The new 12th man
  • 18th June, 2019
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The new 12th man

By Dr Taryn Morris

2 min read

The 2019 Cricket World Cup is off to a blazing start in England and Wales, with men's teams from ten cricket playing nations vying for the ultimate title. Cricket is a game that is no stranger to statistics with terms like average run rate, average strike rate and required run rate mentioned in almost every over* of the game.

Classically, team management would select the eleven person lineup and decide on the batting order based on a player's previous performance statistics. However, as the world of available data and analytics expands, it seems there is a new kid in town that is shaking up the conventions of cricket. It has been reported that for the first time ever, the Indian Selection Committee took into account advanced analyses that were presented to the five person Indian selection team by the team’s data analyst.

Furthermore, data scientists across the globe have been flexing their data science muscles to predict the tournament outcome. By taking factors such as player performance, toss, time of day, home ground advantage and even weather, advanced modelling is being used to predict the likely successes of the teams. Using 58 different metrics and advanced machine learning techniques (ensemble classification and neural networks), one data scientist has predicted that India will take the trophy over the English team under the noses of home ground fans to become the 2019 Cricket World Cup Champions. Furthermore, Pakistan is predicted to edge New Zealand out of the rankings by securing 3rd place in the playoffs. With the increasing influence in each match, one has to question whether data science is cricket’s new 12th man.

The use of data analytics and data science in sports is becoming common place. In 2011, the movie Moneyball shed light on the role that analytics played in Major League Baseball. Based on a true story, a Yale Economics graduate takes a team from zero to hero in one season based on a specific formula of their player statistics. The use of data science has come a long way since the release of that movie with Markov models being used in the NBA and deep learning algorithms used in the NFL. With the rise of degrees, journals, and jobs in sports analytics it seems that the use of data science in sports is only just beginning.

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*An over is a grouping of 6 balls with 50 overs available to each team in the one day international format.

Photo by Alessandro Bogliari on Unsplash

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About Author

Dr. Morris has extensive experience in research, teaching, communication and policy, which equips her to approach problem solving with multiple lenses to find implementable solutions to any given problem. Taryn brings strong a statistical background, nifty data skills and sharp critical thinking to her work. She has a keen interest in using data science for good, particularly to address the increasing environmental problems facing our society today.

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