Dataset used in several JsonPath engines

This dataset contains detailed cricket performance data structured in a nested JSON format. It includes information for multiple players categorized by gender, opponent teams, formats, and individual batter match-ups.

Each player node (e.g., “J Srinath”) provides detailed insight into: - vs_team: Player performance against each international team (e.g., England, Australia, Pakistan). - vs_batter: Individual head-to-head statistics against other players. - formats: Match format-specific data (e.g., ODI, T20I, Test).

📘 Dataset Structure Example: { "root": { "Gender": { "male": { "J Srinath": { "vs_team": { "England": { ...14 items... }, "Australia": { ...14 items... }, "Pakistan": { ...14 items... } }, "formats": { "ODI": { ... }, "Test": { ... } }, "vs_batter": { "Ricky Ponting": { ... }, ... "vs_bowler": {
"L Malinga": { ... }, ... } } } } } }

✨ Key Features: - 🏏 Granular Player Data: Bowler vs Batter and Bowler vs Team breakdowns. - 📊 Format-wise Stats: Data across multiple cricket formats (Test, ODI, T20). - 🌍 International Coverage: Includes performance against top teams like England, Australia, Pakistan, and Sri Lanka. - ⚙️ Machine Learning Ready: Structured JSON ideal for training predictive models (e.g., run prediction, wicket prediction). - 📁 Customizable: Easily expandable to include more players, genders, and match details.

💾 File Description: File name: Cricket_Stat_All_Player_International_Matches File type: .json File size: ~ 1.26 GB (depending on dataset size)

Columns / Structure: | Field | Description | |--------|--------------| | root | Root key containing gender-wise data | | Gender | "male" or "female" category | | Player Name | Name of the player (e.g., “J Srinath”) | | vs_team | Dictionary of performance stats vs teams | | formats | Data grouped by cricket formats | | vs_batter | Player’s record vs individual batters | | vs_bowler | Player’s record vs individual bowler |

🏷️ Suggested Tags: cricket, sports-analytics, json-dataset, machine-learning, player-performance, india, odi, t20, bowler-vs-batter

📜 License: CC BY 4.0 (Attribution License)

🚀 Use Cases: - Player performance prediction using ML. - Team strategy analysis and matchup visualization. - Cricket data visualization dashboards. - Fantasy team data preparation.

The file generated by the parse-logs script. Is used to calculate rate of passing / failing flaky tests.

Dataset

This dataset was created by kermitscp

Released under CC0: Public Domain

Contents

Eurostat data contains many indicators (short-term, structural, theme-specific and others) on the EU-28 and the Eurozone, the Member States and their partners. The database of Eurostat contains always the latest version of the datasets meaning that there is no versioning on the data. Datasets are updated twice a day, at 11:00 and at 23:00, in case new data is available or because of structural change. It is possible to access the datasets through SDMX Web Services, as well as through Json and Unicode Web Services. SDMX Web Services are a programmatic access to Eurostat data, with the possibility to: get a complete list of publicly available datasets; detail the complete structure definition of a given dataset; download a subset of a given dataset or a full dataset. SDMX Web Services: provide access to datasets listed under database by themes, and predefined tables listed under tables by themes; provide data in SDMX 2.0 and 2.1 formats; support both Representation State Transfer (REST) and Simple Object Access Protocol (SOAP) protocols; return responses in English language only; are free of charge. The JSON & UNICODE Web Services are a programmatic access to Eurostat data, with the possibility to download a subset of a given dataset. This operation allows customizing requests for data. You can filter on dimensions to retrieve specific data subsets. The JSON & UNICODE Web Services: provide data in JSON-stat and UNICODE formats; support only Representation State Transfer (REST) protocol; deliver responses in English, French and German language; are free of charge.

