Context

Legalized Sports Betting has arrived. Here is some historical data to backtest.

Content

Data has regular season and playoff results and odds for most of the past decade

Acknowledgements

Data has been scraped from various sources and complied.

Inspiration

Can the bookie be beat?

Analysis of ‘NFL scores and betting data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tobycrabtree/nfl-scores-and-betting-data on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

National Football League historic game and betting info

Content

National Football League (NFL) game results since 1966 with betting odds information since 1979. Dataset was created from a variety of sources including games and scores from a variety of public websites such as ESPN, NFL.com, and Pro Football Reference. Weather information is from NOAA data with NFLweather.com a good cross reference. Betting data was used from http://www.repole.com/sun4cast/data.html for 1978-2013 seasons. Pro-football-reference.com data was then cross referenced for betting lines and odds as well as weather data. From 2013 on betting data reflects lines available at sportsline.com.

Acknowledgements

Helpful sites with interest in football and sports betting include:

https://github.com/fivethirtyeight/nfl-elo-game

http://www.repole.com/sun4cast/data.html

https://www.pro-football-reference.com/

http://www.espn.com/nfl/

http://www.nflweather.com/

http://www.noaa.gov/weather

https://www.sportsline.com/

https://github.com/jp-wright/nfl_betting_market_analysis

http://www.aussportsbetting.com/data/historical-nfl-results-and-odds-data/

Inspiration

Can you build a predictive model to better predict NFL game outcomes and identify successful betting strategies?

--- Original source retains full ownership of the source dataset ---

RealClearPolitics - U.S. Presidential Election - Betting Odds

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods.

About this project

I have been recording available different types of data on soccer matches since 2012, live 24/7. The whole database contains more than 350,000 soccer matches held all around the world from over 27,000 teams of more than 180 countries. An all-in-one package including servers, algorithms and its database are now under the "Analyst Masters" research platform. The app is also free for everyone to get its predictions on Android Play Store . How could it become useful for a data scientist?

Did you know that,

more than 1000 soccer matches are played in a week? the average profit of the stock market from its beginning to now has been less than 10% a year? but you can earn at least 10% on a single match in 2 hours and get your profit in cash It is one of the very rare datasets that you do not need to prove to other companies your method is the most accurate one and get the prize :) . On the other hand you do not have to classify every data point to be rewarded. Just tune or focus to correctly classify only 1% of matches and there you go! Let me give you a simple hint how easily it can become a classification problem rather than a time series prediction:

Example 1: Who wins based on the number of wins in a head 2 head history?

Q) Consider two teams Midtjylland and Randers from Denmark. They have played against each other for very long time. Midtjyland has won Randers over 8 times in the past 10 matches in a 4 year time span. Forget any other complicated algorithm and simply predict who wins this match?

A) That is easy! However, I am also gathering a lot more information than just their history. You can check their head-to-head history and the odds you could get for predicting this match is "1.73" check here.

Example 2: Number of Goals based on their history?

Q) Consider two teams "San Martin S.J." and "Rosario Central" from Argentina. Their odds for wining "Team 1 (Home)", "Draw" and "Team 2 (away)" is [3.16, 3.2, 2.25] respectively. They rank 22 and 13 in their league. They have recently won 45%,35% of their matches in their past 14 matches. Their average head to head goals in their last 7 matches were 1.3 full time (F) and 0.3 until half-time (HT). How many goals do you think they score in their match? (Note that a safe side of number of goals in soccer betting is Over 0.5 goals in HT, Under 1.5 goals in HT, Over 1.5 goals in FT and Under 3.5 goals in FT). Which one do you choose?

A) For sure under 1.5 goals in HT (you get 35%) and under 3.5 goals in FT (you get 30%) . Bingo you get 65% in a single match in 2 hours

Example 3: Based on the money placed for betting on teams who wins the match?

Q) "Memmingen" and "Munich 1860" are well known in Germany. One of our reliable sources of data is the ratio of money placed on betting from 10 hours before the match until it starts. Assume that the ratio of bets on "Munich 1860" to "Memmingen" are recorded every hour as below, which team do you think will win?

