If DOTS and Wilks are powerlifting’s answer to bodyweight comparison, Sinclair is Olympic weightlifting’s — and it works on a fundamentally different principle.
The core idea: chase the world record curve
Instead of fitting a curve to average lifter performance the way Wilks and DOTS do, Sinclair is built directly from world record totals across every bodyweight category. The formula asks: “if this lifter competed in the heaviest weight class instead, and lifted at the same relative level compared to the world record in their own class, what would their total be?”
The formula
Coefficient = 10^(A × (log₁₀(bodyweight ÷ b))²) when bodyweight is below b (the reference bodyweight); otherwise the coefficient is exactly 1.0.
A and b are published by the International Weightlifting Federation and recalculated every Olympic cycle from the current world records. Multiply your total by the coefficient to get your Sinclair Total.
Why it changes every four years
World records move. As lifters push totals higher in each bodyweight class — often unevenly across classes — the curve Sinclair is built from shifts too, so the IWF republishes new A and b constants after each Olympic cycle to keep the formula honest to current performance levels. This is a meaningful difference from Wilks or DOTS, which are fixed once published and only revised occasionally.
What this means for you
A Sinclair Total calculated with last cycle’s constants isn’t directly comparable to one calculated with the current cycle’s constants — always check which cycle’s numbers you’re using before comparing scores across years. Our Sinclair Calculator notes which cycle’s official coefficients it’s using and links back to the IWF for verification.
Sinclair vs Wilks/DOTS: not interchangeable
Because Sinclair is built from world-record data rather than a broader competitive population, and applies to the snatch + clean & jerk total rather than a powerlifting total, it isn’t a drop-in substitute for Wilks or DOTS — it’s solving the same problem for a completely different sport with a different underlying dataset.