How does AI make watching IPL so much more fun?
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The Indian Premier League and its Romance with Technology

Since its inception, the Indian Premier League has taken the cricketing world by storm. Pooling the game’s biggest talent with a mix of underdogs in a thrilling 20 over format has elevated the level of competition and prestige that comes with the game. Every year, the IPL strives to bring the best of the on-field action and intensity directly to the fans. Excitement and high-intensity possibilities ranging from momentum derailing maiden overs to massive ‘knocks’ that can win a match have always been the linchpin of the IPL. And every year the league delivers across the board. It is a sporting event that connects not only our country but the entire world in a slew of breathtaking moments. 

The Premier League (IPL)has always strived to make the game and viewer experience better with each installment. Keeping this in mind, the league has never shied away from the implementation of new technology. The use of a Snickometer to detect the ball against edges of bats, or hot spot tracking that uses heat signatures to determine where the area of impact of the ball has been essential in umpiring decisions since their use.

The use of AI in the game

From the time of its maiden run until the present year, IPL has used techniques that increase the inclusivity of viewers and the game. Some of such systems include :

The hawkeye:

This technology developed by a former player, Dr. Hawkins, using a set of cameras generates a path of the ball from the bowler to the wickets. It takes into account factors like ball spin, seam, swing, etc. All this information is fed into an AI system that will provide a predictive analysis of the ball’s path. This has proven essential in the leg before wicket reviews, stumping, third umpire decision making, etc.

Player performance and selection:

There exist certain wearable devices that can determine player impact and performance. This proves to be a vital point in player selection and match fitness. Moreover, on-field training analysis is being used to design training regimens to improve performance and reactions under pressure.

Statistical Analysis and Generation:

The IPL has spearheaded the game analysis arena by providing statistics on run rates, bowling economy, fielding prowess, most valuable player, etc. All of these statistical analyses are ML generated and help in providing a comprehensive, real-time view of the current state of the game. Thus, adding to the rush when a batsman with a high run rate is pitted against a bowler with an impeccable economy. The speculation then runs wild, the excitement – even wilder.

Match Analysis and Prediction:

There has been a large amount of statistical analysis done on batting and bowling since the league started. Thus, there exists a surplus of data for an AI system to accurately predict based upon run rate, bowler performance, the projected number of runs. This however coupled with the star sports initiative to track player fitness, running between the wickets, catches to wicket conversations, fielding analysis fed to a machine-learning software, can generate an outcome of the match before the last ball is bowled.

Future of AI in the IPL

It is important to note that simple statistics is not where the brilliance of AI in the IPL ends. Given that there have been several editions of the League coupled with data from domestic and international fixtures it is now possible to predict a team’s performance and how they could fare against a certain selection of players. Data processing platforms like ADVIT can be used to train neural networks to ultimately make various calculations. For example – neural networks can be trained using videos of reaction times, running between the wickets, batting styles against a particular kind of delivery to determine a player’s best performances. Then through the use of the said neural networks can be used to determine the ideal team against the opponent. 

Moving forward, the League will want to field the best players. A team’s chance of hoisting the gold shall be dependent upon putting together a unit that works well in tandem and at its best. In this regard selection committees can use data feeds from, domestic and international matches, and theory machine learning to determine players in their best form.

Every stakeholder, be it team owners, players, advertising companies are looking to use the use of AI to their benefit. For example, the IPL allows slots for advertising during gameplay. This, through the use of Deep Learning and AI technology, can be used to display targeted ads, based on region, preferences, online habits, etc. All this will require the neural network to be ‘trained’, which can easily be achieved by the use of deep learning platforms that offer such usage.

Even though the perception of AI is that it is merely involved in algorithm analysis in computer science has been deeply rooted, its use in cricket has propelled its importance and exposure to new heights. The way we watch a match, through a screen, in a stadium has greatly been changed. The statistics of player performance and the likelihood of a team winning generated through ML create an explosive environment. The fans of the winning team – invigorated with excitement by the projection. while the fans of the team that’s slated to lose – hoping for that one underdog moment that could get them across the line – that is cricket. And all of this in part has been brought to the fore, more than ever, through the exemplary use of AI.

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