The data showed that wider tires were generally better, clinchers had caught up or surpassed many tubular tires, and that latex tubes were significantly better than butyl. The results based on the data we had were that tire width, tire type, and tube type were significant and we developed baselines off the resulting equations. Originally, we gathered up as much data as we could find and ran it through a machine learning model to determine the key characteristics (assuming relatively optimal tire pressure) that drove differences in Crr values. With bicycling aerodynamics we are starting to see more and more standardized testing and reporting however tire manufactures don’t seem to have a standard rolling resistance test nor do they often report numbers to consumers. The illustration below simplifies the concept but the key takeaways are that anything you can do to lower the forces against you will help you go faster for the same amount of power or maintain speed for a lower power output.Īs shown above for the typical speeds that athletes ride, rolling resistance can make up a significant portion of the required power output, but how do you make a knowledgeable choice when selecting tires and modeling race plans? Tire Data to the Rescue To get insight into what Crr is let’s first dive into the great force equation of cycling. ![]() To explain the process we went through on these updates, I want to first discuss why rolling resistance is important, talk about a company that is helping to standardise the way we look at tire data, and look at some case studies to highlight just what this means for athletes in real world scenarios. In the subsequent years tire manufactures have come a long way in making faster tires, common tire widths have expanded, and we have branched into modeling some new types of non-road races.īest Bike Split was past due for an update to the system, so we have recently “rolled” out some changes to help fine tune Crr settings, as well as highlight the impact of various tire rolling resistance value changes to overall performance and expected finish times on race day. We also provided a way for users to input known values if they had them to help make the model more accurate. When we first created Best Bike Split we took in as much data as we could find to create some standard baseline coefficient of rolling resistance (Crr) values based on the typical tire types and sizes of the time. With so much emphasis on weight and more recently aerodynamics, we often overlook another major force component that riders must overcome to propel the bike forward: rolling resistance.
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