This repository contains the evaluation results of our study, as well as datasets and model checkpoints. For a detailed overview regarding the provided materials, please refer to README.md.

Automotive Map Matching API Market Outlook

According to our latest research, the global automotive map matching API market size reached USD 1.87 billion in 2024, demonstrating robust momentum driven by rapid advancements in connected vehicle technologies and increasing demand for precise navigation solutions. The market is experiencing a notable growth trajectory, expanding at a CAGR of 13.5% during the forecast period. By 2033, the automotive map matching API market is expected to attain a value of USD 5.90 billion. This impressive growth is primarily fueled by the proliferation of advanced driver assistance systems (ADAS), autonomous vehicle development, and the growing integration of real-time data analytics in automotive navigation platforms.

One of the most significant growth factors for the automotive map matching API market is the rising emphasis on vehicle safety and driver assistance. As the automotive industry accelerates its shift towards semi-autonomous and fully autonomous vehicles, the need for highly accurate map matching APIs becomes paramount. These APIs enable real-time synchronization between vehicle sensors and digital maps, ensuring vehicles maintain precise positioning and trajectory, which is essential for ADAS functionalities such as lane-keeping, collision avoidance, and adaptive cruise control. The increasing regulatory mandates for enhanced vehicle safety are further propelling OEMs and technology providers to invest heavily in advanced map matching solutions, thereby supporting market expansion.

Another key driver is the exponential growth of fleet management solutions across commercial and logistics sectors. Fleet operators are increasingly adopting automotive map matching APIs to optimize routing, monitor vehicle locations, and enhance operational efficiency. The integration of map matching APIs with telematics and IoT platforms allows for seamless real-time tracking, better route planning, and reduced fuel consumption. Moreover, the surge in e-commerce and last-mile delivery services globally has intensified the demand for accurate and reliable navigation systems, further fueling the adoption of map matching APIs within fleet management applications.

The evolution of cloud computing and the proliferation of connected vehicles have also played a pivotal role in the growth of the automotive map matching API market. Cloud-based deployment models offer scalability, flexibility, and cost-effectiveness, making it easier for automotive OEMs and aftermarket players to integrate and update map matching functionalities remotely. This shift towards cloud-based solutions is enabling real-time updates and continuous improvements in navigation accuracy, which is particularly crucial for autonomous vehicles that rely on up-to-date mapping data to operate safely. The convergence of AI, machine learning, and big data analytics with map matching APIs is further enhancing their precision and reliability, opening new avenues for innovation and market growth.

Regionally, North America continues to dominate the automotive map matching API market, owing to the presence of leading automotive technology companies, high adoption rates of connected vehicles, and supportive regulatory frameworks. Europe follows closely, driven by stringent safety standards and a strong focus on autonomous vehicle development. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, increasing vehicle production, and growing investments in smart transportation infrastructure. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by expanding automotive sectors and digital transformation initiatives.

Component Analysis

The component segment of the automotive map matching API market is primarily bifurcated into software and services. The software segment dominates the market, accounting for the largest share in 2024, as automotive OEMs and technology providers focus on developing sophisticated APIs capable of delivering real-time, high-precision map matching functionalities. These software solutions are integral to the seamless operation of navigation systems, ADAS, and autonomous driving platforms. Continuous advancements in artificial intelligence and machine learning algorithms are further enhancing the capabilities of map matching software, enabling more accurate and reliab

League of Legends Relational Database for Match Prediction

Context

This dataset contains detailed match and player data from League of Legends, one of the most popular multiplayer online battle arena (MOBA) games in the world. It includes 35,000 matches and contains 78,000 summoner statistics, capturing a wide range of in-game statistics, such as champion selection, player performance metrics, match outcomes, and more.

The dataset is structured to support a variety of analyses, including:

• Predicting match outcomes based on team compositions and player stats
• Evaluating player performance and progression over time
• Exploring trends in champion popularity and win rates
• Building machine learning models for esports analytics

Whether you are interested in competitive gaming, data science, or predictive modeling, this dataset provides a rich source of structured data to explore the dynamics of League of Legends at scale.

