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Case Study: Cricket Analytics, the game changer!

Indian Premier League Cricket cup and stadium

IPLT20, the biggest Cricket Festival in India (Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))

You don't play for the crowd, you play for the country.

—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL

About Cricket

It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of money and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more.

The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at $6.7 billion in 2019.

Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes.

Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., ESPN cricinfo and cricsheet. These and several such cricket databases have been used for cricket analysis using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances:

A cricket pitch with bowler and batsmen

Cricket Pitch, the focal point in the field (Image credit: Debarghya Das)

Key Data Analytics Objectives

pose estimator

Cricket Pose Estimator (Image credit: connect.vin)

The Challenges

NumPy’s Role in Cricket Analytics

Sports Analytics is a thriving field. Many researchers and companies use NumPy and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as:

Summary

Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling” or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics.

Diagram showing benefits of using NumPy for cricket analytics

Key NumPy Capabilities utilized