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Official-World-Golf-Ranking-Data

Official-World-Golf-Ranking-Data

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Official World Golf Ranking Data Context: The Official World Golf Ranking is a system for rating the performance level of male professional golfers. It was started in 1986. [1] The rankings are based on a player's position in individual tournaments (i.e. not pairs or team events) over a "rolling" two-year period. New rankings are produced each week. During 2018, nearly 400 tournaments on 20 tours were covered by the ranking system. All players competing in these tournaments are included in the rankings. In 2019, 23 tours will factor into the world rankings. [1] The World Ranking Points for each player are accumulated over a two year rolling period with the points awarded for each tournament maintained for a 13-week period to place additional emphasis on recent performances. [2] Ranking points are then reduced in equal decrements for the remaining 91 weeks of the two year Ranking period. Each player is then ranked according to his average points per tournament, which is determined by dividing his total number of points by the tournaments he has played over that two-year period. [2] There is a minimum divisor of 40 tournaments over the two year ranking period and a maximum divisor of a players last 52 tournaments. [2] Simply put, a golfer's World Ranking is obtained by dividing their points total by the number of events they have played, which gives their average. Players are then ranked; a higher average yields a higher rank. [1] Data: The data was acquired from the Official World Golf Ranking website. Stored in a long data format. This file will be updated weekly after the conclusion of every tournament. Tours Included in the Rankings: PGA Tour European Tour Asian Tour (not a charter member of the Federation) PGA Tour of Australasia Japan Golf Tour Sunshine Tour Korn Ferry Tour Challenge Tour PGA Tour Canada Golf Tour Korean Tour PGA Tour Latinoamrica Asian Development Tour PGA Tour China Alps Tour Nordic Golf League PGA EuroPro Tour ProGolf Tour MENA Golf Tour Big Easy Tour China Tour All Thailand Golf Tour Professional Golf Tour of India Abema TV Tour Collection Method: Acquired the data using the Python library BeautifulSoup. Manipulated data using the Pandas NumPy libraries. Contents: 9000 players Acknowledgements: Data scraped from: Official World Golf Ranking Please formally reference this Kaggle dataset. Please contribute analysis and findings as a kernel. Inspirations: Can this dataset be used to predict who will win upcoming PGA Tour tournaments? Can we predict the players that will make the tournament cuts? Disclaimer: The Official World Golf Ranking website contains plenty of messy data in the 'Name' column. There are still records where there is not enough information for me to infer the proper name of the athlete. If the name contains a date within brackets it is because there are two players with the same name. The date is the birth date of the athlete and is used to uniquely identify athletes with the same name. Questions, Concerns Suggestions: Feel free to email me for questions, concerns or suggestions, bradklassenoutlook.com Resources [1] https://en.wikipedia.org/wiki/Official_World_Golf_Ranking [2] http://www.owgr.com/about

12 features

Weeknumeric1 unique values
0 missing
This_Weeknumeric1529 unique values
0 missing
Last_Weeknumeric1520 unique values
5 missing
End_2019numeric1541 unique values
206 missing
Namestring8998 unique values
0 missing
Average_Pointsnumeric1528 unique values
6912 missing
Total_Pointsnumeric1231 unique values
6912 missing
Events_Played_Divisornumeric13 unique values
0 missing
Points_Lost_2020numeric668 unique values
6932 missing
Points_Gained_2020numeric390 unique values
8351 missing
Events_Played_Actualnumeric67 unique values
0 missing
Pro/Amstring2 unique values
0 missing

19 properties

9000
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
29318
Number of missing values in the dataset.
8394
Number of instances with at least one value missing.
10
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
83.33
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
93.27
Percentage of instances having missing values.
Average class difference between consecutive instances.
27.15
Percentage of missing values.

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