Package 'sportsR' reference manual

Title:	A Comprehensive Collection of Sports and Athletics Datasets
Description:	Offers a rich and diverse collection of datasets focused on sports, athletics, physical performance, and related disciplines. The package includes professional and amateur sports data covering team sports such as soccer, basketball, baseball, American football, volleyball, rugby, cricket, hockey, and handball, as well as individual sports including tennis, badminton, table tennis, golf, swimming, cycling, athletics, gymnastics, wrestling, boxing, martial arts, weightlifting, triathlon, rowing, canoeing, climbing, surfing, skiing, snowboarding, and motorsports. Datasets cover player and team performance, match statistics, tournament results, championship standings, Olympic and international competitions, rankings, player demographics, coaching and training, biomechanics, sports medicine, injuries, exercise physiology, fitness assessment, sports nutrition, wearable sensor measurements, talent identification, and sports analytics. Additional datasets include historical competitions, referee decisions, fan engagement, economic indicators, and sports management data obtained from public repositories, official organizations, research publications, and educational resources. Designed for sports scientists, coaches, analysts, researchers, educators, students, and data scientists, this package facilitates exploratory data analysis, statistical modeling, machine learning, visualization, and sports analytics research.
Authors:	Renzo Caceres Rossi [aut, cre] (ORCID: <https://orcid.org/0009-0005-0744-854X>)
Maintainer:	Renzo Caceres Rossi <[email protected]>
License:	GPL-3
Version:	0.1.0
Built:	2026-09-26 09:55:55 UTC
Source:	https://github.com/lightbluetitan/sportsr

ATP Matches in 2019

Description

This dataset, atp_matches_2019, is a data frame containing match-level data for men's professional tennis matches played on the ATP Tour during 2019. It includes information on tournament details, court and surface conditions, player rankings and points, set-by-set scores, and betting odds from multiple bookmakers for each match.

Usage

data(atp_matches_2019)
data(atp_matches_2019)

Format

A data frame with 2610 observations and 36 variables:

ATP: Integer vector indicating the ATP tournament identification number
Location: Character vector indicating the city where the tournament was played
Tournament: Character vector indicating the name of the tournament
Date: Character vector indicating the date the match was played
Series: Character vector indicating the ATP series or category of the tournament
Court: Character vector indicating whether the match was played indoors or outdoors
Surface: Character vector indicating the court surface (e.g., Hard, Clay, Grass)
Round: Character vector indicating the round of the tournament
Best.of: Integer vector indicating the maximum number of sets played (3 or 5)
Winner: Character vector indicating the name of the match winner
Loser: Character vector indicating the name of the match loser
WRank: Character vector indicating the ATP ranking of the winner
LRank: Character vector indicating the ATP ranking of the loser
WPts: Character vector indicating the ATP ranking points of the winner
LPts: Character vector indicating the ATP ranking points of the loser
W1: Integer vector indicating the games won by the winner in set 1
L1: Integer vector indicating the games won by the loser in set 1
W2: Integer vector indicating the games won by the winner in set 2
L2: Integer vector indicating the games won by the loser in set 2
W3: Integer vector indicating the games won by the winner in set 3
L3: Integer vector indicating the games won by the loser in set 3
W4: Integer vector indicating the games won by the winner in set 4
L4: Integer vector indicating the games won by the loser in set 4
W5: Integer vector indicating the games won by the winner in set 5
L5: Integer vector indicating the games won by the loser in set 5
Wsets: Integer vector indicating the total number of sets won by the winner
Lsets: Integer vector indicating the total number of sets won by the loser
Comment: Character vector indicating the match outcome status (e.g., Completed, Retired, Walkover)
B365W: Numeric vector indicating the Bet365 odds for the winner
B365L: Numeric vector indicating the Bet365 odds for the loser
PSW: Numeric vector indicating the Pinnacle Sports odds for the winner
PSL: Numeric vector indicating the Pinnacle Sports odds for the loser
MaxW: Numeric vector indicating the maximum odds offered by any bookmaker for the winner
MaxL: Numeric vector indicating the maximum odds offered by any bookmaker for the loser
AvgW: Numeric vector indicating the average odds offered across bookmakers for the winner
AvgL: Numeric vector indicating the average odds offered across bookmakers for the loser

Details

The dataset name has been kept as 'atp_matches_2019' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the welo package version 0.1.4

English Football League Results 1888-2022

Description

This dataset, english_football, is a data frame containing results for English soccer games in the top 4 tiers from the 1888/89 season to the 2021/22 season. It includes information on match dates, seasons, home and visiting teams, full-time scores, goals scored, division, tier, and match outcomes.

