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Here are 22 public repositories matching this topic..., laberning / openrowingmonitor.

A free and open source performance monitor for rowing machines

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alexadam / sport-stats

Sport stats UI components

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NodeJS wrapper for the MySportsFeeds Sports Data API

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A full-stack dockerized web application to visualize Formula 1 race statistics from 2016 to present, with a Python Flask server and a React front-end with d3.js as data visualization tool.

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Hover on an NBA player's name on any web page to quickly view their career stats. Thousands of active users across Chrome, Firefox, Opera, and Edge.

• Updated Sep 17, 2023

CFBD / cfb.js

JavaScript client library for accessing the CollegeFootballData API

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Source code for "Inside Cricket: A fifth umpire' view of your favorite sport"

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Map application Using pure JavaScript & Leaflet API.

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epkjoja / firefox-resultscroller

A WebExtension to repeatedly scroll a page up and down to show all its contents.

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Website to display all running tracks in Iowa.

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b-goold / aflgames

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Posted on Jul 25, 2023

Mastering AJAX for Sports Data: A Comprehensive Guide to Seamless Web Development

1. introduction.

Mastering the art of making HTTP requests with JavaScript using AJAX (Asynchronous JavaScript and XML) is an essential skill for modern web developers, especially in the sports domain. AJAX empowers developers to fetch sports data from servers and seamlessly update sports web pages without requiring full page reloads. This technology enables dynamic interactions, live score updates, player statistics, match schedules, and other sports-related information, significantly enhancing the overall sports enthusiast's experience.

In this comprehensive guide, we will delve into the fundamentals of making HTTP requests with AJAX to access and manage sports data. Our journey will encompass various aspects, including understanding different types of requests, handling response data, working with sports APIs, and tackling CORS restrictions for retrieving real-time sports information.

2. Exploring Sports API Requests

2.1. overview of the sports api and its request/response model.

The Sports API forms the backbone of data communication for sports-related information retrieval. It facilitates the exchange of data between a client (such as a sports application) and a server responsible for providing sports-related information.

When a sports application desires specific data from the server, it initiates an API request. This request comprises a method (e.g., GET, POST, PUT, DELETE), an API endpoint specifying the target resource, and, optionally, other components like headers and parameters.

Example code snippet:

Explanation: In the code snippet above, we create an XMLHttpRequest object to send a GET request to the designated sports API endpoint. The open method sets up the request with the method, API endpoint URL, and asynchronous flag (true for asynchronous requests). Finally, the send method dispatches the request to the sports data server.

2.2. Various Types of Sports API Requests (GET, POST, PUT, DELETE)

The Sports API caters to different request methods, each serving a unique purpose. The most commonly used methods are GET, POST, PUT, and DELETE.

The GET method is utilized to retrieve sports-related data from the server. It may append parameters in the API endpoint URL or employ the query string to pass data. This method is commonly employed to fetch scores, player statistics, or match schedules.

Explanation: In the above code snippet, a GET request is issued to the sports API, with a date parameter passed through the query string to fetch scores for a specific date.

2.2.2. POST

The POST method is employed to submit sports-related data to the server. It sends data within the request body and is commonly used for creating or updating sports-related resources, such as match results.

Explanation: In the above code snippet, a POST request is made to the sports API to submit match results, with JSON data sent in the request body.

The PUT method is used to update an existing sports-related resource on the server. It is similar to POST but typically employed for complete resource replacement, such as updating player information.

Explanation: In the above code snippet, a PUT request is made to update player information with ID 56789, with JSON data included in the request body.

2.2.4. DELETE

The DELETE method is employed to remove a sports-related resource from the server, such as deleting a player profile.

Explanation: In the above code snippet, a DELETE request is made to delete the player profile with ID 56789.

2.3. Understanding Request Headers and Parameters

Request headers provide additional information to the sports API server about the request, such as the content type, authentication credentials, or preferred language.

Explanation: In the above code snippet, the setRequestHeader method is used to set the Authorization header with a bearer token to authenticate the request.

Request parameters allow passing data to the sports API server as part of the request. In GET requests, parameters are typically appended to the API endpoint URL, while in POST requests, they are sent in the request body.

Explanation: In the above code snippet, the API endpoint URL includes a date parameter to fetch scores for a specific date.

It is crucial to grasp the Sports API protocol, request methods, headers, and parameters when developing sports applications and making API requests for sports data.

3. Unveiling AJAX in Sports Applications

3.1. definition and purpose of ajax in sports applications.

