The UNITY Odds Feed API – Historical Data Access offers a rich dataset of sports betting odds, covering a global array of leagues and events. This API enables users to retrieve detailed historical odds for both pre-match and live/in-play markets. It includes specific betting metrics such as Asian Handicap, Totals (Over/Under), Corners, and Cards, with data sourced from numerous major Asian sportsbooks and exchanges.

This historical feed is particularly well-suited for:

Data scientists and analysts building predictive models

Sportsbooks improving odds-making strategies

Media platforms generating betting insights

Researchers analyzing market efficiency and odds movement

Key Features: Pre-match and In-play Odds: Track how betting lines moved before and during events.

Multi-Sport Coverage: Includes football (soccer), basketball, and tennis—spanning top leagues like the Premier League, NBA, and Grand Slam tournaments.

Market Breadth: Extensive odds data for niche markets such as corners and cards.

Bookmaker Diversity: Historical odds from a wide range of Asian bookmakers and betting exchanges with low spreads and back/lay functionality.

Structured & Filterable: Access raw or formatted data by sport, league, event, or market.

This API delivers the tools needed to extract meaningful insights from betting markets—whether you're building advanced algorithms, enhancing app features, or deep-diving into betting behavior trends.

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 28 January 2022.

--- 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 ---

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?

oddsDataMLB.csv is historic odds data on MLB games for the 2012-2021 seasons. Odds included are closing numbers - money line, game total, over odds, under odds, run line and run line odds. (Run line data not available for 2012 and 2013). Data also includes runs scored and venue information obtained free of charge from and copyrighted by Retrosheet. Interested parties may contact Retrosheet at www.retrosheet.org.

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

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.

Snapshot img

Sports Betting Market Size 2025-2029

The sports betting market size is forecast to increase by USD 221.1 billion, at a CAGR of 12.6% between 2024 and 2029.

The market is experiencing dynamic growth, driven by the digital revolution and the emergence of machine learning technologies. These advancements enable more accurate predictions and personalized betting experiences for consumers, creating a competitive edge for market participants. Popular betting options include football (soccer), basketball, tennis, horse racing, cricket, and various other sports events. However, this market landscape is not without challenges. Stringent government regulations and restrictions pose significant obstacles, requiring companies to navigate complex legal frameworks and comply with evolving policies.
As the industry continues to evolve, staying informed of regulatory changes and adapting to technological advancements will be crucial for market success. Companies that effectively balance innovation and regulatory compliance will be well-positioned to capitalize on the growing opportunities in the market.

What will be the Size of the Sports Betting Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities shaping its various sectors. Artificial intelligence (AI) is increasingly being integrated into promotional campaigns, enhancing user experience through personalized recommendations and real-time analysis. Spread betting, a popular form of wagering, employs advanced statistical modeling and risk management techniques. Problem gambling remains a significant concern, with player protection measures such as responsible gambling initiatives and KYC procedures being implemented. Betting odds are visualized through data visualization tools, enabling users to make informed decisions. Live streaming and in-play betting provide real-time updates, while API integration and odds comparison tools facilitate seamless data access.

Machine learning algorithms are used for fraud detection and customer segmentation, ensuring secure payment gateways and AML compliance. Bonus offers and loyalty programs are employed as customer acquisition and retention strategies. Data analytics and betting algorithms enable efficient risk management and effective marketing campaigns. Data feeds from sports data providers are crucial for accurate betting odds and real-time score updates. First goalscorer and correct score bets add excitement to the betting experience. Prop bets and Asian handicap betting cater to diverse user preferences. Live score updates and game integrity are ensured through rigorous security protocols and data encryption.

Pre-match betting and futures betting offer opportunities for long-term investment. Ongoing market activities and evolving patterns underscore the continuous dynamism of the market.

How is this Sports Betting Industry segmented?

The sports betting industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Platform

  Online
  Offline


Type

  Basketball
  Horse riding
  Football
  Others


Betting Type

  Fixed Odds Wagering
  Exchange Betting
  Live/In-Play Betting
  eSports Betting


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    Australia
    China
    India
    Japan


  Middle East and Africa

    UAE


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Platform Insights

The online segment is estimated to witness significant growth during the forecast period.

