Unlike in statistical compression, where Shannon's entropy is a definitive lower bound, no such clear measure exists for the compressibility of repetitive sequences. Since statistical entropy does not capture repetitiveness, ad-hoc measures like the size $z$ of the Lempel--Ziv parse are frequently used to estimate it. The size $b \le z$ of the smallest bidirectional macro scheme captures better what can be achieved via copy-paste processes, though it is NP-complete to compute and it is not monotonic upon symbol appends. Recently, a more principled measure, the size $γ$ of the smallest string \emph{attractor}, was introduced. The measure $γ\le b$ lower bounds all the previous relevant ones, yet length-$n$ strings can be represented and efficiently indexed within space $O(γ\log\frac{n}γ)$, which also upper bounds most measures. While $γ$ is certainly a better measure of repetitiveness than $b$, it is also NP-complete to compute and not monotonic, and it is unknown if one can always represent a string in $o(γ\log n)$ space. In this paper, we study an even smaller measure, $δ\le γ$, which can be computed in linear time, is monotonic, and allows encoding every string in $O(δ\log\frac{n}δ)$ space because $z = O(δ\log\frac{n}δ)$. We show that $δ$ better captures the compressibility of repetitive strings. Concretely, we show that (1) $δ$ can be strictly smaller than $γ$, by up to a logarithmic factor; (2) there are string families needing $Ω(δ\log\frac{n}δ)$ space to be encoded, so this space is optimal for every $n$ and $δ$; (3) one can build run-length context-free grammars of size $O(δ\log\frac{n}δ)$, whereas the smallest (non-run-length) grammar can be up to $Θ(\log n/\log\log n)$ times larger; and (4) within $O(δ\log\frac{n}δ)$ space we can not only...