Data Analytics in Cricket — ‘Criclytics’
Introduction of Data Analytics
Data Analytics (DA) is a method of analyzing raw data sets in order to make a decision about the information that actually holds by the given data.
There are few steps by which data can be analyzed –
1. Define the problem
2. Collection of relevant data
3. Group the data according to the measurement strategy
4. Analyze the collected data
5. Interpret the result
Types of Data Analysis
1. Descriptive Analysis:- It describes what has happened over a certain period of time.
2. Diagnostic Analysis:- It describes why something happened.
3. Predictive Analytics:- It describes what is going to be happen in future.
4. Prescriptive Analytics:- It describes the action that can be taken to avoid the future problem.
Introduction to Criclytics:
Data analytics now a day is being used to analyze business strategies. But the same process is also started in other platforms such as sports. The process of data analytics in sports is known as ‘Sports Analytics.’
Cricket is one of the sports where data analytics is used extensively. Making the decision using ‘Big Data Analytics’ in cricket is known as ‘Criclytics.’
India is such a country where people are crazy about cricket. Data Analytics in cricket mostly used during the major cricketing tournament like World Cup, Champions Trophy, IPL etc. By the help of Data Analytics in cricket we can get the past performance review of the team. It also helps to track the performance of an individual player, which helps the team management and selection committee to select the best player for a particular tournament by analyzing the data.
Basics Data Related to Cricket
A single cricket match generates lots of data in the form of bowling figures, batting figures and fielding figures.
1. Data related to Batsman- number of runs scored, number of balls faced, strike rate, number of four’s and sixes scored, number of runs scored against a particular bowler, strike rate against a particular bowler and so on.
2. Data Related to Bowler- number of wickets taken, number of over bowled, number of runs given, bowling average and so on.
3. Data related to Fielder- Number of catches, number of run outs, number of run saved and so on.
Apart from this statistical data there is video data showing how the ball has swung during the initial stages of the game, how the player has responded to a particular delivery etc. This presents a huge opportunity to analyze this data and make meaningful insights which helps in taking correct decisions both on and off the field.
Data played a key role to analyze the performance of a team. One team can analyze the teams with whom they are about to compete. They can analyze opposition team’s strengths and weaknesses through rigorous analysis of their scoring patterns, how they scored their runs, when they were vulnerable during innings. They can identify players who had the skills to counter those opponents in different conditions. They can make sure that they have a clear plan for each moment & each situation.
The other application that is gaining significance is Win and Score Predictor (WASP). WASP predicts the score in the first innings and chasing team’s winning probability in the second innings. By considering various factors such as history of games at that particular venue, weather & pitch conditions, scoring rates, and dismissal probabilities etc, WASP will predict the first innings total and the chasing team’s winning probability. WASP also depends on the match situation.
Conclusion & Discussion
Cricket is a sports that contains lot of statistical data. Those data can be put for proper use to predict the results of the game. So it is obvious that Analytics can play a decisive role in the game of cricket. But analytics can be applied only to a certain extent; it can’t be used for all situations.
