bodyType

customContents

entityId

printArticlePageQuery

filter

article

body

urlAlias

articleSectionsContent

articleSeoArticle

articleSourceHeaderArticle

contentSchemaRenderContent

contentSubHeadlineContent

contentHeadlineContent

mainImagePrintArticleArticle

authorBlockPrintArticleArticle

contentSponsorTagContent

articlePianoPaywallContent

scmpYoutubeContent

hooksTrackPageViewArticle

query printArticlePageQuery(
  $entityId: String!
  $customContents: [CustomContentInput]
  $bodyType: BodyFormatTypeEnum
) {
  article(filter: {entityId: $entityId}) {
    entityId
    body(customContents: $customContents, type: $bodyType) {
      json
    }
    urlAlias
    ...articleSectionsContent
    ...articleSeoArticle_3o8td4
    ...articleSourceHeaderArticle
    ...contentSchemaRenderContent
    ...contentSubHeadlineContent
    ...contentHeadlineContent
    ...mainImagePrintArticleArticle
    ...authorBlockPrintArticleArticle
    ...contentSponsorTagContent
    ...articlePianoPaywallContent
    ...scmpYoutubeContent
    ...hooksTrackPageViewArticle
    id
  }
}

fragment articlePianoPaywallContent on Content {
  __isContent: __typename
  ...hooksPaywallPromiseContent
}

fragment articleSectionsContent on Content {
  __isContent: __typename
  sections {
    value {
      ...helpersCheckIfNewsSectionInFirstIndexSection
      ...entityLink
      name
      id
    }
  }
}

fragment articleSeoArticle_3o8td4 on Article {
  ...hooksArticleSeoArticle
  ...helpersCheckIsPostiesArticle
  ...helpersGetArticleTypeArticle
  entityId
  publishedDate
  updatedDate
  summary {
    text
  }
  subHeadline {
    text
  }
  body(customContents: $customContents, type: $bodyType) {
    text
  }
  keywords
  sections {
    value {
      name
      id
    }
  }
  topics {
    name
    entityId
    id
  }
  urlAlias
  authors {
    name
    id
  }
  images {
    style(filter: {style: "768x768"}) {
      url
    }
  }
  readingTime
}

fragment articleSourceHeaderArticle on Article {
  urlAlias
  ...entityLink
}

fragment authorBlockPrintArticleArticle on Article {
  ...defaultArticleDateContent
  ...authorNamesArticle
}

fragment authorNameAuthor on Author {
  ...entityLink
  name
  entityUuid
}

fragment authorNamesArticle on Article {
  authorLocations
  authors {
    entityId
    location
    ...authorNameAuthor
    id
  }
}

fragment blockquoteQuoteContent on Content {
  __isContent: __typename
  ...contentShareWidgetContent
}

fragment checkersCheckContentContainsStarlingImageContent on Content {
  __isContent: __typename
  topics {
    entityId
    id
  }
}

fragment contentHeadlineContent on Content {
  __isContent: __typename
  headline
  socialHeadline
  ...contentHeadlineTagContent
  ...helpersUseContentSponsorContent
}

fragment contentHeadlineTagContent on Content {
  __isContent: __typename
  ...hooksContentHeadlineTagContent
}

fragment contentNewsletterWidgetContent on Content {
  __isContent: __typename
  topics {
    relatedNewsletters {
      ...helpersMassageToNewsletterItemNewsletter
      id
    }
    id
  }
  sections {
    value {
      relatedNewsletters {
        ...helpersMassageToNewsletterItemNewsletter
        id
      }
      id
    }
  }
  ... on Article {
    relatedNewsletters {
      ...helpersMassageToNewsletterItemNewsletter
      id
    }
  }
}

fragment contentSchemaRenderContent on Content {
  __isContent: __typename
  ...blockquoteQuoteContent
  ...imageContent
  ...inlineWidgetContent
  ...photoGalleryWidgetContent
  ...scmpYoutubeContent
}

fragment contentShareWidgetContent on Content {
  __isContent: __typename
  entityId
  urlAlias
  shortUrl
  socialHeadline
  headline
}

