FIFA: A Complete Player Dataset

Posted 2020-11-26 welcomeyou

Winter 2020
FIFA: A Complete Player Dataset
18k+ Latest FIFA Players, with ~80 Attributes Extracted from FIFA
database
eZvh
,k
FIFA: The Fédération Internationale de Football Association[a] (FIFA /?fi?f?/ FEEF?;
French for ‘International Federation of Association Football‘) is an organization which
describes itself as an The Fédération Internationale de Football Association[a] (FIFA /?fi?f?/
FEEF-?; French for ‘International Federation of Association Football‘) is an organization which
describes itself as an international governing body of association football, fútsal, beach soccer,
and eFootball. FIFA is responsible for the organization of football‘s major international
tournaments, notably the World Cup which commenced in 1930 and the Women‘s World Cup
which commenced in 1991.
FIFA was founded in 1904 to oversee international competition among the national associations
of Belgium, Denmark, France, Germany, the Netherlands, Spain, Sweden, and Switzerland.
Headquartered in Zürich, its membership now comprises 211 national associations. Member
countries must each also be members of one of the six regional confederations into which the
world is divided: Africa, Asia, Europe, North & Central America and the Caribbean, Oceania,
and South America.
Although FIFA does not control the rules of football, that being the responsibility of the
International Football Association Board, it is responsible for both the organization of a number
of tournaments and their promotion, which generate revenue from sponsorship. In 2017, FIFA
had revenues of over US $734 million, for a net loss of $189 million, and had cash reserves of
over US$930 million.
Reports by investigative journalists have linked FIFA leadership with corruption, bribery, and
vote-rigging related to the election of FIFA president Sepp Blatter and the organization‘s
decision to award the 2018 and 2022 World Cups to Russia and Qatar, respectively. These
allegations led to the indictments of nine high-ranking FIFA officials and five corporate
代写FIFA: A Complete Player Dataset程序，代写R语言作业
executives by the U.S. Department of Justice on charges including racketeering, wire fraud, and
money laundering. On 27 May 2015, several of these officials were arrested by Swiss authorities,
who were launching a simultaneous but separate criminal investigation into how the organization
awarded the 2018 and 2022 World Cups. Those among these officials who were also indicted in
the U.S. are expected to be extradited to face charges there as well. Many officials were
suspended by FIFA‘s ethics committee including Sepp Blatter and Michel Platini. In early 2017
reports became public about FIFA president Gianni Infantino attempting to prevent the reelections
of both chairmen of the ethics committee, Cornel Borbély and Hans-Joachim Eckert,
during the FIFA congress in May 2017. On May 9, 2017, following Infantino‘s proposal, FIFA
Council decided not to renew the mandates of Borbély and Eckert. Together with the chairmen,
11 of 13 committee members were removed.
• The goal of this project is to use the players’ attributes to predict his annual wage in
dollars.
• About the Data File:
Attributes:
Data file includes latest edition FIFA players’ attributes like:
Age, Nationality, Overall, Potential, Club, Wage, Preferred Foot, International Reputation,
Weak Foot, Skill Moves, Work Rate, Position, Jersey Number, Joined, 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
and GKReflexes.
• Attributes Values:
row number Age age Nationality
nationality Overall overall rating Potential
potential rating Club current club Wage (Target Variable)
current wage in
thousands of dollars
Special Preferred Foot left/right
International Reputation rating on scale of 5 Weak Foot rating on scale of 5
Skill Moves rating on scale of 5 Work Rate attack work
rate/defence work rate
Body Type body type of player Real Face Position
position on the pitch Jersey Number jersey number Joined
joined date club name if
applicable
Contract Valid Until contract end date
Height height of the player Weight weight of the player
LS rating on scale of 100 ST rating on scale of 100
RS rating on scale of 100 LW rating on scale of 100
LF rating on scale of 100 CF rating on scale of 100
RF rating on scale of 100 RW rating on scale of 100
LAM rating on scale of 100 CAM rating on scale of 100
RAM rating on scale of 100 LM rating on scale of 100
LCM rating on scale of 100 CM rating on scale of 100
RCM rating on scale of 100 RM rating on scale of 100
LWB rating on scale of 100 LDM rating on scale of 100
CDM rating on scale of 100 RDM rating on scale of 100
RWB rating on scale of 100 LB rating on scale of 100
LCB rating on scale of 100 CB rating on scale of 100
RCB rating on scale of 100 RB rating on scale of 100
Crossing rating on scale of 100 Finishing rating on scale of 100
HeadingAccuracy rating on scale of 100 ShortPassing rating on scale of 100
Volleys rating on scale of 100 Dribbling rating on scale of 100
Curve rating on scale of 100 FKAccuracy rating on scale of 100
LongPassing rating on scale of 100 BallControl rating on scale of 100
Acceleration rating on scale of 100 SprintSpeed rating on scale of 100
Agility rating on scale of 100 Reactions rating on scale of 100
Balance rating on scale of 100 ShotPower rating on scale of 100
Jumping rating on scale of 100 Stamina rating on scale of 100
Strength rating on scale of 100 LongShots rating on scale of 100
Aggression rating on scale of 100 Interceptions rating on scale of 100
Positioning rating on scale of 100 Vision rating on scale of 100
Penalties rating on scale of 100 Composure rating on scale of 100
Marking rating on scale of 100 StandingTackle rating on scale of 100
SlidingTackle rating on scale of 100 GKDiving rating on scale of 100
GKHandling rating on scale of 100 GKKicking rating on scale of 100
GKPositioning rating on scale of 100 GKReflexes rating on scale of 100
Some Guidance:
• The competition will be opened January 6th 2020 and it will will be closed one week 10
of the winter quarter.