[bets in $ on Munich 1860]/[bets in $ on Memmingen] : {1.01, 1.02, 1.04, 1.1, 1.2, 1.4, 1.58, 2.3, 2.6, 2.8}

A) in 10 hours the amount of money placed on wining Munich 1860 Vs Memmingen increased from 1.01 to 2.8, who is the winner? Easy again, Munich 1860 that gives you 160% as stated here.

Try the dataset and inspect every strategy you may come up with, as I gave you three reliable examples above. Just perform well enough to predict 15 matches correctly in a row, start with $1000 and you are a millionaire. If you can't be that accurate use the Kelly Criterion to divide your whole money into smaller stakes.

Let me do the math for you, if you can only get 90% accuracy on 1% of data points (10 out of 1000 matches a week) and your average profit on each match is only 20%. You earn (9*20% = 180%) and lose 100% for your error in 10 predictions. Your net profit would be 80% in a week or approximately 12% in a day. if you risk only 33% of your whole money on each match then the daily net profit becomes 4%. I guess you can easily calculate how fast you can progress @ 4% daily accumulative profit.

For sure one needs a live data feed to predict the outcome before the match. If everything goes well and enough users are interested I will open the live feed of data for you in a shared folder of Dropbox saved in CSV.

Content

Here is what the dataset contains for 'n' matches:

names6.csv

team names as the order of "home-away" separated using "/" ; size : (n x 1)

results6.csv*

Scores recorded during the match, every 2 rows show scores for one match ; size : (2n x 14)

fresults6.csv*

Final scores after full-time ; size : (n x 2) odds6.csv

odds in the order of: Home-Draw-Away ; size : (n x 3) dollars6.csv*

Ratio of the money spent on teams at 15 minutes intervals ; size : (n x 76) ranks6.csv

their ranks in the league at the day of the match Irrespectively ; size : (n x 2) winrate6.csv

their winrate In the last (maximum 14) matches in 2017 Irrespectively ; size : (n x 2) country6.csv

their country as some countries are difficult to analyze e.g. Belarussia ; size : (n x 1) wins6.csv

number of wins in their last (maximum 6) head to head matches ; size : (n x 1) FT_HT6.csv

average of total goals in their last (maximum 6) head to head matches FT and HT ; size : (n x 2) *. recorded every 15 minutes

Try to predict the match as examples above using the given data in the zip file. I will upload the respective data from Mid of August to Mid September later on.

Inspiration

10 years ago I invented the world's first home-size cooking robot in my father's basement but in the end after cooking for us for 2 years it ended up in nothing. So, you as a data scientist can earn money using this live data stream for yourself if you can perform accurately without outperforming others in the competition just get an acceptable accuracy and you are good to go :)

For more information on the overall platform and its live, pre-match and in-play analysis read at www.analystmasters.com or download the app for FREE to get easy predictions at 5% profit per week. More details on how the app operates is available at https://youtu.be/fqlu0YEyqc0

In 2023, online odds-type sports bets were dominated by soccer in Portugal, which represented more than 71 percent of all sport bets. Tennis occupied the second position in terms of sports where Portuguese sports bets players were most invested, with 22 percent of all bets.

About this Dataset

Trying to analyze historical betting odds for whether MLB games will go over or under the betting line? This dataset is for you. More than 13,000 rows include data for all games played between 2013 and 2018.

Category

Sports

Keywords

baseball,mlb,Betting,odds,probability

Row Count

13162

Price

$100.00

Analysis of ‘UFC Fights (2010 - 2020) with Betting Odds’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mdabbert/ufc-fights-2010-2020-with-betting-odds on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

There are some great UFC datasets out there, but I could not find one that included gambling odds.... So I went and made one myself. This dataset focuses very generally on the fights and hopes to be able to draw very broad conclusions. More a more in depth statistical fight analysis I would recommend Rajeev Warrier's excellent datasetwhich was the inspiration for my work.

Content

This dataset consists of 11 columns of data with basic information about every match that took place between March 21, 2010 and March 14, 2020.

