John Degenkolb
John Degenkolb is a professional bike-rider from Germany. John Degenkolb is contracted at Team dsm-firmenich PostNL and was born on January 7 1989. John Degenkolb weighs 82 kg and is 180 cm long. More info on John Degenkolb will be avaialbe soon. Please take look at the skills of John Degenkolb at the rider-card at this page, those will be updated daily.
Current scores of John Degenkolb
We keep track of all indicator-scores of John Degenkolb (and all other pro-riders) based on results in UCI-races in the past 3 years. Stats are updated every day in order to provide up-to-date scores. It gives you the possibility to check current rider-specialties of all riders. Here you can find the scores of John Degenkolb.
- John Degenkolb has an average strength of 84 points.
- Degenkolb scores 89 points on riding on cobbles, meaning the rider is one of the best cobbled-specialists.
- John Degenkolb scores 73 points on hills.
- Degenkolb gets 26 points on mountains.
- John Degenkolb has 39 points on riding General Classifications.
- Degenkolb is ranked at 25 points in time trial.
- John Degenkolb scores 71 points on sprinting.
- Degenkolb gets 75 points on riding on the flat.
- John Degenkolb indicates 74 points on doing a leadout.
- Degenkolb gained 89 points on riding one-day races, which makes the rider one of the best one-day specialists of the peloton.
- John Degenkolb has 28 points on racing prologues.
- Degenkolb gets a score of 32 points on riding short time-trials.
- John Degenkolb scores 20 points on riding long time-trials.
About CyclingOracle.com
CyclingOracle.com is created by 5 cycling-addicts who found each other in their shared passion for cycling. Tom Nederend (@TomNederend), Arjan Zoer (@ZoerCyclingStat), Daniël Herbers (@StatsOnCycling), Thomas Zwetsloot (@zwetmas) en Stef van Zon (@stefvanzon) invest a lot of their free time in making content for the website and developing the computer algorithm predicting professional cycling races.
Computer-model of CyclingOracle.com
Arjan Zoer is the mastermind behind the smart computer-algorithm. Arjan developed the model and is working on improvements of the model on a daily basis. We will not share the depths of the model publically, but can give some insight in how it works. The model is based on results of riders in the past 3 seasons in which more recent results have a larger impact on the outcome. The model runs for every male and female rider in all UCI-races. That’s a lot of data. The result of race, combined with the profile, quality of the startlist and the UCI-classification of the race, determines on which skills a rider gets ‘points’. Riders score points between 20 and 100 on 13 different skills (categories), being: spring, flat, mountain, hills, time-trial, ITT-long, ITT- short, prologue, cobbles, leadout, GC, one-day races and stage-races. In addition, a rider gets points for his current shape (good results in recent races).
Some examples:
- A rider wins a bunch sprint in Tour de Rwanda. He gets points awarded for ‘flat’ and ‘sprint’, but these points will make less of a difference compared to a bunch sprint-victory in Tour de France given the UCI-classification of the race (2.1) and weak field of participants.
- A rider wins a bunch sprint in Giro d’Italia and his teammates get rewarded points for ‘leadout. If teammates of a sprinter have a lot of leadout-points, the computer lifts the chances of a sprinter to win a flat race which is likely to result in a bunch sprint. Team-quality is part of the model.
- A rider solos to victory in Ronde van Vlaanderen: the rider gets rewarded a mix of points on skills like ‘cobbles’, ‘hills’, ‘one-day races’ and ‘time-trial’.
- A rider wins the sprint of a small-group at a summit-finish of Alpe d’Huez. He gets points for ‘mountain’, but also for ‘sprint’ and ‘stage-races’. Moreover, these points will weigh heavily on a rider’s shape or form in order to predict future results in the same race better.
All these skills will be used to predicted a cycling-race. Depending on the profile and field of participants, the computer predicts the most likely winner. The probability a certain rider will win the race is called ‘Expected Win’.
How to find CyclingOracle.com
WielerOrakel.nl can be found at Twitter, Instagram and YouTube named @wielerorakel. Further the ‘WielerOrakel podcast’ is our most important media outlet, you can listen to us in your favourite podcast-app. In the WielerOrakel podcast, cycling-addicts Tom and Thomas rejoice about the upcoming cycling races. They will be joined by several others, like data-mastermind Arjan Zoer, stat-expert Daniël and other guests from the cycling community. They elaborate on and discuss the predictions by the computer model.
Cycling Oracle Cycling Quiz
Every year the Cycling Oracle Cycling Quiz is organised in Café Scheltema in Leiden (NL). Cycling-lovers from several countries gather to fight fort he Challenge Cup and several other prizes. Follow us on Twitter to know more about the quiz.
Collaboration with CyclingOracle.com?
Do you want to sponsor us of collaborate with CyclingOracle.com? Let us know by sending an e-mal to [email protected] and we will get in touch to explore the possibilities. That’s it for now, see you later.
