When the list of players across IPL teams come out, you often see some surprise packages. Well, this year most people were wondering why Royal Challengers Bangalore (RCB) would pay such a huge price for Glenn Maxwell. We were left wondering why Rajasthan Royals (RR) would pay such a huge price for Chris Morris. While the surprise and shock are relevant, the answers to the rationale lies in the numbers and its analytics.
Yes, Analytics plays a huge role in the selection of teams and in the overall performance. Data is analyzed across the minutest possible details such as a deviation in the stance of a batsman, bat speed, etc.
Let’s look at a few key metrics that the IPL teams consider keenly before selecting a player or creating their wish list of players before an auction.
If they are creating a list of batsmen, the parameters are as below.
1. Pinch Hitting Abilities: This is the ratio of the boundaries (fours + sixes) hit by a batsman to the overall deliveries played by him.
For example, Virat Kohli has faced 4496 balls in all editions of IPL till now. He has hit 503 fours and 201 sixes. The total comes to 704 boundaries. Pinch-hitting abilities score for Virat would be 704/4496 = 0.156.
Let’s compare this with Rohit Sharma. He has faced 4004 balls. He has hit 458 fours and 213 sixes. The total boundaries come to 671.
Pinch-hitting abilities score for Rohit would be 671/4004 = 0.1675 which is just a tad higher. You can feel very happy if you are a Mumbai Indians fan!
2. Finishing capabilities: This denotes the number of innings where the player has finished the match or has remained not out through the innings. It is a simple ratio of no. of innings in which the batsman was not out to the total innings player.
Finishing capabilities = No. of not out innings/ total innings played.
On this parameter, if we compare the above two modern-day greats of the game, let’s see how they fare?
Virat has remained not out on 30 out of 184 innings so his finishing capabilities score would be 30/184=0.163
Compare this with Rohit. He has remained not out in 28 out of 195 innings. So, his score would be 28/195=0.143 which is a bit less than Virat’s score. RCB fans, it’s your turn now to cheer. You have pulled one back!
3. Fast Scoring Ability = This measures the total runs scored divided by the total balls played. It is nothing but the strike rate.
On this parameter in all IPLs put together, Virat’s score is 130.74 while Rohit’s is 130.62. Nothing much to differentiate but Virat is slightly ahead.
4. Consistency score = This measures the total runs scored by the batsman divided by the total number of times he got out.
Consistency Score = Total runs scored/ Total number of times Out
Virat has scored 5878 runs and has been dismissed 154 times across all IPL seasons. His consistency score would be 5878/154=38.17
Rohit has scored 5230 runs and has been dismissed 167 times across all IPL seasons. So, his consistency score would be 5230/167=31.31.
Virat has a significant upper hand if this parameter is considered.
5. Running between wickets= While IPL is mostly about fours and sixes, running between the wickets is crucial. It enables teams to rotate the strike, take advantage of left-hand and right-hand combinations and keep the scoreboard ticking when the boundaries dry up.
Here’s how the score or efficiency of running between wickets is calculated:
(Total Runs – (Fours + Sixes)) / (Total Balls Played – Boundary Balls)
Let’s look at how these two favorite cricketers of most Indians fare on this parameter.
Virat has scored 5878 runs across all IPLs and has hit 704 shots which have been a four or a six. He has played a total of 4496 balls to score these runs.
The Running between Wickets score for Virat is =
(5878-(704))/(4496-704)) = 5174/3792=1.364
The Running between Wickets score for Rohit is =
(5230-(671)/(4004-671)) = 4559/3333=1.367
While there is nothing much to choose from, isn’t it surprising to see that Rohit Sharma scores a tad higher on this parameter?
These scores across the metrics are then compared with benchmarked ranges and weights are assigned to arrive at a list of players (batsmen, bowlers, all-rounders, and wicket keepers) that the IPL team wishes to select.
What’s the takeaway?
Data Analytics, which we often thought, would be used by businesses only, finds great use in sports too. It’s fantastic to see how analytics would help teams to pick the perfect players and execute a winning strategy.
