The Arena of Valor game dataset contains information on individual player performance during matches of the popular mobile multiplayer online battle arena (MOBA) game. The dataset includes details on player IDs, team IDs, chosen heroes, positions played, game stats (such as level, gold, KDA, damage dealt, damage taken, and time played), and match IDs.

Column Details

• Match ID: The unique identifier for each Arena of Valor match.

• Player ID: A unique identifier for each player participating in a match.

• Team ID: A unique identifier for each team in a match.

• Hero: The hero chosen by the player for the match.

• Position: The position played by the player in the match (such as top, mid, jungle, or bottom).

• Level: The level of the player's hero at the end of the match.

• Gold: The amount of gold earned by the player during the match.

• KDA: A measure of the player's performance, including kills, deaths, and assists.

• Damage Dealt: The amount of damage dealt by the player to enemy players during the match.

• Damage Taken: The amount of damage taken by the player from enemy players during the match.

• Time Played: The amount of time played by the player in the match.

You could use this dataset to analyze how different heroes perform in different positions, which players are the most effective in each position, which teams are the most successful, and many other factors related to Arena of Valor gameplay

Note: The dataset is an example and may not accurately represent the actual data structure of an Arena of Valor game dataset.

** The purpose of creating this dataset is solely for educational use, and any commercial use is strictly prohibited and this dataset was large language models generated and not collected from actual data sources.

cover image: https://www.4gamers.co.th/news/detail/236/rov-battlefield

Context

League of Legends (LoL) is a Multiplayer Online Battle Arena, MOBA game developed and serviced by Riot Games. There are a total of three lines (TOP, MID, BOT) with about 150 champions playing the game. Normally, killing an enemy champion and destroying the final Nexus will win the game.

Content

This data is the data of the game records of the blue and red teams for each game. There are two sets of data, one starting and building up to 10 minutes, and the other building up to 15 minutes. In addition, the data is game data of challenger users(Very Very High Rank).

Looking at the data set in a large category, the primary key for each game is the first, the win for each team, the third, the object acquisition for each team, and the fourth, the actions of users for each team.

Object data includes information about dragons, Rift herald, turrets, inhibitor, and barons, and user behavior information includes ward installation, ward removal, kill, death, assist, level, gold, and minion kill.

Acknowledgements

• Object : Tower, inhibitors, dragon, baron, rift herald ... - Tower : Attack turrets to protect each ally - Inhibitors : Opponent suppressor that can summon our team's powerful minions (superminions) - Dragon : Dragon with 4 buff types(Fire, Wind, Water, Earth) - Baron : Epic monster giving a powerful buff - Rift Herald : Objects that hit a certain amount of health over the enemy's turret and suppressor

• Gold : Money to buy items

• Minion : A monster that gives a certain amount of money when killed

Inspiration

• Prediction Win OR Lose
• Win team, Lose team EDA

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

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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.

This dataset provides comprehensive statistics on items available in Smite, a popular multiplayer online battle arena (MOBA) game. Each entry includes detailed information such as item type, tier, cost, total cost, stats provided, and any passive effects associated with the item.

• Item: The name of the item.
• Item Type: Categorization of the item based on its function or purpose in the game, such as physical damage, magical power, defense, utility, or consumables.
• Item Tier: The tier or level of the item, indicating its power and effectiveness relative to other items.
• Cost: The base cost of purchasing the item.
• Total Cost: The total amount of in-game currency required to fully upgrade or purchase the item, including any additional costs for upgrades or enhancements.
• Stats: A breakdown of the numerical attributes and bonuses provided by the item, including factors like health, mana, physical power, magical power, attack speed, penetration, and more.
• Passive Effect: Any unique or passive abilities granted by the item when equipped, which may offer strategic advantages or synergies with specific character builds and playstyles.

This dataset serves as a valuable resource for Smite players, allowing them to analyze item effectiveness, optimize builds, and make informed decisions during gameplay to gain a competitive edge on the battlefield.

Link to notebook used to collect the data.

Introduction:

Embark on an enthralling exploration into the illustrious careers of basketball's most iconic figures in the NBA Legends Dataset. This meticulously curated collection chronicles the remarkable odysseys of legendary players, offering intimate glimpses into their unparalleled skills, unwavering determination, and relentless pursuit of excellence. As a tribute to the enduring legacies and profound impacts these legends have had on the game and countless lives, this dataset encapsulates their transcendent influences, both on and off the court.

Column Descriptions:

1. season: The NBA season during which the game took place.
2. date: The date of the game.
3. age: Kobe Bryant's age at the time of the game.
4. team_played: The team Kobe Bryant played for during the game.
5. game_type: The type of game (regular season, playoffs, etc.).
6. venue: The arena or stadium where the game was held.
7. opponent: The opposing team.
8. win_lose: Indicates whether Kobe's team won or lost the game.
9. point_difference: The difference in points between Kobe's team and the opposing team.
10. game_started: Whether Kobe started the game or came off the bench.
11. minutes_played: The total minutes Kobe played in the game.
12. fieldgoal: The number of field goals Kobe made.
13. fieldgoal_attempts: The total number of field goal attempts by Kobe.
14. fieldgoal_percent: Kobe's shooting percentage for field goals.
15. 3pointers: The number of three-pointers Kobe made.
16. 3pointers_attempts: The total number of three-point attempts by Kobe.
17. 3pointers_percent: Kobe's shooting percentage for three-pointers.
18. freethrows: The number of free throws Kobe made.
19. freethrows_attempt: The total number of free throw attempts by Kobe.
20. freethrow_percent: Kobe's shooting percentage for free throws.
21. offensive_rebounds: The number of offensive rebounds by Kobe.
22. defensive_rebounds: The number of defensive rebounds by Kobe.
23. total_rebounds: The total number of rebounds by Kobe.
24. assists: The number of assists by Kobe.
25. steals: The number of steals by Kobe.
26. blocks: The number of blocks by Kobe.
27. turnovers: The number of turnovers by Kobe.
28. personal_fouls: The number of personal fouls committed by Kobe.
29. points: The total points scored by Kobe in the game.

Influence of NBA Legends:

The enduring legacies of NBA legends transcend basketball, serving as timeless sources of inspiration for athletes and enthusiasts alike. Their remarkable achievements, unwavering work ethics, and unyielding self-belief epitomize the essence of greatness and resilience. As we delve into the intricacies of their journeys through this dataset, may their indelible spirits continue to inspire and motivate us to pursue excellence in every aspect of life

Photo by JC Gellidon on Unsplash