The statistic shows total professional sports player salaries by league in 2019/20. Total salaries paid to NFL players amounted to 5.52 billion U.S. dollars for the 2019/20 season.

With each player taking home a handsome 8.32 million U.S. dollars every year, the NBA is the professional sports league with the highest player wages worldwide. In second place is the Indian Premier League, an annual cricket competition contested each year between eight franchises representing eight different Indian cities.

Big money in the NBA Although the wages that players in the NBA take home are astronomical, the average annual salaries vary from one team to another. Whilst the New York Knicks pay their players an average of 7.08 million U.S. dollars a year, Portland Trail Blazers spent around 10.04 million U.S. dollars annually on each of their stars in 2019/20. NBA salaries have tremendously increased throughout the years reaching 8.32 million U.S. dollars in 2019/20 compared to 4.6 million U.S. dollars in 2015/16.

This dataset merges player per-game and advanced statistics for the NBA's 2022-23 season with player salary data, creating a comprehensive resource for understanding the performance and financial aspects of professional basketball players. The dataset is the result of web scraping player salary information from Hoopshype, and downloading traditional per-game and advanced statistics from Basketball Reference.

Key Features:

• Player Information: Player name, team(s) played for during the season.
• Per Game Statistics: A wide array of per-game statistics, including points scored (PPG), assists (APG), rebounds (RPG), steals (SPG), blocks (BPG), and more.
• Shooting Efficiency: Metrics like field goal percentage (FG%), three-point percentage (3P%), two-point percentage (2P%), and free throw percentage (FT%) for assessing scoring efficiency.
• Advanced Statistics: A wide array of advanced metrics such as value over replacement player (VORP), win shares (WS) and true shooting percentage (TS%)
• Salaries: The financial aspect of the dataset includes player salaries for the 2022-23 season, offering insights into player earning

Potential Uses:

• Player Performance Analysis: Analysts can evaluate player performance in correlation with their earnings, identifying players who provide strong value for their salaries.
• Team Budgeting and Strategy: Teams can use this dataset to assess the financial implications of player acquisitions, trades, and salary cap management.
• Player Earnings Insights: The dataset offers insights into the income distribution of NBA players, helping fans and analysts understand the financial side of professional basketball.
• Data-Driven Decisions: Data scientists can leverage this combined dataset for predictive modeling and machine learning applications, including salary predictions and player valuation.

Acknowledgements: Basketball Reference, Hoopshype

The highest paid athlete in the world in the 2024/2025 season was Cristiano Ronaldo. The Al-Nassr soccer star took home total earnings of 275 million U.S. dollars, of which an estimated 50 million U.S. dollars were off-field earnings through endorsements. Messi vs. Ronaldo continues Despite the ongoing debate among fans and in the media about who is the better soccer player between Cristiano Ronaldo and Lionel Messi, it is the former that led the way in terms of total earnings in 2024/25. As well as winning an incredible amount of trophies, Ronaldo is winning off the pitch, too, thanks to a lucrative sponsorship deal with Nike, and endorsements with DAZN, Electronic Arts, and Tag Heuer. Meanwhile, seven-time Ballon d'Or winner Messi had a relatively equal share of on- and -off field earnings, which totaled 135 million U.S. dollars. Where are all the women? One notable absence in the list of highest paid athletes is any female athletes. In fact, the highest paid female athletes took home a fraction of the earnings of their male counterparts. Tennis star Coco Gauff topped the list of sportswomen, having taken home almost seven million U.S. dollars in prize money and a further 15 million U.S. dollars in endorsements in 2023. Gauff consistently ranked in the top 10 of the WTA's ranking of female tennis players worldwide, and her endorsements include companies such as New Balance, Bose, and Rolex.

The National Basketball Association (NBA) is one of the most popular and widely watched sports leagues in the world, with millions of fans tuning in to watch their favorite teams and players compete on the court. This dataset provides a detailed breakdown of the salaries earned by NBA players based on their individual attributes and career trajectory.

The dataset includes information on a variety of player attributes, including age, height, weight, position, and years of experience. It also includes data on each player's career statistics, such as points per game, rebounds per game, and assists per game, among others.

By analyzing this data, fans and analysts can gain insights into how different attributes and career milestones impact NBA player salaries. They can also identify trends and patterns that might inform player evaluations and team-building strategies.

Overall, this dataset is a valuable resource for anyone interested in understanding the complex factors that drive NBA player salaries and the intricate workings of the league's salary cap system. It offers a comprehensive view of the NBA landscape and sheds light on the factors that contribute to the success and earning potential of individual players.