Data Schema and Dictionary

Data was collected from Riot Games API using Python script(link) from Patch 25.19

The datase consists of 7 csv files:

• MatchStatsTbl - Match Stats given a summonerID and MatchID.Contains K/D/A, Items, Runes,Ward Score, Summoner Spells, Baron Kills, Dragon Kills, Lane, DmgTaken/Dealt, Total Gold, cs,Mastery Points and Win/Loss
• TeamMatchStatsTbl - Containes Red/Blue Champions,Red/Blue BaronKills,Blue/Red Turret Kills, Red/Blue Kills, RiftHearaldKills and Win/loss
• MatchTbl- Contains MatchID,Rank,Match Duration and MatchType.
• RankTbl - Contains RankID and RankName
• ChampionTbl- Contains ChampionID and ChampionName
• ItemTbl - Contains ItemID and ItemName
• SummonerTbl - Contains SummonerID and SummonerName
• SummonerMatchTbl - Links MatchID,SummonerID and ChampionID

Database Features

• This dataset contains 35,422 League of Legends matches and 78,863 summoner statistics from those games.
• Uses Data from over 2,381 summoners.
• Consists of data only from Europe West(EUW)
• Data is sampled from Unranked to Challenger tiers.

Database Setup

-MySQL Database using Linux -Database Schema Script can be found here. (Works with the gtihub project to collect your own data)

Limitations

The Riot API only provides the "BOTTOM" lane for bot-lane players. During Data collection, roles were inferred by combining chapions that often played support with CS metrics to distinguish ADC vs Support — especially for ambiguous picks like Senna or off-meta choices.

Acknowledgements/Privacy

Data is collected using the official Riot Games API. We thank Riot Games for providing the data and tools that make this project possible. This dataset is not endorsed or certified by Riot Games. No personal or identifiable player data (e.g., Summoner Names, Summoner IDs, or PUUIDs) are included. The SummonerTbl has been intentionally excluded from this public release.

Github

The Python scripts used for data collection, as well as various scripts I developed for API calls, database management, and initial data analytics, can be found on GitHub

Digital Freight Matching API Connectors Market Outlook

According to our latest research, the global Digital Freight Matching API Connectors market size reached USD 1.62 billion in 2024, with a robust growth trajectory supported by the increasing adoption of automation in logistics and transportation. The market is projected to grow at a CAGR of 19.7% from 2025 to 2033, reaching an estimated USD 7.90 billion by 2033. This remarkable expansion is primarily fueled by the surge in demand for real-time freight visibility, seamless integrations between shippers and carriers, and the digital transformation sweeping across the supply chain ecosystem.

The growth factors driving the Digital Freight Matching API Connectors market are multi-faceted and deeply rooted in the evolving logistics landscape. One of the most significant contributors is the increasing complexity of global supply chains, which has made traditional freight management methods inefficient and costly. Enterprises are now seeking solutions that offer real-time connectivity, automated load matching, and instant rate discovery, all of which are enabled by advanced API connectors. These connectors facilitate direct, programmatic communication between digital freight platforms, shippers, carriers, and third-party logistics providers, reducing manual intervention and errors. Furthermore, the rise of e-commerce and omnichannel retailing has intensified the need for rapid and efficient freight operations, thereby accelerating the adoption of digital freight matching technologies.

Another key driver is the rapid advancement in cloud computing and IoT technologies. The proliferation of cloud-based solutions has democratized access to sophisticated freight management tools, making it feasible for small and medium-sized enterprises (SMEs) to participate in digital freight ecosystems. Cloud-based API connectors allow for scalable, flexible, and secure integration of disparate logistics systems, enabling stakeholders to access real-time data, optimize routes, and monitor shipments more efficiently. The integration of IoT devices further enhances the value proposition by providing granular visibility into cargo location, condition, and estimated time of arrival, which is critical for both shippers and carriers aiming to improve customer satisfaction and operational efficiency.

