The concept of "comparative advantage", attributed to the economist David Ricardo in 1817, says that it is better to capitalize on your strengths than to shore up your weaknesses. Back then Ricardo was pointing out the futility with which England might compete with Portugal on wine production and how the English should instead leverage their textile industry to trade cloth for wine, but there is far wider relevance across many aspects of life. In competitive sport, and especially in a sports like cycling and triathlon where there are a multitude of different events and courses available, the idea has a lot to offer.
In our blog article on the measure of a cyclist we outlined 8 metrics of a cyclist's ability. Strengths or weaknesses in each one of them will tend make a rider more or less suited to specific events. For example, a rider with a high FTP/Weight ratio will probably be good in events with long climbs. A rider with high AWC/Weight (Anaerobic Work Capacity) and fast AWC recharge may be better in events with lots of short, punchy climbs. A rider who is relatively heavier but also powerful is likely to have a higher power to drag ratio "Watts/CdA" and excel in flatter time trials. The full set of metrics will interplay to make every rider uniquely better or worse than another rider in every event.
Identifying comparative advantage is not so easy. Of course we can immediately say that a Fabian Cancellara is better suited to time trials than summit finishes in the Dolomites. And that a Nairo Quintana is unlikely to win a world championship time trial. In between these extremes, however, there is a very large population of more "average" riders whose morphology is similar, whose weight is visually similar, and whose only differences lie in the subtleties of their power numbers, their actual weight, and their aerodynamic drag. This population includes you, your competitors, or if you are a coach then your athletes and their competition.
It is not just riders who are often imperceptibly different. The same goes for courses. A typical, rolling, neither extremely hilly nor extremely flat course may suit one rider more than another, or it may not. To the naked eye and the human imagination there is no way to tell. It's a similar problem to deciding between a light climbing bike or an aero time trial bike for a rolling course. Apply some Performance Modelling though and the answer, rider versus rider, course versus course, just falls out in front of you. All you have to do is simulate both riders then compare their time on course estimates.
We have always advocated using Performance Modelling to compare any two riders on a particular course. For example, given metrics for "Rider A" it is very easy to simulate his/her performance on the course in question, then to simulate an alternative or competitor "Rider B", and decide who has comparative advantage. If you were managing a team then you might use this sort of approach to recommend one rider over another for a certain event. If you are a coach then you might use this approach to estimate the shortfall between your rider and a competitor. The latter "gap analysis" is the foundation of the key coaching practises of rider profiling, performance benchmarking and goal setting.
A logical extension of Performance Modelling is to change the problem, forget about the specifics of any one alternative rider or competitor, and ask "which of several events should this rider enter to maximise his chances?" This is a question equivalent to the problem (for time triallists) "on which course can I maximise my ranking?" or (for triathletes) "which course gives me the best chance of qualifying for Kona?" Until recently we didn't have an answer to that, but now we do.
CPLPercentile is a metric we have developed to indicate a rider's comparative advantage on a specific course. Very simply: Every time you use CPL to simulate a given rider on a given "official" course we show a time on course estimate. Below that we show a "CPLPercentile" which is a number from 1 (best) to 100 (worst) indicating the simulated rider's ranking relative to the theoretical performance of every other rider in our database. Without going into numbers that is now a very comprehensive population of average and not so average riders. The actual value of CPL percentile doesn't matter. It could be 25 (stronger than average), 50 (average), or 75 (weaker than average). The reason it doesn't matter is because it's simply an indication of where the rider might finish in a typical field of typical but anonymous competitors. What does matter though, and what is a very powerful indiation of which event a rider should favour, is the way CPLPercentile changes across events.
Intelligent Event Selection
By example: Let's say we simulate a rider in Event A. The CPL percentile is 50. This means that with a large number of typical competitors the rider might finish exactly mid-field. We dont' care aboout that the absolute level because we dont know who the field are. Now we simulate the same rider in Event B. The CPL Percentile is 45. Again we dont care about the absolute level. What we do care about is that the percentile is 5 lower. The rider's attributes are giving him/her a comparative advantage in this event. The lower the percentile the higher the comparative advantage. Logically the rider would choose Event B to maximise his/her chances. It's that simple.
In conclusion, CPLPercentile is a new metric to indicate rider's comparative advantage across events. It has become possible thanks to the large population of riders in our database, whose attributes we compare 100% anonymously, and a fair deal of computing power. Finding the event where a rider's metrics give comparative advantage really is as simple as checking the CPLPercentile for two or more candidate events and favoring the one with the lowest CPLPercentile. CPLPercentile is visible absolutely free from our Performance Modelling resource. We would of course be keen to see any questions or feedback.