Racing Case Study

Telemetry Analysis and Race Strategy Breakdowns

When I first started looking at Racing Case Study materials for Formula 1 operations, the biggest gap wasn't in the data itself. It was in understanding how engineers actually communicate between sectors during a race weekend. The onboard telemetry, pace maps, tire wear projections — they're all there in publicly available footage and timing screens, but putting them together into a coherent case study that someone outside the paddock could follow requires a specific workflow. I spent about three seasons building these breakdowns for a semi-professional racing newsletter. The process is tedious and most of the value is in the filtering. You take a race weekend, you pick two or three strategic moments where decisions diverged from the baseline model, and you explain why those divergences mattered. A typical F1 race weekend generates roughly 3 terabytes of data per car. Your job isn't to parse all of it. Your job is to find the one sector where the tire degradation curve flattened unexpectedly and the pit wall either caught it or missed it.

Building a Racing Case Study From Public Data

Here's how I approached it. Start with the race itself and pull the timing gaps sector by sector. Gap charts are free on the FIA's public results portal and on some independent timing sites. What you're looking for is inconsistency. If Car A and Car B run within 0.1 seconds per sector for six laps and then suddenly Car A gains 0.4 seconds in Sector 2 while Sector 1 stays flat, something changed. Tire compound? Draft resistance? Brake temperature management? That's your entry point. Next, overlay the pit strategy. You can find this on the official timing page — stop durations, pit lane speed, tire compound choices per stint. Cross-reference the gap anomalies with pit window timing. In one case I worked on at circuits in Brazil and Singapore, a team chose to delay their second stop by three laps because their degradation model predicted a longer tire life on the hard compound than the simulation suggested. The strategy worked until lap 42 when the tires dropped off sharply. I explained this by comparing the live lap times sector-by-sector against the pre-race simulation projection, which was available from the team's own press release. The delta between projected and actual degradation was roughly 0.3 seconds per lap after that threshold, which cost them about 1.2 seconds overall and one position. The mistake most people make is treating the race result as the story. The story is always in the decision tree. Why did the engineer on the radio call for an undercut instead of staying out? What was the tire wear number at that moment? Was the track temperature dropping? These are the questions that separate a Racing Case Study from a race report.

I also use a simple spreadsheet method. Columns for lap number, sector times, tire age, compound, track temperature, and fuel load. Rows are each car. It takes maybe 20 minutes per race to populate if you're working from public sources. The actual analysis — finding the inflection points, comparing them to the strategy model, and writing it up — takes longer, usually about 45 minutes to an hour depending on how many cars you're tracking. Two cars is manageable. Four or five starts to get messy and you need to decide which ones actually matter for the story you're telling. One edge case I ran into repeatedly: tire degradation isn't linear. Every rookie analyst I mentored learned this the hard way. They'd plot tire wear against lap time deltas and expect a straight line. It's never a straight line. There are plateaus where the tires hold grip surprisingly well, then sudden drops. I found this out after I spent two weekends building degradation curves that looked clean in the spreadsheet but didn't match reality when I watched the onboard footage. The workaround was simple — add a visual check. Watch the racing lines, the throttle application patterns, and the braking points on the onboard. If the driver is lifting early in a corner that they weren't lifting in earlier laps, that's tire degradation. The numbers don't always show it clearly, but the driving style changes first. Another thing I learned the hard way: fuel load matters more than people give it credit for. A car running 15 kilograms less fuel will lap faster, and that speed isn't just from weight savings. It affects brake temperatures, tire wear rates, and even downforce balance. When you're comparing two cars on different strategy paths, you have to account for fuel. Otherwise your gap analysis will be off by 0.1 to 0.2 seconds per lap, which compounds over a stint.

Limitations, though. Public data has blind spots. You won't see real-time tire pressure readings or individual brake disc temperatures unless the team shares them. You'll miss the nuance of a suspension change made in the garage between laps. The strategy radio conversations are private. What you get is the surface picture — lap times, gaps, pit stops, compound choices. It's enough to build a solid case study, but it's not the full picture. I've had readers tell me my analysis was wrong because they knew something I didn't — a mechanical issue, a DRS malfunction, a driver complaint about brake feel. Those things don't show up in the timing sheets. If you're serious about this, I'd recommend supplementing public data with onboard video. You can find most races on the F1 YouTube channel with onboard cameras. It gives you context that numbers alone don't. Watching a driver struggle with rear grip in a specific corner while the timing screen shows a 0.15-second sector drop tells you more than the number does. There's no single downloadable template that will do this for you. The closest thing I found was a basic timing spreadsheet from a motorsport analytics site, but it was outdated and didn't account for fuel load or track temperature variables. I built my own from scratch, and I'd suggest doing the same. The structure is straightforward — lap number in column A, sector 1 time in B, sector 2 in C, sector 3 in D, tire age in E, compound code in F, track temp in G. Add columns as you need them. It's flexible and you own the format.

One more thing that surprised me early on: the best Racing Case Study topics aren't always the races with the most drama. The most interesting strategy decisions happen in races where nothing went dramatically wrong. A clean race where two cars ran identical stints but one gained positions through micro-adjustments — brake bias changes, lift-and-coast techniques, tire pressure tweaks — that's where the engineering story lives. The crash-filled races are easier to write about but harder to learn from because the decision tree gets torn apart by chaos. I've written case studies covering everything from tire strategy in wet conditions to fuel management in sprint races. The method stays the same. Find the divergence. Explain the why. Check your assumptions. Move on to the next race.