Column Definitions:

R_fighter and B_fighter: The names of the fighter in the red corner and the fighter in the blue corner R_odds and B_odds: The American odds of the fighter winning.
date: The date of the fight location: The location of the fight country: The country the fight occurred in Winner: The winner of the fight ('Red' or 'Blue') title_bout: Was this fight a title bout? ('True' or 'False') weight_class: What weight class did this fight occur at? gender: Male or Female

Acknowledgements

I was inspired by the work of Rajeev Warrier

Want More?

My work, including a scraper to help gather data for upcoming events, can be found on my GitHub. I promise I'll add more documentation soon.

--- Original source retains full ownership of the source dataset ---

Developing digital infrastructure and high penetration of connected devices in gaming are increasing the indulgence of sports betting around the world. The global sports betting market has been valued at US$ 102.4 billion in 2024 and has been projected to accelerate at 10% CAGR to reach US$ 265.5 billion by the end of 2034.

Report Attributes	Details
Sports Betting Market Size (2024E)	US$ 102.4 Billion
Forecasted Market Value (2034F)	US$ 265.5 Billion
Global Market Growth Rate (2024 to 2034)	10% CAGR
East Asia Market Share (2034E)	23.1% CAGR
Market Share of Football Betting Segment (2034F)	42%
South Korea Market Growth Rate (2024 to 2034)	10.8%
Key Companies Profiled	IGT 888 Holdings Plc Endeavor Group Holdings Inc. Bet365 Viscus Infotech Ltd. Betsson AB William Hill Plc Churchill Downs Incorporated Sportech Plc Entain plc Kindred Group Plc Flutter Entertainment Plc

Country-wise Insights

Attribute	United States
Market Value (2024E)	US$ 10.9 Billion
Growth Rate (2024 to 2034)	10.5% CAGR
Projected Value (2034F)	US$ 29.4 Billion

Attribute	China
Market Value (2024E)	US$ 11.2 Billion
Growth Rate (2024 to 2034)	10% CAGR
Projected Value (2034F)	US$ 29.1 Billion

Category-wise Insights

Attribute	Online
Segment Value (2024E)	US$ 85 Billion
Growth Rate (2024 to 2034)	10.6% CAGR
Projected Value (2034F)	US$ 233.6 Billion

Attribute	Football
Segment Value (2024E)	US$ 51.2 Billion
Growth Rate (2024 to 2034)	8.1% CAGR
Projected Value (2034F)	US$ 111.5 Billion

Over the third quarter of 2023, bets around the UEFA Champions League represented almost seven percent of all online soccer-related bets, as well as the Portuguese First League, which represented 10 percent. The English Premier League and the Spanish La Liga registered a volume of 12 percent of all online soccer bets in Portugal together.

About

From home page:

English football results, fixed odds, total goals and Asian Handicap betting odds data including the Premiership and other divisions.

Access

CSV files available from 1993 to present.

Openness

No information available.

Market Definition

The Sports Betting Market size was valued at USD 113.54 billion in 2023 and is predicted to reach USD 223.66 billion by 2030 with a CAGR of 10.17% from 2024-2030. Sports betting refers to the activity of predicting the outcome of sporting events and placing wagers on the predicted results. It involves placing bets on various sports such as football, basketball, baseball, tennis, and many others.

Participants in sports betting, commonly known as bettors, analyze factors such as team/player performance, statistics, injuries, and other relevant information to make informed predictions. They then place bets with bookmakers or sportsbooks, who offer odds on different outcomes. If the bettor's prediction is correct, they receive a payout based on the odds offered. Thus, Sports betting is a popular form of gambling and entertainment, enjoyed by millions of people worldwide.

Market Dynamics and Trends

The legalization of sports betting in various regions and countries has significantly contributed to the sports betting market growth. As more jurisdictions recognize the potential economic benefits and address the demand for regulated gambling, the sports betting market expands. Also, the popularity of sports around the world has a direct impact on the sports betting market. Major sporting events, such as the Olympics, FIFA World Cup, Super Bowl, and various league championships, attract a significant number of bettors. The growing fan base and interest in sports lead to increased betting activity, which in turn boosts the growth of the market.

Moreover, aggressive advertising and marketing campaigns by sports betting operators have contributed to market expansion. Promotional activities, sponsorships, and partnerships with sports teams and leagues increase brand visibility and attract new customers. Effective marketing strategies have played a vital role in driving the growth of the sports betting market.

