While watching a cricket match, we interpret and cherish the game through different ways we perceive the game content:
Personal knowledge of the game with regards to batting, bowling, umpire’s signals.
On-screen graphics helping us with score highlights.
Visual and Audio effects from the stadium like applause and appeal.
Commentary from the expert (PwC, 2019).
The primary analysis of statistics like runs, batting, bowling average and centuries, have been in place since quite some time now. However, lately, the level of details and analysis in the game has reached new heights with technology. Artificial Intelligence (AI), together with Big Data analytics tools, is already creating tremors in the technological world. Few have been illustrated below-
Virtual Umpires – Tell me what to do?
It is not just the players; Artificial Intelligence (AI) is also helping the umpires in the game. It will not be incorrect to refer to this as virtual umpires. The third umpire relies on AI-driven visualisation to assist on-field umpire in taking the decision, which is otherwise difficult or rather impossible in many cases for the human eye to catch minute split second details.
On-Field AI Infusion and Future
Who could have thought that sensors can be embedded in the essential playing equipment – bat and ball!! Kookaburra smart ball is the world’s first microchipped cricket ball, manufactured by leading cricket ball company – Kookaburra in unison with tech innovators SportCor. The embedded chip collects the real-time data related to revolutions, ball speed at release, pre-bounce and post-bounce. This data set can be later used as real-time feedback for the coaches and players to strategise and enhance their performance, by broadcasters and match officials to change the way spectators experience the game. Is it not marvellous – a talking ball!!
Prediction Tool – Which team will win?
The technology has advanced to such level that we even have tools like Winning and Score Predictor (WASP) and Cricmetric systems that can predict which team will win based on past performance records of the two teams and pitch conditions. Even in India, these technologies are extensively worked upon, and one such case is a collaboration of sports media firm ESPNCricInfo and Indian Institute of Technology (IIT), Madras to launch a tech platform “Superstats” – a combination of luck index, forecaster and smart stats. The relevant database has around ten years of detailed data which are processed using algorithms based on machine learning. Prof. Raghunathan Rengaswamy, department of Chemical Engineering from IIT Madras, India, who led this project with ESPN team referred the tool as - "While there might be many interpretations for luck, these algorithms rationalize and consistently quantify luck events so that a whole tournament with matches that occurred in disparate circumstances could be compared in an ‘apples-to-apples’ fashion."
Customer Experience
Customer Experience reached a new height during 2017 International Cricket Council (ICC) Champions trophy with the launch of the mobile responsive, widget-based website – live blog coverage, predictive analysis, and live audio commentary. The website was also supported by ICC App compatible on Apple and Android, sharing fans with the exclusive contents of their favourite team. It was the first for sports federations to give fans the personalised experience via Direct Messages on Twitter with @ICC (ICC, 2017a; ICC, 2017b). A similar experience was made available for the fans in Australia, feature – “Monty”, a machine learning model developed in collaboration between Foxtel, Mindshare and Google. Monty alerts the fans up to 5 mins in advance of a wicket falling, so that fan does not miss the moment live, a classic example of engaging the fans.
Hey Duckworth-Lewis-Stern Method, here I come!!!
It was in 2010 ICC World Twenty20 match, where West Indies defeated England even after scoring just 60 for 2 in six overs, in reply to England’s 191 for 5. In a similar rain-curtailed match, Zimbabwe had to score 44 runs in five overs to win over Sri Lanka’s 173 (ESPNcricinfo, 2010). The Duckworth-Lewis-Stern (DLS) method, in the ICC cricket matches since 1997, has been tailored for one-day matches. However, now when Twenty20 matches are the flavour of the season, is it not time to adjust the DLS method with new match format? Yes, and what better way than AI (machine learning based model) to achieve it. Clearly, it's a case of "one-size-doesn't-fit-all".
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