Data standardization is an important part of effective management. However, sometimes people have data that doesn't match. This dataset includes different ways that counties might get written by different people. It can be used as a lookup table when you need County to be your unique identifier. For example, it allows you to match St. Mary's, St Marys, and Saint Mary's so that you can use it with disparate data from other data sets.

The global match data collection market is projected to grow from USD 940 million in 2023 to USD 3,530 million by 2033, at a CAGR of 16.7%. Growing adoption of data-driven decision-making in the sports industry, the increasing popularity of esports, and advancements in sensor technology are the primary factors driving the market growth. The use of match data allows teams, players, and coaches to gain insights into their performance, identify strengths and weaknesses, and make informed decisions. The market is segmented by type (sensor data, video data, and others), application (sports industry and esports), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). North America is the largest market, followed by Europe. The Asia Pacific region is expected to witness the highest growth rate due to the increasing popularity of esports and the growing number of professional sports leagues in the region. Key players in the market include Opta, Sportradar, N3XT Sports, Sportsdata, OUTFORZ, KINEXON Sports, Stats Perform, Baidu Cloud, Bestdata, Gracenote, Genius Sports, Statscore, and Broadage.

2D-3D Match Dataset is a new dataset of 2D-3D correspondences by leveraging the availability of several 3D datasets from RGB-D scans. Specifically, the data from SceneNN and 3DMatch are used. The training dataset consists of 110 RGB-D scans, of which 56 scenes are from SceneNN and 54 scenes are from 3DMatch. The 2D-3D correspondence data is generated as follows. Given a 3D point which is randomly sampled from a 3D point cloud, a set of 3D patches from different scanning views are extracted. To find a 2D-3D correspondence, for each 3D patch, its 3D position is re-projected into all RGB-D frames for which the point lies in the camera frustum, taking occlusion into account. The corresponding local 2D patches around the re-projected point are extracted. In total, around 1.4 millions 2D-3D correspondences are collected.

This dataset provides information about the number of properties, residents, and average property values for Match Street cross streets in Pensacola, FL.

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Serie A Match Results Dataset (2020–2025) was created in response to community requests following the release of my LaLiga Match Results Dataset.

This dataset contains match-level results and performance stats from the Italian Serie A football league, covering seasons 2020 to 2025.

Source: Data was collected using a custom Python web scraper from FBref.com (https://fbref.com/en/comps/11/Serie-A-Stats).

Uses: - Match prediction models - Sports analytics - Feature engineering experiments - Educational ML datasets

Licensing Intended for educational and research use only. All rights remain with original data providers.

This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier.

The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided.

This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.

Match reported 2.5K in Employees for its fiscal year ending in December of 2024. Data for Match | MTCH - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Victory prediction from League of Legend champion selection data

• Victory prediction from League of Legend champion selection data
  • Objectif
  • Dataset description
    • Splits
    • Files
      • Champions
      • Matches
  • Getting started

Objectif

The continuous development of e-sports is generating a daily trail of insightful data in high volume, to the point that justifies the use of exploratory data analysis.

In particular, the multiplayer online battle arena (MOBA) game League of Legends (LoL), organizes one of the most viewed tournaments, attracting over 4 million peak viewers.

The game lets participants choose between more than 161 champions with different characteristics and game play mechanics affecting the dynamics of team composition. Thus, champion selection is of capital importance for pro players.

Multiple works focused on champion selection data in order to predict team victory for DOTA, a MOBA similar to League of Legends, but LoL is still under-researched. And with the regular new patches received, it is difficult to compare predictor performances across time.

To this objective, we are releasing this curated dataset such that others can try their own architectures on victory prediction from champion selection data, thus offering a benchmark dataset for the community.

Dataset description

This dataset has been collected by Devoteam Revolve from Riot Developer API

http://france.devoteam.com/wp-content/uploads/sites/21/2021/05/logo-cartouches-RVB-ROUGE.png" alt="Devoteam logo">

The dataset has a total of 84440 games that are from 2022 at the version 12.12 of the game.

The games are only from the highest ELO players, with ranks of either Master, Grand Master and Challenger. This ranks represents the top 1.2% of all players.

Splits

The dataset comes pre splitted

Files

Dataset organization:

12.12.-splits
├── test
|  ├── df_00000.csv
|  |   ...
|  └── df_xxxxx.csv
|
├── train
|  ├── df_00000.csv
|  |   ...
|  └── df_xxxxx.csv
|
└── val
|  ├── df_00000.csv
|  |   ...
|  └── df_xxxxx.csv
|
└── champion.json

Champions

All champions information can be found under ./12.12.-splits/champion.json

This file allows the conversion from Player_{Player_id}_pick id number to the champion name.

Multiple other information are also freely available such has champion damages, HP, etc ...

Matches

All the matches are collected in the 3 directories:

• ./12.12.-splits/train/
• ./12.12.-splits/val/
• ./12.12.-splits/test/

Each of these directories contain multiple df_xxxxx.csv files detailing up to 100 matches.

