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 ...

Data from my home fantasy baseball league. Is it possible to predict winners based on managerial data? What features predict winning the most?

team - name of manager R - runs scored HR - home runs RBI - Runs batted in SB - Stolen bases AVG - Average OBP - On based percentage K - strikeouts QS - quality starts W - wins SV - saves plus holds ERA - earned run average WHIP - walks plus hits divided by innings pitched MOVES - number of moves by manager bye - whether or not the manager received a bye into playoffs playoffs - whether or not the manager entered into playoffs win_percentage - regular season win percentage regular_rank - regular season standings rank final_rank - final standings rank. 1 represents that they won the league year - the specific year ring_count - how many rings that manager had going into the season draft_position - what position the manager drafted

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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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.

Context

I wanted to practice my scraping skills so I practiced on ESPN Fantasy data.

Content

All players found on the ESPN fantasy page, their 2019 stats, and the 2020 projections.

Acknowledgements

Thank you ESPN.

Inspiration

Which players are expected to perform significantly better in 2020 than in 2019? What page would you like me to scrape next?

Please Upvote if you like my work.

IPL DATA (2008-2019)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5635484%2Fcc4775f61ed72a625e5485a3941e6e45%2FIPL%20pic.jpg?generation=1600083123785212&alt=media" alt=""> Indian Premier League(IPL) is a professional Twenty20 cricket league in India contested during March or April and May of every year by eight teams representing eight different cities in India. The league was founded by the Board of Control for Cricket in India(BCCI) in 2008.

I am not the greatest cricket fan out there but I enjoy cricket as much as the next guy. Although I am a huge fan of Aaron Sorkin and his work. If you are a fan of him like me you must be knowing what movie I am inspiring this from Moneyball(2011)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5635484%2F488e209388026b26e54861cc2755d635%2F229898_Moneyball_2011_1400x2100_US_1.jpg?generation=1600083427047949&alt=media" alt="">

I was awestruck by such exclamations of formation of a best team with the help of some numbers on a sheet that might determine the players abilities.This sounded absolutely preposterous to me but with a little bit of cynicism I continued my research.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5635484%2Fb7cb16ca9cfa9dc410e03d24564a9307%2FMONEYBALLchart.png?generation=1600150528360838&alt=media" alt="">

I was dumbfounded when I had found such determinations were possible and people were using such determinations to create fantasy teams on various sports and leagues.

PECOTA, an acronym for Player Empirical Comparison and Optimization Test Algorithm, is such a sabermetric system for forecasting Major League Baseball player performance. The word is a backronym based on the name of journeyman major league player Bill Pecota, who, with a lifetime batting average of .249, is perhaps representative of the typical PECOTA entry. PECOTA was developed by Nate Silver in 2002–2003 and introduced to the public in the book Baseball Prospectus 2003. Baseball Prospectus (BP) has owned PECOTA since 2003; Silver managed PECOTA from 2003 to 2009. Beginning in Spring 2009, BP assumed responsibility for producing the annual forecasts, making 2010 the first baseball season for which Silver played no role in producing PECOTA projections.

One of several widely publicized statistical systems of forecasts of player performance, PECOTA player forecasts are marketed by BP as a fantasy baseball product. Since 2003, annual PECOTA forecasts have been published both in the Baseball Prospectus annual books and, in more detailed form, on the BaseballProspectus.com subscription-based website. PECOTA also inspired some analogous projection systems for other professional sports: KUBIAK for the National Football League, SCHOENE and CARMELO for the National Basketball Association, and VUKOTA for the National Hockey League.

PECOTA forecasts a player's performance in all of the major categories used in typical fantasy baseball games; it also forecasts production in advanced sabermetric categories developed by Baseball Prospectus (e.g., VORP and EqA). In addition, PECOTA forecasts several summary diagnostics such as breakout rates, improve rates, and attrition rates, as well as the market values of the players. The logic and methodology underlying PECOTA have been described in several publications, but the detailed formulas are proprietary and have not been shared with the baseball research community.

We need such a public system for cricket to forecast the fantasy leagues that are popping up.

As a data science student and enthusiast I tried finding such projects whether existed on kaggle or even the data sets that needed for such projects on IPL. I found no updated data. As the new season of IPL is upon us I wanted to create a data set repository on which such determinations can be made. As I am new to kaggle please guide me so that we can make this possible. I will try to update the data as soon as possible.

Lets make this happen. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5635484%2Fcb066c3eb67397dbf5b55a792c652984%2Fjonah-hill-plays-peter-brand-in-moneyball.0.0.jpg?generation=1600085676716279&alt=media" alt="">

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