As of March 2024, 33 percent of Pokémon GO-aware gamers in the United States were currently playing mobile AR game. In total, 49 percent of respondents aged 35 to 54 years were current Pokémon GO players. Among those who had heard of the game, 48 percent had played it in the past.

Released in July 2016, Pokémon GO was instantly a smash hit, accumulating close to 700 million lifetime downloads. During its first quarter, the app generated a dizzying 228 million downloads before winding down to a double-digit download volume over time. Pokémon GO players are a highly engaged audience, and live events, when possible, are very popular with the player community. In the second quarter of 2025, the Pokémon GO app generated over 8.98 million downloads worldwide.

In July 2016, an augmented reality gaming phenomenon hit our smartphone screens. Pokémon GO had players roaming around parks and streets in the real world in an attempt to capture, train and battle their virtual Pokémon. There were an estimated ** million users of this innovative game in the United States in 2016 and the figure is set to soar to ** million in 2020. The game is particularly beloved in the Asia Pacific region, where the number of users is set to increase from ** million to *** million in the same timeframe.

Pokémon GO in the U.S. Although the original hype around the game has died down some three years later, Pokémon GO has still retained a loyal fan base in the United States. As of February 2019, it was found that **** percent of Android Pokémon GO app owners in the United States accessed the gaming app on a daily basis and **** percent used it monthly. Given the universal appeal of the Pokémon world, the app has a relatively balanced gender split amongst its users in the United States – **** percent of the users in the U.S. were male compared to **** percent who were female.

Pokémon GO rakes in the revenue Although the app is free-to-play, the freemium business model means that revenue is generated via in-app purchases carried out by the user. Within the Pokémon GO app, these purchases include Pokéballs, which are used to capture Pokémon, a Revive power-up for your favorite fallen Pokémon, and a Lucky Egg, which increases the experience gained by a Pokémon in battle. Player spending on the app stood at an impressive **** billion U.S. dollars at the end of 2018, with players in the United States and Japan being particularly keen to get their hands on the best upgrades.

What This Database Contains:

This data set includes 721 Pokemon, including their name, first and secondary type, and basic stats: HP, Attack, Defense, Stamina and Total Stats

Data Description:

Pokemon: Name of each Pokemon Type 1: Each Pokemon has a type, this determines weakness/resistance to attacks Type 2: Some Pokemon are dual type and have 2 Total Stats: sum of all stats that come after this, a general guide to how strong a Pokemon is HP: hit points, or health, defines how much damage a Pokemon can withstand before fainting Attack: the base modifier for normal attacks Defense: the base damage resistance against normal attacks

Collection Method:

This data was scraped from https://pokemon.gameinfo.io/en/pokemon/list/best-pokemon-by-cp and also had some manual insertion and editing of the data itself.

How This Data Can Be Used

This data can be a great resource for finding performance of Pokemon in the game Pokemon Go. As where the stats work differently in Pokemon Go as the traditional game I was struggling to find a data set that had the correct stats and Pokemon in the game. This data can be used for statistical analysis and good machine learning practice as well!

In June 2022, Pokémon GO had approximately 146 thousand daily active users (DAU) via iPhone in the United States. Released in July 2016, the game has remained popular and is still regularly ranked among the leading mobile games worldwide.

Comprehensive YouTube channel statistics for Pokémon GO, featuring 1,160,000 subscribers and 143,580,440 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Gaming category. Track 647 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Dataset

This dataset was created by Brandon Escobedo

Contents

In 2025 year to date, Pokémon GO in-app purchase revenues surpassed 297.44 million U.S. dollars worldwide. The AR game's most profitable year was 2020, when many gamers discovered the title during the COVID-19 pandemic. Pokémon Go: a global phenomenon Pokémon Go took the world by storm in 2016. Within a week of its release on July 19, the mobile app had been downloaded over ten million times worldwide, indicating the enormous hype surrounding the launch of Nintendo’s first venture into mobile gaming. The location-based augmented reality (AR) game involves catching, training, and battling virtual creatures (Pokémon) in real-world locations via smartphone. While the initial Pokémon craze has slowly faded, the game still enjoys millions of active users around the world and the Asia-Pacific region, in particular. Player spending increases in a quest to catch ’em all Pokémon Go ranks among the leading free-to-play (FTP) gaming titles worldwide, generating over four billion U.S. dollars in lifetime IAP revenue. While both app download and usage are free, the game uses a freemium business model that supports in-app purchases. Users can buy additional Pokéballs or other in-game items to level up and increase their chances of becoming a successful virtual Pokémon trainer. In order to maintain players’ interest and participation, developer Niantic continues to expand the game by introducing new features and characters like popular franchise antagonists Team Rocket..

