  We find that improvements in speedrunning world records follow a power law
pattern. Using this observation, we answer an outstanding question from
previous work: How do we improve on the baseline of predicting no improvement
when forecasting speedrunning world records out to some time horizon, such as
one month? Using a random effects model, we improve on this baseline for
relative mean square error made on predicting out-of-sample world record
improvements as the comparison metric at a $p < 10^{-5}$ significance level.
The same set-up improves \textit{even} on the ex-post best exponential moving
average forecasts at a $p = 0.15$ significance level while having access to
substantially fewer data points. We demonstrate the effectiveness of this
approach by applying it to Machine Learning benchmarks and achieving forecasts
that exceed a baseline. Finally, we interpret the resulting model to suggest
that 1) ML benchmarks are far from saturation and 2) sudden large improvements
in Machine Learning are unlikely but cannot be ruled out.