Additionally, regulatory pressures and sustainability initiatives are shaping the trajectory of the Digital Freight Matching API Connectors market. Governments worldwide are introducing stringent emissions regulations, compelling logistics companies to optimize their operations and reduce empty miles. API-driven digital freight matching platforms play a pivotal role in this context by enabling smarter load consolidation, route optimization, and asset utilization. Moreover, as the industry moves towards greater transparency and compliance, the ability of API connectors to facilitate seamless data sharing and auditing becomes indispensable, ensuring that all stakeholders can adhere to evolving regulatory requirements while maintaining operational agility.

From a regional perspective, North America leads the global Digital Freight Matching API Connectors market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to the early adoption of digital freight platforms, a highly developed logistics infrastructure, and a strong presence of key market players. Europe’s market is buoyed by cross-border trade complexities and robust regulatory frameworks, while Asia Pacific is witnessing rapid growth due to expanding e-commerce, urbanization, and government investments in logistics modernization. Latin America and the Middle East & Africa, though smaller in market size, are expected to experience accelerated adoption rates as digitalization initiatives gain traction and local logistics networks evolve.

Component Analysis

The Digital Freight Matching API Connectors m

General Info

This dataset contains over 100,000 ranked matches from the North American (NA) server of League of Legends. Only matches played at Platinum rank or higher are included.

Files

• matchData.csv – Contains structured match-level data (excluding individual player challenges).
• match_data.jsonl – Contains the full set of raw Riot API match JSONs for reference. The specific fields are documented in the Riot API Match-V5 docs.
• players_8-14-25.csv – Lists the players whose match histories were scraped, along with their ranks at the time of scraping.
• match_ids.csv – Contains the match IDs used in the dataset, paired with the rank of the player they were scraped from.

Rank Distribution

Matching Touch Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

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• Comprehensive Data Points for LinkedIn Profiles: Our LinkedIn profile data includes over 40 key data points for every individual and company, ensuring a complete understandin...

BigBox API provides reliable, real-time Home Depot product, category, reviews, and offers data. All data includes comprehensive coverage of each of the search results in a cleanly structured output.

You can originate your request from any zip code (US) to see results as they would appear to customers in the specified location i.e. shipping info. BigBox APIs high-capacity, global infrastructure assures you the highest level of performance and reliability. For easy integration with your Home Depot data apps and services, data is delivered in JSON or CSV format.

Data is retrieved by search term, search results page URL, or for single products, by the Home Depot item ID or by global identifiers such as GTIN, ISBN, UPC and EAN. GTIN-based requests work by looking up the GTIN/ISBN/UPC on Home Depot first, then retrieving the product details for the first matching item ID.

So what's in the data from BigBox API?

Product: - Item & parent ID - UPC - Store SKU - In-store bay &/or aisle - Product specifications - Description - Imagery - Product videos - Buy Box winner: price and fulfillment info - Rating & reviews count - Descriptive attributes

Search results: - Product details per search result: - Position - Related queries - Pagination - Facets

How can BigBox API be used? - Product listing management - Price monitoring - Category & product trends monitoring - Market research & competitor intelligence - Location-specific shipping data - Rank tracking on Home Depot

...and more, depending on your request parameters or the search result.

Who uses BigBox API? This data is leveraged by software developers, marketers & business owners, sales & business development teams, researchers, and data analysts & engineers, in ecommerce, other retail business, agencies and SaaS platforms.

Anyone in your organization who works with your digital presence can develop business intelligence and strategy using this advanced product data.

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

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API Features:

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Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

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• API Access: Our robust APIs and customizable data solutions provide the flexibility and scalability needed to adapt to changing market conditions and business needs.
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• Tailored Solutions: Custom datasets are built to fit your unique business needs and industries.

Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new...

