This is a project based on the FIFA World Cup 2019 and Analyzes the Performance and Efficiency of Teams, Players, Countries and other related things using Data Analysis and Data Visualizations
About this file data.csv includes lastest edition FIFA 2019 players attributes like Age, Nationality, Overall, Potential, Club, Value, Wage, Preferred Foot, International Reputation, Weak Foot, Skill Moves, Work Rate, Position, Jersey Number, Joined, Loaned From, Contract Valid Until, Height, Weight, LS, ST, RS, LW, LF, CF, RF, RW, LAM, CAM, RAM, LM, LCM, CM, RCM, RM, LWB, LDM, CDM, RDM, RWB, LB, LCB, CB, RCB, RB, Crossing, Finishing, Heading, Accuracy, ShortPassing, Volleys, Dribbling, Curve, FKAccuracy, LongPassing, BallControl, Acceleration, SprintSpeed, Agility, Reactions, Balance, ShotPower, Jumping, Stamina, Strength, LongShots, Aggression, Interceptions, Positioning, Vision, Penalties, Composure, Marking, StandingTackle, SlidingTackle, GKDiving, GKHandling, GKKicking, GKPositioning, GKReflexes, and Release Clause.
FIFA 19 is a football simulation video game developed by EA Vancouver as part of Electronic Arts’ FIFA series. As a football fan and game player, I wrote this blog post to perform some data exploration on FIFA 19 Player Dataset from Kaggle.
The dataset contains information of all 18207 players from the latest edition FIFA 19. There are 89 attributes including personal information like age, name, nationality, photo, club, wage, etc, and also player skill information like ball control, dribbling, crossing, finishing, GK skills and etc.
I will focus on the three questions below:
Q1: What’s the ratio of total wages/ total potential for clubs. Which clubs are the most economical ？
Q2: What’s the age distribution like? How is it related to the player’s overall rating?
Q3: How is a player’s skill set influence his potential? Can we predict a player’s potential based on his skills’ set?
What’s the ratio of total wages/ total potential for clubs. Which clubs are the most economical ？ First I would like to look at the data at the clubs’ level. There is a total of 651 clubs collected and on average 27.6 players for each club. So which of the clubs are richest among them and which are more cost-effective？
To find answers to the first question, we can take a look at the wage/potential ratio for each club. The higher the ratio is, the more willingly a club spends money on high potential players.
On average, clubs spend €140 on wage for every potential of their players. So for a common player with 50 potential, his club will pay €7000 every year.
Football analytics
Detailed attributes for every player registered in the latest edition of FIFA 19 database. Scraping code at GitHub repo: https://github.com/amanthedorkknight/fifa18-all-player-statistics/tree/master/2019
Data scraped from https://sofifa.com/
Inspired from this dataset: https://www.kaggle.com/thec03u5/fifa-18-demo-player-dataset