2025 Trailblazer Michael, a data analyst at heart, shares how he prepares for the 2025 Highball to Thurmond 50 Mile. First he explains his method. Then you can dig into his spreadsheet!
Data-Based Ultramarathon Preparation: Spreadsheets for Success
Preparing for an ultramarathon isn’t just about logging miles—it’s about creating a strategic plan based on real data. In this post, I’ll walk through my methodical approach to preparing for Highball to Thurmond, breaking down how I transform raw course information into a precise race-day strategy.
I work as a data scientist and I find comfort in information. One of the aspects of ultrarunning that I enjoy a lot is that you get to be both the one designing the experiment and the agent in the experiment.
Creating Your Master Aid Station Table
The foundation of any ultra plan is understanding the course infrastructure using what is provided by the race. I start by using the aid station chart provided on the race website which includes:
- Aid station locations and distances
- What’s available at each station (food, water, toilets)
- Crew access and parking information
- Drop bag availability
This gives me a bird’s-eye view of the course and helps identify potential challenges. For example, I can immediately see that some aid stations have no parking, while others have limited facilities. This is important for knowing when I will see my crew and how much I need to be carrying with me at any given time.
Breaking Down Segment Distances and Timing
Next, I transform the basic aid station table into a more detailed planning tool by calculating:
- Distance between each aid station (segment distance)
- Estimated time to complete each segment based on goal pace
- Predicted arrival times at each aid station
This helps me visualize my journey through the course and identify particularly challenging segments that might require special attention. For this race, I’m aiming for a 10-hour finish, which means maintaining roughly a 12-minute mile average.
Calculating Precise Fueling Requirements
Once I have my timing estimates, I can calculate exactly what I’ll need for each segment:
Hydration Needs
- Based on 500-600ml per hour (using 600ml soft flasks)
- Adjusted for weather conditions and segment difficulty
- Example: A 5.9-mile segment taking ~70 minutes might require 1-2 bottles
Sodium Requirements
- Using 500mg sodium tablets
- Targeting 200-300mg per hour based on conditions
- Example: For a 2-hour segment, I’ll need approximately 2 tablets
Carbohydrate Planning
- Aiming for ~60g carbs per hour
- Using 300g pouches of carb mix
- Planning when to switch pouches based on consumption
This precise calculation prevents both underfueling (which leads to bonking) and overpacking (which adds unnecessary weight).
Learning from Others’ Race Data
Raw numbers only tell part of the story. To refine my plan, I analyze Strava data from runners who’ve completed the course at my target pace. This provides insights into:
- Where other runners slowed down (indicating challenging terrain)
- Realistic pacing strategies that have succeeded
- Segment-by-segment adjustments needed
This real-world validation often reveals nuances that aren’t evident from the course profile alone—such as technical sections that force even strong runners to slow significantly. After finding the Strava race data that matches my goals, I make revisions to my segment pace expectations.
*Note – I asked Michael for further inside into this step. He said, “I used Andy Lancos’ Strava data. I first looked at results from last year. Found someone who ran in my goal time. Looked up their Strava. Scrolled and scrolled to get to the race (I wish that was easier) took screen shots of the mile splits.”
Adjusting for Elevation and Terrain
With segment distances and timing established, I incorporate elevation data:
- Total elevation gain/loss per segment
- Identification of major climbs and descents
- Adjusted pacing for significant elevation changes
For steep climbs, I plan to slow my pace and potentially increase my fueling. For technical descents, I factor in both the physical demands and the mental focus required.
Creating a Weather-Contingent Gear Plan
As race day approaches, I develop a weather-based gear strategy:
- Base clothing plan for expected conditions
- Contingency items for weather shifts
- Strategic drop bag placement for gear changes
Temperature swings can dramatically impact performance, so I prepare options that can be adjusted at key drop bag locations.
Finalizing the Race Strategy Document
Finally, I compile all this information into a single, comprehensive race strategy document that includes:
- Segment-by-segment timing goals
- Precise fueling and hydration plans for each segment
- Gear changes planned at specific aid stations
- Special notes for challenging course sections
This becomes my race-day guide—a data-driven plan that removes as much guesswork as possible and tries to front load a lot of the mental work and decision making which allows me to focus on execution.
Open Questions Still Remain
While the table provided is a pretty good estimate of my plan there are still a few open questions and data points. One is thinking through if I feel like I could start the race in road shoes or if I would transition back to road shoes towards the end. I could also work in more specific elevation number per segment. Some questions around fueling and pace for certain segments can now be better answered in training by finding routes that match a given segment from my table.
The Advantage of Data-Driven Preparation
This methodical approach might seem excessive to some, but in ultrarunning, preparation is everything. By basing my race strategy on concrete data rather than vague estimates, I minimize race-day surprises and maximize my chances of success.
These days with the use of AI a lot of this is easier than it was. I could provide race details, Strava data, my per hour fueling requirements and my goal time and it could create the table for me. It even wrote a lot of this blog post.
The beauty of this system is its adaptability—the framework remains consistent, but the specific numbers can be adjusted for any course, distance, or goal time. Whether you’re tackling your first 50k or your tenth 100-miler, this data-based approach provides a reliable foundation for ultramarathon success.
What’s your approach to race planning? Do you use a data-driven strategy or prefer a more intuitive method? Share your thoughts in the comments!
Check out Michael’s planning Sheet. Note that there are two tabs. Make sure you stroll all the way left; there’s a lot of helpful information that you can likely apply to your own planning!