The statistic shows the average player salary of the professional sports teams in Los Angeles in 2018. The Los Angeles Clippers of the NBA had an average player salary of about 7.47 million U.S. dollars in 2018.

Michael Jordan is the undisputed leader when it comes to the highest paid athletes in history. The basketball legend and former Chicago Bulls star was estimated to have earned a staggering 4.15 billion U.S. dollars throughout his career, thanks to his on-court accomplishments, endorsements, and ownership of the Charlotte Hornets. Jordan's enduring popularity and iconic status have cemented his place as one of the greatest athletes of all time. Golfers earning big bucks on and off the course Interestingly, the three of the next six athletes on the list of highest paid athletes of all time are all golfers. Arnold Palmer, Jack Nicklaus, and Tiger Woods all earned a significant amount of money during their careers. These golfing greats managed to secure lucrative endorsement deals with major brands and also enjoyed success on the golf course, with multiple major championships to their names. Cristiano Ronaldo leads the way for soccer players Soccer can be considered one of the most popular sports in the world and the highest soccer player in the ranking of highest paid athletes is Cristiano Ronaldo. The Portuguese superstar was in third place on the list, with estimated lifetime earnings of over 2.2 billion U.S. dollars. Ronaldo's on-field achievements, combined with his large numbers of social media followers make him one of the most recognizable and highest-paid athletes of all time. Gender pay gap in sport In terms of female athletes, Serena Williams was the highest placed athlete on the list, coming in at 40th place, with lifetime earnings of 660 million U.S. dollars. Williams is widely regarded as one of the greatest tennis players of all time and won an impressive 23 Grand Slam titles during her career. She has also been a vocal advocate for women's rights and equal pay in sports, paving the way for future generations of female athletes to succeed and prosper. Serena Williams' relatively low rank on the list of highest paid athletes of all time is indicative of the gender pay gap that exists in sports. Despite being one of the most successful and dominant tennis players in history, Williams earned significantly less than many of her male counterparts. For example, Roger Federer, who won fewer Grand Slam titles than Williams, ranked ninth on the list, having earned ovwe 1.50 billion U.S. dollars throughout his career. This disparity in earnings is not unique to tennis, as it is a pervasive issue in sports across the board. Female athletes are often paid less than male athletes for performing the same job, even when they achieve comparable levels of success and generate similar levels of revenue. This gender pay gap within sports can be attributed to a variety of factors, including differences in media coverage, sponsorships, and endorsement deals.

Player salaries for all 30 Major League teams for 2011 through 2024 seasons. Data includes salaries for active players and players on the Major League injured list.

The data for this project was sourced from Spotrac, accessed on January 9, 2025. Spotrac provides detailed information on player contracts, salaries, and other financial data for professional sports.

In this article, we analyze how different representation models of professional football players affect their salaries in salary negotiations. We distinguish between self-representation, representation by relatives and representation by player agencies. Based on the principal agent theory and against the background of asymmetric information, we hypothesize that the self-representation model has the most lucrative effect on salaries. Furthermore, we argue that the number of players represented by an agency has a negative effect on salaries. To test our hypotheses, we use a unique panel dataset containing 3,775 players from the top five European leagues over five collection dates. In addition to market values and salaries, we also include individual and team performance. Furthermore, we use information on the different representation models and, in the case of player agencies, information on the particular agency. In our study, we found no significant effect of the representation model on the salary of professional football players, which challenges the justification of agencies in general.

Context

• The National Basketball Association (NBA) is an American men's professional basketball league.
• It is composed of 30 teams (29 in the United States and 1 in Canada) and is one of the four major professional sports leagues in the United States and Canada.
• It is the premier men's professional basketball league in the world.
• The league was founded in New York City on June 6, 1946, as the Basketball Association of America (BAA).
• It changed its name to the National Basketball Association on August 3, 1949, after merging with the competing National Basketball League (NBL).
• The NBA's regular season runs from October to April, with each team playing 82 games.

• The league's playoff tournament extends into June. As of 2020, NBA players are the world's best paid athletes by average annual salary per player.

• The NBA is an active member of USA Basketball (USAB), which is recognized by the FIBA (International Basketball Federation) as the national governing body for basketball in the United States.

• The league's several international as well as individual team offices are directed out of its head offices in Midtown Manhattan, while its NBA Entertainment and NBA TV studios are directed out of offices located in Secaucus, New Jersey.

The NBA is the third wealthiest professional sport league after the National Football League (NFL) and Major League Baseball (MLB) by revenue.

