I am looking for a closed-form formula for the approximation of an inverse Sinc as $$y=\frac{\pi x}{sin(\pi x)}$$ for x between -0.85 and 0.85, with 1% accuracy.
My first inclination is to do a polynomial curve-fit but wanted to check if anyone already had the solution or a better approach?
I am ultimately using this to approximate X as a function of Y for: $$K Sinc(X)=Sinc(Y)$$
As JeanMarie suggested in his answer, Pade approximants are probably the most interesting approach.
A simple and rather accurate one would be $$\dfrac{\pi x}{\sin(\pi x)}=\frac{1+\frac{13 \pi ^2 }{396}x^2+\frac{5 \pi ^4 }{11088}x^4} { 1-\frac{53 \pi ^2 }{396}x^2+\frac{551 \pi ^4}{166320} x^4}$$ which shows a maximum error at the upper bound $0.85$ (the approximated value being $\approx 5.87170$ for an exact value $\approx 5.88196$.
Using it, if you need sot solve for $x$ equation $$K \text{sinc}(x)=\text{sinc}(y)$$ you will need to solve $$K \frac{ 1-\frac{53 }{396}x^2+\frac{551 }{166320} x^4}{1+\frac{13 }{396}x^2+\frac{5 }{11088}x^4} =\text{sinc}(y)$$ which is just a quadratic equation in $x^2$.
Defining $a=\frac{\text{sinc}(y)} K$, the solutions will then be $$x^2=\frac{6 \left(\pm\sqrt{35} \sqrt{-3985 a^2+130862 a+25583}-455 a-1855\right)}{75 a-551}$$
For illustration purposes, let us use $y=\frac 12$ and $K=3$. This will give as possible positive solutions $x=2.31087$ and $x=6.34097$ while the exact solution would be $x=2.31069$.
Edit
You may be intested by comparing Taylor expansions $$\text{sinc}(x)=1-\frac{x^2}{6}+\frac{x^4}{120}-\frac{x^6}{5040}+\frac{x^8}{362880}-\frac{x^{10}}{3 9916800}+O\left(x^{12}\right)$$ $$\frac{ 1-\frac{53 }{396}x^2+\frac{551 }{166320} x^4}{1+\frac{13 }{396}x^2+\frac{5 }{11088}x^4}=1-\frac{x^2}{6}+\frac{x^4}{120}-\frac{x^6}{5040}+\frac{x^8}{362880}-\frac{x^{10}}{1 005903360}+O\left(x^{12}\right)$$
If you are concerned by the range $0 \leq x \leq 0.85 \pi$, a curve fit would give $$\text{sinc}(x)=\frac{ 1-0.132083 x^2+0.00311873 x^4}{1+0.0345805 x^2+0.000554186 x^4}$$ for a maximum error lower than $10^{-5}$ everywhere.