AJAX, short for Asynchronous JavaScript and XML, represents a powerful technique extensively utilized in sports web development. Its primary purpose is to make asynchronous requests to a server without reloading the entire web page. This empowers sports applications to fetch live sports data from the server, update match results, and dynamically display real-time scores, schedules, and player statistics, delivering a smoother and more interactive user experience for sports enthusiasts.

Code Snippet 1: Making a Basic AJAX Request for Live Scores

Explanation: In this sports-related code snippet, we create a new XMLHttpRequest object using the `new XMLHttpRequest()` constructor. We then open a GET request to the specified sports API endpoint (`'https://api.sportsdataexample.com/live-scores'`) using the `open()` method. The third parameter, `true`, indicates that the request should be asynchronous. We set up an `onreadystatechange` event handler to listen for changes in the request state. Once the request is complete (`readyState === 4`) and the response status is successful (`status === 200`), we parse the JSON response using `JSON.parse()` and process and display the liveScores data accordingly.

3.2. Advantages of Using AJAX in Sports Applications over Traditional Page Reloads

3.2.1. real-time sports updates.

AJAX allows sports applications to provide real-time updates on match scores, player statistics, and other sports-related data. Users can see live scores and updates without having to refresh the entire page, making the experience more engaging and up-to-date.

3.2.2. Enhanced Performance and Speed

By making asynchronous requests, AJAX minimizes the need for full page reloads when fetching data. This results in faster loading times and improved performance, crucial for delivering timely sports updates and preventing user frustration.

3.2.3. Interactive Features

Sports applications can leverage AJAX to implement interactive features like live commentary, match timelines, and in-depth player statistics that dynamically update as events unfold. This level of interactivity enhances the overall user experience and keeps sports enthusiasts engaged.

Code Snippet 2: Updating Match Timeline with AJAX

Explanation: In this sports application example, we define an `updateMatchTimeline(matchId)` function that makes an AJAX request to retrieve the match timeline data for a specific match using the provided `matchId`. Once the response is received and the status is successful, we parse the JSON response using `JSON.parse()` and update the match timeline section of the page with the fetched data.

Note: For the complete tutorial and additional insights on Making HTTP Requests with JavaScript, you can access the guide Making HTTP Requests for Sports Data with JavaScript: Introduction to AJAX .

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coding for sports analytics: resources to get started

These days, if you want to work in sports analytics, you need to know how to code. There's really no way around it. And while that can be scary for someone who's never written a line of code before, it's not as daunting as it seems.

The reality is that there are a variety of excellent (often free!) resources for learning how to code. Some of them are very general, some are focused on a specific programming language, and some are focused on a specific use case, like sports analytics.

Below is a list of fantastic resources for learning how to code specifically for sports analytics. Because sports analytics is typically done in either R or Python, most of what you'll find below is focused on those two languages, however, many of the methods used in R and Python can be applied to other languages and use cases beyond sports.

This list is based on (read: "essentially copied directly from") a thread I posted on the Measurables Twitter account earlier this year. I'm duplicating it here simply because some people might find a blog post easier to navigate than a Twitter thread. I'll be keeping this list updated with any new resources I discover.

If you're unsure where to start, reach out! You can find my contact info here .

🏈 Ben Baldwin's guide to using nflscrapR , an R package for NFL data analysis

🏈 Thomas Mock's guide to improving your nflscrapR graphics

🏈 Nathan Braun's eBook "Learn to Code with Fantasy Football"

🏈 Parker Fleming's Introduction to College Football Data with R and cfbscrapR

🏈 Garret McClintock's Introduction to College Football Data Using Python

🏈 Michael Lopez's "R for NFL analysis"

⚾ Daniel Poston's Scikit-Learn (a Python package) tutorial with baseball data

⚾ Brice Russ's "How To Use R For Sports Stats, Part 1: The Absolute Basics" on FanGraphs

⚽ FC Python , a site that teaches Python through soccer data

⚽ FC RStats , a site that teaches R through soccer data

⚽ Devin Pleuler's Soccer Analytics Handbook

⚽ Tom Whelan's "Python for Fantasy Football"

🏒 Evan Oppenheimer's "R for Hockey Analysis — Part 1: Installation and First Steps" on Toward Data Science

🏒 Meghan Hall's "An Introduction to R With Hockey Data" on Hockey-Graphs

🏀 Ryan Davis's tutorials for processing NBA data in Python

🏀 Robert Clark's "Python Sports Analytics Made Simple"

🏑 Chris Fry's tutorial on graphing in R using field hockey data

Updated 9/24/20 with new resources

sportsdataverse

The SportsDataverse's Node.js Package for Sports Data.

