Strava has long been the dominant app for athletes to record and analyse their training. It is renowned for its robust tracking and community features. However, many of its most powerful tools now sit behind a subscription paywall. This model is completely understandable, businesses need to generate revenue but it is an obstacle for users seeking some advanced features but who don't need everything that Strava has to offer. That’s where Sauce comes in. Now Strava have had some financial obstacles in recent years including lay-offs so this shouldn't discourage anyone from taking out a subscription. I should also say my personal preference for analysis remains the Garmin connect web application.
What is Sauce for Strava?
Simon Bromley of BikeRadar recently reviewed Sauce which prompted me to give it a try. Sauce was developed by Justin Mayfield, it is a browser extension designed to extend Strava's functionality with advanced analytical tools. Sauce’s primary features include performance metrics like Peak Power, Normalized Power, Intensity Factor and Training Stress Score (TSS). Additionally, it offers detailed analysis of peak efforts over various durations and a Performance Predictor tool that can help you snag your next KOM/QOM. Now there are lots of other tools that can give this information such as GoldenCheetah but Sauce integrates with the Strava web application directly in your browser.
Sauce may fall foul of recent changes to the Strava T&C regarding third party applications however it remains your data. What is perhaps more controversial is that Sauce isn’t limited to analyzing your own activities. You can use it to examine the performance data of your riding partners or even your favorite professional athletes, provided the data is available.
Features
The home screen adds a drop down on the left which gives you Peak VAM, Peak Power, Peak W/KG, Peak Pace etc. I'm not sure how useful the very short durations are as they are prone to gpx glitches but for 1 hour values for cycling the VAM (climbing speed) and W/KG are interesting metrics. However it does give an idea of how your performance decreases over duration. On the right you see your average power, intensity factor, ftp and how many beers you can drink at the post ride cafe stop!
Below is a cross country ski featuring a 325 meter climb. VAM drops from 850 m/h to 630 m/h over the course of the climb. The climb took just under 30 minutes so the 1 hour figure includes a lot of flat.
Zooming in on the 30 minutes effort gives a bit more information about the climb
and you can analyse further
If you click on a KOM symbol you see a power profile for that effort
and if you click on the segment itself you can access the Performance Predictor If you’re aiming to improve your times on specific segments or race courses. This feature lets you tweak various inputs, such as terrain type, rolling resistance, and aerodynamic drag, to estimate the effort required to achieve a target time. By experimenting with these variables, you can identify the most effective strategies for improvement – whether that’s increasing your power output, reducing system weight, optimizing aerodynamics, or even relying on a tailwind. The tool also provides a breakdown of the forces acting on you during a ride – including gravity, aerodynamics, and rolling resistance – and how much of your power output they consume. This insight helps pinpoint whether focusing on power-to-weight ratio or reducing drag would yield the best results.
A basic version of Sauce is free and doesn’t rely on ads. However, users can support Mayfield’s work through Patreon. For £2 per month, Patreon supporters gain access to additional features like longitudinal fitness tracking. Sauce is available as a browser extension for Google Chrome, Mozilla Firefox including the mobile browser, and Apple Safari.
While all this extra data is interesting I'm not sure how useful it really is. With the Garmin ecosystem I can plan trainings such as 4x8 minute efforts and actually see those in the Garmin Connect application which is of more interest to me. I cam across the Sauce article when looking for this kind of analysis for training files outside of Garmin connect.