• Students will be evaluated based on:
1. Validity of the model
2. The R square
3. The R square adjusted
4. The simplicity of the model (number of predictors used to create the model).
• Students should report leverage points good ones and bad ones, and explain how they
dealt with these kind of points.
• Students should report missing values, and explain how they dealt with these kind of
missing data.
• Students should report transformations used to get their last and best Model.
• Students should report their best predictor(s).
• Students are only allowed to use lm, bestpredictor, glm functions in R
• Students are not allowed to use any of the Machine Learning techniques or function to
predict the players’ annual salary.
• The score of this project will be given to all members of the group assigned at the
beginning of the quarter.
• Each group will have 3-4 members. If some students drop from the class, two students
are still enough to finish the project.
• Students should have the lecture name and number as part of their group nickname…
(such as: Lec 1 AlmoGroup).
• Students are first ranked based on all groups in our three lectures, then the final ranking
and grades will be based independently on each lecture.
• Submission File Format
The file should contain a header and have the following format:
Ob,WageNew
1 ,1690.1
2 ,1877
3 ,1752
.
.
etc.
You can download an example submission file (sample_submission.csv) on the Data page.
What’s the difference between a private and public leaderboard?
The Kaggle leaderboard has a public and private component to prevent participants from
“overfitting” to the leaderboard. If your model is “overfit” to a dataset, then it is not
generalizable outside of the dataset you trained it on. This means that your model would have
low accuracy on another sample of data taken from a similar dataset.
Public Leaderboard
For all participants, the same 50% of predictions from the test set are assigned to the public
leaderboard. The score you see on the public leaderboard reflects your model’s accuracy on this
portion of the test set.
Private Leaderboard
The other 50% of predictions from the test set are assigned to the private leaderboard. The
private leaderboard is not visible to participants until the competition has concluded. At the end
of a competition, we will reveal the private leaderboard so you can see your score on the other
50% of the test data. The scores on the private leaderboard are used to determine the competition
winners. Getting Started competitions are run on a rolling timeline so the private leaderboard is
never revealed.
How do I create and manage a team?
When you accept the competition rules, a team will be created for you. You can invite others to
your team, accept a merger with another team, and update basic information like team name by
going to the More < Team page.
We‘ve heard from many Kagglers that teaming up is the best way to learn new skills AND have
fun. If you don‘t have a teammate already, consider asking if anyone wants to team up in
the discussion forum.
What are kernels?
Kaggle Kernels is a cloud computational environment that enables reproducible and
collaborative analysis. Kernels supports scripts in R and Python, Jupyter Notebooks, and
RMarkdown reports. Go to the Kernels tab to view all of the publicly shared code on this
competition. For more on how to use Kernels to learn data science, visit the Read more about our
decision to implement a rolling leaderboard on getting started competitions here.
How do I contact Support?
Kaggle does not have a dedicated support team so you’ll typically find that you receive a
response more quickly by asking your question in the appropriate forum. (For this competition,
you’ll want to use the House Prices discussion forum).
Support is only able to help with issues that are being experienced by all participants. Before
contacting support, please check the discussion forum for information on your problem. If you
can’t find it, you can post your problem in the forum so a fellow participant or a Kaggle team
member can provide help. The forums are full of useful information on the data, metric, and
different approaches. We encourage you to use the forums often. If you share your knowledge,
you‘ll find that others will share a lot in turn!
If your problem persists or it seems to be affecting all participants then please contact us.
Rules
One account per participant: You cannot sign up to Kaggle from multiple accounts and therefore
you cannot submit from multiple accounts.
No private sharing outside teams
Privately sharing code or data outside of teams is not permitted. It‘s okay to share code if made
available to all participants on the forums.
Team Mergers
Team mergers are allowed and can be performed by the team leader. In order to merge, the
combined team must have a total submission count less than or equal to the maximum allowed as
of the merge date. The maximum allowed is the number of submissions per day multiplied by the
number of days the competition has been running.
Team Limits
Maximum of 4 members per team.
Submission Limits
You may submit a maximum of 3 entries per day.
You may select up to 2 final submissions for judging.
Competition Timeline
Start Date: 01/13/2020
Merger Deadline: None
Entry Deadline: None
End Date: 03/23/2020 3:00 AM
Due to the public nature of the data, this competition does not count towards Kaggle ranking
points.
• We ask that you respect the spirit of the competition and do not cheat. Hand-labeling is
forbidden.
Competition URL
http://www.kaggle.com/c/ fifa2019wages
如有需要，请加QQ：99515681 或邮箱：[e-mail] 微信：codehelp