The dataset for people who double on Fifa and Data Science

Content

17,000+ players
50+ attributes per player ranging from ball skills aggression etc.
Player's attributes sourced from EA Sports' FIFA video game series, including the weekly updates
Players from all around the globe
URLs to their homepage
Club logos
Player images male and female
National and club team data

Weekly Updates would include :

Real life data (Match events etc.)
The fifa generated player dataset
Betting odds
Growth

Data Source

Data was scraped from https://www.fifaindex.com/ first by getting player profile url set (as stored in PlayerNames.csv) and then scraping the individual pages for their attributes

Improvements

You may have noticed that for a lot of players, their national details are absent (Team and kit number) even though the nationality is listed. This may be attributed to the missing data on fifa sites.

GITHUB PROJECT

There is much more than just 50 attributes by which fifa decides what happens to players over time, how they perform under pressure, how they grow etc. This data obviously would be well hidden by the organisation and thus would be tough to find

Important note for people interested in using the scraping: The site is not uniform and thus the scraping script requires considering a lot of corner cases (i.e. interchanged position of different attributes). Also the script contains proxy preferences which may be removed if not required.

Exploring the data

For starters you can become a scout:

Create attribute dependent or overall best teams
Create the fastest/slowest teams
See which areas of the world provide which attributes (like Africa : Stamina, Pace)
See which players are the best at each position
See which outfield players can play a better role at some other position
See which youngsters have attributes which can be developed

And that is just the beginning. This is the playground.. literally!

Data description

The file FullData.csv contains attributes describing the in game play style and also some of the real statistics such as Nationality etc.
The file PlayerNames.csv contains URLs for different players from their profiles on fifaindex.com. Append the URLs after the base url fifaindex.com.
The compressed file Pictures.zip contains pictures for top 1000 players in Fifa 17.
The compressed file Pictures_f.zip contains pictures for top 139 female players in Fifa 17.
The compressed file ClubPictures.zip contains pictures for emblems of some major clubs in Fifa 17.

Inspiration

I am a huge FIFA fanatic. While playing career mode I realised that I picked great young players early on every single time and since a lot of digital learning relies on how our brain works, I thought scouting great qualities in players would be something that can be worked on. Since then I started working on scraping the website and here is the data. I hope we can build something on it.

With access to players attributes you can become the best scout in the world. Go for it!

Tables

Clubnames

@kaggle.artimous_complete_fifa_2017_player_dataset_global.clubnames

21.76 KB
633 rows
2 columns


CREATE TABLE clubnames (
  "name" VARCHAR,
  "url" VARCHAR
);

Fulldata

@kaggle.artimous_complete_fifa_2017_player_dataset_global.fulldata

1.07 MB
17588 rows
53 columns


CREATE TABLE fulldata (
  "name" VARCHAR,
  "nationality" VARCHAR,
  "national_position" VARCHAR,
  "national_kit" DOUBLE,
  "club" VARCHAR,
  "club_position" VARCHAR,
  "club_kit" DOUBLE,
  "club_joining" TIMESTAMP,
  "contract_expiry" DOUBLE,
  "rating" BIGINT,
  "height" VARCHAR,
  "weight" VARCHAR,
  "preffered_foot" VARCHAR,
  "birth_date" TIMESTAMP,
  "age" BIGINT,
  "preffered_position" VARCHAR,
  "work_rate" VARCHAR,
  "weak_foot" BIGINT,
  "skill_moves" BIGINT,
  "ball_control" BIGINT,
  "dribbling" BIGINT,
  "marking" BIGINT,
  "sliding_tackle" BIGINT,
  "standing_tackle" BIGINT,
  "aggression" BIGINT,
  "reactions" BIGINT,
  "attacking_position" BIGINT,
  "interceptions" BIGINT,
  "vision" BIGINT,
  "composure" BIGINT,
  "crossing" BIGINT,
  "short_pass" BIGINT,
  "long_pass" BIGINT,
  "acceleration" BIGINT,
  "speed" BIGINT,
  "stamina" BIGINT,
  "strength" BIGINT,
  "balance" BIGINT,
  "agility" BIGINT,
  "jumping" BIGINT,
  "heading" BIGINT,
  "shot_power" BIGINT,
  "finishing" BIGINT,
  "long_shots" BIGINT,
  "curve" BIGINT,
  "freekick_accuracy" BIGINT,
  "penalties" BIGINT,
  "volleys" BIGINT,
  "gk_positioning" BIGINT,
  "gk_diving" BIGINT,
  "gk_kicking" BIGINT,
  "gk_handling" BIGINT,
  "gk_reflexes" BIGINT
);

Nationalnames

@kaggle.artimous_complete_fifa_2017_player_dataset_global.nationalnames

3.24 KB
47 rows
2 columns


CREATE TABLE nationalnames (
  "name" VARCHAR,
  "url" VARCHAR
);

Playernames

@kaggle.artimous_complete_fifa_2017_player_dataset_global.playernames

635.04 KB
17588 rows
2 columns


CREATE TABLE playernames (
  "name" VARCHAR,
  "url" VARCHAR
);