FIFA19|Player & Team Analysis and Value Predict

Data Description:

This data set is taken from Kaggle: https://www.kaggle.com/karangadiya/fifa19 and Github: https://github.com/4m4n5/fifa18-all-player-statistics/tree/master/2019

The data was scrapped from the sofifa website using a python crawling script. The website contains the data from the EA Sports' game FIFA and gets updated regularly with the release of new versions of the game. data developed by Electronic Arts for the latest edition of their FIFA game franchise. Through several research projects done on soccer analytics, it has been established in the field of academia that the use of data from the FIFA franchise has several merits that traditional datasets based on historical data do not offer. Since 1995 the FIFA Soccer games provide an extensive and coherent scout of players worldwide.

For each attribute, we have an integer from 0 to 100 that measures how good a player is at that attribute. Examples of attributes are: dribbling, aggression, vision, marking and ball control. Observe that it seems to be unfeasible to accurately characterize players in these attributes automatically. Thus, all of those are gathered and curated by the company whose job is to bring the gameplay closer to reality as possible, hence preserving coherence and representativeness across the dataset.

The FIFA 19 dataset that has been used for this analysis provides statistics of about 18000 players on over 90 different attributes. These attributes are optimal indicators to determine the performance of a player at a particular playing position.

"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.

My Kaggle notebook: https://www.kaggle.com/lykin22/fifa19-player-team-analysis-and-value-predict

Note: - For best experience of my work(quality content) please view from my kaggle notebook link!!