Source Analysis: worldfootballR

[dcaribou]

Analyse integration of worldfootballR functions as a new data source (see #202) in this project.

Functional analysis

Transfermarkt functions 🟢

As of Oct 3rd 2023, the table below shows the most relevant scraping functionality available in worldfootballR but missing in transfermarkt-datasets, together with the results of some preliminar analysis of the function and returned data.

Function	Analysis summary	Decision
tm_team_transfers()	Lack of history	🔴
tm_player_injury_history()	Looks promising	🟢
tm_player_transfer_history()	Lack of IDs / Invalid data	🔴

Understat 🟡

TBD

FBref 🟡

TBD

Technical effort 🟡

Create acquisition script (Low)

# arguments: season

players <- read.file("raw/transfermarkt-scraper/<season>/players.json")
players_injury_history <- tm_player_injury_history(player_urls=players$url)
write.file(players_injury_history, "raw/worldfootballR/<season>/players_injury_history.txt")

Additional dbt models and documentaion (Low)

Orchestrate run (High Low)

Makefile, Github actions, Re-visit dvc stages, Dagster...

• Add a new acquiring script
• Add a new acquire workflow
• Model in dbt

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🟢 means Accepted
🔴 means Ruled out
🟡 means Pending

[dcaribou]

After the latest enhancements to generalise the project to include new acquirers (see #202), including worldfootballR can now be considered low effort.
Issue #258.