Difficulty in Making Ends Meet Details Download JSON-STAT Difficulty in Making Ends Meet Preview Download PX Difficulty in Making Ends Meet Details Download XLSX Difficulty in Making Ends Meet

Persons achieving National Physical Activity Guidelines Details Download JSON-STAT Persons achieving National Physical Activity Guidelines Preview Download PX Persons achieving National Physical Activity Guidelines Details Download XLSX Persons achieving National Physical Activity Guidelines

🏏 IPL Player Statistics (2016–2025) – From Cricsheet

📖 Overview

This dataset provides detailed IPL player performance statistics from 2016 to 2025.
It has been processed from Cricsheet ball-by-ball data into a structured JSON format.
Each player’s record is organized against opponent teams, batters, and bowlers, enabling deep matchup analysis.

📂 Data Source

• Base data: Cricsheet (open-source cricket ball-by-ball data).
• Processed & aggregated into JSON by me for Kaggle community.

📁 File Details

• File Name: ipl_2016_2025_player_stats.json
• Format: JSON
• Size: ~28.8 MB

🔑 Structure

Each player has 3 main categories:

• op_team → Stats vs opponent teams
• op_batter → Bowling stats vs individual batters
• op_bowler → Batting stats vs individual bowlers

Example JSON Snippet

 "DA Warner": {
    "op_team": {
      "Royal Challengers Bengaluru": {
        "Bt_Runs": 307,
        "Bt_Balls": 202,
        "Bw_Runs": 0,
        "Bw_Balls": 0,
        "Matches": 10,
        "Bw_economy": 0,
        "Bt_Strike_rate": 151.98,
        "Bt_Avg": 30.7,
        "Bt_Runs_list": [
          14,
          100,
          8,
          6,
          17,
          54,
          66,
          19,
          22,
          1
        ],
        "Bt_Balls_list": [
          9,
          55,
          6,
          6,
          18,
          39,
          40,
          13,
          14,
          2
        ],
        "Bw_Runs_list": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "Bw_Balls_list": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "Bt_W_list": [
          1,
          0,
          1,
          0,
          1,
          1,
          1,
          1,
          1,
          1
        ],
        "Bw_W_list": [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
        ],
        "four_list": [
          2,
          5,
          0,
          1,
          3,
          7,
          4,
          4,
          3,
          0
        ],
        "Six_list": [
          1,
          5,
          1,
          0,
          0,
          1,
          5,
          0,
          1,
          0
        ],
        "Gain_Wicket": 0,
        "Lose_Wicket": 8,
        "isMatches": false,
        "Bw_Avg": 0.0
      },

Split the city's report data into smaller statistical units such as valid subcategories, locate the data with a fixed rule independent URL, provide JSON transmission format for different applications or software to collect and integrate data.

This dataset consists in 22 JSON files representing a season of the Spanish Football League ("La Liga").

The dataset represents several hierarchically related elements, however, only the Match, Event and Player elements contain relevant information for analysis. The rest of the elements simply serve to keep the data structured, by seasons and matchdays. The dataset collects information from several seasons between the years 2000 and 2022. The attributes of each of the elements that make up the dataset are described below:

Season: JSON documents represent a season, their root contains the following information:

• competition: Name by which the competition is known
• country: Country where the competition is held
• season_id: Identifier of the season, example: Season 2021/22
• season_url: Relative URL of the season's web page
• rounds: List of Round elements, the days into which the championship is divided

Rounds: (or matchdays) Collection of matches:

• number: Name of the matchday, e.g.: Matchday 1.
• matches: List of Match elements, matches that are played on the same day/s of the championship.

Match: contains relevant match information.