Usage

data(english_football)
data(english_football)

Format

A data frame with 203956 observations and 12 variables:

Date: Character vector indicating the date of the match
Season: Numeric vector indicating the season
home: Character vector indicating the home team
visitor: Character vector indicating the visiting team
FT: Character vector indicating the full-time score
hgoal: Integer vector indicating the number of goals scored by the home team
vgoal: Integer vector indicating the number of goals scored by the visiting team
division: Character vector indicating the division
tier: Numeric vector indicating the tier
totgoal: Integer vector indicating the total number of goals scored in the match
goaldif: Integer vector indicating the goal difference
result: Character vector indicating the match result

Details

The dataset name has been kept as 'english_football' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the footBayes package version 2.0.0

Italian Football League Results 1934-2022

Description

This dataset, italian_football, is a data frame containing results for Italian soccer games in the top tier from the 1934/35 season to the 2021/22 season. It includes information on match dates, seasons, home and visiting teams, full-time scores, and goals scored.

Usage

data(italian_football)
data(italian_football)

Format

A data frame with 27684 observations and 8 variables:

Date: Date vector indicating the date of the match
Season: Numeric vector indicating the season
home: Character vector indicating the home team
visitor: Character vector indicating the visiting team
FT: Character vector indicating the full-time score
hgoal: Integer vector indicating the number of goals scored by the home team
vgoal: Integer vector indicating the number of goals scored by the visiting team
tier: Numeric vector indicating the tier

Details

The dataset name has been kept as 'italian_football' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the footBayes package version 2.0.0

Baseball Team Statistics (2019)

Description

This dataset, mlb_teams_2019, is a data frame containing season-level team statistics for Major League Baseball teams during the 2019 season. It includes information on league affiliation, wins, and offensive statistics such as runs, hits, home runs, RBI, stolen bases, walks, strikeouts, and batting average.

Usage

data(mlb_teams_2019)
data(mlb_teams_2019)

Format

A data frame with 30 observations and 14 variables:

Team: Factor w/ 30 levels indicating the name of the MLB team
League: Factor w/ 2 levels indicating the league the team belongs to (American or National)
Wins: Integer vector indicating the number of games won by the team
Runs: Integer vector indicating the total number of runs scored by the team
Hits: Integer vector indicating the total number of hits by the team
Doubles: Integer vector indicating the total number of doubles hit by the team
Triples: Integer vector indicating the total number of triples hit by the team
HomeRuns: Integer vector indicating the total number of home runs hit by the team
RBI: Integer vector indicating the total number of runs batted in by the team
StolenBases: Integer vector indicating the total number of stolen bases by the team
CaughtStealing: Integer vector indicating the total number of times the team was caught stealing
Walks: Integer vector indicating the total number of walks drawn by the team
Strikeouts: Integer vector indicating the total number of strikeouts by the team
BattingAvg: Numeric vector indicating the team's overall batting average

Details

The dataset name has been kept as 'mlb_teams_2019' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the Lock5Data package version 4.0.1

Baseball Team Statistics (2024)

Description

This dataset, mlb_teams_2024, is a data frame containing season-level team statistics for Major League Baseball teams during the 2024 season. It includes information on league affiliation, wins, and offensive statistics such as runs, hits, home runs, RBI, stolen bases, walks, strikeouts, and batting average.

Usage

data(mlb_teams_2024)
data(mlb_teams_2024)

Format

A data frame with 30 observations and 14 variables:

Team: Character vector indicating the name of the MLB team
League: Character vector indicating the league the team belongs to (American or National)
Wins: Integer vector indicating the number of games won by the team
Runs: Integer vector indicating the total number of runs scored by the team
Hits: Integer vector indicating the total number of hits by the team
Doubles: Integer vector indicating the total number of doubles hit by the team
Triples: Integer vector indicating the total number of triples hit by the team
HomeRuns: Integer vector indicating the total number of home runs hit by the team
RBI: Integer vector indicating the total number of runs batted in by the team
StolenBases: Integer vector indicating the total number of stolen bases by the team
CaughtStealing: Integer vector indicating the total number of times the team was caught stealing
Walks: Integer vector indicating the total number of walks drawn by the team
Strikeouts: Integer vector indicating the total number of strikeouts by the team
BattingAvg: Numeric vector indicating the team's overall batting average