Background

This dataset contains stats, attributes, and descriptions of Pokémon in Pokémon Go. Use this dataset to find patterns between different primary or secondary types, regions, and categories of Pokémon.

Data

• number - The number of the Pokémon in the Pokédex
• pokemon_name - The name of the Pokémon
• pic_url - A URL to a .png file of the Pokémon
• main_type - The primary type of the Pokémon
• secondary_type - The secondary type of the Pokémon (or NULL only 1 type)
• region - Region where Pokémon first appeared in the Pokémon games
• category - Category of the Pokémon
• height - Height of the Pokémon
• weight - Weight of the Pokémon
• pokemon_family - Derivative family of the Pokémon (ex. Charmander, Charmeleon, Charizard will all be Charmander Family)
• attack - Pokémon base attack stat in Pokémon Go
• defense - Pokémon base defense stat in Pokémon Go
• stamina - Pokémon base stamina stat in Pokémon Go
• cp_range - Pokémon Combat Power (CP) range in Pokémon Go
• hp_range - Pokémon Hit Point (CP) range in Pokémon Go
• capture_rate - Pokémon capture rate in Pokémon Go ('N/A' values indicate that data was not available from the derived data source)
• flee_rate - Pokémon flee rate in Pokémon Go ('N/A' values indicate that data was not available from the derived data source)
• male_perc - Percentage of Pokémon found that are male ('N/A' values indicate Pokémon is genderless, only male, or only female)
• female_perc - Percentage of Pokémon found that are female ('N/A' values indicate Pokémon is genderless, only male, or only female)
• resistance - Dictionary of Pokémon resistances to certain types in the following form: {'percent_resistant': ['type_1', 'type_2']}
• weakness - Dictionary of Pokémon weaknesses to certain types in the following form: {'percent_weak': ['type_1', 'type_2']}
• wild_avail - Available in the Wild?
• egg_avail - Available from the Eggs?
• raid_avail- Available as the Raid Boss?
• research_avail - Available from Field Research?
• shiny - Shiny form available?
• shadow - Shadow form available?
• pkedex_desc - Pokémon's Pokédex description
• poss_attacks - List of possible attacks available to the Pokémon

Source

https://pokemongo.fandom.com/

This dataset provides detailed information on 1007 Pokémon from the popular mobile game Pokémon GO. It includes a wide range of attributes such as base stats, move sets, rarity, and acquisition methods. The data was collected using the RapidAPI Pokémon GO API, offering researchers and data enthusiasts a rich resource for analysis, machine learning projects, and game strategy development.

Key features of this dataset include:

• Comprehensive coverage of 1007 Pokémon
• 24 attributes for each Pokémon, including battle stats, type, and rarity
• Information on acquisition methods (wild, egg, raid, etc.)
• Move set details for both fast and charged moves
• Game mechanics data such as capture and flee rates

This dataset is ideal for:

• Analyzing Pokémon strengths and weaknesses
• Developing machine learning models for Pokémon classification or prediction
• Studying game balance and design in Pokémon GO
• Creating tools for players to optimize their gameplay strategies

Whether you're a data scientist, game developer, or Pokémon enthusiast, this dataset offers a wealth of information to explore and analyze the world of Pokémon GO.

Pokémon Go is an augmented reality mobile game developed and published by Niantic in collaboration with Nintendo. In Poland, the mobile gaming app reached its highest download number in July 2023, amounting to ***** thousands downloads.

Context

This data is a re-formatted version of the popular Pokemon with Stats table uploaded by Alberto Barradas to make some data manipulation easier. Some of the manipulation could be done within Python or R, but some of us are not that savvy. Hopefully, this makes entry-level analysis easier for beginner users.

