Wages in the United States increased 4.72 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

MLB Players Salaries And Performance

Analyzing Salaries, Contract Length, and Team Performance

By Nate Reed [source]

About this dataset

This dataset contains information about Major League Baseball players’ salaries and contracts, sourced from USA Today. It includes information like the player's salary for the current season, total contract value, position they play, number of years their contract is for and average annual salary. This dataset allows you to explore MLB player contracts at a deeper level, examine differences between players' salaries across different positions and teams, identify which teams are paying their players the most per annum or over the duration of full contracts

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For more datasets, click here.

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How to use the dataset

This dataset provides detailed salary and contract information for Major League Baseball players. It contains all the most up-to-date information about each player's contract, including salary, total value, position, years, average annual salary, and team affiliation. With this data you can analyze trends in player salaries and contracts to identify opportunities for maximizing profits.

You can also use this data to compare the relative worth of players at different positions across teams. Use it to research trade value of players - including estimated trade values based on their contracts - as well as provide statistical analysis of the effects that player moves have had on teams' success. Additionally, you can utilize it to build predictive models that use past contracts to predict future salary increases or decreases when negotiating new contracts with existing or prospective players.

Ready to get started? Here are a few tips on how best to utilize this dataset: - Examine the Total Value column first since it is often a key indicator in determining a player's worth; - Look at previous years’ salaries by team for comparision purposes;
- Factor in performance metrics like OPS (on-base plus slugging percentage), ERA (earned run average), WHIP (walks + hits/innings pitched), FIP (fielding independent pitching); - Take into account intangibles such as fan interest/popularity; - Utilize averages across different positions and teams – are certain players way underpaid compared his peers? Conversely are certain overpaid compared his peers? Finding these mismatches could potentially create an arbitrage opportunity if a trade were made.

By understanding how successful teams build rosters using Major League Baseball Player Salaries and Contracts datasets you too can be empowered with data driven decisions when investing in your fantasy baseball team or MLB organization!

Research Ideas

• Analyzing which teams are spending the most on salary, and determining how that is affecting their performance.
• Comparing positions to see which positions earn more money across teams and leagues.
• Identifying trends in salaries for larger contracts vs smaller ones, to help players and teams determine better negotiating strategies for future signings

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: salaries.csv | Column name | Description | |:----------------|:-------------------------------------------------------------| | salary | The amount of money a player is paid for a season. (Numeric) | | name | The name of the player. (String) | | total_value | The total value of the player's contract. (Numeric) | | pos | The position the player plays. (String) | | years | The length of the player's contract. (Numeric) | | avg_annual | The average annual salary of the player. (Numeric) | | team | The team the player plays for. (String) |

Acknowledgements

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

####About Dataset

This dataset was retrieved from the page https://ai-jobs.net/salaries/download/

This site collects salary information anonymously from professionals all over the world in the AI, ML, Data Science space and makes it publicly available for anyone to use, share and play around with.

The primary goal is to have data that can provide better guidance in regards to what's being paid globally. So newbies, experienced pros, hiring managers, recruiters and also startup founders or people wanting to make a career switch can make better informed decisions.

work_year: The year the salary was paid. experience_level: The experience level in the job during the year with the following possible values: EN: Entry-level / Junior MI: Mid-level / Intermediate SE: Senior-level / Expert EX: Executive-level / Director employment_type: The type of employement for the role: PT: Part-time FT: Full-time CT: Contract FL: Freelance job_title: The role worked in during the year. salary: The total gross salary amount paid. salary_currency: The currency of the salary paid as an ISO 4217 currency code. salary_in_usd: The salary in USD (FX rate divided by avg. USD rate of respective year via data from fxdata.foorilla.com). employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code. remote_ratio: The overall amount of work done remotely, possible values are as follows: 0: No remote work (less than 20%) 50: Partially remote/hybrid 100: Fully remote (more than 80%) company_location: The country of the employer's main office or contracting branch as an ISO 3166 country code. company_size: The average number of people that worked for the company during the year: S: less than 50 employees (small) M: 50 to 250 employees (medium) L: more than 250 employees (large)

Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in the United States increased to 31.18 USD/Hour in May from 31.06 USD/Hour in April of 2025. This dataset provides - United States Average Hourly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

This Dataset containes the details of the AI, ML, Data Science Salary (2020- 2025). Salary data is in USD and recalculated at its average fx rate during the year for salaries entered in other currencies.