Men's College Basketball

It provides users with the capability to access the ESPN API’s men's college basketball game play-by-plays, box scores, and schedules.

College Football

It provides users with the capability to access the ESPN API’s college football game play-by-plays, box scores, and schedules to analyze the data for themselves.

EPA and WPA

It provides users with the capability to access the cfbfastR team's expected points added and win probability metrics.

It provides users with the capability to access the nflfastR team's game play-by-plays, box scores, and schedules. Additionally, the package provides users with functions to access the ESPN NFL API endpoints during live game-play.

It provides users with the capability to access ESPN's NHL endpoints for game play-by-plays, box scores, and schedules.

The sports scene for 2024 is filled with excitement and a...

Livescore apps are a staple in the digital sports landscap...

Stepping into 2024, sports data APIs are set to be the nex...

Top 22 Sports Analytics Projects & Datasets (Updated for 2024)

Sports analytics is one of the fastest growing job segments in Big Data, having grown by 27% over the last decade, according to the Bureau of Labor Statistics.

Sports analysts use various techniques, including statistical and quantitative analysis and predictive forecasting, to make on-the-field and off-the-field decisions. The idea was popularized by Moneyball when the Oakland Athletics used analytics to propel the team to the playoffs.

Sports analytics jobs are highly competitive and require experience in the field. Completing a sports analytics project is one of the best ways to gain recognition and hands-on experience working with sports data and analysis. We’ve compiled some of the best sports analytics projects and datasets to help you practice, including:

• MLB Mlb Sports Analytics Projects

NBA Sports Analytics Projects

Fantasy sports analytics projects, general sports analytics projects, types of sports analytics projects.

Teams can use sports analytics data to perform a variety of analyses. However, the majority of sports data science projects fall into four categories:

1. Predicting outcomes: These projects use data to forecast player or team performance. These models are used to determine the spreads or the results of games.

2. Competitor valuation: These projects value the impact a player has or the strength of a team.

3. Identifying problem areas: These analytics projects determine areas where players or teams can improve. For example, you could analyze a team’s free throw percentage on wins to see the impact improving free throw percentage would have.

4. Analyzing the game: Finally, these projects assess trends in the game, studying strategies or style of play. For example, this NBA data analytics project examined whether the 2-for-1 play was worth it.

MLB Sports Analytics Projects

Almost all MLB baseball teams employ data scientists and statisticians to predict player performance and gain a competitive edge. Baseball analytics projects typically examine performance or gauge the valuation of a team or player. Here are some MLB analytics projects you can try:

1. Swish Analytics Take-Home: Pitch Predictions

This sports analytics take-home from Swish Analytics is more of a shorter data challenge. You’re provided with a table of the pitches from the 2011 MLB season and metadata. And your goal is to build a model to predict the probability of a fastball, slider, curveball, etc.

This take-home challenge requires about 3-5 hours to complete, and it’s used as part of the interview process at Swish Analytics. Ultimately, the challenge asks you to build and evaluate a model that could be used in a production environment, including data analysis, feature engineering, and code assembly.

2. Gauging When Players Peak

This guided baseball analytics project is excellent for beginners. Using MATLAB, the project walks you through importing baseball data, calculating batting statistics, creating visualizations, and analyzing player careers.

Thanks to the step-by-step tutorial, this project provides a solid introduction to MLB stats analysis, and you’ll be able to answer the questions: What defines a great MLB hitter? And at what point do great hitters peak in their careers? If you want to re-create the project, use data from Baseball-Reference .

3. HOF Analysis with Random Forests

This project delves deep into understanding the factors that may influence a baseball player’s induction into the Hall of Fame. By using Random Forests and local importance scores, it offers a quantitative approach to what has often been a subject of speculation.

To begin, fetch the data from the Lahman package and address any missing values during data collection and cleaning. Following this, integrate multiple datasets, merging batting statistics, fielding statistics, and awards to create a comprehensive view. During modeling, Random Forests will be utilized for classification without splitting the data, allowing you to explore the entirety of it. The local importance scores will be especially useful in identifying the most significant variables for this classification.

In your analysis and interpretation, examine the importance of these variables and contrast them across different classes. A deep dive into outliers will further enhance your understanding, providing nuanced insights into the dataset.