The description of each column can be read in the below table.

The column which possess {Player_id} in their name are repeated 10 times, one for each player.

For example, the column name Player_{Player_id}_team can be found in each csv as 10 different columns with names ranging from Player_1_team to Player_10_team.

The Measurable AI Dating App Consumer Transaction Dataset is a leading source of in-app purchases , offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our in-app and email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - User overlap between competitors - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia - EMEA (Spain, United Arab Emirates) - USA - Europe

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Features/subscription plans purchased - No. of orders per user - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact michelle@measurable.ai for a data dictionary and to find out our volume in each country.

Match

## Overview

Match is a dataset for object detection tasks - it contains Planes annotations for 1,500 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Match reported $3.43B in Debt for its fiscal quarter ending in March of 2025. Data for Match | MTCH - Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Hockey Match Tracking

## Overview

Hockey Match Tracking is a dataset for object detection tasks - it contains Players annotations for 201 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Match reported $172.59M in Operating Profit for its fiscal quarter ending in March of 2025. Data for Match | MTCH - Operating Profit including historical, tables and charts were last updated by Trading Economics this last July in 2025.

According to April 2020 survey data of adults in the United States, four percent of respondents aged 18 to 29 years were currently using Match.com. Adults aged 30 to 44 years were most likely to use the social dating site, as 11 percent of respondents from that age group confirmed being current users.

Match.com is a part of Match Group, an American internet company that owns and operates a selection of online dating sites including Tinder, OkCupid and PlentyOfFish. The annual dating revenue of the Match Group in 2018 was 1.7 billion U.S. dollars. Overall, dating apps are among the more successful social media apps in terms of revenue generation.

According to April 2020 survey data of adults in the United States, seven percent of respondents living in suburban communities were currently using Match.com. Only three percent of survey respondents living in rural areas confirmed being users of the social dating site.

This dataset contains historical data of all the matches played in IPLT20 Men's Cricket since 2008. The match-wise data contained in the dataset include host country, match venue, first and second batting teams, scores of teams, winners, winning margins, season winners, and others.

Match stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

This dataset provides information about the number of properties, residents, and average property values for Match Play Drive cross streets in Humble, TX.

This dataset contains detailed ball-by-ball information from various cricket matches. It provides an in-depth view of match events, such as player performance, wickets, and scoring patterns, enabling analysis of team strategies, individual contributions, and overall match outcomes.

Dataset Features:

• Match ID: A unique identifier for each match.
• Date: The date on which the match was played.
• Venue: The stadium or location where the match took place.
• Bat First: The team that batted first in the match.
• Bat Second: The team that batted second in the match.
• Innings: The innings number (1 or 2) during the match.
• Over: The over in which the ball was bowled.
• Ball: The specific ball in the over.
• Batter: The player on strike facing the delivery.
• Non-Striker: The player at the non-striker's end.
• Bowler: The bowler delivers the ball.
• Batter Runs: The runs scored by the batter from a specific ball.
• Extra Runs: Additional runs awarded due to extras (integer value.).
• Runs From Ball: Total runs scored off the delivery, including extras.
• Ball Rebowled: Indicates whether the ball was re-bowled (Yes - 1/No - 0).
• Wicket: Indicates whether a wicket was taken (Yes - 1/No - 0).
• Method: Describes how the batter got out (e.g., bowled, caught, LBW).
• Player Out: The name of the player dismissed.
• Innings Runs: Total runs scored in the respective innings.
• Innings Wickets: Total wickets lost in the innings.
• Target Score: The score the batting team is chasing (if applicable).
• Runs to Get: Runs needed to win at that point in the match.
• Balls Remaining: Number of balls left in the innings.
• Winner: The team that won the match.
• Chased Successfully: Indicates whether the target was successfully chased (1 for Yes, 0 for No).

Usage:

This dataset is ideal for cricket analytics and machine learning tasks, including: - Analysing player and team performance trends. - Training predictive models for match outcomes. - Developing simulation tools for cricket strategy optimisation. - Identifying key moments and contributors in matches.

Coverage:

The dataset encompasses critical match and ball-level details, capturing the intricacies of cricket gameplay. It is suitable for exploring various analytical dimensions, such as player efficiency, bowling performance, and team tactics.

License:

CC0 (Public Domain)

Who can use it:

This dataset is designed for data scientists, sports analysts, machine learning practitioners, and cricket enthusiasts interested in leveraging data for sports analytics.

How to use it:

• Build predictive models for match outcomes and player performances.
• Analyse player contributions in different match contexts.
• Conduct exploratory data analysis on cricket match events.
• Simulate match scenarios to evaluate team strategies.

Match reported $117.57M in Net Income for its fiscal quarter ending in March of 2025. Data for Match | MTCH - Net Income including historical, tables and charts were last updated by Trading Economics this last July in 2025.