However, in some countries incKEY MARKET SEGMENTS

By Platform

Offline


Online

By Betting Type

Fixed Odds Wagering


Exchange Betting


Live/In-Play Betting


eSports Betting


Others

By Sports Type

Football


Basketball


Baseball


Horse Racing


Cricket


Hockey


Others

By Region

North America



  The U.S.


  Canada


  Mexico




Europe



  The UK


  Germany


  France    


  Italy    


  Spain    


  Denmark    


  Netherlands    


  Finland    


  Sweden    


  Norway    


  Russia    


  Rest of Europe  




Asia-Pacific



  China


  Japan


  India


  South Korea


  Australia


  Indonesia


  Singapore


  Taiwan


  Thailand


  Rest of Asia-Pacific




RoW



  Latin America


  Middle East


  Africa

REPORT SCOPE AND SEGMENTATION:

    Parameters


    Details




    Market Size in 2023


    USD 113.54 Billion




    Revenue Forecast in 2030


    USD 223.66 Billion




    Growth Rate


    CAGR of 10.17% from 2024 to 2030




    Analysis Period


    2023–2030




    Base Year Considered


    2023




    Forecast Period


    2024–2030




    Market Size Estimation


    Billion (USD)




    Growth Factors




      Legalization of sports betting in various regions and countries fuels the market


      The popularity of sports around the world boosts the market growth.


      Aggressive advertising and marketing campaigns by sports betting operators drives the market.






    Countries Covered


    28




    Companies Profiled


    10




    Market Share


    Available for 10 companies




    Customization Scope


    Free customization (equivalent up to 80 working hours of analysts) after purchase. Addition or alteration to country, regional, and segment scope.

Context

Find insights that might help inform betting decisions.

Content

Betting odds and results for European football/soccer leagues from 1993 - 2021. Key to leagues:

England E0 - Premier league E1,E2,E3 - Divisions 1, 2 & 3 respectively

Scotland SC0 - Premier league SC1,SC2,SC3 - Divisions 1, 2 & 3 respectively

Germany D1,D2 - Bundesliga 1 & 2 respectively

Spain SP1,SP2 - La Liga Premera & Segunda respectively

Italy I1,I2 - Serie A & B respectively

France F1,F2 - Le Championnat & Division 2

Netherlands N1 - KPN Eredivisie

Belgium B1 - Jupiler League

Portugal P1 - Liga I

Turkey T1 - Ligi 1

Greece G1 - Ethniki Katigoria

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards HBP = Home Team Bookings Points (10 = yellow, 25 = red) ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

B365H = Bet365 home win odds B365D = Bet365 draw odds B365A = Bet365 away win odds BSH = Blue Square home win odds BSD = Blue Square draw odds BSA = Blue Square away win odds BWH = Bet&Win home win odds BWD = Bet&Win draw odds BWA = Bet&Win away win odds GBH = Gamebookers home win odds GBD = Gamebookers draw odds GBA = Gamebookers away win odds IWH = Interwetten home win odds IWD = Interwetten draw odds IWA = Interwetten away win odds LBH = Ladbrokes home win odds LBD = Ladbrokes draw odds LBA = Ladbrokes away win odds PSH and PH = Pinnacle home win odds PSD and PD = Pinnacle draw odds PSA and PA = Pinnacle away win odds SOH = Sporting Odds home win odds SOD = Sporting Odds draw odds SOA = Sporting Odds away win odds SBH = Sportingbet home win odds SBD = Sportingbet draw odds SBA = Sportingbet away win odds SJH = Stan James home win odds SJD = Stan James draw odds SJA = Stan James away win odds SYH = Stanleybet home win odds SYD = Stanleybet draw odds SYA = Stanleybet away win odds VCH = VC Bet home win odds VCD = VC Bet draw odds VCA = VC Bet away win odds WHH = William Hill home win odds WHD = William Hill draw odds WHA = William Hill away win odds

