The Problem DLS Solves
Imagine India scores 300 runs in 50 overs. Australia, in reply, are 150/2 after 25 overs when rain washes out the rest of the game. Who wins? A simple run-rate comparison would suggest a tie, as both are scoring at six runs per over. But this isn't fair.
Australia still has eight wickets in hand — a massive advantage. They were on track to score much more than 300. The DLS method was created to solve this exact problem: how to create a fair target or outcome in a limited-overs match that is shortened by weather or other interruptions. It provides a more statistically sound solution than just comparing run rates.
Cricket's Two Most Valuable Resources
The core principle of DLS is that a batting team has two resources to score runs: the number of overs they have left to bat and the number of wickets they have in hand. The more of each resource a team has, the greater their potential to score. A team that is 50/0 after 10 overs is in a much stronger position than a team that is 50/5 after 10 overs, even though their run rate is identical. The first team has all 10 wickets as a resource, while the second has already lost half of its run-scoring potential. DLS mathematically quantifies the value of these two resources at every stage of an innings.
The 'Resource Percentage' Chart
This is where it gets a little technical, but the concept is simple. The DLS method uses a complex chart that assigns a percentage value to the combined resources (overs remaining and wickets lost) a team possesses. At the start of a 50-over innings, a team has 100% of its resources available (50 overs, 10 wickets). As overs are bowled and wickets fall, this resource percentage depletes. Losing an early wicket is more 'expensive' in terms of lost resources than losing a wicket in the final over, when there are few overs left to utilise anyway. This chart, based on analysis of hundreds of thousands of limited-overs matches, is the engine of the DLS calculation. It’s not a guess; it's a statistical model of a team's scoring potential.
Calculating a New Target
So, how is a target revised? Let’s go back to our example. Team A (India) scores 300, using up nearly 100% of their resources. Team B (Australia) is set to start their chase, but rain delays the start and their innings is reduced to 40 overs. According to the DLS chart, a 40-over innings with 10 wickets in hand represents, for example, 89% of a full 50-over innings' resources. Therefore, Team B's target is adjusted to be 89% of Team A’s score. The new target wouldn’t be 301, but something closer to (300 x 0.89) + 1 = 268 in 40 overs. If the interruption happens mid-innings, the calculation finds the 'par score' – the score the chasing team should have reached with the resources they have used so far. If they are above par, they are ahead of the game. If they are below, they are behind.
From Duckworth-Lewis to Stern
The method was originally devised by two English statisticians, Frank Duckworth and Tony Lewis, and was officially adopted by the ICC in 1999 as the D/L method. For years, it was the global standard. In 2014, Professor Steven Stern of Australia became the custodian of the method and made some refinements. He updated the statistical model to better account for modern scoring rates, especially the high-octane aporoach seen in T20 cricket. The formula was updated to reflect that teams can score much faster in the final overs than they could in the 1990s. This updated version is what we know today as the Duckworth-Lewis-Stern, or DLS, method.
Is the DLS Method Actually Fair?
DLS is often criticised, usually by the team that loses. Common complaints are that it doesn’t account for the specific strengths of a batting lineup (e.g., a team with explosive finishers) or for game 'momentum'. It is purely a statistical model. Sometimes, the revised targets can feel incredibly steep, especially if the chasing team has lost early wickets before the rain interruption. However, despite these criticisms, it is globally accepted as the most accurate and fairest system available. It’s a logical solution to an illogical problem. Without it, the only alternative in many rain-affected games would be a 'no result', which is far more unsatisfying for players and fans alike.
