Battle Royale style video games have taken over the world. PUBG is 100 players are dropped on an empty island and must explore, hunt, and eliminate other players to only one player remains until the play area ends. There are 3 playing modes in the game. One can play alone, with a friend (a duo), or with 3 other friends (a squad).
The objective of this project is to find out the best strategy to win in PUBG and infer the probability of the team cheating in the game from this data.
In this project what we want is analyzing the data and find out what affects the player's win by answering some questions such as:
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How many players are joinded in one match?
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Who is the highest win (Solos, Duos or Squads)?
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What is the amount of work as a team between players?
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What is the average person that kills many players?
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How many most kills are recorded?
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How many 99% of people kills have?
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The relationship between kills and headshots Kills?
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How do we catch the fraudsters in the game?
Let's let the data do the talking!
- Python in Jupyter notebook
- Numpy and Pandas for data manipulation
- Matplotlib, Seaborn and plotly for plotting
Reference: Kaggle Your Machine Learning and Data Science Website here. The dataset contains 4446966 rows and 29 columns, there are 4446966 players participated, they comprised 2026745 groups, and played 47734 matches included in the data folder as CSV file. For better understanding of database there is a columns description below:
| Field Name | Description |
|---|---|
| groupId | Players team ID |
| assists | Number of assisted kills. The killed is actually scored for another teammate |
| boosts | Number of boost items used by a player. These are for example: energy dring, painkillers, adrenaline syringe |
| damageDealt | Damage dealt to the enemy |
| DBNOs | Down But No Out - when you lose all your HP but you're not killed yet. All you can do is only to crawl |
| headshotKills | Number of enemies killed with a headshot |
| heals | Number of healing items used by a player |
| killPlace | Ranking in a match based on kills. |
| killPoints | Ranking in a match based on kills points |
| kills | Number of enemy players killed. |
| killStreaks | Max number of enemy players killed in a short amount of time |
| longestKill | Longest distance between player and killed enemy. |
| matchDuration | Duration of a mach in seconds. |
| matchType | Type of match. There are three main modes: Solo, Duo or Squad. In this dataset however we have much more categories. |
| maxPlace | The worst place we in the match. |
| numGroups | Number of groups (teams) in the match. |
| revives | Number of times this player revived teammates. |
| rideDistance | Total distance traveled in vehicles measured in meters. |
| roadKills | Number of kills from a car, bike, boat, etc. |
| swimDistance | Total distance traveled by swimming (in meters). |
| teamKills | Number teammate kills (due to friendly fire). |
| vehicleDestroys | Number of vehicles destroyed. |
| walkDistance | Total distance traveled on foot measured (in meters). |
| weaponsAcquired | Total distance traveled on foot measured (in meters). |
| winPoints | Ranking in a match based on won matches. |
And our target column:
| winPlacePerc | Normalised placement (rank). The 1st place is 1 and the last one is 0. |
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First, we will clean up the data by finding and removing nulls and duplicate values. After that, the average of dividing the number of players in one map is calculated, then the average is calculated for each of (Killers, Runners , Drivers, Swimmers, The Healers, Solos, Duos and Squads). This data helps us determine the highest percentage or strategy for winning players in the first place. Finally, show and model the data and use what we've learned to improve this game play, answering our questions.
Please feel free to let me know if you have any questions. Email: tahani.almutery@gmail.com and maram.alfaifi@hotmail.com