Content

Here are a list of changes to the table that were made: 1. Added a column called ROW_ID that counts every record as one entry; 2. Added a column called ROW_COUNT that gives every record a value of 1; 3. Renamed the "#" column to be POKEDEX_NUM because a hash character is not an ideal column name; 4. In the NAME column, some of the Pokemon names had odd characters (like Nidorana, so I altered them). This was only done to <5 Pokemon and does not change the record meaningfully; 5. For records where the Pokemon does not have a second type, I labelled each of those records as NO_TYPE2 which I feel is more helpful than a blank value; 6. Removed spaces in the Type 1 and Type 2 column heading names; 7. Capitalized all column heading names; 8. Renamed Special Attack and Special Defense column heading names; 9. Added a 1 or 0 field for Legendary to complement the boolean field; 10. Using a simple text search formula in excel, created a 1 or 0 field to indicate if the record is a Mega Pokemon or not; 11. Since some Pokemon can appear in multiple forms, created a field that only shows 1 for a unique Pokemon and returns a 0 for any additional forms that are in the table;

Acknowledgements

Again, this table comes from Alberto Barradas and he deserves all due credit. Adding the same tags as the original dataset.

Here is the original dataset description: *This data set includes 721 Pokemon, including their number, name, first and second type, and basic stats: HP, Attack, Defense, Special Attack, Special Defense, and Speed. It has been of great use when teaching statistics to kids. With certain types you can also give a geeky introduction to machine learning.

This are the raw attributes that are used for calculating how much damage an attack will do in the games. This dataset is about the pokemon games (NOT pokemon cards or Pokemon Go).

The data as described by Myles O'Neill is: ID for each pokemon Name: Name of each pokemon Type 1: Each pokemon has a type, this determines weakness/resistance to attacks Type 2: Some pokemon are dual type and have 2 Total: sum of all stats that come after this, a general guide to how strong a pokemon is HP: hit points, or health, defines how much damage a pokemon can withstand before fainting Attack: the base modifier for normal attacks (eg. Scratch, Punch) Defense: the base damage resistance against normal attacks SP Atk: special attack, the base modifier for special attacks (e.g. fire blast, bubble beam) SP Def: the base damage resistance against special attacks Speed: determines which pokemon attacks first each round*

The data for this table has been acquired from several different sites, including: pokemon.com pokemondb bulbapedia

One question has been answered with this database: The type of a pokemon cannot be inferred only by it's Attack and Deffence. It would be worthy to find which two variables can define the type of a pokemon, if any. Two variables can be plotted in a 2D space, and used as an example for machine learning. This could mean the creation of a visual example any geeky Machine Learning class would love.*

This data set (800 x 13, excluding header) includes 721 unique Pokémon, including their Pokedex ID, Name, first and second type, and basic stats such as HP, Attack, Defense, Special Attack, Special Defense, and Speed.

These are the raw attributes that are used for calculating how much damage an attack will do in the games. This dataset is about the Pokémon games (NOT Pokémon cards or Pokémon Go).

The data as described by Myles O'Neill is:

pokedex_id: ID for each Pokémon from pokedex name: Name of each Pokémon type_1: Each Pokémon has a type, this determines weakness/resistance to attacks type_2: Some Pokémon are dual type and have 2 total_attack: sum of all stats that come after this, a general guide to how strong the Pokémon is health_points: hit points, or health, defines how much damage a Pokémon can withstand before fainting attack: the base modifier for normal attacks (eg. Scratch, Punch) defense: the base damage resistance against normal attacks special_attack: special attack, the base modifier for special attacks (e.g. fire blast, bubble beam) special_defense: the base damage resistance against special attacks speed: determines which Pokémon attacks first each round generation: from which generation is the Pokémon is_legendary: is the Pokémon legendary

The data for this table has been acquired from several different sites, including:

pokemon.com pokemondb bulbapedia

One question has been answered with this database: The type of a Pokémon cannot be inferred only by it's Attack and Defense. It would be worthy to find which two variables can define the type of a Pokémon, if any.

Note: My edit -> renamed columns to remove some misunderstandings. Pokedex ID will not be the dataset ID per say.

Traffic analytics, rankings, and competitive metrics for pokemongo.com as of September 2025

Changed From Previous Version

• Added Original Game Pokemon's Status Data.
• Added Capture_Rate, Flee_Rate, Movement_Type Data.
• Deleted Some Columns.

Preface

Hello. My name is Takamasa Kato, From Japan.
I have just joined Kaggle, and studying Data-Science, Machine-Learning, trying competition at Kaggle.
I like playing game, especially like Pokemon, So Now I challenge Machine-Learning using Pokemon data.
As part of my study, make a Pokemon stats dataset. this dataset includes latest generations Pokemon. If you need Pokemon data, or searching latest data, please feel free to use. Last, I'm very Sorry for my bad English. If you find any problems or mistakes, please point them out.

