Justice was finally served a little over a week ago. Strava removed over two million rides recorded as normal rides, but apparently accomplished on an e-bike. They made the announcement on their subreddit, where they addressed a full global backfill aimed at problems expressed by many commenters.Strava is using Machine Learning (ML) models to help clean up logged riding errors, intentional or not. James, an engineer at Strava, explained in the post that the recent crackdowns were threefold.First, they introduced a new ML model, specifically aimed at catching e-bikes. This Enhanced E-Bike Detection flags and removes activities logged as a normal ride, but that were clearly recorded with electric assist.Secondly, they cleaned up the leaderboard. Strava accomplished this by reprocessing the top 100 activities on every global ride segment. They claim this move helps confirm that the leaderboards for these segments are free of vehicle and e-bike use and also of incorrect sport types, such as logging a ride as a run.Lastly, Strava introduced ML that focuses on its run leaderboard, better indicating when a logged run is actually completed on a bike.And, according to Stravas post, the results of these new actions are pretty astonishing. On the pedal-assist side, they removed 2.3 million apparent e-bike rides. They also detected and removed 1.6 million logged activities using a vehicle. And, because of this, 293k athletes restored to their rightful spot in the top 10.How Does Strava Know?This is a good question, and Strava provides more information in a previous Reddit post from almost a year ago. This time, Nick from the product team provides some context to the longstanding cheating issues the app has had. While this previous post doesnt focus on e-bikes, we can gleam some ideas as to how Strava is tackling these issues.Every run activity was broken up into chunks from 800m to marathon length. If a user broke the world record during any of those chunks, we know it cant be a real run. So, we automatically exclude that portion of the activity from segment leaderboards. This keeps the sections recorded in cars or on bikes off leaderboards. But a system like this has a lot of drawbacks. Notably, it doesnt work on hills. There is no world record for hills, especially not hills with different gradients and surfaces. It also doesnt work if a car drives slowly.Nick also notes that the system can be difficult to detect cycling cheaters, especially regarding descending.For cycling, we also break the activity into chunks and have rules based on the limits of human performance. But in cycling, its much trickier to determine what the world record for riding over uneven grades actually is. If you sprint faster than world-class sprinter Mark Cavendish on a flat or net-uphill road, we know thats not possible and exclude that part of the activity. But its possible for an amateur cyclist to go faster than Cavendish on a given downhill. On the uphills, its difficult to say what the limit of performance is. We experimented with using VAM, but these efforts still let vehicles through.Strava goes into more detail on how their ML systems work on a post on their website. But, essentially, they look at users times that break world records and flag them. Yes, there may be some controversy around users legitimately getting top spots and having their activity flagged. However, we imagine there are ways of proving a run or ride if youre setting a world record.Some commenters asked for details on how the ML models detect e-bike rides, but Strava hasnt released them yet, as it did with its previous ML models. But, they acknowledge that their work isnt done, and appreciate the subreddit for bringing the anamolous activities to their attention.The post Strava is Now Using Enhanced E-Bike Detection to Crack Down on KOM Cheating, Removing 2.3 Million Rides appeared first on Bikerumor.