Ranking　problems　are　commonly　encountered　in　practical　applications,　including　order　priority　ranking,　wine　quality　ranking,　and　piston　slap　noise　performance　ranking.　The　responses　of　these　ranking　applications　are　often　considered　as　continuous　responses,　and　there　is　uncertainty　on　which　scoring　function　is　used　to　model　the　responses.　In　this　paper,　we　address　the　scoring　function　uncertainty　of　continuous　response　ranking　problems　by　proposing　a　ranking　model　averaging　(RMA)　method.　With　a　set　of　candidate　models　varied　by　scoring　functions,　RMA　assigns　weights　for　each　model　determined　by　a　K-fold　crossvalidation　criterion　based　on　pairwise　loss.　We　provide　two　main　theoretical　properties　for　RMA.　First,　we　prove　that　the　averaging　ranking　predictions　of　RMA　are　asymptotically　optimal　in　achieving　the　lowest　possible　ranking　risk.　Second,　we　provide　a　bound　on　the　difference　between　the　empirical　RMA　weights　and　theoretical　optimal　ones,　and　we　show　that　RMA　weights　are　consistent.　Simulation　results　validate　RMA　superiority　over　competing　methods　in　reducing　ranking　risk.　Moreover,　when　applied　to　empirical　examples-order　priority,　wine　quality,　and　piston　slap　noise-RMA　shows　its　effectiveness　in　building　accurate　ranking　systems.