It has been quite a while since the last analysis and by now we are more than half way the Tour de France 2019. One of the most interesting Tour in years if you ask me. Julian Alaphilippe in yellow, no truly dominant team and many surprising stages. The past four stages took place in the Pyrenees. In this post we will take a look at the past mountain stages and their relative difficulties.
This is one of the times I truely deviated from my plan. There is a lot of work to do with the prediction algorithms, but instead of focusing I made a small app that can be used to visualize rider and team preparations for the Giro d’Italia. Talking about the Giro, did you already see our statistics on the Giro d’Italia race page? Before we continue there are 2 important things:
Introduction Perhaps long overdue, but here we will review all predictions we published since the beginning of the year up to the Amstel Gold Race. We distinguish two types of races: one day races (ODR) / classics and stage races / multiday races (MDR). The predictions for these two race types are generated with different models, but for each race type the model specification was the same for all predictions up to now.
The E3 Binckbank Classic 2019 is approaching fast. When doing some ‘field research’ on the official website I discovered that the organizers actually put a GPX file on their website. Basically this file contains the GPS data in a standardized format. It also includes the elevations in meters. I was intrigued, and decided to give it a go and have some fun with it.
Milan San Remo is the longest one-day race on the UCI calendar. Because the stage is largely flat it is known as a sprinters classic, despite the fact that the parcours does have some climbs. In general the length of the stage and intermediate obstacles thin out the field considerably and the final sprint is frequently between a severely reduced ‘peloton’. In Figure 1 you can find an interactive plot with the Milan San Remo results since 2008.
In this post we are going to continue with the clustering problem that we started in February. The idea remains the same, we are going to try to automatically group riders, but we changed our approach considerably. In the first step we identify four rider clusters: time trialists, sprinters, GC guys/climbers and classics specialists. After that we zoom in on the sprint cluster and the clustering algorithm comes up with three distinct sprinter types.
Today we address a complex, but fun and interesting problem: clustering riders. This is by far a new idea, everybody knows Elia Viviani and Dylan Groenewegen are classified as ‘sprinters’, whereas Chris and Froome are ‘GC guys’. The label a rider receives is simply based on past results. We are going to investigate whether we can take this a step further by (1) using a uniform point system for race results and (2) including other dimensions than just outcomes.
It’s the beginning of January, which means it’s time for some serious cycling soon! But before we start, let’s refresh our memory and look at the team changes. We won’t focus on the changes itself, for an overview you can take a look at the pages on Velon.cc or Cyclingnews. Instead, we will investigate how these changes impact the (relative) strenght of the teams.
UCI World Tour Review It’s getting darker, colder and most people start to get a bit more depressed. For me it definitely does not help there are no interesting road races left to watch :sob:. And no; I’m not a replay kind of guy ;) The upside is I got some time to spare to look at the data of the 2018 worldtour.
Veni Vidi Vici Today we focus on Peter Sagan, one of my favourite cyclists. I don’t know any Slowakian words to describe him in, but I guess best is the classical Latin phrase ‘Veni Vidi Vici’. It means ‘I came I saw and I conquered’. Although the phrase is in the past tense, Peter it still doing it as we speak!