Content

1. GM, GP; GS: games played; games started 2.PTS: points 3.FGM, FGA, FG%: field goals made, attempted and percentage 4.FTM, FTA, FT%: free throws made, attempted and percentage 5.3FGM, 3FGA, 3FG%: three-point field goals made, attempted and percentage 6.REB, OREB, DREB: rebounds, offensive rebounds, defensive rebounds 7.AST: assists 8.STL: steals 9.BLK: blocks 10.TO: turnovers 11.EFF: efficiency: NBA's efficiency rating: (PTS + REB + AST + STL + BLK − ((FGA − FGM) + (FTA − FTM) + TO)) 12.PF: personal fouls 13.MIN: minutes 14.AST/TO: assist to turnover ratio 15.PER: Player Efficiency Rating: John Hollinger's Player Efficiency Rating 16.PIR: Performance Index Rating: Euro league's and Euro cup's Performance Index Rating: (Points + Rebounds + Assists + 17.Steals + Blocks + Fouls Drawn) − (Missed Field Goals + Missed Free Throws + Turnovers + Shots Rejected + Fouls Committed)

Inspiration

This is one of the most common questions of everyone's life that how are these basketball players such rich and how are they getting paid, and even the stats about how everything is done on a team.

The highest paid athlete in the world in 2024 was soccer superstar Cristiano Ronaldo, who racked up on and off field earnings of around 260 million U.S. dollars. However, the Portuguese legend was one of only 12 soccer players to make it into the top 100 ranking of highest paid athletes. Meanwhile, 36 stars from the National Basketball Association made it into the top 100.

The statistic shows the average player salary of the professional sports teams in Chicago in 2017. The Chicago Cubs of the MLB had an average player salary of about 6.54 million U.S. dollars in 2017.

The Most Cost-Effective MLB Hitters

Analyzing Performance and Salary Impact

By Andy Kriebel [source]

About this dataset

This 2013 Major League Baseball hitting statistics dataset compiles the data from Lahman’s Baseball Database and includes salary, team and a variety of other stats for each player. The data covers all levels from amateur to professional, and provides a wealth of information about the past year's performance in baseball. With this dataset, you can analyze batting averages for home runs, RBIs, stolen bases and more—as well as average salaries across players. It is an invaluable resource for anyone looking to get insight into the very best in baseball performance over the last year. Whether you're an avid fantasy league enthusiast or just curious about major league stats this statistic set is sure to help you see who was making waves on or off the field in 2013!

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How to use the dataset

This kaggle dataset consists of all the 2013 Major League Baseballl (MLB) hitting statistics for each player, including their salary, team, and other stats. The main objective of this dataset is to uncover the most cost effective MLB hitters of 2013 by analyzing their stats in relation to how much they are paid. This data can be used by baseball fans looking to gain insights into the performance and salaries of MLB players in 2013 as well as fantasy baseball owners trying to identify value-for-money players for their teams.

In order to make use of this dataset, you will need some knowledge on commonly used baseball stats like runs batted in (RBI), runs scored (R), batting average (AVG), on base percentage (OBP) etc. These stats provide information on players' offensive contributions to the game while fielding and pitching statistics will not be included in this specific dataset. You can then analyse these individual player statistics in comparison with each other or against league averages or trends across various franchises and different leagues such as American League or National League teams over a range of seasons such as 2009 - 2019 season.

Some interesting analysis that you could draw from this data include correlations between higher salaries and a number home runs hit per season, exploring whether there is any truth behind ‘big-hitting’ superstars being paid more than consistent players playing important roles but do not hit many homeruns; cross-referencing which Franchises have more cost effective hitters versus what type/ style of play; identifying if there has been any changes based on handedness i.e left / right handed batters performance & salary; etc… There is certainly much potential with this interesting set available!

Research Ideas

• Creating an interactive visualization allowing users to see the top 10 most cost-effective MLB batters of 2013 based on a number of criteria such as salary, batting stats, or games played.
• Comparing how teams’ payrolls shifted after particular seasons and seeing how budget changes affected different player groups (e.g., high-salary vs low-salary players).
• Utilizing this data to develop a predictive model for estimating future salaries for current MLB players by analyzing the historical performance of other similar players in correlation with their salaries

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: MLB Stats.csv | Column name | Description | |:----------------|:------------------------------------------------------------------------------------| | Player Name | Name of the player. (String) | | weight | Weight of the player in pounds. (Integer) | | height ...

Comprehensive YouTube channel statistics for Pro Sports, featuring 112,000 subscribers and 4,728,570 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Sports category and is based in US. Track 413 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Every contract a player has signed appears as it's own row in this dataset along with information about when and who they were signed to. Joining of the contract and draft data was done via fuzzywuzzy off of the player name and position. Players with common/duplicate names should be scrutinized or excluded and their contract and draft data may have misjoined.

