Baselight

Trending FIFA

This dataset can be used to find the factors determining a FIFA player's

@kaggle.willianoliveiragibin_trending_fifa

  • 72.08 KB
  • 2306 rows
  • 12 columns
player_name

Player Name

age

Age

national_team

National Team

positions

Positions

overall

Overall

potential_overall

Potential Overall

current_club

Current Club

contract_start

Contract Start

contract_end

Contract End

value

Value

wage

Wage

total_stats

Total Stats

T. Almada22ArgentinaCAM, CM, CF7987Atlanta United2022 202539500000100002050
L. Palma23HondurasLW6975Celtic2023 20282200000220001794
R. Lavia19BelgiumCDM7386Chelsea2023 20307000000320001829
W. Zaïre-Emery17FranceCM, CDM7789Paris Saint Germain2022 20252400000090002080
Gabri Veiga21SpainCM, CAM7889Al Ahli Jeddah2023 202631500000280001944
J. Bellingham17EnglandCAM, CM6482Sunderland2023 2028150000010001714
K. Havertz24GermanyCAM, RW, ST8287Arsenal2023 2028460000001100002044
A. Vermeeren18BelgiumCDM, CM7487Antwerp2022 2026950000070001883
R. Højlund20DenmarkST7789Manchester United2023 202825500000770001841
J. Bellingham20EnglandCAM, CM8791Real Madrid2023 20291120000001900002265

CREATE TABLE playr_soccer_trending (
  "player_name" VARCHAR,
  "age" BIGINT,
  "national_team" VARCHAR,
  "positions" VARCHAR,
  "overall" BIGINT,
  "potential_overall" BIGINT,
  "current_club" VARCHAR,
  "contract_start" VARCHAR,
  "contract_end" VARCHAR,
  "value" BIGINT,
  "wage" BIGINT,
  "total_stats" BIGINT
);

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