The online market is experiencing notable expansion, fueled by technological advancements and favorable regulatory shifts. Key drivers of this growth include the expanding betting market due to continuous innovation in online channels, the increasing availability of mobile platforms with the widespread use of the Internet and smartphones, and the structural migration of customers from retail to online betting in emerging markets. Improvements in platform quality and user experience, particularly through betting applications, further enhance the appeal of online betting. With digitalization on the rise and smartphone penetration increasing, regions such as APAC and MEA present significant opportunities for growth in the online sports betting sector.

Technological advancements have also brought about the integration of various features, such as machine learning algorithms for risk management and player protection, responsible gambling initiatives, API integration, and odds comparison tools. In-play be

The Challenge

The online sports gambling industry employs teams of data analysts to build forecast models that turn the odds at sports games in their favour. While several betting strategies have been proposed to beat bookmakers, from expert prediction models and arbitrage strategies to odds bias exploitation, their returns have been inconsistent and it remains to be shown that a betting strategy can outperform the online sports betting market. We designed a strategy to beat football bookmakers with their own numbers:

"Beating the bookies with their own numbers - and how the online sports betting market is rigged", by Lisandro Kaunitz, Shenjun Zhong and Javier Kreiner.

Here, we make the full dataset publicly available to the Kaggle community. We also provide the codes, raw SQL database and the online real-time dashboard that were used for our study on github.

Our strategy proved profitable in a 10-year historical simulation using closing odds, a 6-month historical simulation using minute to minute odds, and a 5-month period during which we staked real money with the bookmakers. We would like to challenge the Kaggle community to improve our results:

• Can your strategy consistently beat the sports betting market over thousands of bets across leagues around the world?
• Do time series odds movements offer insightful information that a betting strategy can exploit?
• Can you outperform the bookmakers’ predictions included in the odds data by creating a better model?

What's inside the Beat The Bookie dataset

10 year historical closing odds:

• 479,440 football games from 818 leagues around the world
• Games from 2005-01-01 to 2015-07-30.
• Maximum, average and count of active odds at closing time (start of the match)
• Betting odds from up to 32 providers
• Details about the match: date and time, league, teams, 90-minute score

14-months time series odds:

• 92,647 football games from 1005 leagues around the world
• Games from 2015-09-01 to 2016-11-22
• Hourly sampled odds time series, from up to 32 bookmakers from 72 hours before the start of each game
• Details about the match: date and time, league, teams, 90-minute score

The dataset was assembled over months of scraping online sport portals.

We hope you enjoy your sports betting simulations (but remember... the house always wins in the end).

Acknowledgements

Ben Fulcher was of great help when we were drafting the paper. Ben has also developed a very nice toolbox for time-series analysis, which might be relevant for the analysis of this dataset.

Use our trusted SportMonks Football API to build your own sports application and be at the forefront of football data today.

Our Football API is designed for iGaming, media, developers and football enthusiasts alike, ensuring you can create a football application that meets your needs.

Over 20,000 sports fanatics make use of our data. We know what data works best for you, so we ensured that our Football API has all the necessary tools you need to create a successful football application.

• Livescores and schedules Our Football API features extremely fast livescores and up-to-date season schedules, meaning your app will be the first to notify its customers about a goal scored. This also works to further improve the look and feel of your website.

• Statistics and line-ups We offer various kinds of football statistics, ranging from (live) player statistics to team, match and season statistics. And that’s not all - we also provide pre-match lineups for all important leagues.

• Coverage and historical data Our Football API covers over 1,200 leagues, all managed by our in-house scouts and data platform. That means there’s up to 14 years of historical data available.

• Bookmakers and odds Build your football sportsbook, odds comparison or betting portal with our pre-match and in-play odds collated from all major bookmakers and markets.

• TV Stations and highlights Show your customers where the football games are broadcasted and provide video highlights of major match events.

• Standings and topscorers Enhance your football website with standings and live standings, and allow your customers to see the top scorers and what the season's standings are.

The UNITY Soccer API is a powerful solution for delivering highly accurate, real-time football (soccer) odds to sportsbooks, betting apps, affiliate platforms, and data-driven systems. As part of the broader UNITY Odds Feed API, the Soccer API is engineered for speed, scalability, and flexibility—allowing seamless integration of betting markets across the world’s most popular sport.