4. Predicting World Series Winners

This project comes from the Baseball Data Science blog, which attempts to answer a classic pre-season sports analytics question: Which team is most likely to win it all?

This project uses tree-based models to determine top teams, and after training, it proved reasonably successful. For example, of the Top 5 teams predicted to be World Series winners in 2020, four teams made deep playoff runs, with the No. 2 team (Dodgers, 25%) winning it all.

Another source: See FiveThirtyEight’s MLB ELO ratings and read about how their MLB predictions work.

5. Predicting Pitcher Saves

This project - which you can see in a step-by-step tutorial here - attempts to forecast which MLB pitcher will have the most saves at the beginning of the season.

Using BeautifulSoup to scrape Baseball-Reference data, the author, Ethan Feldman, starts with a simple regression model, which just used the previous season’s saves as the only feature.

Ultimately, the project does prove difficult as there is significant variability in the number of saves, making this an excellent project for further model testing and development.

There are numerous NBA sports analytics projects and questions you can explore. See the top NBA articles on Towards Data Science if you’re looking for inspiration. Or you can follow along with these basketball analytics projects and datasets and create your own:

6. Predicting NBA MVPs

Predicting player performance is a common subject of sports analytics projects, and this one attempts to use machine learning to determine the most likely player to win the MVP award.

You can follow a tutorial , which will show you how to import data and apply various machine learning models, including linear regression, random forests, and XGBoost.

The models presented in this tutorial correctly predicted the 2021-22 MVP winner Nikola Jokic and the other Top 3 spots (however, the No. 3 prediction was No. 2 in the actual MVP race).

7. Predicting NBA Salaries

By leveraging data-driven insights to understand NBA player salaries, we can enhance league competitiveness and provide teams with a more accurate valuation of players. This leads to a smarter and more strategic signing.

In this project , we will delve into NBA salaries, focusing on data from the 2020-21 season onwards and particularly on Free Agents (FAs). The aim is to predict future salaries, giving a true reflection of a player’s worth on the court.

To get started, source your data using BRScraper from Basketball Reference. Next, analyze prevailing trends and apply regression models, including Random Forest and Gradient Boosting. To assess the results, lean on metrics like RMSE and R². Finally, delve into SHAP values to truly understand the key factors determining salaries. The end goal is to equip teams with the insights needed for well-informed contractual decisions.

8. NBA Draft Success Analysis

Drafting NBA players is an inexact science; however, some NBA franchises are more successful than others. For example, the Sacramento Kings have a poor draft record, one reason the franchise has missed the playoffs for 16 consecutive seasons.

This tutorial walks you through determining draft rankings based on player performance, draft position, and other factors.

9. Predicting Double-Doubles

Predicting a double-double based on the number of games played by a player, the number of games played in a season, and other variables is challenging. But this project attempts to predict if one player, Nikola Vučević, will score a double-double in any game.

You can follow this tutorial to build a regression model in R to make such a prediction. Ultimately, the model correctly predicts double-doubles 61% of the time. Enrich the dataset and see if you can improve the model’s accuracy.

10. Simulating NBA Games in Python

This tutorial from Ken Jee evaluates win probability in games based on team points scored and team points against.

You’ll find a variety of sports analytics datasets on Jee’s site you can use. One option: This straightforward model uses only the team’s historical average. That’s why it’s an excellent project for beginners.

If you wanted to take this project further, you could incorporate historical player data to enrich the model.

Fantasy Sports can use data science to give your team a competitive advantage. In particular, most fantasy sports analytics projects look at the line-up and draft optimization, as well as predicting player performance. Here are some projects to try to improve your fantasy sports teams:

11. DraftKings Data Analytics Take-Home

Although this isn’t a project per se, DraftKings analytics take-home will help you practice skills and prepare for a sports analytics interview. We broke this data down into three parts.

1. Data Sense Test - Describe what you see in the chart above.

2. SQL Challenge - Writing queries to pull fantasy sports metrics.

3. Python Challenge - A quick test of your applied programming skills.

Many analyst roles at fantasy sports companies require take-homes like this. However, this is also a short SQL and sports analytics practice assignment.

12. Optimizing Your Fantasy Line-Up

Here’s an approach to daily fantasy football strategy. Build a model to value players based on a “cost per point” metric. This model valuates players by their predicted points divided by their latest salary cost.

