Context

This public data includes pitch-by-pitch data for Major League Baseball (MLB) games in 2016. With this data you can effectively replay a game and rebuild basic statistics for players and teams.

Content

games_wide - Every pitch, steal, or lineup event for each at bat in the 2016 regular season.

games_post_wide - Every pitch, steal, or lineup event for each at-bat in the 2016 post season.

schedules - The schedule for every team in the regular season.

*The schemas for the games_wide and games_post_wide tables are identical.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

Dataset Source: Sportradar LLC

Use: Copyright Sportradar LLC. Access to data is intended solely for internal research and testing purposes, and is not to be used for any business or commercial purpose. Data are not to be exploited in any manner without express approval from Sportradar. Display of data must include the phrase, “Data provided by Sportradar LLC,” and be hyperlinked to www.sportradar.com.

Baffled why your team traded for that 34-year-old pitcher? Convinced you can create a new and improved version of WAR? Wondering what made the 1907 Cubs great and if can they do it again?

The History of Baseball is a reformatted version of the famous Lahman’s Baseball Database. It contains Major League Baseball’s complete batting and pitching statistics from 1871 to 2015, plus fielding statistics, standings, team stats, park stats, player demographics, managerial records, awards, post-season data, and more.

Scripts, Kaggle’s free, in-browser analytics tool, makes it easy to share detailed sabermetrics, predict the next hall of fame inductee, illustrate how speed scores runs, or publish a definitive analysis on why the Los Angeles Dodgers will never win another World Series.

We have more ideas for analysis than games in a season, but here are a few we’d really love to see:

• Is there a most error-prone position?
• When do players at different positions peak?
• Are the best performers selected for all-star game?
• How many walks does it take for a starting pitcher to get pulled?
• Do players with a high ground into double play (GIDP) have a lower batting average?
• Which players are the most likely to choke during the post-season?
• Why should or shouldn’t the National League adopt the designated hitter rule?

See the full SQLite schema.

Content

files: - https://projects.fivethirtyeight.com/mlb-api/mlb_elo.csv

- https://projects.fivethirtyeight.com/mlb-api/mlb_elo_latest.csv

MLB Elo

This file contains links to the data behind The Complete History Of MLB and our MLB Predictions.

mlb_elo.csv contains game-by-game Elo ratings and forecasts back to 1871. mlb_elo_latest.csv contains game-by-game Elo ratings and forecasts for only the latest season.

The data contains two separate systems for rating teams; the simpler Elo ratings, used for The Complete History Of MLB, and the more involved — and confusingly named — "ratings" that are used in our MLB Predictions. The main difference is that Elo ratings are reverted to the mean between seasons, while the more involved ratings use preseason team projections from several projection systems and account for starting pitchers. More information can be found in this article.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Context

The Major League Soccer Union releases the salaries of every MLS player each year. This is a collection of salaries from 2007 to 2017.

Content

Each file contains the following fields:

• club: Team abbreviation
• last_name: Player last name
• first_name: Player first name
• position: Position abbreviation
• base_salary: Base salary
• guaranteed_compensation: Guaranteed compensation

Acknowledgements

Jeremy Singer-Vine over at Data is Plural scraped the PDF's released by the MLS Union and put the data in a nice little package of CSV files for everyone.

I downloaded this dataset from: https://github.com/data-is-plural/mls-salaries MIT License

Inspiration

Who in the MLS makes the most money? Are they worth it? I make about $900 bazillion each year, can I afford a soccer team?