Fix convex sets $\Delta,\Pi$ and let $r: \Pi \times \Delta \in [0,\infty]$ be linear (i.e., concave and convex) in its first parameter for every fixed second parameter.
I'm looking for a situation where 1) the corresponding zero-sum game has no value, i.e., $$ \sup_{\pi \in \Pi} \inf_{\delta \in \Delta} r(\pi,\delta) \neq \inf_{\delta \in \Delta} \sup_{\pi \in \Pi} r(\pi,\delta) $$ but 2) there exists a minimax solution, i.e., there exists $\delta_0 \in \Delta$ such that $$ \sup_{\pi \in \Pi} r(\pi,\delta_0) = \inf_{\delta \in \Delta} \sup_{\pi \in \Pi} r(\pi,\delta). $$ (Note that I've swapped the order of variables from the usual game presentation to match the statistical setting when $r$ is interpreted as (average) risk.)
Clearly, such a situation cannot arise when both $\Pi,\Delta$ are compact and $r : \Pi \times \Delta$ is concave-convex because such games always have values by the minimax theorem. When I've come across counterexamples to a game having a value, this usually also results in there being no minimax solution.
Am I missing an obvious obstruction, or can someone point me to an example, or to where I can read more about existence of minimax solutions when the game has no value?