| Type: | Package |
| Title: | A Comprehensive Collection of Sports and Athletics Datasets |
| Version: | 0.1.0 |
| Maintainer: | Renzo Caceres Rossi <arenzocaceresrossi@gmail.com> |
| 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. |
| License: | GPL-3 |
| Language: | en |
| URL: | https://github.com/lightbluetitan/sportsr, https://lightbluetitan.github.io/sportsr/ |
| BugReports: | https://github.com/lightbluetitan/sportsr/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| Suggests: | ggplot2, testthat (≥ 3.0.0), dplyr, knitr, rmarkdown |
| Depends: | R (≥ 4.1.0) |
| Imports: | utils |
| Config/roxygen2/version: | 8.0.0 |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2026-08-21 00:22:16 UTC; Renzo |
| Author: | Renzo Caceres Rossi
 [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2026-08-26 20:20:02 UTC |
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 arenzocaceresrossi@gmail.com
See Also
Useful links:
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)
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)
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)
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)
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)
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)
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
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()
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()
}
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)
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)
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)
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