How Data Science is helping teams win at the Cricket World Cup

Remember the days when a giant scoreboard somewhere in the stands of a cricket stadium would be manually updated by a person for every run scored? Gone are those days when runs added gave a different feeling of satisfaction. Enter 21st century Cricket, where technology has changed the way we see, play, or feel.

Data Science, for one, has been key to this transformation beyond keeping track of runs. In a highly competitive world of cricket, Data Science helps to achieve wins even in unlikely situations and in this article we'll see how. But first, here's three things that you need to quickly recap on how Data Science is changing the game.

With the advance of statistical modeling that earlier was used to calculate D/L method, run rates, averages, strike rates, it is today used in Hawk-eye, Hot Spots, or simply, image recognition and segmentation technologies. These algorithms have progressed so far ahead that they record every little data for each ball faced or bowled by a batsman or a bowler. Together, this data allows every cricketer to use it in analyzing their performance and improve their game. While the strengths and weaknesses are deduced from algorithm-based models, later we'll see which of these are greatly used by players and coaches alike to push the boundaries of their game. Predicting the outcome of a game based on certain factors such as the playing conditions, player and team's batting or bowling performances have given a strong backing for statistical models to generate a prediction for which team will win in modern day cricket. This is widely used across the business of sport for media purposes, fan engagement and virtual games, and ofcourse, the big betting market which now has also raised the stakes, just like women's cricket has come a long way. Again, the idea behind using statistical modeling in cricket is not so that we can kill the fun in the game by knowing who will eventually win the game, that is not even possible and is subject to uncertainties. However, statistical models help to change the predicted outcomes by identifying and focusing on what would likely cause the resulting outcome. So just like players, the management can widely use the supervised learning approach to effectively build teams based on the right combination of players and modeling team's strength with a calculated focus on winning. Some of the tools or models used from the field of Data Science are as follows:

Support Vector Machines: Cricket performance is affected by several factors. Amongst the dominant ones that influence the outcome, it's crucial to separate those which effectively can be controlled and also change the game to the player or team's advantage. Support Vector Machine is one such supervised machine learning algorithm used as a cricket performance prediction model to identify low performance actions. It helps to improve results by reducing the number of input vectors, or divisors, by learning from a history of sports data. These Machine Learning experiments are tested and verified before suggesting models for improvement basis player's batting postures, bowling or batting shot actions, fielding movements. SVMs have proved their effectiveness in accurately classifying sports movements for optimal performance basis individual actions.

Statistics for insights: IBM's Data Analytics wing ScoreWithData stunned the cricket fans in 2015 Cricket World Cup when they predicted South African bowler Imran Tahir would be the top performer in the quarter final match. Their model used player records, social media data, world cup performances before forecasting the probability of player performances. Similarly, hiring a dedicated analytics team can help to choose the right players against the opposition and influencing the outcome of a cricket match. It also empowers captains to take appropriate decisions based on pitch reports, weather conditions, opposition's strengths and weaknesses, and individual form before predicting the probability of success.

Finally, before we get to statistical modeling, let's give the due recognition to Python for making all this possible. Python is a popular coding language which helps to download data, clean and sort it in a way that helps to arrange it for modeling. Python goes a long way with its functionality in diving deep into data. For e.g. it can be used to separate and analyse only the necessary data such as 1v1 player face-offs, player v specific teams, etc. By segregating these stats into order, the machine learning models feed on this data to hereafter create the magic of providing insights.

Cricket is growing and Data Science is inevitably adding value that cannot be ignored, just like the other fields. At the same time, data is also growing and it is easier to collect data such as the one from the recent Women's Cricket World Cup which is readily available on Kaggle. Now if you're interested in finding out how this data is processed and analysed, you can enroll for a course in Skillslash's Data Science and AI which covers a wide range of concepts across Python, Statistics and Machine Learning to name a few.

Click here: https://skillslash.com/data-science-course to find out more about the program which offers guaranteed real work experience upon completion of the course and lots more.