The UNITY Soccer API is a robust, enterprise-grade solution that powers football betting platforms with real-time, historical, and highly accurate data. With extensive market coverage, flexible customization, and deep global reach, it supports any betting-related use case—whether you're building a full-scale sportsbook, launching a mobile app, or analyzing data for predictive modeling.

Combined with a powerful support infrastructure, seamless integration tools, and competitive bookmaker data, the UNITY Soccer API is the ideal foundation for your next-generation football betting solution.

Sports Betting Predictive Analysis Dataset

This comprehensive synthetic dataset contains 1,369 rows and 10 columns specifically designed for predictive modeling in sports betting analytics. The dataset provides a rich foundation for machine learning applications in the sports betting domain, featuring realistic match data across multiple sports with comprehensive betting odds, team information, and outcome predictions.

Dataset Overview Table

Key Applications

Machine Learning Use Cases

• Outcome Prediction Models: Train classification algorithms to predict match winners
• Odds Analysis: Analyze betting market efficiency and identify value bets
• Feature Engineering: Create derived features for advanced predictive models
• Model Validation: Compare predicted vs actual outcomes for performance metrics
• Risk Assessment: Evaluate betting strategy performance and risk management

Data Science Applications

• Exploratory Data Analysis: Understand patterns in sports betting markets
• Statistical Modeling: Build probabilistic models for outcome prediction
• Time Series Analysis: Analyze temporal trends in betting odds and outcomes
• Comparative Analysis: Study performance differences across sports and teams
• Visualization Projects: Create interactive dashboards for betting analytics

Research Applications

• Academic Research: Study sports betting market dynamics
• Algorithm Development: Test new machine learning approaches
• Benchmarking: Compare different predictive modeling techniques
• Educational Projects: Learn data science concepts with realistic data
• Portfolio Development: Demonstrate skills in sports analytics domain

Data Characteristics

Realistic Market Simulation

• Betting odds within industry-standard ranges (1.2 - 5.0)
• Sport-specific logic (draws only applicable for Football and Hockey)
• Strategic null value placement to simulate real-world data gaps
• Temporal consistency across 2-year historical period
• Unique match identifiers for easy reference and tracking

Comprehensive Coverage

• Multi-Sport Analysis: Five major sports for diverse modeling scenarios
• Balanced Distribution: Even representation across all sports categories
• Team Diversity: Unique city-based team names preventing data leakage
• Outcome Variety: Includes wins, losses, and draws where applicable
• Prediction Comparison: Both model predictions and actual results included

Snapshot img

Horse And Sports Betting Market Size 2025-2029

The horse and sports betting market size is forecast to increase by USD 252 million at a CAGR of 11.4% between 2024 and 2029.

The market is experiencing significant growth, driven by several key trends. One of the primary factors fueling market expansion is the increasing digital connectivity, enabling more consumers to place bets online. Another trend is the rising adoption of advanced technologies such as artificial intelligence (AI) and machine learning, which enhance the betting experience and improve accuracy. However, stringent government regulations pose a challenge to market growth, requiring operators to comply with complex rules and restrictions. Despite these challenges, the market is expected to continue its upward trajectory, offering ample opportunities for stakeholders.

What will be the Size of the Horse And Sports Betting Market During the Forecast Period?

Request Free Sample

The market In the US continues to experience significant growth, driven by the increasing number of internet users and smartphone users. Digital infrastructure and wireless connectivity have enabled online betting to become a convenient and accessible option for individuals seeking to place wagers on athletic events.



Horse racing and horse racing wagering remain popular categories within this market, with past performance and track conditions being key factors in bettors' decision-making processes. The trend towards digitalization is further evidenced by the rise of casino organizations offering fixed odds wagering on horse races, as well as the emergence of esports betting. According to Datareportal, there are currently over 300 million monthly active internet users In the US, and the implementation of 5G networks is expected to further enhance the user experience for mobile device users.

How is this Horse And Sports Betting Industry segmented and which is the largest segment?

The horse and sports betting industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Platform

  Offline betting
  Online betting


Type

  Fixed odds wagering
  Exchange betting
  Live betting
  esports betting
  Others


Geography

  Europe

    Germany
    UK
    Italy


  APAC

    China
    India
    Japan


  North America

    Canada
    US


  South America

    Brazil


  Middle East and Africa

By Platform Insights

The offline betting segment is estimated to witness significant growth during the forecast period.

