Session abstract
What you’ll learn
In software, we optimize systems through measurement, not guesswork. We instrument, measure, debug, and iterate. This talk applies that approach to a surprising area: competitive sim racing. Two laps around the same track. Same car, same driver. One is 1.3 seconds faster. Where did the time go? Modern racing simulators provide detailed telemetry data. This includes speed traces, throttle input, brake pressure, steering angle, lap deltas, sector times, gear changes, and more. In this session, we’ll treat that data like any performance profile. We’ll compare two laps, find lost time, visualize driver inputs, and spot ways to improve. Along the way, we will connect common software practices to racing performance: Profiling becomes lap and sector analysis. Debugging means isolating inconsistent driver inputs. Automated reporting offers faster feedback after each session. Structured testing transforms into controlled setup and driving experiments. This isn’t about becoming a race car driver. It’s a technical session on using developer skills to tackle a real-world performance issue. Sim racing is our case study, and this framework applies wherever performance relies on measurement, feedback, and iteration. Attendees will learn a practical method for turning messy performance data into valuable insights. They will also get ideas for creating small tools that reduce guesswork and speed up improvement. No racing background is needed.