Bing Sports serves Instant Answers to sports-related queries from hundreds of millions of users every day with a latency of <100ms. Answering sports-related factual questions is challenging because of the dynamic nature of the domain, contextual nature of queries, and a large variety of possible intents. In this paper, we discuss various blocks of our scalable multilingual sports answer triggering pipeline. The pipeline mainly comprises of two main stages: Query Understanding (QU) and Ranking. QU leverages blocks like domain classifier, named entity recognizer, intent classifier, and entity linker to detect queries for which a sports answer should get triggered and also accurately identify user intent and entities in queries. The ranking stage is driven using blocks like pre-web ranker, answer lookup, and post-web suppression and reranking for ranking various candidates and choose the best one to be shown as the final triggered sports answer. We leverage various heuristics, deep learning models like UniLM and large language models like GPT-4o for both QU and ranking. Lastly, we present evaluation results for various blocks in our pipeline on a set of ~80K Bing queries from May 2024.