🇦🇺 Australia English Search API for looking up addresses and roads within the catchment. The api can search for both address and road, or either. This dataset is updated weekly from VicMap Roads and Addresses, sourced via www.data.vic.gov.au. # Use The Search API uses a data.gov.au datastore and allows a user to take full advantage of full test search functionality. An sql attribute is passed to the URL to define the query against the API. Please note that the attribute must be URL encoded. The sql statement takes for form as below: SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('[term]', ' ', ' %26 ')) LIMIT 10 The above will select the top 10 results from the API matching the input 'term', and return the display name as well as an x and y coordinate. The full URL for the above query would be: https://data.gov.au/api/3/action/datastore_search_sql?sql=SELECT display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('[term]', ' ', ' %26 ')) LIMIT 10) # Fields Any field in the source dataset can be returned via the API. Display, x and y are used in the example above, but any other field can be returned by altering the select component of the sql statement. See examples below. # Filters Search data sources and LGA can also be used to filter results. When not using a filter, the API defaults to using all records. See examples below. ## Source Dataset A filter can be applied to select for a particular source dataset using the 'src' field. The currently available datasets are as follows: - 1 for Roads - 2 for Address - 3 for Localities - 4 for Parcels (CREF and SPI) - 5 for Localities (Propnum) ## Local Government Area Filters can be applied to select for a specific local government area using the 'lga_code' field. LGA codes are derrived from Vicmap LGA datasets. Wimmeras LGAs include: - 332 Horsham Rural City Council - 330 Hindmarsh Shire Council - 357 Northern Grampians Shire Council - 371 West Wimmera Shire Council - 378 Yarriambiack Shire Council # Examples Search for the top 10 addresses and roads with the word 'darlot' in their names: SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('darlot', ' ', ' & ')) LIMIT 10) example Search for all roads with the word 'perkins' in their names: SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('perkins', ' ', ' %26 ')) AND src=1 example Search for all addresses with the word 'kalimna' in their names, within Horsham Rural City Council: SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('kalimna', ' ', ' %26 ')) AND src=2 and lga_code=332 example Search for the top 10 addresses and roads with the word 'green' in their names, returning just their display name, locality, x and y: SELECT distinct display, locality, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('green', ' ', ' %26 ')) LIMIT 10 example Search all addresses in Hindmarsh Shire: SELECT distinct display, locality, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE lga_code=330 example

Success.ai’s B2B Company Data API provides direct, on-demand access to in-depth firmographic insights for over 70 million companies worldwide. Covering key attributes such as industry classification, company size, revenue ranges, and geographic footprints, this API ensures your sales, marketing, and strategic planning efforts are informed by accurate, continuously updated, and AI-validated data.

Whether you’re evaluating new markets, refining your ICP (Ideal Customer Profile), or enhancing ABM campaigns, Success.ai’s B2B Company Data API delivers the intelligence needed to target the right organizations at the right time. Supported by our Best Price Guarantee, this solution empowers you to make data-driven decisions and gain a competitive edge in a complex global marketplace.

Why Choose Success.ai’s B2B Company Data API?

1. Comprehensive Global Coverage

   • Access profiles of over 70 million companies spanning multiple industries, sectors, and regions.
   • Confidently enter new markets, identify niche segments, and discover growth opportunities across the globe.

2. AI-Validated Accuracy

   • Benefit from 99% data accuracy through AI-driven validation, ensuring every insight is reliable and actionable.
   • Trust that your decisions are backed by current, high-quality information, minimizing risk and guesswork.

3. Continuous Data Updates

   • Real-time refreshes keep you aligned with evolving market conditions, organizational changes, and industry dynamics.
   • Always operate with the most relevant data, ensuring your outreach and strategies remain timely and impactful.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage for all applications.

Data Highlights:

• 70M+ Verified Company Profiles: Leverage a vast database to discover new accounts, refine targeting, and guide strategic initiatives.
• Firmographic Insights: Gain visibility into industry classifications, company sizes, revenue tiers, and regional footprints.
• Continuously Updated: Stay current with market expansions, mergers, and new entrants, seizing opportunities early.
• Best Price Guarantee: Optimize ROI by accessing top-tier data at the most competitive prices on the market.