Bb1X2 = Number of BetBrain bookmakers used to calculate match odds averages and maximums BbMxH = Betbrain maximum home win odds BbAvH = Betbrain average home win odds BbMxD = Betbrain maximum draw odds BbAvD = Betbrain average draw win odds BbMxA = Betbrain maximum away win odds BbAvA = Betbrain average away win odds

MaxH = Market maximum home win odds MaxD = Market maximum draw win odds MaxA = Market maximum away win odds AvgH = Market average home win odds AvgD = Market average draw win odds AvgA = Market average away win odds

Key to total goals betting odds:

BbOU = Number of BetBrain bookmakers used to calculate over/under 2.5 goals (total goals) averages and maximums BbMx>2.5 = Betbrain maximum over 2.5 goals BbAv>2.5 = Betbrain average over 2.5 goals BbMx<2.5 = Betbrain maximum under 2.5 goals BbAv<2.5 = Betbrain average under 2.5 goals

GB>2.5 = Gamebookers over 2.5 goals GB<2.5 = Gamebookers under 2.5 goals B365>2.5 = Bet365 over 2.5 goals B365<2.5 = Bet365 under 2.5 goals P>2.5 = Pinnacle over 2.5 goals P<2.5 = Pinnacle under 2.5 goals Max>2.5 = Market maximum over 2.5 goals Max<2.5 = Market maximum under 2.5 goals Avg>2.5 = Market average over 2.5 goals Avg<2.5 = Market average under 2.5 goals

Key to Asian handicap betting odds:

BbAH = Number of BetBrain bookmakers used to Asian handicap averages and maximums BbAHh = Betbrain size of handicap (home team) AHh = Market size of handicap (home team) (since 2019/2020) BbMxAHH = Betbrain maximum Asian handicap home team odds BbAvAHH = Betbrain average Asian handicap home team odds BbMxAHA = Betbrain maximum Asian handicap away team odds BbAvAHA = Betbrain average Asian handicap away team odds

GBAHH = Gamebookers Asian handicap home team odds GBAHA = Gamebookers Asian handicap away team odds GBAH = Gamebookers size of handicap (home team) LBAHH = Ladbrokes Asian handicap home team odds LBAHA = Ladbrokes Asian handicap away team odds LBAH = Ladbrokes size of handicap (home team) B365AHH = Bet365 Asian handicap home team odds B365AHA = Bet365 Asian handicap away team odds B365AH = Bet365 size of handicap (home team) PAHH = Pinnacle Asian handicap home team odds PAHA = Pinnacle Asian handicap away team odds MaxAHH = Market maximum Asian handicap home team odds MaxAHA = Market maximum Asian handicap away team odds
AvgAHH = Market average Asian handicap home team odds AvgAHA = Market average Asian handicap away team odds

Acknowledgements

Data obtained from Football-Data Photo by Waldemar Brandt on Unsplash

Inspiration

1. What's the proportion of luck (if any) in positive bet results?

2. What's the proportion of misfortune (if any) in negative bet results?

3. Is there a relationship between game odds and the actual game outcome?

4. Comparing single-game (separate stakes) vs multiple game (single stake) bets. Which is more likely to win or lose given a fixed amount to stake?

5. How much influence does form (trend) have in deciding the team's outcome of their next game?

6. Are some leagues more difficult to predict than others?

Please help suggest any other insights I might have missed.

Remember to BeGambleAware

https://i.ibb.co/2M2MsgH/upvote7.png" alt="">

GetData.IO -

Snapshot img

Horse and Sports Betting Market Report 2023-2027

The Global Horse and Sports Betting Market size is estimated to grow by USD 171.87 million accelerating at a CAGR of 9.64% between 2023 and 2027.

Our report offers in-depth analysis of market drivers, trends, opportunities, and challenges, with segmentation by Platform (offline betting and online betting), Type (fixed odds wagering, exchange betting, live betting, esports betting, and others), and Geography (Europe, APAC, North America, South America, and Middle East and Africa). Additionally, Technavio provides valuable insights through value chain analysis, parent market analysis, Porter’s Five analysis, vendor analysis, and COVID-19 impact data. The report also includes a thorough analysis of historical market data from 2017 to 2022.