However, the next step is determining the optimal line-up, and the author walks through two options: Random Walk or Integer Linear Programming to select the best line-up combination for your team.

Here’s another look at using Linear Programming for fantasy football optimization.

13. Winning English Premier League Fantasy

Bias and player favoritism affect team performance in English Premier League fantasy. Players tend to pick their favorites, and not necessarily footballers with the best ROI.

This tutorial shows you how to build an algorithm in Python to pick the best team , consisting of players with the best ROI.

14. Forecasting Performance Based on Defense

Does the strength of a defense affect a player’s performance in NFL fantasy football?

This fantasy football project found a slight correlation, e.g., when a player plays against a better defense, their production tends to decrease.

Another option: You can take this further and gauge performance against individual defensive players. For example, you could determine wide receiver performance against a top cornerback or quarterback performance against a leading pass rusher.

There’s an endless variety of sports analytics projects you can try. Here are some ideas for performing geographic clustering, predictions with random forests, and creating play-by-play visualizations with NFL data.

15. Realigning Divisions with Machine Learning

Professional sports teams are put into divisions that aren’t always geographically efficient. For example, the Dallas Cowboys play in the NFC East and New York, Pennsylvania, and Washington, DC teams. Using a clustering algorithm, you can build a model to realign teams based on geographic distance.

This tutorial shows you how to use a K-means algorithm to minimize travel distance between teams. Ultimately, you can apply this technique to various geographic clustering problems.

16. Plotting NFL Play-by-Play Data in Python

Check out this tutorial using the Python package nfl_data_py to ingest NFL play-by-play data to build visualizations.

The tutorial walks you through plotting passing yards by quarterbacks throughout the 2021-22 NFL season. However, you can adapt this project to perform a variety of analyses.

You’ll find some ideas for questions you can analyze in NFL data analytics projects , like how defensive statistics affect points allowed or how quarterback play has changed historically.

17. Predicting World Cup Game Winners

With the 2022 World Cup right around the corner, this sports machine learning project is super relevant. In 2018, researchers tested three models for predicting World Cup winners : Poisson regression, random forest, and ranking methods.

Using a random forest model, they simulated the World Cup 100,000 times, using FIFA rating, average team age, and player ability as essential variables. The model performed moderately well, predicting 11 of the Round of 16 teams correctly.

The model predicted Germany would win it all; however, Germany lost in the Group Stage. Also, check out this article on simulating the 2022 World Cup for more ideas.

18. How Golf Drives Affect Scoring

What’s the better approach: Long drives that are crooked or shorter, more accurate drives? Ken Jee looked at this question to see which method strongly affected points. See his video for more explanation about this project .

19. International Football Results from 1872 to 2023

This dataset on international football matches provides an extensive compilation of football matches over a span of more than 150 years. For any football enthusiast, this is a goldmine of data waiting to be uncovered.

To start with the analysis, begin with data cleaning and pre-processing. Even though the dataset appears comprehensive, it’s vital to ensure it’s devoid of missing values, inconsistencies, or duplicates. Doing so greatly refines the precision of the insights that will be derived. After cleaning the data, dive into EDA using histograms, then transition to Temporal Analysis for historical trends.

For deeper insights, you can also study a specific nation’s metrics over time or identify historical rivalries by analyzing performance against specific countries.

20. F1 Performance Analysis

Formula 1 is a sport that’s as much about strategy and data as it is about speed. With races determined by split-second decisions, the information provided in this dataset can offer invaluable insights into the performance of each F1 racers over the season.

To begin, ensure that all datasets are consistent, free from discrepancies, and interlinked correctly. Perform an EDA to visualize metrics such as wins, podium finishes, and other pivotal performance metrics of the racers.

For a more challenging take, you could also analyze a racer’s performance over tracks and identify which tracks they perform the best in.

21. Are sports supplements effective?

In sports, athletes and enthusiasts alike use supplements to improve their overall performance. This dataset bridges the gap between claims of effectiveness and scientific validation.

An Exploratory Data Analysis (EDA) will reveal which legal supplements truly enhance performance, endurance, and strength according to rigorous scientific scrutiny.

22. Toughest Sports in Terms of Skills

Are you wondering which sport is truly the hardest?

This dataset offers a unique perspective by evaluating sports based on various skills. Through detailed analysis, it seeks to quantify the complexity and challenge of different sports, providing data-driven insights into this decades long discussion.