Details

The dataset name has been kept as 'mlb_teams_2024' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the Lock5Data package version 4.0.1

PGA Tournament Data

Description

This dataset, pga_results, is a data frame containing player-level results and performance statistics from PGA Tour tournaments. It includes information on tournament and player identifiers, scoring, fantasy points (DraftKings, FanDuel, and SuperDraft), cut status, finishing position, tournament details such as course, date, purse and season, and strokes gained statistics across different aspects of the game.

Usage

data(pga_results)
data(pga_results)

Format

A data frame with 3676 observations and 34 variables:

Player_initial_last: Character vector indicating the player's name in initial-last format
tournament.id: Integer vector indicating the tournament identification number
player.id: Integer vector indicating the player identification number
hole_par: Integer vector indicating the par for the hole
strokes: Integer vector indicating the number of strokes taken
hole_DKP: Numeric vector indicating the DraftKings points earned per hole
hole_FDP: Numeric vector indicating the FanDuel points earned per hole
hole_SDP: Integer vector indicating the SuperDraft points earned per hole
streak_DKP: Integer vector indicating the DraftKings streak bonus points
streak_FDP: Numeric vector indicating the FanDuel streak bonus points
streak_SDP: Integer vector indicating the SuperDraft streak bonus points
n_rounds: Integer vector indicating the number of rounds played
made_cut: Integer vector indicating whether the player made the cut
pos: Integer vector indicating the player's finishing position
finish_DKP: Integer vector indicating the DraftKings points earned for finishing position
finish_FDP: Integer vector indicating the FanDuel points earned for finishing position
finish_SDP: Integer vector indicating the SuperDraft points earned for finishing position
total_DKP: Numeric vector indicating the total DraftKings points earned
total_FDP: Numeric vector indicating the total FanDuel points earned
total_SDP: Integer vector indicating the total SuperDraft points earned
player: Character vector indicating the full name of the player
tournament.name: Character vector indicating the name of the tournament
course: Character vector indicating the name of the golf course
date: Character vector indicating the date of the tournament
purse: Numeric vector indicating the total prize money offered at the tournament
season: Integer vector indicating the season or year of the tournament
no_cut: Integer vector indicating whether the tournament had no cut
Finish: Character vector indicating the player's final finishing position
sg_putt: Numeric vector indicating strokes gained putting
sg_arg: Numeric vector indicating strokes gained around the green
sg_app: Numeric vector indicating strokes gained approach
sg_ott: Numeric vector indicating strokes gained off the tee
sg_t2g: Numeric vector indicating strokes gained tee to green
sg_total: Numeric vector indicating total strokes gained

Details

The dataset name has been kept as 'pga_results' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the ISAR package version 1.0.5

sportsR: A Comprehensive Collection of Sports and Athletics Datasets

Description

This package provides a diverse collection of datasets focused on sports, athletics, physical performance, and related disciplines. The package includes professional and amateur sports data covering team sports such as soccer, basketball, baseball, American football, volleyball, rugby, cricket and more.

Details

sportsR: A Comprehensive Collection of Sports and Athletics Datasets

A Comprehensive Collection of Sports and Athletics Datasets.

Author(s)

Maintainer: Renzo Caceres Rossi [email protected]

View Available Datasets in sportsR

Description

This function lists all datasets available in the 'sportsR' package. If the 'sportsR' package is not loaded, it stops and shows an error message. If no datasets are available, it returns a message and an empty vector.

Usage

view_datasets_sportsR()
view_datasets_sportsR()

Value

A character vector with the names of the available datasets. If no datasets are found, it returns an empty character vector.

Examples

if (requireNamespace("sportsR", quietly = TRUE)) {
  library(sportsR)
  view_datasets_sportsR()
}
if (requireNamespace("sportsR", quietly = TRUE)) {
  library(sportsR)
  view_datasets_sportsR()
}

Golden State Warriors Basketball - 2016

Description

This dataset, warriors_2016, is a data frame containing game-by-game team statistics for the Golden State Warriors during the 2016 NBA season. It includes information on game location, opponent, win/loss outcome, points scored, and detailed shooting, rebounding, and other box score statistics for both the Warriors and their opponents.

