Type: Package
Title: Tools for Football Player Scouting in Indonesia
Version: 0.1.3
Description: Provides tools to scrape, clean, and analyze football player data from Indonesian leagues and perform similarity-based scouting analysis using standardized numeric features. The similarity approach follows common vector-space methods as described in Manning et al. (2008, ISBN:9780521865715) and Salton et al. (1975, <doi:10.1145/361219.361220>).
License: MIT + file LICENSE
Encoding: UTF-8
Imports: dplyr, rvest, purrr, tibble, stringr, readr, proxy
RoxygenNote: 7.3.3
URL: https://github.com/tioanta/indonesiaFootballScoutR
BugReports: https://github.com/tioanta/indonesiaFootballScoutR/issues
NeedsCompilation: no
Packaged: 2026-02-04 02:38:20 UTC; BRI
Author: Tio Anta Wibawa [aut, cre]
Maintainer: Tio Anta Wibawa <tio158@gmail.com>
Depends: R (≥ 4.1.0)
Repository: CRAN
Date/Publication: 2026-02-06 20:00:13 UTC

Clean and standardize football player data

Description

This function converts character-based numeric fields into numeric values and prepares player data for further analysis.

Usage

clean_player_db(df)

Arguments

df

A data frame containing raw football player data. Must include at least columns name, age, and market_value_est.

Details

The function performs safe numeric conversion and does not remove rows with missing values.

Value

A data frame with cleaned and standardized player data.

Examples

df <- data.frame(
  name = c("Player A", "Player B"),
  age = c("21", "23"),
  market_value_est = c("€500k", "€750k"),
  club = c("Club A", "Club B"),
  league_country = c("Indonesia", "Indonesia"),
  stringsAsFactors = FALSE
)

clean_player_db(df)

Retrieve similar players based on cosine similarity

Description

Retrieve similar players based on cosine similarity

Usage

get_similar_players(model, player_name, top_n = 5)

Arguments

model

A trained scouting model returned by train_scout_brain().

player_name

Character string specifying the reference player.

top_n

Integer indicating the number of similar players to return.

Details

Similarity is computed using cosine similarity on standardized numeric features. The reference player is excluded from the results.

Value

A data frame with similarity scores for the most similar players.

Examples

df <- data.frame(
  name = c("Player A", "Player B", "Player C"),
  age = c(21, 23, 22),
  market_value_est = c(500, 750, 600),
  club = c("Club A", "Club B", "Club C"),
  league_country = c("Indonesia", "Indonesia", "Indonesia"),
  stringsAsFactors = FALSE
)

model <- train_scout_brain(df)
get_similar_players(model, "Player A", top_n = 2)

Initialize scouting workflow

Description

This function initializes an in-memory scouting workflow. It does not create any directories or write files.

Usage

init_real_scout()

Details

This function is retained for API compatibility but performs no file system operations in order to comply with CRAN policies.

Value

NULL. Called for side effects only.

Examples

init_real_scout()

Save raw scouting data

Description

Save raw scouting data

Usage

save_raw_data(df, file = NULL)

Arguments

df

A data frame containing scouting data.

file

Optional file path. If NULL, no file is written.

Value

If file is provided, the file path. Otherwise, NULL.

Examples

df <- data.frame(
  name = "Player A",
  age = 21,
  market_value_est = 500,
  club = "Club A",
  league_country = "Indonesia"
)

tmp <- tempfile(fileext = ".csv")
save_raw_data(df, file = tmp)

Scrape players from a club page

Description

Scrape players from a club page

Usage

scrape_club(club_url, league_country)

Arguments

club_url

Character string specifying the club URL.

league_country

Character string indicating league or country.

Value

A tibble containing player data for the club.

Scrape football player data from a league

Description

Scrape football player data from a league

Usage

scrape_league(league_url, league_country = "Unknown League")

Arguments

league_url

Character string specifying the league URL.

league_country

Character string indicating league or country.

Details

This function performs web scraping and returns the data in memory. No files are written to disk.

Value

A tibble containing raw player data.

Scrape a single player row

Description

Scrape a single player row

Usage

scrape_player(node)

Arguments

node

HTML node corresponding to a player row.

Value

A tibble with player information.

Train a similarity-based scouting model

Description

This function prepares numeric player features for similarity-based scouting analysis.

Usage

train_scout_brain(df)

Arguments

df

A cleaned data frame containing player information.

Details

The returned object is intended to be used as input for get_similar_players().

Value

A list containing:

data

A numeric matrix of standardized player features.

players

Character vector of player names.

Examples

df <- data.frame(
  name = c("Player A", "Player B"),
  age = c(21, 23),
  market_value_est = c(500, 750),
  club = c("Club A", "Club B"),
  league_country = c("Indonesia", "Indonesia"),
  stringsAsFactors = FALSE
)

model <- train_scout_brain(df)