Key Features of the B2B Company Data API:

1. On-Demand Data Enrichment

   • Instantly enhance CRM records or marketing databases with verified company profiles, eliminating guesswork.
   • Maintain data hygiene and ensure teams always work with accurate, current intelligence.

2. Advanced Filtering and Query Capabilities

   • Query the API to segment companies by industry, location, employee count, or revenue.
   • Zero in on the precise accounts that match your ideal customer profile, improving conversion and engagement rates.

3. Real-Time Validation and Reliability

   • Rely on continuous data refreshes and AI validation for impeccable data integrity.
   • Reduce wasted effort and improve decision-making backed by trustworthy insights.

4. Scalable and Flexible Integration

   • Seamlessly integrate the API into CRMs, analytics tools, or marketing platforms, streamlining workflows.
   • Adjust parameters as market conditions evolve, ensuring your data needs always match your strategic priorities.

Strategic Use Cases:

1. Account-Based Marketing (ABM)

   • Identify high-value accounts aligned with your ICP using firmographic data.
   • Deliver personalized outreach, increasing engagement, deal size, and overall ABM success.

2. Market Expansion and Product Launches

   • Enter new markets with confidence by identifying industry leaders, rising players, and underserved segments.
   • Validate product-market fit and refine go-to-market strategies using data-driven insights.

3. Competitive Benchmarking and Analysis

   • Monitor industry landscapes and track competitor growth to anticipate trends and pivot strategies proactively.
   • Stay ahead of market shifts by aligning solutions with evolving customer needs.

4. Partner and Supplier Sourcing

   • Discover reliable partners, suppliers, or distributors based on firmographic filters.
   • Strengthen supply chains, reduce risks, and ensure stable growth through informed partner selection.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality B2B company data at industry-leading prices, maximizing ROI for data-driven initiatives.

2. Seamless Integration

   • Incorporate the API into existing workflows easily, eliminating manual data imports and siloed processes.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven choices, refine targeting, and improve conversion rates.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on particular industries, regions, or company sizes, adapting as your goals shift.

Additi...

League of Legends MatchID dataset V2.0

As people who like data analysis , but young enough to still like gaming, we thought that League of legends would be a great game to analyze. Due to competitive play some statistics and predictions were quite welcome. There are of course a lot of websites that offer that by them selves, but we think that League community needed an open dataset to work with, as there was none that offered some real volume of data. There came the idea for a bigger dataset which would offer other people to drive their projects without the struggle of long lasting process of parsing matches with Riot API (which has a limit of 500 calls per 10 minutes...so yea)

This is NOT the finished project, but more like a post along the way. The dataset only consists of one column and its basically useless by it self. The file consists of 223 715 match IDs of ranked games . Each column represents the MatchId of a single match played in League, which can be than accessed with Riot API The purpose is only to allow others like us, to continue the research with Riot API with some pre gathered data and save them some precious time that way.

The final dataset "League of Legends MatchesDataset V1.0" we will be posting, consists of 100 000 matches in JSON which will be directly suitable for data analysis.

Link to the dataset: WIP

We are also open sourcing the data gathering program (written in python)

GitHub link: GitHub program

This project has been posted by me (Lan Vukušič) as data scientist but the main credit goes to lead programmer Matej Urbas who is responsible for the data gathering in this project and without whom the project would not exist.

We are happy to give the dataset out for free, to let the comunity use that dataset. We would love to see what people are going to create. We know that we are "rookies" in that field but would still like to contribute to evergrowing field of data science. So if there is really anything that should be changed in upcoming updates please feel free to message us and tell us your thoughts.

Contacts : leaguedataset@gmail.com

Best regards

League of Legends MatchID dataset V1.0 and League of Legends MatchID dataset V2.0 aren't endorsed by Riot Games and doesn't reflect the views or opinions of Riot Games or anyone officially involved in producing or managing League of Legends. League of Legends and Riot Games are trademarks or registered trademarks of Riot Games, Inc. League of Legends © Riot Games, Inc.