The data is processed and updated on a weekly basis so the rankings may change over time during the year.

Attribute Information

• work_year: The year the salary was paid.
• experience_level: The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director
• employment_type: The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance
• job_title: The role worked in during the year.
• salary: The total gross salary amount paid.
• salary_currency: The currency of the salary paid as an ISO 4217 currency code.
• salary_in_usd: The salary in USD (FX rate divided by avg. USD rate of respective year) via statistical data from the BIS and central banks.
• employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code.
• remote_ratio : The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote/hybird 100 Fully remote (more than 80%)
• company_location: The country of the employer's main office or contracting branch as an ISO 3166 country code.
• company_size: The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Acknowledgements

https://aijobs.net/

Photo by Anastassia Anufrieva on Unsplash

The average monthly salary for active Child Protective staff as of the last day of the fiscal year by staff type. The county and region of the employees are determined by the office to which they are assigned. More information at www.dfps.texas.gov NOTE: Child Protective Investigations (CPI), Child Care Investigations (CCI), and Child Protective Services (CPS) Staff are all included. Child Care Investigations (CCI), which is a part of CPI and include Day Care Investigations (DCI) and Residential Child Care Investigations (RCCI) are only available from 2018 onward. This is due to the split of those job functions from Child Care Licensing, which was a part of DFPS until 2017, when it was transferred to the Health and Human Services Commission (HHSC). This addresses Texas Human Resource Code Section 40.0516(11).

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Cash, AR, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 9,820, while the mean income for the highest quintile (20% of households with the highest income) is 128,088. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 174,444, which is 136.19% higher compared to the highest quintile, and 1776.42% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cash median household income. You can refer the same here

This dataset provides annual personal income estimates for State of Iowa produced by the U.S. Bureau of Economic Analysis beginning in 1997. Data includes the following estimates: personal income, per capita personal income, wages and salaries, supplements to wages and salaries, private nonfarm earnings, compensation of employees, average compensation per job, and private nonfarm compensation.

Personal income is defined as the sum of wages and salaries, supplements to wages and salaries, proprietors’ income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Personal income for Iowa is the income received by, or on behalf of all persons residing in Iowa, regardless of the duration of residence, except for foreign nationals employed by their home governments in Iowa. Per capita personal income is personal income divided by the Census Bureau’s annual midyear (July 1) population estimates.

Wages and salaries is defined as the remuneration receivable by employees (including corporate officers) from employers for the provision of labor services. It includes commissions, tips, and bonuses; employee gains from exercising stock options; and pay-in-kind. Judicial fees paid to jurors and witnesses are classified as wages and salaries. Wages and salaries are measured before deductions, such as social security contributions, union dues, and voluntary employee contributions to defined contribution pension plans.

Supplements to wages and salaries consists of employer contributions for government social insurance and employer contributions for employee pension and insurance funds.

Private nonfarm earnings is the sum of wages and salaries, supplements to wages and salaries, and nonfarm proprietors' income, excluding farm and government.

Compensation to employees is the total remuneration, both monetary and in kind, payable by employers to employees in return for their work during the period. It consists of wages and salaries and of supplements to wages and salaries. Compensation is presented on an accrual basis - that is, it reflects compensation liabilities incurred by the employer in a given period regardless of when the compensation is actually received by the employee.

Average compensation per job is compensation of employees divided by total full-time and part-time wage and salary employment.

Private nonfarm compensation is the sum of wages and salaries and supplements to wages and salaries, excluding farm and government.