The market encompasses both online and offline platforms. While online betting is growing in popularity, offline betting remains the largest segment due to various factors. Some individuals prefer the traditional betting experience and are not comfortable with technology. Additionally, government regulations in certain regions limit sports betting to offline channels. Offline betting, including horse racing, is accessible through local bookies and betting shops, allowing customers to bet on credit. The convenience and flexibility of paying later contribute to the continued popularity of offline betting. Despite advancements in technology and the rise of online platforms, offline betting maintains a significant presence In the market.

Get a glance at the Horse And Sports Betting Industry report of share of various segments Request Free Sample

The offline betting segment was valued at USD 219.80 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 48% 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.

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

In Europe, the market has experienced significant growth due to the increasing popularity of online betting and the widespread use of smartphones. With Internet connectivity rates averaging between 50% and 60% among European internet users, online betting platforms have gained traction. Regulatory frameworks have become more permissive towards both online and offline betting, creating a secure environment for sports enthusiasts. The presence of numerous bookmakers in major European countries such as Germany, the UK, France, Italy, and Poland, along with the popularity of various sports activities, has further fueled market expansion. Overall, the digital infrastructure and wireless connectivity have enabled easy access to athletic events, making horse and sports betting an increasingly popular pastime in Europe.

Market Dynamics

Our horse and sports betting market researchers analyzed the data with

Exploring Canadian Lottery 6/49 – Probability and Historical Analysis This project dives into the world of the Canadian Lottery 6/49, where players choose six numbers from a pool of 1 to 49 for a chance to win significant prizes. While the excitement of lotteries often lies in their unpredictability, there is a world of probability and statistical insight behind each draw. This project offers a detailed analysis of both the winning odds and historical patterns in the 6/49 game.

Key Features: Probability Calculation: We begin by calculating the odds of winning the lottery’s grand prize with a single ticket, as well as with multiple tickets. These calculations highlight just how rare a winning combination truly is, providing perspective on lottery participation.

Historical Winning Combinations: By analyzing historical draw data, we observe the frequency and patterns of winning numbers. Are there numbers that appear more often, or combinations that seem “luckier”? This data can help answer questions players may have about “hot” or “cold” numbers.

Multi-Ticket Probability Simulator: We introduce a feature that calculates the chances of winning with multiple tickets, offering insights into how probability scales with ticket purchases. This tool shows how additional tickets impact one’s chances, even if the odds remain steep.

Matching Combinations: This section calculates the likelihood of partially matching numbers (e.g., 2, 3, 4, or 5 numbers), giving players a clearer understanding of their odds of winning lesser prizes.

Fun and Informative Insights: Beyond the numbers, the project provides interactive features and visualizations, making it an engaging way to explore lottery statistics and understand probability theory in a real-world context.

Why This Project? This project is an educational tool for exploring probability, pattern analysis, and data visualization using a universally recognizable concept: the lottery. By looking at historical data, calculating odds, and using simulations, this project aims to make the abstract concepts of probability more accessible and engaging. It’s also an ideal way to apply and showcase data analysis skills with a fun, practical dataset.

Let’s dive into the numbers and uncover the statistical truths behind one of Canada’s most popular games of chance!

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Discover Earnings: Will DFS Defy the Odds?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Korea Racing Authority provides odds data for combinations of entry numbers for quiver, a method of purchasing horse racing tickets that predicts both the 1st and 2nd place entry numbers for races held at racecourses in Seoul, Busan-Gyeongnam, and Jeju, regardless of the order of arrival. (Provided data are racecourse, race date, race number, entry number combination 1, entry number combination 2, and odds data.) - If neither the race year and month nor the race date are entered as requested variables, information for the past month of the most recent race date is displayed. ※ Additional explanation of betting types - Win: This is a method to predict 1 horse to finish in 1st place. - Consecutive: This is a method to predict 1 horse to finish in 1st to 3rd place. - Place: This is a method to predict 2 horses to finish in 1st to 3rd place, regardless of order. - Place: This is a method to predict 2 horses to finish in 1st and 2nd place, regardless of order. - Twin: This is a method of predicting the two horses that will finish in 1st and 2nd place in that order. - Triple: This is a method of predicting the three horses that will finish in 1st, 2nd, and 3rd place in that order. - Tri-Twin: This is a method of predicting the three horses that will finish in 1st, 2nd, and 3rd place in that order.

