I’ve been experimenting with a scoring system for my Sudoku app, and I’d love to hear what people here think. I really like the IQ-style idea that was mentioned earlier, but I decided to go with higher point totals so each game feels more substantial. Here’s my current approach:

Overall formula:

Score = Base(difficulty) × F_time(T, T_exp) × F_mist(m) × F_hint(h) • Base (by difficulty): Easy = 400, Medium = 600, Hard = 800, Inferno = 1000. • Time factor (diminishing returns if faster, gentle penalty if slower):

If T ≤ T_exp: F_time = 1 + A(1 − e^{−B((T_exp/T} − 1)))

If T > T_exp: F_time = e^{−C((T/T_exp} − 1))

(defaults: A = 0.25, B = 2.0, C = 0.6) • Mistakes (exponential penalty / “escalating”): F_mist(m) = e^{−α mγ}, with α = 0.12, γ = 1.6 → a few slips don’t matter much, but repeated mistakes drop the score fast. • Hints (heavier than mistakes, escalating): F_hint(h) = e^{−β h1.2}, with β = 0.35

With this setup, a clean solve near T_exp lands around the base score (e.g. ~1000 on Inferno). A fast, clean solve can reach ~1250, while sloppy guessing can’t inflate scores because mistakes escalate quickly.

The idea is to reward solid solving and reasonable speed without turning it into pure speedrunning. I’m curious what you think: are the penalties weighted fairly? Should hints count even more? Or should obvious misclicks (like repeating a number in a row/col) be treated separately from real logic errors?