One of the challenges of leading cycling trips or belonging to a club is describing how difficult a ride is. Someone you have never met asks: “Do you think I can do this ride?” A difficult question to answer given one would need to know the individual’s abilities, the condition of their bike as compared to the route planned.
There are rubrics that attempt to do this. While I have not looked at them exhaustively (and some are listed below), in general they suffer from the same problem – they are written for cyclists who can already ride well. In other words, for a middle-age out-of-shape newbie, knowing that another 500 Metres of elevation on a 160KM ride will add 2KM of distance is irrelevant. What is relevant, is whether that person could survive a 40KM ride if they barely completed a 20KM ride.
Cycling on a Gradient
To attempt to address this, I am proposing a Cycling Grading chart. It is composed of two parts. Firstly, it has a colour coded series of rides inspired from skiing. At the beginning is the baby-bunny – a ride suitable for a young child. At the top is the Pro which includes a distance of two full centuries (320KM). In between is the rest of us.
The level is the relative ranking (starting at the baby bunny and going from there). The numeric values represent the primary factors under optimal conditions:
- Distance: how many kilometres the ride is.
- Score: the relative ranking at this point. Note that generally the light score is about 1/4 of the next colour band’s score (e.g. Green-Light is 80 or about 25% of Blue-Light).
- Speed: the expected average velocity over the duration of the ride.
- Hours: the number of hours in the saddle. Generally Green and Blue rides are half days; Reds start into the full’ish days and then one gets into the full day Black rides.
- Climb: the cumulative elevation gain in metres that is expected (and therefore not counted) within in a ride.
- Notes: some general comments for those thinking of riding.
The second part, and the subject of the next blog – Part II, is a series of factors that determines where a particular ride will land. The usual suspects will be there: distance, elevation and wind. I have added a few more factors that affect the speed or mental well-being of a cyclist – particularly an inexperienced rider. For example, a 20KM ride on a quiet trail is not nearly as exhausting as the same ride on a busy highway.
The Effort Curve
At the heart of this model is the effort curve shown below. To the casual reader this would seem to be a data driven mathematical construct – it is not. Instead it is my guess-tamation of how difficult a ride is relative to the starting point – the baby-bunny. From the table introduced above, the distances are under optimal conditions. The reason the curve is not linear is that a person struggling to ride 20KM under ideal conditions does not struggle 50% more to ride 30KM; they struggle A LOT MORE. The formulas for the curve is provided as part of the graphic and all that I ask is that you don’t laugh at my math.
Uses for the Cycling Gradient
This is not a precise tool (although I do believe that people smarter than me could make is ‘less unprecise’); instead this is a contextual tool. If you are running a ride in a club and you want to describe how difficult it will be, it does that fairly well. It should be noted that the scale works better going up then down. For example, a rider may have to work hard going from Green to Blue rides. However, a rider comfortable in the Red Zone may find both of them ridiculously easy.
In communicating my rides I plan to rate them on this scale. I will use the algorithm to get an approximate sounding of the ride relative to the scale. From there, I will use judgement as to whether the ride is easier (e.g. less blue – more greenish) or harder. Hopefully in time and use, a consensus will form such that there is more or less generally agreement that this ride is a solid dark green – unless there is wind, rain, heavy traffic, etc.
Other Gradients Found
A non-exhaustive list of other gradients can be found below. They have been selected as much by stumbling on them in Google then be any definitive analysis. Feel free to comment and add your preferred measure that you have found useful.