Usage

data(warriors_2016)
data(warriors_2016)

Format

A data frame with 82 observations and 33 variables:

Game: Integer vector indicating the game number in the season
Date: Factor w/ 82 levels indicating the date the game was played
Location: Factor w/ 2 levels indicating whether the game was played at home or away
Opp: Factor w/ 29 levels indicating the opposing team
Win: Factor w/ 2 levels indicating whether the Warriors won or lost the game
Points: Integer vector indicating the points scored by the Warriors
OppPoints: Integer vector indicating the points scored by the opponent
FG: Integer vector indicating the number of field goals made by the Warriors
FGA: Integer vector indicating the number of field goals attempted by the Warriors
FG3: Integer vector indicating the number of three-point field goals made by the Warriors
FG3A: Integer vector indicating the number of three-point field goals attempted by the Warriors
FT: Integer vector indicating the number of free throws made by the Warriors
FTA: Integer vector indicating the number of free throws attempted by the Warriors
Rebounds: Integer vector indicating the total rebounds by the Warriors
OffReb: Integer vector indicating the offensive rebounds by the Warriors
Assists: Integer vector indicating the assists by the Warriors
Steals: Integer vector indicating the steals by the Warriors
Blocks: Integer vector indicating the blocks by the Warriors
Turnovers: Integer vector indicating the turnovers committed by the Warriors
Fouls: Integer vector indicating the personal fouls committed by the Warriors
OppFG: Integer vector indicating the number of field goals made by the opponent
OppFGA: Integer vector indicating the number of field goals attempted by the opponent
OppFG3: Integer vector indicating the number of three-point field goals made by the opponent
OppFG3A: Integer vector indicating the number of three-point field goals attempted by the opponent
OppFT: Integer vector indicating the number of free throws made by the opponent
OppFTA: Integer vector indicating the number of free throws attempted by the opponent
OppRebounds: Integer vector indicating the total rebounds by the opponent
OppOffReb: Integer vector indicating the offensive rebounds by the opponent
OppAssists: Integer vector indicating the assists by the opponent
OppSteals: Integer vector indicating the steals by the opponent
OppBlocks: Integer vector indicating the blocks by the opponent
OppTurnovers: Integer vector indicating the turnovers committed by the opponent
OppFouls: Integer vector indicating the personal fouls committed by the opponent

Details

The dataset name has been kept as 'warriors_2016' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the Lock5Data package version 4.0.1

Golden State Warriors Basketball - 2019

Description

This dataset, warriors_2019, is a data frame containing game-by-game team statistics for the Golden State Warriors during the 2019 NBA season. It includes information on game location, opponent, win/loss outcome, points scored, and detailed shooting, rebounding, and other box score statistics for both the Warriors and their opponents.

Usage

data(warriors_2019)
data(warriors_2019)

Format

A data frame with 82 observations and 33 variables:

Game: Integer vector indicating the game number in the season
Date: Factor w/ 82 levels indicating the date the game was played
Location: Factor w/ 2 levels indicating whether the game was played at home or away
Opp: Factor w/ 29 levels indicating the opposing team
Win: Factor w/ 2 levels indicating whether the Warriors won or lost the game
Points: Integer vector indicating the points scored by the Warriors
FG: Integer vector indicating the number of field goals made by the Warriors
FGA: Integer vector indicating the number of field goals attempted by the Warriors
FG3: Integer vector indicating the number of three-point field goals made by the Warriors
FG3A: Integer vector indicating the number of three-point field goals attempted by the Warriors
FT: Integer vector indicating the number of free throws made by the Warriors
FTA: Integer vector indicating the number of free throws attempted by the Warriors
Rebounds: Integer vector indicating the total rebounds by the Warriors
OffReb: Integer vector indicating the offensive rebounds by the Warriors
Assists: Integer vector indicating the assists by the Warriors
Steals: Integer vector indicating the steals by the Warriors
Blocks: Integer vector indicating the blocks by the Warriors
Turnovers: Integer vector indicating the turnovers committed by the Warriors
Fouls: Integer vector indicating the personal fouls committed by the Warriors
OppPoints: Integer vector indicating the points scored by the opponent
OppFG: Integer vector indicating the number of field goals made by the opponent
OppFGA: Integer vector indicating the number of field goals attempted by the opponent
OppFG3: Integer vector indicating the number of three-point field goals made by the opponent
OppFG3A: Integer vector indicating the number of three-point field goals attempted by the opponent
OppFT: Integer vector indicating the number of free throws made by the opponent
OppFTA: Integer vector indicating the number of free throws attempted by the opponent
OppRebounds: Integer vector indicating the total rebounds by the opponent
OppOffReb: Integer vector indicating the offensive rebounds by the opponent
OppAssists: Integer vector indicating the assists by the opponent
OppSteals: Integer vector indicating the steals by the opponent
OppBlocks: Integer vector indicating the blocks by the opponent
OppTurnovers: Integer vector indicating the turnovers committed by the opponent
OppFouls: Integer vector indicating the personal fouls committed by the opponent