fragment contentSponsorTagContent on Content {
  __isContent: __typename
  ...helpersUseContentSponsorContent
}

fragment contentSubHeadlineContent on Content {
  __isContent: __typename
  subHeadline {
    json
  }
  summary {
    json
  }
  ...contentSchemaRenderContent
}

fragment defaultArticleDateContent on Content {
  __isContent: __typename
  ...hooksArticleDateUtilsContent
}

fragment entityLink on BaseWithApplicationAndUrlAlias {
  __isBaseWithApplicationAndUrlAlias: __typename
  __typename
  entityId
  entityUuid
  urlAlias
  urlRedirect {
    toUrl
    id
  }
  application {
    entityId
    id
  }
  ... on Section {
    fullSectionPath {
      entityId
      id
    }
    parentSection
  }
  ...helpersAppBarVariantBase
  ...helpersApplicationBase
  ...helpersCheckIsPlusArticle
  ...helpersStyleSectionContent
}

fragment helpersAppBarVariantBase on BaseWithApplicationAndUrlAlias {
  __isBaseWithApplicationAndUrlAlias: __typename
  ...helpersCheckIsLiveArticle
  ...helpersCheckIsPlusPage
  ...helpersCheckIsPostiesArticle
  ...helpersCheckIsPostiesPage
  ...helpersCheckIsPostMagazineArticle
  ...helpersCheckIsStyleArticle
  ...helpersSectionContent
  ...helpersStyleSectionContent
}

fragment helpersApplicationBase on BaseWithApplicationAndUrlAlias {
  __isBaseWithApplicationAndUrlAlias: __typename
  ...helpersCheckIsPostiesArticle
  ...helpersCheckIsPostiesPage
  ...helpersCheckIsPlusPage
}

fragment helpersCheckIfNewsSectionInFirstIndexSection on Section {
  name
}

fragment helpersCheckIsAmpEnabledArticle on Article {
  entityId
  types {
    value {
      entityId
      id
    }
  }
  sections {
    value {
      entityId
      id
    }
  }
  disableOffPlatformContent
  sponsorType
  published
  ...helpersCheckIsPlusArticle
  ...helpersCheckIsPostiesArticle
}

fragment helpersCheckIsLiveArticle on Article {
  flattenTypeIds
  liveArticle {
    status
    id
  }
}

fragment helpersCheckIsMainTopicContent on Content {
  __isContent: __typename
  topics {
    entityId
    id
  }
}

fragment helpersCheckIsPlusArticle on Content {
  __isContent: __typename
  ... on Article {
    paywallTypes {
      entityUuid
      id
    }
  }
}

fragment helpersCheckIsPlusPage on Page {
  urlAlias
}

fragment helpersCheckIsPostMagazineArticle on Article {
  sections {
    value {
      entityId
      id
    }
  }
}

fragment helpersCheckIsPostiesArticle on Article {
  sections {
    value {
      entityId
      id
    }
  }
}

fragment helpersCheckIsPostiesPage on Page {
  urlAlias
}

fragment helpersCheckIsStyleArticle on Content {
  __isContent: __typename
  sections {
    value {
      entityId
      id
    }
  }
}

fragment helpersGetArticleTypeArticle on Content {
  __isContent: __typename
  ... on Article {
    types {
      value {
        entityId
        id
      }
    }
  }
}

fragment helpersMassageToNewsletterItemNewsletter on Newsletter {
  entityId
  name
  alternativeName
  description {
    text
  }
  summary {
    text
  }
  homepageDescription {
    text
  }
}

fragment helpersSanitizeArticleTypeEntityIdsArticle on Article {
  types {
    value {
      entityId
      id
    }
  }
}

fragment helpersSanitizeAuthorTypesArticle on Article {
  authors {
    types
    id
  }
}

fragment helpersSanitizedAuthorEntityIdsArticle on Article {
  authors {
    entityId
    id
  }
}

fragment helpersSectionContent on Section {
  fullSectionPath {
    entityId
    id
  }
}

