transfers
data on european football player transfers (by ewenme)
worldcup
A Comprehensive Database on the FIFA World Cup (Men's and Women's) (by jfjelstul)
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
transfers
Posts with mentions or reviews of transfers.
We have used some of these posts to build our list of alternatives
and similar projects.
- Sub feature requests/moderation feedback
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[Data Visualization and Analysis] Transfer Market 2021-22 (Liverpool vs Man City edition)
The dataset was curated by Ewenme on GitHub. Please show them some love by starring their repository.
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Premier league transfer spending adjusted for inflation and median market growth 1992-2021
Drogbas transfer is listed as 34,6m pounds in the data, as said I have not cross referenced any fees and they are straight from Transfermarkt
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Premier League transfer spending adjusted for inflation and median growth of the transfer market 1992-2021
I started by downloading all the transfer data from Transfermarkt (Someone had done the work for me in GitHub). I plotted out the data and adjusted each transfer for inflation. After that all transfers were plotted out, and a linear regression could be made to create a market growth coefficient.
worldcup
Posts with mentions or reviews of worldcup.
We have used some of these posts to build our list of alternatives
and similar projects.
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Generate Data Warehouse
Hi guys, I have a school project for building a Data warehouse using an open-source dataset. I currently dive into one dataset which is The Fjelstul World Cup Database (Github: https://github.com/jfjelstul/worldcup). This dataset has multiple tables which combine a variety of properties. It's hard to figure out the whole following process to build a Dataware house from scratch. I just get some ideas and make out a sample of this. Can you guys help me examine that? What are the best ways to build Fact and Dimension tables from this generous dataset? What properties and table I should put in that case?
- Where to find database of World Cup penalty statistics? Including penalties awarded and the score line of when this happened
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[OC] Brazil and Argentina are the top goal-scorers in World Cup history.
Source: Fjelstul World Cup Database
- Ask about Day 5 of #25daysofDAXFridays
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Announcing the 25 days of DAX Fridays! second edition!
let Source = Web.BrowserContents("https://github.com/jfjelstul/worldcup/tree/master/data-csv"), #"Extracted Table From Html" = Html.Table( Source, {{"FileName", ".js-navigation-open.Link\-\-primary"}}, [RowSelector = ".Box-row + *"] ), fxCSVs = (FileName as text) as table => let Source = Csv.Document( Web.Contents( "https://raw.githubusercontent.com/jfjelstul/worldcup/master/data-csv/" & FileName ), [Delimiter = ",", Encoding = 65001, QuoteStyle = QuoteStyle.None] ), #"Promoted Headers" = Table.PromoteHeaders(Source, [PromoteAllScalars = true]) in #"Promoted Headers", #"Invoked Custom Function" = Table.AddColumn( #"Extracted Table From Html", "CSV_Data", each fxCSVs([FileName]) ) in #"Invoked Custom Function"
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Ask about Day 1 of #25daysofDAXFridays
= Csv.Document(Web.Contents("https://github.com/jfjelstul/worldcup/blob/master/data-csv/award_winners.csv"),[Delimiter=",", Encoding=65001, QuoteStyle=QuoteStyle.None])
What are some alternatives?
When comparing transfers and worldcup you can also consider the following projects:
project - Predict how many points an European football team will end the season with, according to the characteristics of its players. Project for the Big Data Computing course at Sapienza University of Rome (2021-22)
qcoder - Lightweight package to do qualitative coding
football_analytics - 📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.
sofifa-web-scraper - It has over 18k detailed players info and stats from EA FC 24 scrapped from SoFIFA.com.