More Data Analytics Projects to Try

Suppose you’re looking for more projects to build your data science portfolio and present your data science project ; look at our list of data analytics projects , which feature more general tasks. You might also try a data science project from our list of 30 ideas and datasets or a Python data science project .

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You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.

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• DSA with JS - Self Paced
• JS Tutorial
• JS Exercise
• JS Interview Questions
• JS Operator
• JS Projects
• JS Examples
• JS Free JS Course
• JS A to Z Guide
• JS Formatter
• JavaScript Tutorial

JavaScript Basics

• Introduction to JavaScript
• JavaScript Versions
• How to Add JavaScript in HTML Document?
• JavaScript Statements
• JavaScript Syntax
• JavaScript Output
• JavaScript Comments

JS Variables & Datatypes

• Variables and Datatypes in JavaScript
• Global and Local variables in JavaScript
• JavaScript Let
• JavaScript Const
• JavaScript var

JS Operators

• JavaScript Operators
• Operator precedence in JavaScript
• JavaScript Arithmetic Operators

JavaScript Assignment Operators

• JavaScript Comparison Operators
• JavaScript Logical Operators
• JavaScript Bitwise Operators
• JavaScript Ternary Operator
• JavaScript Comma Operator
• JavaScript Unary Operators
• JavaScript Relational operators
• JavaScript String Operators
• JavaScript Loops
• 7 Loops of JavaScript
• JavaScript For Loop
• JavaScript While Loop
• JavaScript for-in Loop
• JavaScript for...of Loop
• JavaScript do...while Loop

JS Perfomance & Debugging

• JavaScript | Performance
• Debugging in JavaScript
• JavaScript Errors Throw and Try to Catch
• Objects in Javascript
• Introduction to Object Oriented Programming in JavaScript
• JavaScript Objects
• Creating objects in JavaScript (4 Different Ways)
• JavaScript JSON Objects
• JavaScript Object Reference

JS Function

• Functions in JavaScript
• How to write a function in JavaScript ?
• JavaScript Function Call
• Different ways of writing functions in JavaScript
• Difference between Methods and Functions in JavaScript
• Explain the Different Function States in JavaScript
• JavaScript Function Complete Reference
• JavaScript Arrays
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• Best-Known JavaScript Array Methods
• What are the Important Array Methods of JavaScript ?
• JavaScript Array Reference
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• JavaScript String Reference
• JavaScript Numbers
• How numbers are stored in JavaScript ?
• How to create a Number object using JavaScript ?
• JavaScript Number Reference
• JavaScript Math Object
• What is the use of Math object in JavaScript ?
• JavaScript Math Reference
• JavaScript Map
• What is JavaScript Map and how to use it ?
• JavaScript Map Reference
• Sets in JavaScript
• How are elements ordered in a Set in JavaScript ?
• How to iterate over Set elements in JavaScript ?
• How to sort a set in JavaScript ?
• JavaScript Set Reference
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• Arrow functions in JavaScript
• JavaScript this Keyword
• Strict mode in JavaScript
• Introduction to ES6
• JavaScript Hoisting
• Async and Await in JavaScript

JavaScript Exercises

• JavaScript Exercises, Practice Questions and Solutions

A ssignment Operators

Assignment operators are used to assign values to variables in JavaScript.

Assignment Operators List

There are so many assignment operators as shown in the table with the description.

Below we have described each operator with an example code:

Addition assignment operator(+=).

The Addition assignment operator adds the value to the right operand to a variable and assigns the result to the variable. Addition or concatenation is possible. In case of concatenation then we use the string as an operand.

Subtraction Assignment Operator(-=)

The Substraction Assignment Operator subtracts the value of the right operand from a variable and assigns the result to the variable.

Multiplication Assignment Operator(*=)

The Multiplication Assignment operator multiplies a variable by the value of the right operand and assigns the result to the variable.

Division Assignment Operator(/=)

The Division Assignment operator divides a variable by the value of the right operand and assigns the result to the variable.

Remainder Assignment Operator(%=)

The Remainder Assignment Operator divides a variable by the value of the right operand and assigns the remainder to the variable.

Exponentiation Assignment Operator

The Exponentiation Assignment Operator raises the value of a variable to the power of the right operand.

Left Shift Assignment Operator(<<=)

This Left Shift Assignment O perator moves the specified amount of bits to the left and assigns the result to the variable.

Right Shift Assignment O perator(>>=)

The Right Shift Assignment Operator moves the specified amount of bits to the right and assigns the result to the variable.