fragment helpersStyleSectionContent on Section {
  fullSectionPath {
    entityId
    id
  }
}

fragment helpersUseContentSponsorContent on Content {
  __isContent: __typename
  topics {
    entityId
    sponsor {
      ...sponsorTagSponsor
      name
      type
      id
    }
    id
  }
  ... on Article {
    sponsorType
  }
  ...helpersCheckIsMainTopicContent
}

fragment hooksArticleDateUtilsContent on Content {
  __isContent: __typename
  createdDate
  publishedDate
  updatedDate
}

fragment hooksArticleSeoArticle on Article {
  ...helpersCheckIsPostiesArticle
  ...helpersCheckIsPostMagazineArticle
  ...helpersCheckIsStyleArticle
  ...hooksContentHeadlineTagContent
  ...helpersCheckIsAmpEnabledArticle
  entityId
  headline
  socialHeadline
  types {
    value {
      entityId
      id
    }
  }
  authors {
    entityId
    id
  }
  images {
    generic_og: style(filter: {style: "og_image_scmp_generic"}) {
      url
    }
    styles(filter: {styles: ["og_image_posties_article", "og_postmag", "og_image_style", "og_image_scmp_analysis", "og_image_scmp_explainer", "og_image_scmp_editorial", "og_image_scmp_fact_check", "og_image_scmp_debate", "og_image_scmp_live", "og_image_scmp_obituary", "og_image_scmp_opinion", "og_image_scmp_review", "og_image_scmp_series", "og_image_scmp_generic"]}) {
      url
      style
    }
    type
  }
  urlAlias
}

fragment hooksContentHeadlineTagContent on Content {
  __isContent: __typename
  __typename
  ... on Article {
    types {
      value {
        name
        entityId
        description {
          text
        }
        id
      }
    }
    ...helpersCheckIsStyleArticle
  }
  flag
  sections {
    value {
      name
      id
    }
  }
  ...hooksContentHelperContent
}

fragment hooksContentHelperContent on Content {
  __isContent: __typename
  ...helpersSanitizeArticleTypeEntityIdsArticle
  ...helpersSanitizeAuthorTypesArticle
  ...helpersSanitizedAuthorEntityIdsArticle
  flag
}

fragment hooksPaywallPromiseContent on Content {
  __isContent: __typename
  entityId
  ...helpersCheckIsPostiesArticle
}

fragment hooksTrackPageViewArticle on Article {
  entityId
  publishedDate
  headline
  sections {
    value {
      name
      id
    }
  }
  urlAlias
  authors {
    name
    id
  }
  entityUuid
}

fragment imageContent on Content {
  __isContent: __typename
  headline
  ...checkersCheckContentContainsStarlingImageContent
}

fragment inlineWidgetContent on Content {
  __isContent: __typename
  ...contentNewsletterWidgetContent
}

fragment mainImagePrintArticleArticle on Article {
  images {
    title
    url
    isSlideshow
    size_652x446: style(filter: {style: "652x446"}) {
      url
    }
  }
}

fragment photoGalleryDialogContent on Content {
  __isContent: __typename
  __typename
  ...contentShareWidgetContent
}

fragment photoGalleryWidgetContent on Content {
  __isContent: __typename
  ...photoGalleryDialogContent
}

fragment scmpYoutubeContent on Content {
  __isContent: __typename
  entityId
  urlAlias
  publishedDate
}