This study uses prediction market data from the nation’s historical election betting markets to measure electoral competition in the American states during the era before the advent of scientific polling. Betting odds data capture ex ante expectations of electoral closeness in the aggregate, and as such improve upon existing measures of competition based on election returns data. Situated in an analysis of the1896 presidential election and its associated realignment, I argue that the market odds data show that people were able to anticipate the realignment and that expectations on the outcome in the states influenced voter turnout. Findings show that a month ahead of the election betting markets accurately forecast a McKinley victory in most states. This study further demonstrates that the market predictions identify those states where electoral competition would increase or decline that year and the consequences of these expected partisanship shifts on turnout. In places where the anticipation was for a close race voter expectations account for a turnout increase of as much as 6%. Participation dropped by 1% to 6% in states perceived as becoming electorally uncompetitive. The results support the conversion and dealignment theories from the realignment literature.

Coronavirus disease 2019 (COVID-19) can lead to acute organ dysfunction, and delirium is associated with long-term cognitive impairment and a prolonged hospital stay. This retrospective single-center study aimed to investigate the risk factors for delirium in patients with COVID-19 infection receiving treatment in an intensive care unit (ICU). A total of 111 patients aged >18 years with COVID-19 pneumonia who required oxygen therapy from February 2021 to April 2022 were included. Data on patient demographics, past medical history, disease severity, delirium, and treatment strategies during hospitalization were obtained from electronic health records. Patient characteristics and risk factors for delirium were analyzed. Old age (P < 0.001), hypertension (P < 0.001), disease severity (Sequential Organ Failure Assessment score) (P < 0.001), mechanical ventilator support (P < 0.001), neuromuscular blocker use (P < 0.001), and length of stay in the ICU (P < 0.001) showed statistically significant differences on the univariable analysis. Multivariable analysis with backward selection revealed that old age (odds ratio, 1.149; 95% confidence interval, 1.037–1.273; P = 0.008), hypertension (odds ratio, 8.651; 95% confidence interval, 1.322–56.163; P = 0.024), mechanical ventilator support (odds ratio, 226.215; 95% confidence interval, 15.780–3243.330; P < 0.001), and length of stay in the ICU (odds ratio, 30.295; 95% confidence interval, 2.539–361.406; P = 0.007) were significant risk factors for delirium. In conclusion, old age, ICU stay, hypertension, mechanical ventilator support, and neuromuscular blocker use were predictive factors for delirium in COVID-19 patients in the ICU. The study findings suggest the need for predicting the occurrence of delirium in advance and preventing and treating delirium.

SES: Socioeconomic status variables included annual household income and education level.HRB: Health related behaviors include smoking status (current, past, or never), and alcohol intake (current, past, or never).Comorbidities include diabetes, BMI.Odds ratios (Ors) of the hypothyroidism by laboratory data for self-reported glaucoma for National Health and Nutrition Examination Survey (NHANES) population.

The Korea Racing Authority provides racing performance data for races held at racecourses in Seoul, Busan, Gyeongnam, and Jeju. (The provided data includes the racecourse name, race date, race number, single win winning entry number, single win odds, consecutive win winning entry number, consecutive win odds, place winning entry number, place odds, quinquenat winning entry number, quinquenat odds, quinquenat winning entry number, quinquenat odds, quinquenat winning entry number, quinquenat odds, trifecta winning entry number, trifecta odds, trifecta winning entry number, and trifecta odds.) - If you do not enter all request messages related to dates such as race year, race year/month, and race date in the request message, information for the past month based on the race date will be displayed. ※ Horse racing terms Betting odds - Single win: This is a method to correctly predict one horse to finish in 1st place. - Consecutive win: This is a method to correctly predict one horse to finish in 1st to 3rd place. - Place bet: This is a method to predict two horses that will finish in 1st, 2nd, and 3rd place, in any order. - Place bet: This is a method to predict two horses that will finish in 1st and 2nd place, in any order. - Win bet: This is a method to predict two horses that will finish in 1st and 2nd place, in order. - Triple bet: This is a method to predict three horses that will finish in 1st, 2nd, and 3rd place, in any order. - Tri-win bet: This is a method to predict three horses that will finish in 1st, 2nd, and 3rd place, in order.