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Tour of the Alps - what can we learn about fatigue resistance? Take home messages

Updated: Apr 19


With 'Tour of the Alps' in full swing it seems timely to talk about the recent research that we had published. This new paper looks at performance differences and fatigue resistance in 'Tour of the Alps' between U23 and Elite riders and between different rider types, namely Allrounders, Domestiques, and General Contender (GC)!


You can find the paper here


https://www.researchgate.net/publication/346426308_Workload_characteristics_and_race_performance_of_U23_and_elite_cyclists_during_an_UCI_2_Pro_multistage_race_Tour_of_the_Alps


Tour of the Alps


Tour of the Alps is a race that is typically used by professional teams in their build up to the Giro d'Italia. It is a multi day race which takes place in the heart of the Alps...as a result it could be described as fairly hilly (to say the least)!!


A typical stage looks a little something like this...


Courtesy of Tour of the Alps


So what did we do?


We collected data from 1st (WorldTour), 2nd (ProTour), and 3rd category (Continental) teams across 2 editions of the race!


We then classified riders in 2 ways


1) Age - either under U23 and Professional


2) Rider type - we used anthropometric data to classify riders as either Sprinters (there were no sprinters!), All-rounders or Climbers. Climbers were then subdivided into Domestiques and GC. Riders were classified as 'GC' if they finished in the top 10 overall.


We processed power meter and heart rate data from all the riders in the study and calculated external and internal load metrics (total work and eTRIMP respectively) and mean maximal power (MMP) values from 5s to 30 minutes. MMP values are simply the highest average power recorded for a defined duration - so 10 min MMP would be the highest power recorded by a rider for 10 mins!


However, we didn't stop there....


We actually looked at MMP values for 5s - 30min after riders had already completed a defined amount of work. We looked at MMP values after 1000, 1500, 2000, 2500 and 3000kJ of work had been completed. This way we could assess how the amount of power riders can produce (the power-duration curve) changes as they fatigue! Or in other words how fatigue resistance riders are!




Take Home 1: U23 are a lot less fatigue resistance that elite riders!





When we plotted the MMP values for U23 and Professionals after various amounts of work we saw something really interesting. We saw that there is a much bigger 'downward shift' in the power duration curve in U23 riders as they fatigue (see the graphs above) compared with Professionals. This indicates that U23 riders are a lot less fatigue resistant than professional riders!


Interestingly we didn't see any differences between Professional and U23 riders for the maximal MMP curve (top line in the graph above)


We also found that U23 have to work a lot harder than Professional riders during a stage. While the external workloads (total work) was not different between U23 and Professional - ie U23 are having to produce roughly the same amount of power during a stage. The internal workload (as measured by HR) was much higher for U23 than for Professional cyclists! Professionals also spent a lot less time in higher HR zones, essentially for the same power the Professional riders were cruising while the U23 riders were working pretty hard! This might help explain the differences we see in MMP values


Take Home 2: Fatigue resistance is different between rider types




When we looked at the difference between rider types we again saw something quite interesting. We saw that the 'downward shift' in the power duration relationship was very different between GC, Domestiques, and All-rounders.


Again however there was no difference in the maximal MMP values!


The most interesting finding was that the power-duration curve in GC riders hardly changes at all. This means that GC riders (riders that finished in the top 10 overall) are able to produce the same power at the end of a hard stage as they can at the start.


Unlike between U23 and Professionals we didn't see any differences in internal workloads between groups. However GC and Domestiques did spend more time riding between 5-8 W.kg - this might simply be because they are a bit fitter and can therefore sustain these power outputs for longer!


One possible explanation for these findings is that the GC riders were not putting out maximal efforts at the start of a stage! Early in a stage GC may have only been putting out just enough power to keep up with the bunch but importantly could have gone harder if needed. In comparison All-rounders and possible Domestiques were already going full gas, even very early on in the race!


Take Home 3: We can predict performance amazingly accurately!!





While all these findings are really interesting, we should never forgot to ask the question


What does this mean for performance!!!?


In this study we classified performance by how close to the overall winner's time a rider finished the race. For example, if the winner completed the stage race in 1500 minutes and a rider was 15 mins behind on GC we counted them as being 1% behind!


We used differences in the power profile between ride types to come up with an algorithm that predicted race performance based on how much power an individual rider could produce at any given point in the race. When we plotted the results of the algorithm against the actual results we got the graph that can been seen above! Essentially the algorithm predicted performance to an accuracy of 99.6%!!


We also showed that you can also predict the number of UCI points a rider might score!


Take home conclusion


So there we have it! This work has shown that maximal MMP values do not necessarily predict differences in race performance! Instead it is the amount of power that riders can put out at the back end of a race that really matters! Fatigue resistance is therefore a really important factor to consider when looking at how a rider might perform!!


And Finally....


These papers are always a massive team effort so a huge thanks to Peter Leo, for leading the project as well as Iñigo Mujika, Dan Lorang, Justin Lawley, Andrea Giorgi, and Dieter Simon! A pleasure to work with you all!


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