Bitwise AND Assignment Operator(&=)

The Bitwise AND Assignment Operator uses the binary representation of both operands, does a bitwise AND operation on them, and assigns the result to the variable.

Btwise OR Assignment Operator(|=)

The Btwise OR Assignment Operator uses the binary representation of both operands, does a bitwise OR operation on them, and assigns the result to the variable.

Bitwise XOR Assignment Operator(^=)

The Bitwise XOR Assignment Operator uses the binary representation of both operands, does a bitwise XOR operation on them, and assigns the result to the variable.

Logical AND Assignment Operator(&&=)

The Logical AND Assignment assigns the value of  y  into  x  only if  x  is a  truthy  value.

Logical OR Assignment Operator( ||= )

The Logical OR Assignment Operator is used to assign the value of y to x if the value of x is falsy.

Nullish coalescing Assignment Operator(??=)

The Nullish coalescing Assignment Operator assigns the value of y to x if the value of x is null.

Supported Browsers: The browsers supported by all JavaScript Assignment operators are listed below:

• Google Chrome
• Microsoft Edge
• Internet Explorer

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Sports Datasets for Data Modeling, Visualization, Predictions, Machine-Learning

🏈 football data sets.

• Detailed NFL Play-by-Play Data 2009-2018 : Regular season plays from 2009-2016 containing information on: players, game situation, results, win probabilities and miscellaneous advanced metrics.

College Football Stats & Data Sets

• CFB Stats – detailed downloadable CFB stats in CSV file-format. Data includes: Kicking Statistics, Kickoff-return Statistics, Kickoff Statistics, Passing Statistics, Punt-return Statistics, rushing, scoring and punting data. Conferences: Sun Belt, SEC, PAC-12, Mountain-West, MAC, Independents, CUSA, Big-Ten, American Conference, Big 12, ACC, And FBS.

🎾 Tennis Data Sets

⚽ soccer data sets.

• UEFA Champions League Dataset : Historical UCL statistics including top goal scorer, all time rankings and more.

FIFA Video-game Data Sets

• FIFA 2022 complete player dataset : Including FIFA 2015-2022 – CSV downloadable dataset with 19k+ players and 100+ attributes.
• Fifa 18  Player Dataset : Attributes for players featured in the 2018 edition of the Electronic Arts game Fifa18 .

Results Data Sets

• Historical soccer results datasets – Historical soccer data sets reference, featuring game half-time and full-time scores, player stats from European and International soccer leagues. back to 1994. Downloadable in CSV format. Updated weekly.
• football.db : Open source, free and public domain soccer database & schema for use in any (programming) language.
• International football results from 1872 to 2018 : 40,000 results of soccer matches from the very first official match in 1872 up until 2018.

World Cup Data Sets

• World Cup Dataset : This data set shows all information about historical World Cups as well as all match data.

🏀 Basketball Data Sets

• Michael Jordan Career Regular Season Statistics by Game (csv)
• Shaquille O’Neal Career Regular Season Statistics by Game (csv)
• WNBA 2014 player stats by game (csv)
• NBA shot logs : Data on shots taken during the 2014-2015 season, which player took the shot, where on the floor was the shot taken from, who was the nearest defender, how far away was the nearest defender, time on the shot clock, and much more.
• NBA Player of the Week Data : Player of the week data from 1984-5 to 2018-9 seasons, scraped from the  Basketball real gm site .
• NCAA Basketball : This dataset contains data about NCAA Basketball teams, teams, and games. It covers play-by-play and box scores from 2009 and final scores from 1996.

🏎️ Racing Data Sets

• Ergast Formula One Dataset : An experimental web service which provides a historical record of motor racing data for non-commercial purposes.
• Formula 1 Race Data : Results dataset covering formula seasons from 1950 to the 2017 F1 season. Data includes constructors, drivers, lap times, pit stops.
• MotoGP Dataset – The Motogp.com statistics database

⚾ Baseball Data Sets

• Historical MLB Scores & Odds Dataset 2010-2020 . Historical odds & scores data from MLB seasons 2010-2020 inclusive – including run-lines, opening and closing moneylines and totals (over/under).
• Lahman’s Baseball Database : A complete history of major league baseball stats from 1871 to 2018, including batting and pitching stats, standings, team stats, managerial records, post-season data, and more.

🏒 Hockey Data Sets

• National Hockey League Player Offensive statistics Data Set (Csv) – yearly offensive statistics of every NHL player from the 1940 season to the 2018 season.

