Cognitive analytics for cricket
From context-lagging information to real-time decision-making, through cricket’s POV
A cricket team’s greatness lies in the number of victories it generates. But what about the players? The context varies considerably from one performance to another. And yet, game’s statistics continue to provide us with single-dimensional evaluations through landmarks. Is it the only way out?
The gap between the statistics needed to understand players and the landmark-driven statistics available has grown deeper and deeper with time. Ironically, in the same period, the game has increasingly relied on human observations to finely evaluate and profile performances, thus generating a huge amount of cricket data to dig into, in the form of ball-by-ball commentary, player memoirs and even autobiographies, besides animated discussions during live televised matches. Any genuine cricket follower can recognize the finer aspects when provided with an opportunity to watch the performances live and can provide analysis ranging from individual players to comprehending match situations, in real-time.
Yet, how much of our cognitive understanding of the game finds its way into the game’s statistics? While many efforts have been made to create analytical layers with available data to a certain extent, they fail to provide an all-round perspective on the game like the game’s followers can.
Humans can provide real-time insights with accuracy because of their ability to churn several data points thrown at them independently, before arriving at conclusions – all with a limited time-span at their disposal. For the game’s statistics to be able to scale these steps, it needs to not only provide unbiased insights (with the combination math and technology) but the ability to consume and provide them in real-time as well, like humans do – through the help of Cognitive Analytics.
How can Cognitive Analytics help develop cricket statistics?
The game’s ever-growing database of literature, videos, and granular data as well as several opinions that it can trigger for each on-field event has the capability to provide the base to derive all-round, context-driven insights and conclusions. Such an effort requires the use of core Cognitive Analytics techniques – Natural Language Processing, Probabilistic Reasoning and Machine Learning amongst other technologies – to efficiently analyze context and find real-time answers hidden within massive amounts of unstructured information.
While descriptive and inquisitive analytics have already found their places given technological developments over last two decades, the need of time-bound predictive and prescriptive analytics has only grown in prominence. Progression to real-time DIPP framework provides a genuine opportunity to apply the principle of learning over knowing. Through incorporating basics of Cognitive Analytics in to its methodology, muCricket manages to connect the gap between the game’s immense database of information as well as its stakeholder’s varied opinions to provide us with real-time contextual decision-making possibilities.
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