granular view of 9,074 games, totaling 941,009 events from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France from 2011/2012 season to 2016/2017 season as of 25.01.2017. There are games that have been played during these seasons for which I could not collect detailed data. Overall, over 90% of the played games during these seasons have event data.

The dataset is organized in 3 files:

• events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
• ginf.csv - contains metadata and market odds about each game. odds were collected from oddsportal.com
• dictionary.txt contains a dictionary with the textual description of each categorical variable coded with integers

Past Research

I have used this data to:

• create predictive models for football games in order to bet on football outcomes.
• make visualizations about upcoming games
• build expected goals models and compare players

Inspiration

There are tons of interesting questions a sports enthusiast can answer with this dataset. For example:

• What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models
• When are teams more likely to score?
• Which teams are the best or sloppiest at holding the lead?
• Which teams or players make the best use of set pieces?
• In which leagues is the referee more likely to give a card?
• How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous?
• Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers)
• Which teams have a bias for attacking on a particular flank?

And many many more...

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