Miscellaneous Sports Data Sets and Databases

• Cricheet.org structured ball-by-ball data for international and IPL cricket matches, 2015 to 2019 inclusive.
• FiveThirtyEight – Data driven sports journalism and analysis with datasets regularly published to Github.
• SPORTS-1M : 1M sports videos of average length-5.5mins labelled for 487 sports classes.
• 120 years of Olympic history : A historical data set on the Olympic Games, including all the Games from Athens 1896 to Rio 2016
• Daily and Sports Activities Data Set : Motion sensor data of nineteen sports activities performed by 8 subjects in their own style for 5 minutes.
• NHL Game Data : Game, team, player and play data including x,y coordinates measured for each game in the NHL in the past 6 years.

Extracting / Scraping Sports Data from websites

• Web Scraping NBA Stats

If you have a sports-dataset you would like us to host, please contact us.

A sports-journalist & Las Vegas native, Thomas lives for professional basketball, NFL, college football and Ice-Hockey. When he is not watching sports (or analyzing sports data), he can usually be found reading a book in a secluded corner. Tom is also a contributor at GamblingIndustryNews.com & Vegas-Odds.com.

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SportsDataverse

About the sportsdataverse.

The first conversation on the SportsDataverse projects happened at the Carnegie Mellon Sports Analytics Conference . The paper our lead engineer, Saiem Gilani, wrote for the conference was selected as the winner for the Data and Software contribution, Open Track for their reproducible research competition.

The conference materials can be found here:

• cfbfastR - An R package to quickly obtain clean and tidy college football play by play data (Data sources: CollegeFootballData, ESPN)
• hoopR - A utility to quickly obtain clean and tidy men's basketball play by play data (Data sources: NBA Stats API, ESPN, KenPom)
• wehoop - A utility to quickly obtain clean and tidy women's basketball play by play data (Data sources: WNBA Stats API, ESPN)
• baseballr - Provides numerous utilities for acquiring and analyzing baseball data from online sources (Data sources: Baseball Reference, FanGraphs, MLB Stats API, NCAA)
• fastRhockey - A utility to scrape and load hockey play-by-play data and statistics (Data sources: Premier Hockey Federation, NHL)
• worldfootballR - allow users to extract various world football results and player statistics from popular football (soccer) data sites (Data sources: FB Reference, Transfermarkt, Understat, Fotmob)
• sportyR - Create scaled 'ggplot' representations of playing surfaces. Playing surfaces are drawn pursuant to rule-book specifications.
• ggshakeR - Analysis and visualization R package that works with publically available soccer data (Compatible data sources: FB Reference, StatsBomb, Understat)
• soccerAnimate - Create 2D animations of soccer tracking data (Compatible data sources: Metrica Sports, Catapult)
• oddsapiR - Access sports odds from the Odds API (Data sources: The Odds API)
• hockeyR - Various functions to scrape hockey play-by-play data (Data sources: NHL, Hockey Reference)
• gamezoneR - Package for working with NCAA Men’s Basketball play-by-play data (Data sources: STATS LLC’s GameZone)
• mlbplotR - Create 'ggplot2' and 'gt' Visuals with Major League Baseball Logos
• cfbplotR - A set of functions to visualize college football teams in 'ggplot2'
• cfb4th - A set of functions to analyze NCAA Football 4th Downs
• softballR - Scrapes and cleans college softball data (Data sources: NCAA, ESPN)
• nwslR - Compiles dataset for the National Women's Soccer League (NWSL)
• usfootballR - MLS and NWSL play-by-play data (Data sources: ESPN)
• recruitR - A college football recruiting package (Data sources: CollegeFootballData, 247sports)
• puntr - Package for puntalytics
• chessR - A set of functions to enable users to extract chess game data from popular chess sites (Data sources: Lichess, Chess.com)

SportsDataverse.org

Python packages.

Documentation

Node.js modules

Connect with us

Work with us

Are you interested in working with sports data, developing open-source packages and helping teach others to do the same? Our group is dedicated to the cause of trying to include people from more diverse backgrounds and underrepresented groups in sports.

Additionally, we are making the publicly available sports data much more accessible to the common person. The desired goal is to create a supportive community that will provide guidance and mentor those who want to be a part of the solution.

Shoot me a message on Twitter - @saiemgilani !

Get in touch

Is there something you would like to work on in the SportsDataverse? Whether it's related to work or just a casual conversation, we are here and ready to listen. Please don't hesitate to reach out to us.