Predicting query performance under concurrency is a difficult task that has many applications in capacity planning, cloud computing, and batch scheduling. We introduce Contender, a new resourcemodeling approach for predicting the concurrent query performance of analytical workloads. Contender's unique feature is that it can generate effective predictions for both static as well as adhoc or dynamic workloads with low training requirements. These characteristics make Contender a practical solution for real-world deployment. Contender relies on models of hardware resource contention to predict concurrent query performance. It introduces two key metrics, Concurrent Query Intensity (CQI) and Query Sensitivity (QS), to characterize the impact of resource contention on query interactions. CQI models how aggressively concurrent queries will use the shared resources. QS defines how a query's performance changes as a function of the scarcity of resources. Contender integrates these two metrics to effectively estimate a query's concurrent execution latency using only linear time sampling of the query mixes. Contender learns from sample query executions (based on known query templates) and uses query plan characteristics to generate latency estimates for previously unseen templates. Our experimental results, obtained from PostgreSQL/TPC-DS, show that Contender's predictions have an error of 19% for known templates and 25% for new templates, which is competitive with the state-ofthe-art while requiring considerably less training time.