The contest was scheduled for two days, although rain meant that it ran into three. Five thousand people attended the St George's Club for the first day's play, and over the course of the match, an estimated 100,000 (approximately 4.2m in today's money) was gambled on the outcome. The Canadians, somewhat exhausted and bedraggled by a journey that had brought them up the St Lawrence River and across Lake Ontario by boat before catching trains to New York, batted first and made 82 against an US attack that consisted solely of two men born in Yorkshire: Sam Wright and Harry Groom. Canada's star was David Winckworth, who made 12 with the bat before sending down a few round-arm thunderbolts of his own, taking four wickets as the Americans were dismissed for 64. Winckworth again top-scored with 14 as Canada stacked up another 63, setting the USA 82 to win.

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome. CAMP employs data mining methods and enables effective data-driven decision-making for selection and drafting, coaching and training, team line-ups, and strategy development. CAMP incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate CAMP on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Man of the M}atch (MoM). The top two rated players by CAMP match with MoM in 83\% of the 961 games. Thus, the CAMP rating of the best player closely matches that of the domain experts. By this measure, CAMP significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (DLS) method.

The evolution of digital technology and the increasing popularity of sports inspired the innovators to take the experience of users with a proclivity towards sports to a whole new different level, by introducing Fantasy Sports Platforms FSPs. The application of Data Science and Analytics is Ubiquitous in the Modern World. Data Science and Analytics open doors to gain a deeper understanding and help in the decision making process. We firmly believed that we could adopt Data Science to predict the winning fantasy cricket team on the FSP, Dream 11. We built a predictive model that predicts the performance of players in a prospective game. We used a combination of Greedy and Knapsack Algorithms to prescribe the combination of 11 players to create a fantasy cricket team that has the most significant statistical odds of finishing as the strongest team thereby giving us a higher chance of winning the pot of bets on the Dream 11 FSP. We used PyCaret Python Library to help us understand and adopt the best Regressor Algorithm for our problem statement to make precise predictions. Further, we used Plotly Python Library to give us visual insights into the team, and players performances by accounting for the statistical, and subjective factors of a prospective game. The interactive plots help us to bolster the recommendations of our predictive model. You either win big, win small, or lose your bet based on the performance of the players selected for your fantasy team in the prospective game, and our model increases the probability of you winning big.

India lost two early wickets in the first session of the initial innings of the ICC Test Championship Final. There is already much history about him and England. This History was enough to create the hype around him. On the first note, the ground was the same where India loses their debut world cup winning chance in the captaincy of Kohli. Southampton has seen India losing enough times.

This work presents an analysis of the Duckworth-Lewis-Stern (DLS) method for One Day International (ODI) cricket matches. The accuracy of the DLS method is compared against various supervised learning algorithms for result prediction. The result of a cricket match is predicted during the second inning. The paper also optimized DLS resource table which is used in the Duckworth-Lewis (D/L) formula to increase its predictive power. Finally, an Unpredictability Index is developed that ranks different cricket playing nations according to how unpredictable they are while playing an ODI match.

Whether we realise it or not, technology such as artificial intelligence and VR are already ubiquitous in our everyday lives. While the broader debate swirls around the extent that machines will replace human function, the underlying technology is already at work, altering the way we live and work. In many ways, the sports industry is at the cutting edge of this change. It has embraced technology to the point that many teams now rely on it to help them win games, improve players' ability and coaching, manage their operations as well as interact with their fans. The fourth industrial revolution is upon us, bringing with it unlimited insights from its interconnected everyday data.

Deep learning has proved its supremacy in the world of supervised learning, where we clearly define the tasks that need to be accomplished. But, when it comes to unsupervised learning, research using deep learning has either stalled or not even gotten off the ground! There are a few areas of intelligence which our brain executes flawlessly, but we still do not understand how it does so. Because we don't have an answer to the "how", we have not made a lot of progress in these areas. If you liked my previous article on the functioning of the human brain to create machine learning algorithms that solve complex real world problems, you will enjoy this introductory article on Hierarchical Temporal Memory (HTM). I believe this is the closest we have reached to replicating the underlying principles of the human brain. In this article, we will first look at the areas where deep learning is yet to penetrate.

In the professional sport of cricket, batting order assignment is of significant interest and importance to coaches, players, and fans as an influencing parameter on the game outcome. The impact of batting order on scoring runs is widely known and managers are often judged based on their perceived weakness or strength in setting the batting order. In practice, a combination of experts’ intuitions plus a few descriptive and sometimes conflicting performance statistics are used to assign an order to the batters in a team line-up before the games and in player replacement due to injuries during the games. In this paper, we propose the use of learning methods in automatic line-up order assignment based on several measures of performance and historical data. We discuss the importance of this problem in designing a winning strategy for cricket teams and the challenges this application introduces to the community and the currently existing approaches in AI.

Elite sport is now awash with data. As athletes and management look to gain every competitive advantage they possibly can, they are gathering information about all aspects of individual and team performances in booth training and matchplay, as well as a raft of other metrics. The confidence with machine learning often needed to get coaches to the pinnacle of their field means that some are still reluctant to cede ground to algorithms and machines, but inherent prejudices and the fallibility of human memory mean that the brain is an inefficient tool for processing complex information, especially in the time required during sports games. This is especially true for team sports, where they must monitor a number of players at once. Machine Learning can be applied to sports in a range of ways, with data now accessible about almost anything.

Microsoft's attempt to use machine learning to improve on the Duckworth-Lewis method in cricket has been dismissed by the current custodian of the system. For the uninitiated, the Duckworth-Lewis method, or D/L method for short, is used to calculate the score that the team batting second in a limited overs cricket match needs to reach if the match is affected by rain. For example, if the team batting first scores 400 in 50 overs for five wickets, but rain reduces the second team's innings to 40 overs, the D/L method may put forward a score of 300. It was invented by statisticians Frank Duckworth and Tony Lewis and was first used in 1997 in a match between England and Zimbabwe. It is now regularly used at matches at all levels.