Quantifying Uphill Effort
In a world flooded with every metric and peice of data you could imagine there has always been an area that lacks a solid foundation or qualitative measure, that is for uphill running. What effect does running at 6:00 per km uphill on a 6% gradient take out of you? Am I improving my uphill climbing pace? Are my 2min hill reps actually at the correct intensity and are they consistent? There are ways to measure performance in real time by looking at HR or relating to your RPE scale, however these measures can become distorted by many different variables such as fatigue from previous training, hydration, sleep ect. HR and RPE are incredibly useful when you run a session as it gives you a gauge on what effort you should be running at, but when it come to analysis, providing feedback and tracking improvements Normalised Graded Pace is an incredibly important metric.
Normalised Graded Pace is derived from the cycling world and power data. Here athletes and coaches use Normalised Power or NP, to track or analyse the effect of a very noisy or variable power file actually has on the rider due to the time above and below threshold power. The algorithm used to calculate NP compares a steady effort to a fluctuating effort. This can be used in running, but this time looking at pace and gradient. Running data files are a lot less fluctuating than cycling power files, however a run at 5:00 per km on the flat compared to a run at 5:00 per km on a route with 500m of climbing in are going to stress the athlete in very different ways. Take for example the selection from a data file from running up Jenkins hill. this is a 2.07km hill at 20.1% run at a pace of 8:52 per km. If the athlete were to run at this pace on the flat the physiological effect on the body is going to be very low, however the effect of running up this incline calculate a NGP of 3:44 per km for the 18:23 duration. This then relates to an effort of 3:44 per km for 18:23 on the flat which would cover 4.89km over double the distance on the section we are analysing.
From NGP we can also manage the rTSS (run training stress score) of hillier runs or runs on variable terrain. This metric feeds into the PMC (performance management chart) which enables coaches and athletes to track fitness and fatigue over time and also predict and manage peak performances as well as recovery weeks or days. This data is very sensitive to correct threshold pace settings for athletes as the rTSS is based on the runners threshold pace values and calculates the physiological effect a certain run has on the runner. For example an 8km easy run on the flat may give a rTSS value of 40 whereas an 8km run to the top of skiddaw climbing 900m would give a rTSS value of 93 thus having a much higher training load on the athlete. Ultimately increasing fitness but also increasing fatigue. It is therefore very important to have the correct threshold paces set for athletes, this is easily set up via a 5km or 10km best effort.
There is always some degree of variability when running in the mountains which makes it so special, this is the technicality of the ground underfoot and also weather conditions. The NGP algorithm does not take into account the fact that you may have been scrambling on a grade 3 climb to reach the summit, producing a much slower pace, or there may have been snow on the ground making the footing much slippier, or you may have had a very strong headwind. This brings back into play your HR values and RPE, all together these metrics along with communication to your coach start to build a much clearer picture of quantifying training and racing in the hills.