• id: Match identifier used at BeSoccer.com
• status: Code representing the status of the match: Played (1), Not Played (0)
• home_team: Name of the home team
• away_team: Name of the away team
• result: List of two integers representing the match score
• date_time: Date and time at which the match started
• referee: First and last name of the referee of the match
• href: URL relative to the match page
• home_tactic: Tactical arrangement of the home team, e.g.: 4-3-3
• home_lineup: List of players in the starting lineup of the home team
• home_bench: List of the home team's substitute players
• away_tactic: Tactical arrangement of the away team, e.g. 4-3-3
• away_lineup: List of players in the home team's starting lineup
• away_bench: List of substitute players of the away team

Event: contains information that defines each of the relevant actions that occur during a soccer match. Events can be described by the following attributes:

• player: Player identifier. Relative URL
• team: Team of the player who participates in the event
• minute: Minute of the match in which the event occurs
• type: Event type (Enumeration)

Players: Player information:

• name: First name
• fullname: Player's full name
• dob: Date of birth
• country: Nationality
• position: Position the player usually occupies: GOA (GoalKeeper), DF (Defender), MID (Midfielder), STR (Striker)
• foot: Dominant Foot: Right-footed, Left-footed, Two-footed, Unknown
• weight: Weight of player in kilograms
• height: Player height in centimeters
• elo: Measurement of the player's skills on a scale of 1 to 100
• potential: Estimate of the maximum ELO that a player can reach on a scale of 1 to 100.
• href: Relative URL of the player's record

This dataset contains detailed season-wise performance statistics of all players who participated in the Indian Premier League (IPL) between 2016 and 2025.

The dataset was created by processing open-source ball-by-ball IPL data from CricSheet into a structured JSON file that makes analysis easy.

📊 Features Included

Batting stats: Runs, balls faced, strike rate, average, fours, sixes.

Bowling stats: Runs conceded, balls bowled, wickets, economy, wickets per match.

Fielding stats: Catches and dismissals.

Season totals: Total matches played, aggregated per-player values.

Per-match breakdown (within each season): Runs, balls, fours, sixes, wickets per match.

This dataset provides national theater box office statistics for films distributed by the Administrative Institution National Film and Audiovisual Culture Center. The data is up to the last Sunday before the announcement date and does not include films that have not been screened for less than 7 calendar days. The earliest CSV format data in this dataset begins on July 30, 2018, and the earliest JSON format data begins on March 1, 2020. JSON format queries require entering the start and end dates (in the format of year, month, and day), and can provide data for a maximum of 90 days at a time.

On this page, the Cologne police provide you with the 2015 police crime statistics for the Cologne city area as raw data.

You can access a graphical presentation and further statistics here:
http://www.polizei.nrw.de/koeln/klasse_60.html
www.koeln.polizei.nrw.de

Source: Police North Rhine-Westphalia "http://www.polizei.nrw.de/koeln/index.html">http://www.polizei.nrw.de/koeln/index.html

Age structure:

Children up to the age of 14 (the 14th birthday counts as the cut-off date for criminal responsibility)

Young people aged 15 to under 18

Adolescents aged 18 to under 21

Adults aged 21 to under 60

Seniors aged 60+

Land Registry yearly statistical summary.

The JSON file contains statistics for the year of 2000/2001.

Land Registry yearly statistical summary.

The JSON file contains statistics for the year of 2007/2008.

See collection description. Nested json.

Land Registry yearly statistical summary.

The JSON file contains statistics for the year of 2001/2002.

Context

I couldn't find any data related to hero attributes, roles, etc. on Kaggle. Hence, I decided to write a simple web scrapper and put in here.

Content

It has 3 files. 1. dota_heroes.json - This file has data of all heroes ranging from hp to legs. 2. hero_category.json - This file has info on classes of heroes - Strength, Int, Agi. You know what I'm talking about. 3. hero_roles.json - This file contains what roles the hero fits in - Carry, Nuker, Supports, etc.

Acknowledgements

I scraped this data from dota2 gamepedia. So.. thanks to them. I've used bs4, requests, json libraries for the purpose.

Inspiration

If there's any issue with data or anything else you might want to see in the dataset, leave a comment. I'll see what I can do.

Land Registry monthly statistics on instruments received for registration.

The JSON file contains statistics for the month of September 2014.

Household Gross Income Preview Download JSON-STAT Household Gross Income Preview Download PX Household Gross Income Details Download XLSX Household Gross Income