fragment sponsorTagSponsor on Sponsor {
  name
  type
  images {
    url
  }
}


sections

types

disableOffPlatformContent

sponsorType

published

headline

socialHeadline

authors

images

publishedDate

updatedDate

summary

subHeadline

keywords

topics

readingTime

authorLocations

entityUuid

Mary Hui

One of the most beautiful things about trail running is its free-form, non-standardised nature. No two racecourses are the same and different athletes excel on different terrains, from mountainous to runnable. This makes competitions, from the local to the international level, quite unpredictable – and that is where a lot of the fun lies.
Still, there’s value in trying to ascribe some method to the trail madness, even if only for curiosity’s sake.
Now, a team of statistics and data science researchers have devised a predictive framework for assessing trail running performance. Riccardo Fogliato, Natalia L. Oliveira and Ronald Yurko, PhD candidates in statistics and data science at Carnegie Mellon University in Pittsburgh, propose a framework called Trail Running Assessment of Performance (TRAP), which assesses runners’ performance both before and during a race.
The framework takes into account three factors: the runner’s ability to reach the next checkpoint (or put another way, their probability of dropping out); the runner’s expected passage time at the next checkpoint; and predicted intervals for the passage time.



 

 
 


 
   
    

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The researchers observe that drop-out rates in trail running races are relatively high, even among experienced competitors. For example, in Ultra Trail du Mont Blanc races that they examined, rates range from 30 per cent to 42 per cent – slightly higher for women compared to men and lower for younger runners compared to older runners.
The researchers also observe that more than half of all runners gain at least one position in the overall ranking at almost every checkpoint throughout a race. This means that a smaller fraction of runners lose multiple positions in the ranking throughout the race, implying that they slow down substantially as they tire.

With these observations in mind, the researchers’ TRAP framework focuses on two variables for each runner at a particular checkpoint in a race that has X number of checkpoints: at what time does the runner cross a checkpoint; and does the runner drop out at that checkpoint? Drawing on this information as the race progresses, the framework can help predict when the runner will pass the next checkpoint and subsequent ones after that, and whether the runner will drop out, either at or before the next checkpoint.
The predictive framework draws on other information as well. At the checkpoint level, various characteristics are taken into account, including: distance from the previous checkpoint, cumulative distance from the start of the race, altitude of the checkpoint, whether there’s food and drink, and medical aid and bathrooms.
At the runner-level, information is scraped from existing databases (such as the race organisers’ website, and the International Trail Running Association (ITRA)), including nationality, gender, age, and race history. The framework also takes into account whether a runner is under or over-performing at each checkpoint – that is, arriving at a checkpoint later or earlier than expected.
The liberation of your first ‘DNF’ in trail running
So what does this all amount to? First, the researchers want to make runner information from ITRA more accessible by providing the tools to scrape the data from the website. Next, feeding that data into their predictive model will help inform many aspects of a race.
For race organisers, the information can help guide racecourse design, and also troubleshoot where best to place checkpoints, volunteers, and medical staff. For spectators and coaches, the information can help them understand when a runner will reach the next checkpoint, whether they are having an off day and need additional help, and how likely it is they will reach the next checkpoint and complete the race.
As the researchers note, what they propose represents “a transferable and scalable framework: the model needs not be retrained for new races and the accuracy of its predictions increases with data”.


As we rack up an increasing amount of data through more and more races, as well as apps from the likes of Strava and Garmin Connect, there is potential for the researchers to fine-tune and make their predictive framework even more accurate.
Trail running is notorious for being a minefield of uncertainty and that is part of what makes the sport so addictive. The researchers’ trail running predictive framework offers the potential to try to make a bit more sense of the uncertainty.


How researchers are using data and statistics to predict trail-running performances

The Ultra Trail du Mont Blanc has a 32 to 42 per cent drop-out rate. Can statisticians use data to predict an ultra runner’s chance of success? Photo: UTMB

Statistics and data scientists are using three variables to predict trail races, including a runner’s chance or reaching the next check point. Photo: Oddoux Franck

Sport

Outdoor

Trail Running

How to Guides


A team of data scientists are creating a framework to predict trail running performances and the probability of a runner dropping out of the race



Extreme fitness

Oxfam Trailwalker

The Adventure Trail Podcast

Ultra trail of Mont Blanc - UTMB

Expresses the views, ideas and conclusions of the author. (Eg. Enough of Crazy Rich Asians, think of crazy poor Hongkongers.)


Column

Trail Mix

Trail Mix | How researchers are using data and statistics to predict trail-running performances

A team of data scientists are creating a framework to predict trail running performances and the probability of a runner dropping out of the race