‘DRS (Decision Review System)’ Technology in CRICKET

Photo by Manoranjan on Unsplash

DRS (Decision Review System) is a technology used in cricket to review umpire decisions on the field. It was first introduced in Test cricket in 2008 and has since been implemented in limited-overs formats. DRS uses various technologies, including ball-tracking and hot-spot, to help umpires make more accurate decisions.

The basic idea behind DRS is to give teams a limited number of opportunities to challenge umpire decisions they feel are incorrect. Each team is allowed to make a maximum of two unsuccessful reviews per innings in Test cricket, and one unsuccessful review per innings in limited-overs cricket.

Photo by Mudassir Ali on Unsplash

The technology used in DRS includes the following:

1. Ball-tracking technology: This technology uses multiple cameras to track the path of the ball. It can determine the trajectory of the ball from the point of release to where it bounces, helping to predict where it would have gone had it not hit the batsman or any other object.
2. Hot-spot technology: This technology uses thermal imaging cameras to detect heat generated by friction. It can show where the ball has made contact with the bat or pad, helping to determine whether a batsman is out or not out.
3. Snickometer: This technology is used to detect faint sounds made when the ball hits the bat or pad. It can help to determine whether the batsman has made contact with the ball and whether he has edged it to the wicketkeeper or slips.
4. UltraEdge: This technology is an improvement on Snickometer, using a combination of sound and video evidence to determine whether the ball has touched the bat.

To implement DRS in cricket, the following steps are typically taken:

1. A team can ask for a review if they believe that an umpire’s decision is incorrect.
2. The on-field umpire makes a signal to indicate that the review has been requested.
3. The third umpire, who is usually located off the field, reviews the footage and makes a decision based on the technology available.
4. The third umpire informs the on-field umpire of his decision, and the on-field umpire makes a signal to indicate whether the decision has been upheld or overturned.

To demonstrate how DRS works in practice, let’s take an example of a hypothetical situation in a cricket match.

Imagine that a batsman has been given out lbw (leg before wicket) by the on-field umpire. The batting team thinks that the decision was incorrect and decides to use a review. Here’s how the process would unfold:

1. The batting team requests a review of the decision.
2. The on-field umpire makes a signal to indicate that the review has been requested.
3. The footage from the ball-tracking camera is examined to determine whether the ball would have hit the stumps. If the ball was going to hit the stumps, the decision is upheld. If not, the decision is overturned and the batsman is not out.
4. The footage from the hot-spot camera is examined to see whether the ball made contact with the bat or pad. If the ball makes contact with the bat, the decision is overturned and the batsman is not out. If not, the decision is upheld.
5. The footage from the Snickometer or ultra-edge technology is examined to determine whether there was an edge. If an edge is detected, the decision is overturned and the batsman is not out. If not, the decision is upheld.
6. The third umpire informs the on-field umpire of the decision, and the on-field umpire makes a signal to indicate whether the decision has been upheld or overturned.

Here’s an example of some Python code that could be used to analyze data from DRS:

let’s assume that we have a dataset containing information about all the DRS reviews taken in a cricket tournament, including the decision made by the on-field umpire, the result of the review, and the reason for the review.

import pandas as pd

# Load the dataset into a pandas dataframe
df = pd.read_csv('DRS_reviews.csv')

# Calculate the overall success rate of DRS reviews
success_rate = df['Result'].value_counts(normalize=True)['Upheld']

print(f"The overall success rate of DRS reviews is {success_rate*100:.2f}%")

# Calculate the success rate of DRS reviews for each reason
reason_success_rates = df.groupby('Reason')['Result'].value_counts(normalize=True).unstack()

# Print the success rates for each reason
print(reason_success_rates)



# @suyashchandrakar

In this code, we first load the dataset into a pandas data frame using the read_csv function. We then calculate the overall success rate of DRS reviews by counting the number of reviews that were upheld (i.e., the decision of the on-field umpire was overturned) and dividing it by the total number of reviews.

Next, we group the reviews by the reason for the review and the result of the review using the groupby function. We then use the value_counts function to count the number of reviews for each combination of reason and result and use the unstack function to reshape the resulting data frame so that the success rates for each reason are shown in columns.

Finally, we print the overall success rate and the success rates for each reason.

Note that the DRS_reviews.csv the file should be replaced with the actual filename and path of the dataset being used.

This code could be used to gain insights into the effectiveness of DRS reviews in the tournament. The overall success rate of reviews could indicate whether DRS is improving the accuracy of umpire decisions. The success rates for each reason could help identify areas where DRS is particularly effective or ineffective. For example, if reviews for lbw decisions have a much lower success rate than reviews for caught-behind decisions, this could suggest that there are issues with the ball-tracking technology used in DRS.