Mix And Match Colour Matching Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Sports API Market Outlook

According to our latest research, the global Sports API market size reached USD 2.84 billion in 2024, reflecting robust momentum driven by the digital transformation of the sports industry. The market is projected to expand at a CAGR of 9.8% from 2025 to 2033, culminating in a forecasted market size of USD 6.62 billion by 2033. This growth trajectory is underpinned by rising demand for real-time sports data integration, the proliferation of fantasy sports platforms, and increasing investments in digital fan engagement solutions. As per our latest research, technology adoption and evolving consumer expectations are the primary catalysts accelerating the global Sports API market.

The surging adoption of digital platforms across the sports ecosystem is a significant growth driver for the Sports API market. Sports organizations, broadcasters, and third-party app developers are increasingly leveraging APIs to deliver seamless access to live scores, player statistics, and other critical data. The shift towards immersive fan experiences has compelled stakeholders to prioritize real-time data delivery, which is only possible through robust API infrastructures. The proliferation of connected devices and mobile applications has further fueled the need for efficient data exchange, making Sports APIs indispensable for enhancing audience engagement and monetization strategies. As sports fans demand instant updates and interactive content, the integration of APIs ensures that data flows smoothly across multiple digital touchpoints, supporting the evolution of fan-centric business models.

Another key factor propelling the Sports API market is the exponential growth of fantasy sports and sports betting platforms. These sectors rely heavily on accurate, up-to-the-second data feeds, including player performance metrics, live scores, and match outcomes. APIs serve as the backbone for aggregating and distributing this data, enabling real-time analytics, predictive modeling, and customized user experiences. The regulatory landscape for sports betting is also evolving, with more countries legalizing online betting, which in turn increases the demand for secure and reliable Sports API solutions. Furthermore, advancements in artificial intelligence and machine learning are being integrated with Sports APIs, empowering developers to deliver smarter recommendations, automated insights, and advanced analytics for end-users. This technological convergence is expected to further amplify market growth over the forecast period.

The transformation of media and broadcasting is another pivotal growth factor for the Sports API market. Traditional sports broadcasting is being rapidly replaced by over-the-top (OTT) streaming services and digital platforms, which rely on APIs for real-time content delivery, personalization, and syndication. Media companies are investing in API-powered solutions to enhance their coverage, offer interactive features, and cater to a global audience with multilingual and multi-device support. Moreover, APIs allow broadcasters to integrate third-party content, such as betting odds, social media feeds, and user-generated content, creating a comprehensive and engaging viewing experience. The growing competition among broadcasters to provide differentiated services is expected to drive sustained investments in Sports API technologies, solidifying their role as a critical enabler of digital transformation in sports media.

From a regional perspective, North America continues to dominate the Sports API market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to the presence of established sports leagues, advanced digital infrastructure, and a vibrant ecosystem of technology providers and sports organizations. Europe is witnessing rapid adoption due to the popularity of football, cricket, and other sports, coupled with increasing investments in digital fan engagement. Asia Pacific is emerging as a high-growth market, driven by the rising popularity of eSports, expanding internet penetration, and the proliferation of mobile devices. Latin America and the Middle East & Africa are also showing promising growth, albeit from a smaller base, as sports organizations in these regions embrace digital innovation to reach wider audiences and unlock new revenue streams.

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All of the data used on the Finder.HealthCare.gov web application is available through this API. There are multiple collections of data available through the API.
1. Public Options Data - This data set includes Medicaid, CHIP, High Risk and Territory data along with all of the other public options available. The appropriate options are returned based on the criteria submitted in the API call.
2. Individual and Family Health Insurance Options Data - Paginated individual and family health insurance plan data, a subset of the full plan data including pricing, is returned for plans that match the criteria submitted in the API call for available plans. Full plan data is returned when a specific plan is requested with all appropriate criteria.
3. Small Group Insurance Options Data - Paginated Small Group health insurance product data, a subset of the full product data including pricing, is returned for products that match the criteria submitted in the API call for available products. Full product data is returned when a specific product is requested with all appropriate criteria.