This streamlit app allows for some exploration of the data: https://nfl-contracts.streamlit.app/

Dictionary: - draft_year: the year they were drafted - rnd: the round they were drafted in - pick: the pick # they were selected at - tm: the team that drafted them - player: the name of the player - g: the number of games the player played in their career - search_key: the key that was used for joining, retained as it could be valuable for displaying - year_signed: the year the contract was signed - signing_tm: the team that signed them (this needs to be cleaned to match with 'tm') - value: the total US dollar value of their contract at signing (not adjusted for inflation) - gtd: the total US dollar guaranteed value of their contract at signing (not adjusted for inflation)

value and gtd were normalized to the NFL salary cap for that year (value_norm and gtd_norm). This allows for comparison of contract values across years since the salary cap (generally) increases every year roughly aligned with inflation.

The Sports Leagues Dataset (SLD) contains statistical data of the major professional sports leagues in the United States: NFL (National Football League), NBA (National Basketball Association), NHL (National Hockey League) and MLB (Major League Baseball). One collect five topics (Player Expenses, Player Salaries, Players Performance, Team Salaries, Team Valuation) of two dimensions (Finance and Performance) in different seasons (2000-2007) from three data sources (Forbes, Spotrac and Sports Reference).

Please consider citing https://doi.org/10.5281/zenodo.3256432 if you found this dataset useful:

[1] André Albino Bastos, Matheus de Oliveira Salim, Wladmir Cardoso Brandão. (2019). SLD: The Sports Leagues Dataset (Version 1.0) [Data set]. Zenodo.

As of 2023, soccer players in the English Premier League were better compensated than many other professional sports leagues, with player salaries amounting to around ** percent of the league's total revenue. Meanwhile, player salaries in North America's Big Four sports leagues hovered around half of each league's revenue, while cricketers in the Indian Premier League only earned ** percent of league revenue.

Baseball Data

Description

Major League Baseball Data from the 1986 and 1987 seasons.

Usage

Hitters

Format

A data frame with 322 observations of major league players on the following 20 variables.

• AtBat: Number of times at bat in 1986

• Hits: Number of hits in 1986

• HmRun: Number of home runs in 1986

• Runs: Number of runs in 1986

• RBI: Number of runs batted in in 1986

• Walks: Number of walks in 1986

• Years: Number of years in the major leagues

• CAtBat: Number of times at bat during his career

• CHits: Number of hits during his career

• CHmRun: Number of home runs during his career

• CRuns: Number of runs during his career

• CRBI: Number of runs batted in during his career

• CWalks: Number of walks during his career

• League: A factor with levels A and N indicating player's league at the end of 1986

• Division: A factor with levels E and W indicating player's division at the end of 1986

• PutOuts: Number of put outs in 1986

• Assists: Number of assists in 1986

• Errors: Number of errors in 1986

• Salary: 1987 annual salary on opening day in thousands of dollars

• NewLeague: A factor with levels A and N indicating player's league at the beginning of 1987

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. This is part of the data that was used in the 1988 ASA Graphics Section Poster Session. The salary data were originally from Sports Illustrated, April 20, 1987. The 1986 and career statistics were obtained from The 1987 Baseball Encyclopedia Update published by Collier Books, Macmillan Publishing Company, New York.

References

Games, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, www.StatLearning.com, Springer-Verlag, New York

Examples

summary(Hitters)

lm(Salary~AtBat+Hits,data=Hitters)

Dataset imported from https://www.r-project.org.

Motivation

Disc golf is a sport where players throw discs from a teeing area and try to get them into a basket in a few strokes as possible. Similarly to ball golf, players compete with a bag of discs that include drivers, midranges, and putters, and try to achieve a score of par or better every hole. The Disc Golf Pro Tour is the main professional disc golf tour that holds tournaments across the United States and Europe where Disc Golfers from all around the world can compete. The DGPT tracks putting and driving statistics for each player across all of their events and the top 40% of finishers in each event receive cash for their performance.

