The UNITY Basketball API is a dynamic and high-performance data feed delivering accurate, real-time basketball betting odds to sportsbooks, betting applications, affiliate platforms, and analytical tools. As a part of the UNITY Odds Feed API suite, it empowers clients to offer rich, responsive betting experiences across many basketball events, including the world’s most watched leagues.

The UNITY Basketball API offers an end-to-end solution for integrating global basketball betting odds into any digital platform. Its combination of real-time performance, extensive market support, broad league coverage, and technical flexibility makes it an ideal tool for sportsbook operators, betting app developers, affiliates, and analysts alike.

Whether you're looking to enhance a live betting experience, scale your sportsbook globally, or build advanced data models, the UNITY Basketball API delivers the accuracy, speed, and support you need to succeed.

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.

UNITY is a next-generation Odds Feed and Betting API built for the dynamic needs of modern sportsbooks, betting websites, mobile apps, and professional trading teams/individuals. With comprehensive sports coverage—including top leagues in Football (Soccer), Basketball, and Tennis—UNITY provides real-time odds updates and supports seamless in-play betting across a wide range of markets like Asian Handicap, Over/Under Totals, Corners, and Cards.

The platform combines a robust odds feed with a fully functional Betting API, enabling direct bet placements and full automation of trading strategies. UNITY integrates effortlessly with major Asian bookmakers and betting exchanges, supporting both back and lay positions through a single, central wallet.

Designed with developers in mind, UNITY includes detailed documentation, code samples, and a staging environment for integration and testing. Its customizable data feed allows users to filter by sport, league, event, or market and choose between raw or formatted content, making it a flexible solution for platforms of any size.

Backed by technical support and continuous system updates, UNITY ensures your betting operation stays ahead of the curve. Whether you're building a new platform or enhancing an existing one, UNITY delivers the tools and reliability to take your betting experience to the next level.

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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 betting, live sc

In response to the COVID-19 pandemic, NYC Parks temporarily closed several amenities, including Athletic Facilities (e.g. basketball courts, tennis courts, fields, etc.). This data collection contains the status of each Athletic Facility, and is subject to change. Although the data feed is refreshed daily, it may not reflect current conditions.

Data Dictionary:

https://docs.google.com/spreadsheets/d/1aaYE82BS-SYh-xjI-t_oyJcNEPFWJNPfdI7T220-rv4/edit#gid=1499621902

Analysis of ‘Parks Closure Status Due to COVID-19: Athletic Facilities’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/aa7ba96f-9285-4853-a05a-e9c6b247f928 on 13 February 2022.

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

In response to the COVID-19 pandemic, NYC Parks temporarily closed several amenities, including Athletic Facilities (e.g. basketball courts, tennis courts, fields, etc.). This data collection contains the status of each Athletic Facility, and is subject to change. Although the data feed is refreshed daily, it may not reflect current conditions.

Data Dictionary:

https://docs.google.com/spreadsheets/d/1aaYE82BS-SYh-xjI-t_oyJcNEPFWJNPfdI7T220-rv4/edit#gid=1499621902

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

The EEG data were recorded from 20 human volunteers (10 females) while they were performing a sequential neurofeedback-behaviour task, with the neurofeedback reflecting the occurrence of beta bursts over sensorimotor cortex (C3 or C4) quantified in real time.

Each participant was recorded three times over three different days. On each recording day, the participant performed the neurofeedback training task with each hemisphere using the EEG signals recorded from C3 or C4 (in a random order), and the contralateral hand for the motor task. Participants completed four experimental runs for each hemisphere on each training day. Each experimental run consisted of a 30 s of rest recording for calibration, 10 continuous trials in the training condition, and another 10 continuous trials in the no training condition. The order of training and no training blocks in each experimental run was randomized. In total, we recorded data from 20 hemispheres with 120 trials in each of the 'training' and 'no training' conditions for each hemisphere for each of the real feedback and sham feedback groups.

The details of the experimental design and behavioural task are described in He et al (2020).

The data files are in MATLAB format. The dataset consists of 120 raw data files (20 subject × 3 days × 2 hemispheres). Due to the size of this dataset (38 GB), it has been split into segments. However, you may still have trouble downloading it, in which case please contact ben.micklem@bndu.ox.ac.uk.