Get Additional Information on this Report, Request Free Sample in PDF

Horse And Sports Betting Market Analysis and Insights

The increasing digital connectivity is a key driver boosting the market growth. Global Internet usage, reaching 60% of the population in 2020, is driving a digital revolution. This transformation is reshaping sectors like horse racing and sports betting, with a shift towards online platforms. Increased Internet access and smartphone use enable convenient engagement in betting activities. Companies like 888 Holdings invest in technology to enhance services, contributing to the growth of the global horse and sports betting market during the forecast period.

The increasing adoption of artificial intelligence (AI) and machine learning is a key horse and sports betting market trend. Betting industry vendors are increasingly adopting advanced technologies such as AI, machine learning, and virtual reality to attract customers and expand their businesses globally. Machine learning and AI algorithms play a key role in providing accurate predictive analysis for sports betting, considering real-time data like weather conditions, player performance, and fan sentiment. This data, aggregated from various sources, helps bookmakers and bettors formulate effective strategies, resulting in significant revenues. The global horse and sports betting market's growth is further propelled by the integration of AI tools into sports betting solutions, enhancing customer relationships and setting odds.

The stringent government regulations is a key challenge hindering the horse and sports betting market growth. Global horse and sports betting face varied regulations. The UAE deems illegal online activities, including gambling, punishable under Federal Legal Decree No. 5. In Canada, the Criminal Code restricts betting, permitting only authorized forms like pari-mutuel for charitable causes. Asia-Pacific (APAC) countries, like Japan, have limited regulated online sports betting, and Hong Kong allows betting solely through the government-authorized Hong Kong Jockey Club.

The Middle East considers sports betting and online gambling illegal. European nations, such as Spain and France, have diverse regulations, with government-owned entities obtaining licenses. However, high taxes, employment restrictions, and player limitations pose challenges, potentially hindering global market growth.

Horse And Sports Betting Market Segmentation

The market share growth by the offline betting segment will be significant during the forecast period. In countries such as the US, where casinos are widely prevalent and licenses are given by individual states, it is challenging for new vendors to penetrate the market and operate through the online channel. This is a key driver for the growth of the offline market segment during the forecast period.

Horse and Sports Betting Market

By Platform

Get a glance at the market contribution of various segments Request Free Sample

The offline betting segment was valued at USD 190.76 million in 2017 and continued to grow until 2021. Offline betting is especially popular among people who are not comfortable with online platforms. Lack of technology adoption, the prevalence of government regulations that allow sports betting only through offline channels, and privacy issues related to online betting platforms have increased the adoption of offline betting for horse racing and other sports activities. Local bookies allow customers to bet on sports on credit through betting shops or betting agencies, which provides flexibility and convenience to customers to pay later. Such factors wil increase segment growth during the forecast period.

APAC is estimated to contribute 41% to the growth of the global market during the forecast period. Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

Horse and Sports Betting Market

By APAC

For more insights on the market share of various regions Request Free Samples

In most European countries, the popularity of offline an

The latest research report by IMARC Group, The United States sports betting market is expected to exhibit a growth rate (CAGR) of 12.54% during 2024-2032.

Brazil were given the highest odds by bookmakers of winning the 2022 World Cup ahead of the tournament, with an estimated probability of 20 percent or 4/1 odds. The team with the second-highest odds was France, with 20 percent or 6/1 odds. Further details of the odds of all teams listed can be found in the supplementary notes.

In November 2023, Nevada had a sports betting handle of approximately 921.62 million U.S. dollars, up from the previous month's total of 815.65 million U.S. dollars. Land-based sports betting has been legal in the U.S. state of Nevada since 1949. When the Professional and Amateur Sports Protection Act (PASPA) was passed by Congress in 1992, Nevada was handed a legal monopoly on single-game wagering in the United States. Meaning that for decades it was possible to play college sports betting, MLB sports betting, NBA sports betting, and live and off-track horse betting in the state. Nowadays, sports betting has also been legalized in other U.S. states.

Joburg Super Kings (JSK) vs Durban Super Giants (DSG) Match Prediction for 29th Match on 03 Feb 2024. View Betting Tips, Betting Odds, Match and Player Stats on Cricket-Betting.com