All of the data used on the Finder.HealthCare.gov web application is available through this API. There are multiple collections of data available through the API.
1. Public Options Data - This data set includes Medicaid, CHIP, High Risk and Territory data along with all of the other public options available. The appropriate options are returned based on the criteria submitted in the API call.
2. Individual and Family Health Insurance Options Data - Paginated individual and family health insurance plan data, a subset of the full plan data including pricing, is returned for plans that match the criteria submitted in the API call for available plans. Full plan data is returned when a specific plan is requested with all appropriate criteria.
3. Small Group Insurance Options Data - Paginated Small Group health insurance product data, a subset of the full product data including pricing, is returned for products that match the criteria submitted in the API call for available products. Full product data is returned when a specific product is requested with all appropriate criteria.

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.

Search API for looking up addresses and roads within the catchment. The api can search for both address and road, or either. This dataset is updated weekly from VicMap Roads and Addresses, sourced via www.data.vic.gov.au.

Use

The Search API uses a data.gov.au datastore and allows a user to take full advantage of full test search functionality.

An sql attribute is passed to the URL to define the query against the API. Please note that the attribute must be URL encoded. The sql statement takes for form as below:

SELECT distinct display, x, y
FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a"
WHERE _full_text @@ to_tsquery(replace('[term]', ' ', ' %26 '))
LIMIT 10

The above will select the top 10 results from the API matching the input 'term', and return the display name as well as an x and y coordinate.

The full URL for the above query would be:

https://data.gov.au/api/3/action/datastore_search_sql?sql=SELECT display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('[term]', ' ', ' %26 ')) LIMIT 10)

Fields

Any field in the source dataset can be returned via the API. Display, x and y are used in the example above, but any other field can be returned by altering the select component of the sql statement. See examples below.

Filters

Search data sources and LGA can also be used to filter results. When not using a filter, the API defaults to using all records. See examples below.

Source Dataset

A filter can be applied to select for a particular source dataset using the 'src' field. The currently available datasets are as follows:

• 1 for Roads
• 2 for Address
• 3 for Localities
• 4 for Parcels (CREF and SPI)
• 5 for Localities (Propnum)

Local Government Area

Filters can be applied to select for a specific local government area using the 'lga_code' field. LGA codes are derrived from Vicmap LGA datasets. Wimmeras LGAs include:

• 332 Horsham Rural City Council
• 330 Hindmarsh Shire Council
• 357 Northern Grampians Shire Council
• 371 West Wimmera Shire Council
• 378 Yarriambiack Shire Council

Examples

Search for the top 10 addresses and roads with the word 'darlot' in their names:

SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('darlot', ' ', ' & ')) LIMIT 10)

example

Search for all roads with the word 'perkins' in their names:

SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('perkins', ' ', ' %26 ')) AND src=1

example

Search for all addresses with the word 'kalimna' in their names, within Horsham Rural City Council:

SELECT distinct display, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('kalimna', ' ', ' %26 ')) AND src=2 and lga_code=332

example

Search for the top 10 addresses and roads with the word 'green' in their names, returning just their display name, locality, x and y:

SELECT distinct display, locality, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE _full_text @@ to_tsquery(replace('green', ' ', ' %26 ')) LIMIT 10

example

Search all addresses in Hindmarsh Shire:

SELECT distinct display, locality, x, y FROM "4bf30358-6dc6-412c-91ee-a6f15aaee62a" WHERE lga_code=330

example

Datasys helps you understand and engage your audience wherever they are. Every day, we process up to 600 million privacy compliant records of online behavior, giving you a clear, actionable view of how consumers interact with your brand across websites, devices, and platforms.

In a world where people use multiple devices throughout their day, traditional cookies alone can’t tell the whole story. Datasys combines cookies with modern, privacy-friendly identifiers to connect the dots, making your targeting more accurate and your campaigns more effective.

Reach the right people at the right time on the right screen, all while respecting their privacy and building trust.