Task

The participants were pseudo-randomly assigned to a sham feedback group or a real feedback group, with ten participants in each group. The neurofeedback training composed of multiple short trials. Each trial consisted of a 2 s period where the participants were instructed to get ready and a 4 s neurofeedback phase, which was followed by black screen presented for a time randomly drawn between 2 and 3 s and then a movement go-cue. The participants were instructed to perform a thumb of finger pinch movement as fast as possible in response to the go-cue to generate a force overshooting a predefined force level (50% of the maximum voluntary force measured before starting the task).

Instructions given to the participants were the same for both groups. In the training trials, the participants were instructed to keep the basketball floating at the top of the screen, which would require them to suppress the beta bursts. In the no training trials, the participants were instructed to simply pay attention to the movement of the ball displayed on the screen and get ready for the subsequent movement go-cue.

Each participant was recorded three times over three different days. On each recording day, the participant performed the neurofeedback training task with each hemisphere using the EEG signals recorded from C3 or C4 (in a random order), and the contralateral hand for the motor task. Participants completed four experimental runs for each hemisphere on each training day. Each experimental run consisted of a 30 s of rest recording to calibrate the threshold for triggering the vertical movement of the basketball, 10 continuous trials in the training condition, and another 10 continuous trials in the no training condition. The order of training and no training blocks in each experimental run was randomized. In total, we recorded data from 20 hemispheres with 120 trials in each of the training and no training conditions for each hemisphere for each of the real feedback and sham feedback groups.

Group information

Real feedback group: Subject - 1 4 6 7 10 11 13 15 17 20 Sham feedback group: Subject - 2 3 5 8 9 12 14 16 18 19

Data description

For each subject (e.g., Sub1), there are three subfolders (i.e., Day1, Day2, Day3) with two files (i.e., Raw_C3.mat and Raw_C4.mat) in each subfolder. The data are in Matlab format. In each .mat file, there are five fields indicate five data streams recorded using OpenVibe. The name of each data stream can be found in rawData.info.name.

The data stream 'openvibeSignal' contains the 32 channels raw data, including: *** 24 monopolar EEG, channel 1-24: FP1, FP2, Fz, FCz, Cz, CPz, Pz, Oz, FC1, C1, CP1, FC3, C3, CP3, FC2, C2, CP2, FC4, C4, CP4, P3, P4, O1, O2. *** 2 bipolar EMG from the flexor carpi radialis of both arms, channel 25-26: EMGL, EMGR. *** 2 accelerometer measurements for both hands recorded from z-axis: Aclz, Acrz. *** 2 pinch force: FrcL, FrcR. Note that these channel information were not included in the structure, but they were the same as indicated above for all recordings.

The data stream 'TriggerStream' contains the trigger information during the experiment. Here below are some keys triggers used to segment the trials: *** 100 or 101: The start of a block, with 100 and 101 indicating no-training and training conditions, respectively. *** 1-10: The onset of the basketball movement in trial 1-10. Note that there were 10 trials in each block. *** -2: Trigger for the participants to get ready before the basketball movement. *** 14: Tigger for the pinch task.

The data stream 'BallMove' contains the recorded positions of the basketball for each individual trial, which indicated the neurofeedback training performance.

The other two streams including 'openvibeMarkers' and 'Matlab' could be ignored. In each data stream, the variable 'time_series' indicated the raw data and 'time_stamps' indicated the time stamps for each sample (column) in 'time_series'.

The following script was used to match the time stamps between 'openvibeSignal' and other two streams:

rawData{1,1}.time_stamps = rawData{1,1}.time_stamps+str2double(rawData{1,1}.info.created_at) Here we assume rawData{1,1} was the 'openvibeSignal' data stream.

The sampling frequency was 2048 Hz.

The peak frequencies with maximum movement-related power reduction was: BetaC3 = [15 19 15 20 18 17 22 19 18 16 20 22 20 18 24 19 20 22 23 19]; BetaC4 = [15 19 15 18 16 21 18 19 17 18 19 20 22 24 19 18 21 20 23 18]; For each hemisphere, a 5-Hz frequency band around the peak frequency was used for the neurofeedback training.