Details

The dataset name has been kept as 'warriors_2019' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the Lock5Data package version 4.0.1

WTA Matches in 2019

Description

This dataset, wta_matches_2019, is a data frame containing match-level data for women's professional tennis matches played on the WTA Tour during 2019. It includes information on tournament details, court and surface conditions, player rankings and points, set-by-set scores, and betting odds from multiple bookmakers for each match.

Usage

data(wta_matches_2019)
data(wta_matches_2019)

Format

A data frame with 2472 observations and 32 variables:

WTA: Integer vector indicating the WTA tournament identification number
Location: Character vector indicating the city where the tournament was played
Tournament: Character vector indicating the name of the tournament
Date: Character vector indicating the date the match was played
Tier: Character vector indicating the WTA tier or category of the tournament
Court: Character vector indicating whether the match was played indoors or outdoors
Surface: Character vector indicating the court surface (e.g., Hard, Clay, Grass)
Round: Character vector indicating the round of the tournament
Best.of: Integer vector indicating the maximum number of sets played
Winner: Character vector indicating the name of the match winner
Loser: Character vector indicating the name of the match loser
WRank: Character vector indicating the WTA ranking of the winner
LRank: Character vector indicating the WTA ranking of the loser
WPts: Character vector indicating the WTA ranking points of the winner
LPts: Character vector indicating the WTA ranking points of the loser
W1: Integer vector indicating the games won by the winner in set 1
L1: Integer vector indicating the games won by the loser in set 1
W2: Integer vector indicating the games won by the winner in set 2
L2: Integer vector indicating the games won by the loser in set 2
W3: Integer vector indicating the games won by the winner in set 3
L3: Integer vector indicating the games won by the loser in set 3
Wsets: Integer vector indicating the total number of sets won by the winner
Lsets: Integer vector indicating the total number of sets won by the loser
Comment: Character vector indicating the match outcome status (e.g., Completed, Retired, Walkover)
B365W: Numeric vector indicating the Bet365 odds for the winner
B365L: Numeric vector indicating the Bet365 odds for the loser
PSW: Numeric vector indicating the Pinnacle Sports odds for the winner
PSL: Numeric vector indicating the Pinnacle Sports odds for the loser
MaxW: Numeric vector indicating the maximum odds offered by any bookmaker for the winner
MaxL: Numeric vector indicating the maximum odds offered by any bookmaker for the loser
AvgW: Numeric vector indicating the average odds offered across bookmakers for the winner
AvgL: Numeric vector indicating the average odds offered across bookmakers for the loser

Details

The dataset name has been kept as 'wta_matches_2019' to avoid confusion with other datasets in the R ecosystem. This naming convention helps distinguish this dataset as part of the sportsR package and assists users in identifying its specific characteristics.

Source

Data taken from the welo package version 0.1.4

Package 'sportsR'

Help Index

ATP Matches in 2019

Description

Usage

Format

Details

Source

English Football League Results 1888-2022

Description

Usage

Format

Details

Source

Italian Football League Results 1934-2022

Description

Usage

Format

Details

Source

Baseball Team Statistics (2019)

Description

Usage

Format

Details

Source

Baseball Team Statistics (2024)

Description

Usage

Format

Details

Source

PGA Tournament Data

Description

Usage

Format

Details

Source

sportsR: A Comprehensive Collection of Sports and Athletics Datasets

Description

Details

Author(s)

See Also

View Available Datasets in sportsR

Description

Usage

Value

Examples

Golden State Warriors Basketball - 2016

Description

Usage

Format

Details

Source

Golden State Warriors Basketball - 2019

Description

Usage

Format

Details

Source

WTA Matches in 2019

Description

Usage

Format

Details

Source