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Emma Norsgaard

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88 DK Emma norsgaard 92093
Emma Norsgaard
COB 89 HLL 58 MTN 33 GC 43 ITT 85 SPR 88
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Information Norsgaard

Name
Emma Norsgaard
Nationality
denmark Denmark
Team
Lidl-Trek
Birthday
26 July 1999
Age
25 years and 333 days
Weight
65 kg
Length
172 cm

Emma Norsgaard

Emma Norsgaard is a professional bike-rider from Denmark. Emma Norsgaard is contracted at Lidl-Trek and was born on July 26 1999. Emma Norsgaard weighs 65 kg and is 172 cm long. More info on Emma Norsgaard will be avaialbe soon. Please take look at the skills of Emma Norsgaard at the rider-card at this page, those will be updated daily.

Current scores of Emma Norsgaard

We keep track of all indicator-scores of Emma Norsgaard (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 Emma Norsgaard.

  • Emma Norsgaard has an average strength of 88 points, meaning the rider is one of the best cyclists in the world.
  • Norsgaard scores 89 points on riding on cobbles, meaning the rider is one of the best cobbled-specialists.
  • Emma Norsgaard scores 58 points on hills.
  • Norsgaard gets 33 points on mountains.
  • Emma Norsgaard has 43 points on riding General Classifications.
  • Norsgaard is ranked at 85 points in time trial.
  • Emma Norsgaard scores 88 points on sprinting, meaning the rider belongs to the best sprinters in the pro-peloton.
  • Norsgaard gets 89 points on riding on the flat, meaning the rider is one of the best flat-specialists.
  • Emma Norsgaard indicates 83 points on doing a leadout.
  • Norsgaard gained 84 points on riding one-day races.
  • Emma Norsgaard has 88 points on racing prologues, meaning the rider is specialized at riding prologues.
  • Norsgaard gets a score of 90 points on riding short time-trials.
  • Emma Norsgaard scores 79 points on riding long time-trials.

About CyclingOracle

CyclingOracle is created by six cycling-addicts who found each other in their shared passion for cycling. Tom Nederend (@TomNederend), Arjan Zoer (@ZoerCyclingStat), Daniël Herbers (@StatsOnCycling), Thomas Zwetsloot (@zwetmas), Fleur Kok (@fleurrkok) and 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

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

Follow us on X (@wielerorakel) to stay up to date with new updates, podcast episodes, predictions, and statistics. On Instagram (@cyclingoracle) we share not only predictions but also rising stars, Team of the Month features, and interviews with riders.

In the WielerOrakel Podcast, cycling fanatics Tom and Thomas get excited about the races, joined by guest appearances from data brain Arjan and stats wizard Daniël, as they provide context to the computer’s predictions.

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

For sponsorship or other collaboration opportunities, you can email [email protected]

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