Overview

• This dataset has already released Pokemons' data in Pokemon Go.

Describe

• No
  ・Each Pokemon has a number of Pokedex. It's listed in this column.
  ・Some Pokemon has multiple appearances, status, evolution or generation. In these cases, listed same numbers.
• Name_En
  ・Name for each Pokemon.
• Type1
  ・Each Pokemon has a type. The type is listed in this column.
• Type2
  ・Some Pokemon has two types(dual type). If the Pokemon is dual type, the second type is listed in this column.
• Weather_Boost1
• Weather_Boost2
• Origin_Base_xx
  ・Status for each Pokemon that not Pokemon Go, but Original Pokemon Game's.
  ・It has 6 Base Status (HP, Attack, Defense, Special Attack(SP_Attack), Special Defense(SP_Defense), Speed).
• ATK (Attack)
  ・One of Pokemon's status. It determines how much damage the Pokemon deals when using a Move.
• DEF (Defense)
  ・One of Pokemon's status. It determines how much damage the Pokemon receives when it's hit with a Move.
• HP (Hit Point)
  ・One of Pokemon's status. It determines how much damage a Pokemon can receive before fainting.
• Max_LV40(LV50)_CP
  ・It is the maximum allowable CP(Combat Point) at LV40(LV50) if the Pokemon has highest individual values.

This statistic presents the distribution of active Pokémon GO users in the United States as of February 2019, sorted by gender. During the measured period, it was found by App Ape that male users accounted for **** percent of the gaming app's active user accounts on the Android platform.

Pokemon data scrape from https://www.pvpoke.com, contains: dex, speciesName, speciesId, atk, def, hp, primaryType, secondaryType, fastMoves, chargedMoves, released, eliteMoves & legacyMoves - Updates automatically every month.

As of October 2020, men accounted for almost ** percent of the monthly active users (MAU) of Pokémon GO in Japan. The augmented reality game was developed by Niantic, Inc. and was first released in 2016. It is one of the leading mobile games in Japan.

This data set includes 721 Pokemon, including their number, name, first and second type, and basic stats: HP, Attack, Defense, Special Attack, Special Defense, and Speed. It has been of great use when teaching statistics to kids. With certain types you can also give a geeky introduction to machine learning.

This are the raw attributes that are used for calculating how much damage an attack will do in the games. This dataset is about the pokemon games (NOT pokemon cards or Pokemon Go).

The data as described by Myles O'Neill is:

• #: ID for each pokemon
• Name: Name of each pokemon
• Type 1: Each pokemon has a type, this determines weakness/resistance to attacks
• Type 2: Some pokemon are dual type and have 2
• Total: sum of all stats that come after this, a general guide to how strong a pokemon is
• HP: hit points, or health, defines how much damage a pokemon can withstand before fainting
• Attack: the base modifier for normal attacks (eg. Scratch, Punch)
• Defense: the base damage resistance against normal attacks
• SP Atk: special attack, the base modifier for special attacks (e.g. fire blast, bubble beam)
• SP Def: the base damage resistance against special attacks
• Speed: determines which pokemon attacks first each round

The data for this table has been acquired from several different sites, including:

• pokemon.com
• pokemondb
• bulbapedia One question has been answered with this database: The type of a pokemon cannot be inferred only by it's Attack and Deffence. It would be worthy to find which two variables can define the type of a pokemon, if any. Two variables can be plotted in a 2D space, and used as an example for machine learning. This could mean the creation of a visual example any geeky Machine Learning class would love.

This paper is set within the context of a rapidly urbanizing India with shrinking avenues of physical play and social engagement. The loss of traditional play spaces has redefined leisure activities for youth beyond offline sites into online spaces. These newer leisure engagements including gaming, binge-watching and social media interactions are predominantly virtual and sedentary. This shift, and the restrictions in their physical movements, reduce youth interactions with their social and material environments. Operationalizing Soja’s “Thirdspace,” this paper argues that Pokémon Go generates hybrid habitats at the confluence of leisure, youth, and digital gaming. Through qualitative interviews and co-playing sessions, this study draws from an engagement experience pool spanning 400 h of gameplay with five respondents in the Indian context. It examines how the everyday leisure activities of youth in augmented environments can lead to new spatio-cultural meanings, redefine immediate social environments, and create dynamic possibilities for youth development.