Data

The DGPT data set contains 97 rows and 25 columns where each row is a disc golfer’s statistics from the first 11 events of 2024 Disc Golf Pro Tour season. Each row includes a players average scoring, throwing, and putting statistics, as well as cumulative and average placement and earning statistics. This data set include each player who finished above the cash line at least 1 tournament in the first 11 events of the DGPT season. The columns are as follows:

• player player name
• birdie_avg the average number of birdies from an 18 hole round
• bogey_avg the average number of bogeys from an 18 hole round
• PKD the percentage of shots a player has ‘parked’ (within 10 ft of the basket)
• C1R the percent of shots throw within ‘Circle 1’(0-33ft) in regulation
• C2R the percent of shots throw within ‘Circle 2’(33-66ft) in regulation
• FWY the percent fairways hit with drives
• SCR the percent of successful ‘scrambles’ the player achieves
• OB/18 the average total out of bounds shots for 18 holes
• C1X the percent of putts made from C1 excluding tap in putts(0-10ft)
• C2P the percent of putts made from C2 (33-66ft)
• tot_SG:TG the total ‘Strokes Gained’ on the rest of the field from the tee to the green
• tot_SG:P the total ‘Strokes Gained’ on the rest of the field putting
• events total events played for the 2024 DGPT season
• rounds total rounds played for the 2024 DGPT season
• wins wins from the 2024 DGPT season
• podiums total podium placements from the 2024 DGPT season
• top_10s total top 10 placements from the 2024 DGPT season
• top_20s total top 20 placements from the 2024 DGPT season
• avg_place average event placement from the 2024 DGPT season
• top_10% percent of events where the player placed in the top 10
• top_20% percent of events where the player placed in the top 20
• total_earnings the players total earnings from the 2024 DGPT season
• total_strokes the total number of shots the player has taken during the DGPT season

Important Disc Golf Terms

Parked(PKD): When a player throws their disc within 10 feet of the basket with a tee shot on par 3s or an approach shot on par 4s and 5s. A score of eagle or better also counts as a shot being parked.

Circle 1 in Regulation(C1R): When a player’s disc comes to rest within 10m(33ft) of the basket in the expected amount of shots. This is their tee shot on a par 3, 2nd shot on a par 4, and third shot on a par 5. C1 is defined by a 10m circle around the basket.

Circle 2 in Regulation(C2R): This has the same definition as Circle 1 in Regulation, but Circle 2 extends to within 20m(66ft) of the basket.

Fairways Hit(FWY): This is the percent of holes where the player lands on the fairway with their drive. This includes being inside Circle 1 or 2 on a drive.

Scramble(SCR): A scramble is when a player scores par or better on a hole after missing the fairway or being out-of-bounds. Scramble percentage is the percentage of holes where a player scored par or better after an errant drive.

Circle 1X putting(C1X): The putting percentage of the player from within Circle 1 (10m/66ft), excluding tap in putts within 10ft of the basket.

Circle 2 putting(C2P): The putting percentage of the player from Circle 2 (10m-20m/33ft-66ft).

Strokes Gained Tee to Green(SG:TG): This is the number of strokes by which the player has exceeded the average throwing performance of the field on drives and approaches.

Strokes Gained Putting(SG:P): This is the number of strokes by which the player has exceeded the average putting performance of the field in circle 1 and 2.

Questions

1. What statistics are most strongly correlated with successful performances (total_earnings, wins, avg_place)?
2. If a player were to improve in a specific statistic, how would you expect his scoring to change(average birdies or bogeys)?
3. Does the old golf saying “Drive for show, putt for dough” apply to disc golf? Are wins or total earnings more correlated with putting?

References

Profession Disc Golf Association via. StatMando, 2024, 2024 MPO DGPT Season Stats. https://statmando.com/stats/season-stats-main-dgpt-2024-mpo

Profession Disc Golf Association via. StatMando, 2024, Stats for Majors and Elite Events: 2024 (MPO). https://statmando.com/s...

To ensure uncertainty in match outcomes, professional sporting leagues have used various competitive balance policies, including player salary caps, revenue sharing among teams and player drafts. The Australian Football League (AFL) introduced a player draft in 1986, and to refine its operation, a draft value index (DVI) was introduced in 2015. The DVI allocates a numeric value to each individual player draft pick, with these values determined by the AFL using historic player compensation or wage and salary data. The AFL DVI plays an essential role in the operation of its player draft; however, other research has questioned the validity of such indexes. This paper aims to produce an alternative to the AFL DVI. The former index uses career compensation as the determinant of value, whereas we use other measures of player performance. First, various models were developed to predict on-field performance, such as games played (both in a recruit’s career and season) after a draftee was selected for the first time by a team. This was then retrofitted to the pick used to select these draftees to create the new DVIs. Even though the predicted DVI followed an inverse monotonic function like the existing index, the decline in value for the DVI produced here was less steep, unlike the AFL’s. This allowed us to conclude that players’ salaries did not always strongly correlate to performance. The change in performance between players selected at different points in the draft did not vary as much as their wages. Though this scheme is applied to the AFL, the underlying concept could be directly exported to other player drafts.