Strava Deliberately Removes 4 Million Activities from Leaderboards – Here’s Why and How

Strava has just removed millions of activities from its public leaderboards. Here’s how they identified and removed cheats and errors.

The Clean-up Explained

At the end of January 2026, Strava engineers announced the completion of a global recalculation of every single ride and run leaderboard. The purpose was to remove incorrectly recorded activities, including rides uploaded as e-bike activities.

A similar process was used to the one employed back in May 2025, when activities recorded in cars were similarly expunged.

This wasn’t a routine update. It involved a full backfill of the top results for every segment on Strava, using advanced machine learning models. The goal was to better distinguish legitimate human-powered efforts from activities assisted by motors or recorded under incorrect sport types.

How Many Activities Were Removed?

• 2.3 million e-bike activities
• 1.6 million vehicle activities

The effect was that approximately 293,000 athletes were rightfully restored to the Top 10 leaderboard positions.

Perhaps surprising, is the sheer number of erroneous activities that exist and no doubt some remain that can’t quite be so easily flagged as incorrect.

How the Model Works

Strava has shared with this site information on how its exclusion algorithm works. Piecing it all together, the model works like this:

1. Calculate a series of 57 “features” from every run and ride activity uploaded to Strava.
   • This includes simple calculations like averages and variances of velocity or acceleration.
   • More complicated ride characteristics are also used, such as “jerk” (the derivative of acceleration) and “average VAM on climbs” (a cycling-specific metric based on human performance limits). The model also considers the effect of varying gradients and momentum.

1. Strava then utilises an in-house feature, “Sendrix Coefficient,” developed based on testing of a fast staff cyclist (aka Jimi Sendrix). This assesses how quickly someone can accelerate from a dead stop to 20mph (ca. 32 km/h) and how often this can be repeated before fatigue, helping differentiate accelerating cars from fatiguing cyclists.
2. The model uses xAI (explainable Artificial Intelligence) SHAP values to understand how each of the 57 features contributes to the assessment, weighing data towards “cars” or “bikes”. While individual features might have overlap between vehicles and bikes, looking at all features together clarifies the differences. For instance, a top speed of 80mph (ca. 129 km/h) would heavily weigh towards “car,” whereas 25mph (ca. 40 km/h) might not be as informative (every leaderboard cyclist can reach that speed on the flat), requiring the model to reference other features.
3. Finally, individual scores are determined for each before summing them to determine a probability on a scale of 0 to 1 that a vehicle is present.

And there we were, thinking that all Strava had to do was check if we were going 50mph (ca. 80 km/h) uphill.

How the Model Is used

The model has been trained and used over several months and applied to new activities being uploaded. The model has to act on its assessment of the probability of a vehicle being used.

• When the probability exceeds a “classification threshold,” the activity is flagged before it reaches any leaderboards.
• Strava users are then proactively prompted to crop out the vehicle portion or make the activity private for flagged activities.

 

The model was initially trained on tens of thousands of Strava activities and improved in use over the last year or so. An interesting stat is that Strava claim to flag 81% of activities containing vehicles which might seem a little low but considering the complexity of the task maybe isn’t so bad.

The model was then used in the background over many months to check and remove incorrect, historic activities from leaderboards.

These clean-ups affect some areas more than others. For example, in touristy areas there are many more e-bikes.

Tech Stack

• Strava built this ML model using gradient boosted decision trees via a popular software library called XGBoost.

Now you know!

What This Means for Strava Users

Even though 3.9 million activities were removed from competitive lists, the activities themselves aren’t deleted, for example, E-bike rides will still appear in feeds and profiles.

For any of your activities that were automatically excluded from leaderboard results due to incorrect sport type or improbable data, Strava suggests that you correct the original, namely:

• Crop or split the activity to remove invalid parts
• Correct the sport type (e.g., change a bike ride mistakenly logged as a run)
• Submit a support ticket if the exclusion was in error

Note: Once an activity is excluded from segment results, it won’t reappear unless corrected or reclassified.

Community Reaction

Responses online have been mostly positive but mixed. Naturally, the more competitive among us are pleased that leaderboards are cleaner.

There is limited but valid comeback. For example, e-bikers are a valid part of the community and should not be excluded from Strava but, I would definitely argue, they should be excluded from leaderboards of the wrong sport type. Others question exactly two the activities are flagged. Hopefully the earlier sections of this article go some way to reassuring us that there is a degree of conservatism involved and a process for Strava users to correct errors.

Conclusion

Strava has made significant efforts for over a year to clean up its act. Or, more correctly, clean up our acts on our behalf, as it is us that are the source of the mis-recordings.

That said leaderboards and segments are strategic and fundamental to Strava’s uniqueness. It’s in the company’s own interest to keep its prized assets as clean as humanly possible.

 

Last Updated on 3 February 2026 by the5krunner