To say that Indians enjoy cricket would be an understatement. The game is played in almost every corner of India, rural or urban, popular with young and old alike, linking billions in India, unlike any other sport. Cricket enjoys enormous public coverage, and a large sum of money and fame is at stake. Technology has practically changed the game over the past few years. Audiences are spoilt for options of online media, matches, affordable access to smartphone live cricket viewing, and more. Everyone likes to go back in time, analyze how teams and players worked, and speak about how new achievements were made. In reality, fans derive information from records before the tournament begins and forecast wins and losses. No wonder that Big Technology has already taken over the sporting landscape.
As the coach who made laptops and data processing trendy in cricket, everyone recalls Bob Woolmer. Another man who firmly believes in the strength of data is Greg Chappell. However, using data in cricket is only in its infancy and much less mature than in any of the other sports. In comparison, it is the national teams that, at the moment, are genuinely exploiting analytics. Domestic and club teams currently do not have adequate data of reasonable quality to achieve practical insights. Cricket is a numbers game – the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the amount of times a batsman reacts to a form of bowling attack in a certain manner, etc. The opportunity to delve into cricketing statistics by effective analytics methods, fuelled by computational computing technologies, enhances efficiency and research business prospects, general market, and cricket economics.
There is a new entrant to the scene with the introduction of the Decision Review System (DRS) on the global level, the next generation of analytics by sensors, set to put teams, commentators, and viewers closer to the location where the real activity takes place. Today, cricket game records and statistics are available in rich and almost limitless troves, e.g., ESPN Cricinfo. These and other cricket datasets have been used for cricket research using the most modern machine learning and statistical simulation algorithms. Media, television outlets, and elite game-related sporting bodies use technologies and analytics to determine crucial criteria for increasing the likelihood of winning matches by looking at factors like batting performance moving average, score forecasting, gaining insights into fitness and performance of a player against different opposition, player contribution to wins and losses for making strategic decisions on team composition.
To boost overall team performance and increase winning opportunities, sports data analytics is used in cricket and many other sports.
Even after the game adjusts strategies for economic gains and development by the squad and related firms, real-time data analytics will help gain insights. In addition to historical research, statistical models are used to assess potential match results that involve big crunching and know-how in data science, visualization techniques, and the ability to incorporate new findings in the analysis.
There are ten full-time Member States, 57 affiliate Member States and 38 associate Member States under the International Cricket Council (ICC), which rounds up the number to 105 Member States. Imagine the amount of data produced with the ball-by-ball data of 5,31,253 cricket players in approximately 5,40,290 cricket matches at 11,960 cricket grounds worldwide every day for 365 days. Cricket Analytics will come into play and keep track of any of these players’ results, calorie intake, fitness levels, crowd engagement, and even more in search of better success on the pitch.
The perspectives that one can obtain from Cricket Analytics offer ample context knowledge for broadcasters, athletes, and fans to make the necessary decisions regarding the success of the squad. Any of the sophisticated high-tech instruments used in the field today to catch and track the rampant amounts of cricket data are CCTV cameras, portable computers, and sensors. The data analytics team analyses and accrues the previous data on the results, player profiles, player outcomes, and others to develop the team’s optimal configuration and answer questions such as whom to select for the last one.
Statistics on Outcomes:
On a scorecard, each ball that is bowled and any shot that is played or fielded is registered. To evaluate the documentation for batsmen, bowlers, fielders, and also Umpires, this detail is then aggregated and sliced & diced. Players use the research to develop their game and strategize against the opponent and establish the narration of commentators’ events. It also applies to fan matches to compare & contrast with other teams, through commercial brands, etc. Data scraping is the process of importing information from a website into a spreadsheet or local file saved on your computer. ESPN Cricinfo’s Statsguru is among the many places where you can extract the data and do wonders with it.
Assessment from video feeds:
For the experts in the squad to understand their players’ bowling, batting, and fielding strategies, video-based research began mainly as a teaching method. It also became a guide to consider the opposing players’ strengths and limitations and map game plans as the broadcasting feed became available. For pundits and spectators, ball monitoring offered a new way of looking at the ball’s movements on the pitch and the batsman’s ability to react to the bowling. Visualizations like a Wagon wheel and pitch maps offered valuable insights into the players’ strengths/shortcomings and strategies adapted to the opposition’s methods.
We are expected to see even more innovative cricket solutions around player monitoring, incident detection, etc. With more computer vision strategies being implemented in the game.
Analytics based on a sensor:
The need for evidence has also grown from “What & Who” (outcomes) to other issues such as “Why & How” (diagnosis) and “When & Where” with insights complementing the players’ intuition on the field (moments). To consider their energy levels and fitness, players use smart vests during training. We have sensor-based technology such as Snickometer, Hotspot, and LED bails on the DRS front that help us understand an incident’s origin and make the right decision. On the bowling front, the Speed Gun tests the bowler’s release speed of the ball. There were also attempts to explain the ball’s seam and spin movement using sensors inside a cricket ball. One of the new developments in smart sensors’ use to help the batsman appreciate the data and observations behind a cricket shot is on the batting side. The PowerBat technology from Spektacom uses sensors in the cricket bat to recognize relevant parameters such as bat pace, twist, ball effect on the bat, etc. A batsman will use this knowledge to understand the effectiveness of his/her batting technique against a specific bowler. This encourages players and spectators to look at the effect of the shot and diagnose and consider why the result occurred.
Data Cleaning and preprocessing:
Beyond the traditional test match format, IPL extended cricket on a much larger scale. The number of matches played in different formats per season has risen. So have the technology, algorithms, and emerging technology to study sports data and simulation models. Field visualization, player monitoring, ball tracking, the player shot analysis, and many other factors involved in the distribution of the ball, its angle, spin, velocity, and trajectory are needed for cricket data analysis. Together, both of these aspects have increased the difficulty of cleaning and preprocessing information.
There can be a wide range of factors in cricket, like any other sport, relating to monitoring different numbers of players on the field, their strengths, the ball, and several options for possible acts. The sophistication of data analytics and modeling is directly proportional to the form of predictive questions asked during the study, which are strongly dependent on the representation of data and the model. In terms of computing, problems get much more complicated. Data comparisons as forecasts of dynamic cricket action are sought, such as what would have happened if the batsman had struck the ball at a certain angle or velocity.
Finding out the hidden parameters, patterns, and attributes that lead to a cricket match’s outcome helps the stakeholders notice game insights that are otherwise hidden in numbers and statistics. This is a fascinating field with a plethora of opportunities.
An increasing number of sports analytics practitioners combine Python and R to make sense of numbers in their jobs. Are you also a cricket fan, and does analytics interest you?
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– Neha Shetty