Data is one of the most valuable asset in present times and data analytics is gaining importance in almost every field, including sports. After being extensively used in sports like baseball and football, it is now a hot property in cricket as well.
Back in the days where Test cricket was the only format, data involved just plain numbers such as scores and wickets. Then as ODIs came into existence, parameters such as strike rates and economy rates originated. Gradually, video analysis too became an integral part of team strategies.
Now with the advent of T20 cricket, ball-by-ball data streams are being used to generate real time insights. Modern-day analytics makes you move from plain numbers to more complex parameters, which take into account a number of factors to be calculated. This gives a more meaningful insight.
Here are a few areas where data analytics is playing a key role in modern day cricket, especially in the various T20 leagues:
Using Data Analytics to Build a Team:
Gone are the days when franchises looked for big names and their brand values. With the emergence of data analysis, almost all teams now look at stats and get players accordingly. Value added to the team is of more importance than name and reputation. In auctions, if a certain team fails to buy a particular player, they look for someone who has similar stats.
Runs, strike rates, wickets and economies are not enough to judge a player. For instance, economy rates of a player is compared with other bowlers in his team and a normalized economy rate is calculated which gives more precise information about the bowler’s abilities.
Someone with an economy of 8 where most of his fellow bowlers had economies in excess of 9 or 10, brings in more value than someone with an economy of 7 where the remaining bowlers go for 6 an over.
Same goes for strike rates and averages for batsmen as well as bowlers. Someone with a strike rate of 120 in a 130-run innings is more valuable than one with 150 strike rate in a 220-run innings.
Hence, as we can see, raw data are not good enough parameters to judge a player’s value. Other factors such as pitch conditions, opposition strength and performance of other teammates contribute a great deal in assessing the reliability of a player and data analytics makes it possible to take these factors into account.
Improving Individual Performances (Technical Analysis):
Performance analysts have become the right hands of coaches, providing insights based on data and video, on where a particular individual’s strength and weakness lies, which particular players he is weak against and why, why is someone out of form and how can he get back to form and many other aspects.
No doubt coaches and mentors can do the same job with their experience and expertise in the technicalities, but where analytics brings the difference is breaking into the minute details such as angle of the bat, timing of release of the ball and shuffling of the batsmen in the crease with exact precision.
With the help of an individual’s historic data, and his current data, analytics makes it easier to find out why someone is out of form or why someone is doing better than earlier. Similarly, aspects from other individuals with similar traits can be inculcated into certain players to improve their skills and accuracy.
Team Selection (Tactical Analysis):
Not only analytics help in choosing players and improvement of individuals, it also helps in forming strategies based on the opposition. Quite often team selections are done on the basis of results of the data analysis.
A certain bowler might be good against a particular kind of batsmen and if the opposition does possess such batsmen and if the bowler has historically good record against them, then there are high chances that he will play the game.
If a batsman who otherwise is not a regular member in the playing XI, has historically good record against the opposition bowlers, he might be slotted in to play.
Forming strategies becomes a lot easier with the emergence of video and data analysis. The analysts find out the strengths and weaknesses of oppositions and help a great deal to the coaching staff.
Minute details such as pitch maps, seam movement, percentage of balls left or hit, areas on pitch where someone has better strike rate are something only analytics can provide. With so much details in hand, the planning becomes easier and all boils down to the execution on the field.
Real time insights are an integral part of the strategies as well. Information on a team’s performance in a certain period of play has a big impact in the approach of the opposition.
For instance, a team chasing 200 might not start hitting every ball from the beginning, if it knows the opposition has weak middle and death overs bowling and have conceded runs at, say 15 per over in the last 5 overs. On the other hand, a team might look to attack from the beginning, chasing 130, if the opposition has the record of, say 6 wickets in overs 12-20.
Injury Prediction and Fitness Tracking:
Using data it is now possible to predict if a particular player is going to have an injury. There are a number of fitness tracking devices available now, which makes it possible to collect data related to the physical condition of a player. This data is then used to measure the fitness levels of players.
Many a times players hide injuries, fearing about their place in the team and various other factors, which puts a team at risk. But with data, the analysts can easily find if a certain player is fully fit for the match, or if he/she is approaching a major injury based on his historic data.
Apart from the above mentioned areas, data analytics has also resulted in increased viewership and helped in the commercial aspects of the game. Based on the past activities of viewers the broadcasters develop ways to make the game more attractive and interactive with the viewers.