Product Overview Scale your Identity Resolution and Contact Enrichment capabilities with the world’s largest commercially available Email-to-Phone linkage dataset. Covering over 850 Million verified pairs across 190+ countries, this dataset bridges the gap between digital identifiers (Email) and physical reachability (Mobile/Phone).

We provide a deterministic link between email addresses and phone numbers, enabling enterprises to resolve customer identities, prevent fraud, and enrich CRM records with high-accuracy mobile data. Unlike regional providers, our Global Identity Graph aggregates data from telco partnerships, e-commerce signals, and opt-in consortiums to deliver a single, unified solution for global operations.

Key Questions This Data Answers Identity & Risk Teams:

Is this email address associated with a valid, active mobile number?

Does the phone number country match the user's IP location? (Critical for Fraud Detection)

Is this a VOIP/Burner line or a legitimate contract mobile number?

Marketing & Sales Teams:

What is the direct mobile number for this prospect?

How can I reactivate dormant email leads via SMS or Telemarketing?

Which records in my CRM are missing phone numbers?

Common Use Cases 1. Fraud Prevention & Risk Scoring Stop synthetic fraud at the gate. By validating that an incoming email is tied to a legitimate, long-standing mobile number, you can drastically reduce account takeover (ATO) and fake sign-ups.

Signal: Match status (Match/No Match) acts as a strong trust signal.

Line Type: Flag risky VOIP or non-fixed VOIP lines immediately.

1. CRM Enrichment & Reverse Append Breathe life into legacy databases. Take a list of email addresses (hashed or plain text) and append verified phone numbers to unlock new channels like SMS, WhatsApp, or Direct Sales calls.

Fill Rates: Achieve industry-leading match rates (30-60% depending on region).

Refresh: Update old landlines to current mobile numbers.

1. Identity Verification (KYC/AML) Strengthen Know Your Customer (KYC) workflows by adding a passive layer of verification. Confirm that the user providing an email owns the associated mobile device without adding friction to the UX.

2. Omnichannel Marketing Create a unified customer view. Link a user's email activity (Newsletter opens) with their mobile identity to orchestrate synchronized Email + SMS campaigns.

Data Dictionary & Schema Attributes We provide a rich output schema. You send us an Email (Plain Text, MD5, SHA1, or SHA256); we return the following:

Core Identity Fields:

email_address: The input email (or hash).

phone_number: The matched phone number in E.164 format (e.g., +14155550123).

match_score: Confidence score of the linkage (0-100).

last_seen_date: Timestamp of the most recent signal validating this link.

Phone Metadata:

country_code: ISO 2-letter country code (e.g., US, GB, DE).

carrier_name: Name of the telecom provider (e.g., Verizon, Vodafone).

line_type: Classification of the number (Mobile, Landline, Fixed VOIP, Non-Fixed VOIP, Toll-Free).

is_active: Boolean flag indicating if the line has shown recent activity.

Linkage Metadata:

linkage_type: Source of the match (Deterministic vs. Probabilistic).

source_category: Aggregated source type (e.g., E-commerce, Telco, Utility).

Global Coverage & Scale Our 850M+ matches are not just US-centric. We offer significant density in key global markets:

North America: ~350M Matches

Europe (GDPR Compliant): ~250M Matches

APAC: ~150M Matches

LATAM: ~100M Matches

Methodology & Compliance Privacy First: We strictly adhere to GDPR, CCPA, and TCPA regulations. All European data is sourced from consent-based frameworks.

Hashing Supported: We accept and return hashed data (MD5/SHA256) for privacy-safe mapping in clean rooms (Snowflake/AWS).

Verification: Our "Active Line" check pings the HLR (Home Location Register) to ensure the number is currently in service, reducing SMS bounce rates.

Delivery & Formats Real-Time API: <100ms latency for live verification at checkout.

Batch Upload: Secure SFTP or S3 bucket transfer for large-scale CRM enrichment.

Formats: JSON, CSV, Parquet.