Sports visualization

🧵🚴‍♀️🌍 Women’s World Championship — Road Race It will be a great race for sure. Long race and a demanding circuit, with 8 laps. What kind of rider profile will handle this route best? The ability to climb will be important, but I also think puncheur capabilities will matter a lot on this circuit. And with the length of the race, endurance will play a big part — which could favour the Classics specialists. How to understand the graphs Y-axis: Average score for Sprint, Hill and Mountain. X-axis: Flat score. A high score here is often associated with strong riders with good endurance, and riders who tend to perform well in the Classics. Dataset: @ZoerCyclingStat Score is based on 3 years of results and stage profiles. Let’s analyse the riders and nations 👇

🧵Endelig Tour de France 🚴🚴‍♂️ Bli kjent med ryttere, lag og få noen Tourmanager tips Først ut er Bahrain Datasett: @ZoerCyclingStat #tourmanager #letour #tdf

Team presentation @letour Find out who is the fastest sprinter, who attacks on short, steep climbs as a strong puncheur, who shines on long mountain climbs, and who powers breakaways as a strong rouleur. First team out is .....Uno-X 👇

🧵Amstel Gold Race @amstelgold for men and women Favourites and odds Loooong day out with multiple smaller climbs. Analyzed riders which have good score on Flat (typically solid stamina/endurance) and high average of Hill & Mountain. Ended up with strong rouleurs Based on @ZoerCyclingStat dataset

🧵Paris-Roubaix femmes Favourites, strongest teams, top10 odds 👇 x-axes: score on cobbles y-axes: avg on ITT and Flat [typically strong rider] @ZoerCyclingStat dataset which is based on 3 years of results.