Did AI Predict the Winner of the Cricket World Cup?
There are few things in the world that cannot be measured. And everything that can be measured, can be predicted. The world of sports is ripe with data for analyzing, making it the ideal playground for predicting the outcomes of matches using Artificial Intelligence (AI) and Machine Learning (ML). And with the Cricket World Cup in England just concluding, we can see how effective and accurate these technologies really are.
The World Cup of Data
Cricket has always been a numbers game, but it wasn’t until now that we’ve consolidated all of this information to give the data context to every aspect of the game and predict the outcome of a tournament. The recently launched Superstats platform combined historical data from 10 participating teams over the course of the last 11 world cups and tested it against the 2015 tournament to gauge accuracy. They also utilized two different strategies for predictive analytics – identifying patterns in a classifier approach versus training data and prioritization for each feature in a neural network approach. And the results were truly astounding.
In the ensemble classification method, England was correctly predicted as the likely winners based on their recent performance. However, India was (marginally) a tournament favorite based on the neural network predictions. Even though India narrowly missed out to New Zealand for a place in the final, the AI was not able to account for the temperamental British weather which may have contributed in part to their loss as the match was forced into a rain-extended 2-day battle.
Data science is not merely helpful for predicting winners but it also helps extract valuable insights for other use cases. From players and coaches to sponsors and advertisers, data science has made a huge difference in how we watch, play, and enjoy cricket.
If you can’t measure it, you can’t improve it!
This is not the first time data science has been used in sports, or even in cricket. Machine Learning and AI have been analyzing teams’ performances to supplement coaching techniques, helping referees and umpires make informed decisions on the field, and enhancing the spectator experience for viewers at home and in the stadiums, for at least the last 20 years. But what does the future hold?
Fitness devices such as Fitbit have been around for a while now, but more sophisticated devices are currently being developed to fit the needs of specific sports. Furthermore, data is being gathered from individual players to accurately assess their contribution to the team, whether they play in an offensive, defensive, or opportunistic role. Leveraging AI, data scientists can derive correlations between qualitative traits and quantitative variables, and then measure those variables to predict the corresponding qualitative value of players.
AI can also be used to identify patterns in opponents’ tactics, which was a defining feature at this year’s Wimbledon tennis championship. But the potential of AI can go beyond the players by providing spectators a new level of fan engagement and help automate sports journalism to expand coverage. We also know that we are just beginning to extrapolate the potential of AI and there are sure to be many more exciting data science developments in the sports over the coming years.
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