Strava can predict your race finish times

Engadget - Apr 24th, 2025

Strava, the popular fitness tracking app, has announced the launch of a new feature called Performance Predictions, designed specifically for distance runners. This feature aims to provide estimated finish times for various race distances, including 5K, 10K, half marathon, and marathon. The predictions are generated using a machine learning model that analyzes over 100 data points, drawing on the performance of both the individual runner and similar athletes on the app. This new tool, accessible under the Progress tab for subscribers, updates predictions after each run and takes into account rest periods, offering a dynamic and personalized training experience.

Strava's move to incorporate advanced predictive analytics into its platform follows its recent acquisition of Runna, a personalized running plan app. With nearly 1 billion runs logged on Strava last year, the introduction of Performance Predictions signifies a significant step towards integrating AI to enhance user engagement and performance tracking. While this feature may motivate some runners by setting clear goals, it might also add pressure to meet the app's expectations. As Strava continues to expand its offerings, these developments highlight the growing intersection of fitness technology and personalized training solutions.

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Story submitted by Fairstory

RATING

7.0

Fair Story

Consider it well-founded

The article provides a clear and timely overview of Strava's new developments, including the acquisition of Runna and the introduction of the Performance Predictions feature. It effectively communicates the main points in an engaging and readable manner, making it accessible to a general audience. However, the lack of cited sources and transparency regarding the basis of its claims limits the story's accuracy and source quality. While it presents a balanced view of the potential benefits and pressures of the new feature, it could have benefited from additional perspectives or expert commentary. Overall, the article is informative and relevant to Strava users and fitness enthusiasts, but it lacks depth in terms of source attribution and transparency.

Accuracy

The story accurately reports on Strava's recent developments, including the acquisition of Runna and the introduction of the Performance Predictions feature. These claims are consistent with available sources. However, the story lacks specific details about the acquisition's impact on Strava's services and does not provide precise information on the machine learning model's data points. The claim that users logged almost 1 billion runs last year is plausible but needs verification from official Strava statistics.

Balance

The article presents a balanced view of Strava's new feature by acknowledging both its potential benefits and the added pressure it might impose on users. However, it could have included perspectives from users or experts to provide a more comprehensive view of the feature's impact. By focusing mainly on the feature's technical aspects, the story misses an opportunity to explore user experiences or industry expert opinions.

Clarity

The article is well-written and easy to understand, with a clear structure and logical flow. It effectively communicates the main points about Strava's new features without unnecessary jargon. The tone is neutral and informative, making it accessible to a general audience. However, more detailed explanations of technical aspects could enhance clarity for readers unfamiliar with machine learning.

Source quality

The story does not cite any sources directly, which limits the ability to assess the credibility and reliability of the information. While the details about Strava's features align with known facts, the lack of attributed sources or expert commentary affects the overall source quality. Including statements from Strava representatives or industry analysts could have strengthened the article's reliability.

Transparency

The article lacks transparency regarding the basis of its claims. It does not explain the methodology behind the machine learning model or provide data supporting the claim of 1 billion runs. Additionally, there is no disclosure of potential conflicts of interest or affiliations that might influence the reporting. Greater transparency in these areas would improve the article's credibility.