More terms and definitions are available on https://apps.bea.gov/regional/definitions/.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Money Creek Township, Minnesota, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 24,649, while the mean income for the highest quintile (20% of households with the highest income) is 234,434. This indicates that the top earners earn 10 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 365,119, which is 155.74% higher compared to the highest quintile, and 1481.27% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Money Creek township median household income. You can refer the same here

This dataset provides annual personal income estimates for State of Iowa produced by the U.S. Bureau of Economic Analysis beginning in 1997. Data includes the following estimates: personal income, per capita personal income, wages and salaries, supplements to wages and salaries, private nonfarm earnings, compensation of employees, average compensation per job, and private nonfarm compensation. Personal income is defined as the sum of wages and salaries, supplements to wages and salaries, proprietors’ income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Personal income for Iowa is the income received by, or on behalf of all persons residing in Iowa, regardless of the duration of residence, except for foreign nationals employed by their home governments in Iowa. Per capita personal income is personal income divided by the Census Bureau’s annual midyear (July 1) population estimates. Wages and salaries is defined as the remuneration receivable by employees (including corporate officers) from employers for the provision of labor services. It includes commissions, tips, and bonuses; employee gains from exercising stock options; and pay-in-kind. Judicial fees paid to jurors and witnesses are classified as wages and salaries. Wages and salaries are measured before deductions, such as social security contributions, union dues, and voluntary employee contributions to defined contribution pension plans. Supplements to wages and salaries consists of employer contributions for government social insurance and employer contributions for employee pension and insurance funds. Private nonfarm earnings is the sum of wages and salaries, supplements to wages and salaries, and nonfarm proprietors' income, excluding farm and government. Compensation to employees is the total remuneration, both monetary and in kind, payable by employers to employees in return for their work during the period. It consists of wages and salaries and of supplements to wages and salaries. Compensation is presented on an accrual basis - that is, it reflects compensation liabilities incurred by the employer in a given period regardless of when the compensation is actually received by the employee. Average compensation per job is compensation of employees divided by total full-time and part-time wage and salary employment. Private nonfarm compensation is the sum of wages and salaries and supplements to wages and salaries, excluding farm and government. More terms and definitions are available on https://apps.bea.gov/regional/definitions/.

Analysis of ‘Hr Analytics Job Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mfaisalqureshi/hr-analytics-and-job-prediction on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Hr Data Analytics This dataset contains information about employees who worked in a company.

Content

This dataset contains columns: Satisfactory Level, Number of Project, Average Monthly Hours, Time Spend Company, Promotion Last 5
Years, Department, Salary

Acknowledgements

You can download, copy and share this dataset for analysis and Predictions employees Behaviour.

Inspiration

Answer the following questions would be worthy 1- Do Exploratory Data analysis to figure out which variables have a direct and clear impact on employee retention (i.e. whether they leave the company or continue to work) 2- Plot bar charts showing the impact of employee salaries on retention 3- Plot bar charts showing a correlation between department and employee retention 4- Now build a logistic regression model using variables that were narrowed down in step 1 5- Measure the accuracy of the model

--- Original source retains full ownership of the source dataset ---

Average and median gender pay ratio in annual employment income and in annual wages, salaries and commissions. Data are available by National Occupational Classification (NOC) and age group.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

This dataset contains the salaries of Data Science Professionals for year 2020 and 2021.

About Dataset :

work_year : The year during which the salary was paid. There are two types of work year values: 2020 Year with a definitive amount from the past 2021e Year with an estimated amount (e.g. current year)

experience_level : The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type : The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title : The role worked in during the year. salary The total gross salary amount paid.

salary_currency : The currency of the salary paid as an ISO 4217 currency code.

salaryinusd : The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com).

employee_residence : Employee's primary country of residence in during the work year as an ISO 3166 country code.

remote_ratio : The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)

company_location : The country of the employer's main office or contracting branch as an ISO 3166 country code.

company_size : The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Dataset Source - ai-jobs.net Salaries

Statistics on average annual salaries of graduates of full-time UGC-funded programmes who were in Full-time Employment

Wages in Poland increased to 8962.28 PLN/Month in the first quarter of 2025 from 8477.21 PLN/Month in the fourth quarter of 2024. This dataset provides - Poland Average Gross Monthly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Average weekly earnings at industry level including manufacturing, construction and energy, Great Britain, monthly, non-seasonally adjusted. Monthly Wages and Salaries Survey.