Tag: Dashboards

Protecting Young Athletes: reflections from my talk at the 1st CYSM Conference in London

Last Friday I had the pleasure of speaking in London at the Centre for Youth Sports Medicine (CYSM) Annual Conference 2026, on a topic close to my heart: how we protect young athletes while helping them develop. My talk was titled “Protecting young athletes: performance progressions, realistic expectations and injury prevention,” and what follows is a short summary of what I covered, the science behind it, and a new project I shared at the end

Young athletes are not mini-adults

The starting point of the talk is a simple but frequently ignored idea: a young athlete is not a scaled-down version of a senior one. Youth development sits on an individually unique and constantly changing base of physical growth, biological maturation and behavioural development. Because of this, success at an early age does not guarantee success at senior level, and selection systems in most sports quietly favour early maturers — children who happen to be bigger and stronger sooner, not necessarily those with the most long-term potential.

This matters because the way we coach, test and select needs to account for where a child actually is in their development, not simply how many years have passed since their birth.

Age is not just a number

A large part of the talk dealt with the difference between three kinds of age:

  • Chronological age — years since birth.
  • Training age — how many years a young person has spent in structured training.
  • Biological age — where the individual actually sits on the maturation curve, based on physiological markers.

Two children of the same chronological age can be years apart biologically. Around the growth spurt — the period of Peak Height Velocity (PHV) — this gap becomes especially important for training and injury risk. I walked through the methods we use to assess maturity status and timing, from skeletal age estimation (Tanner–Whitehouse, Greulich–Pyle, Fels) to anthropometric approaches based on height, leg length and body mass. I also flagged an important caution from our own work: prediction methods developed on one population can systematically over- or under-predict in another, so the difference between skeletal and chronological age should be used to put test results into context rather than as an absolute truth (for some recent work on this, read this paper from Dr Lorenzo Lolli here).

Talent, the relative age effect, and the road from youth to senior

I then turned to talent identification and the relative age effect — the well-documented bias in which athletes born early in the selection year are over-represented in youth squads and ranking lists. Across athletics and team sports such as Italian football, the data show how strongly birth-quarter skews youth selection. Yet relatively younger or later-maturing athletes often progress better when they transition to senior level — the so-called “underdog hypothesis.”

This connects to one of the central findings I shared, drawn from analyses of tens of thousands of athletes’ careers: early success is not a prerequisite for success as an adult. In jumping events in track and field, only around 8% of males and 16% of females ranked in the world top 50 at age 16 went on to reach the top 50 as seniors. In sprints, on average only about 17% of men and 21% of women were in the top 50 both as under-18s and as seniors. Different pathways can lead to similar outcomes, and many talented juniors never make the transition — for reasons ranging from maturation and selection bias to injury, burnout, loss of funding, or simply moving to another sport.

Injuries and load in young athletes

The final scientific section focused on injury. Young athletes face a distinct set of growth-related conditions — Osgood-Schlatter disease at the knee, Sever’s disease at the heel, gymnast’s wrist, Little League shoulder and elbow, apophysitis around the hip, and more. Our own prospective work in a full-time athletics academy was, to our knowledge, the first to examine growth rates and skeletal maturation as injury risk factors in a large adolescent cohort engaged in full time athletics. We found that rapid growth in stature and leg length, a younger skeletal age and a faster maturity tempo were all associated with an increased risk of bone and growth-plate injuries.

This raises hard questions about how we manage load. Most studies still quantify training simply as “time,” without accounting for what athletes actually do in that time — two athletes can train for the same number of hours doing completely different work. Better load monitoring, including the thoughtful use of wearables and AI, is one of the areas where I think we can genuinely improve.

The book chapter: The Young Athlete

Much of this material is drawn together in a chapter I co-authored with Gennaro Boccia, Paolo Riccardo Brustio, James Baker and Eirik Halvorsen Wik — Chapter 9, “The Young Athlete,” in the Sports Physician Handbook (Fourth Edition of the FIMS Team Physician Manual), edited by Pitsiladis, Yung, Hutchinson and Pigozzi (Academic Press, 2026, pp. 199–235, the link the book is here).

The chapter brings together the themes of the talk into a single reference for clinicians and practitioners: defining the elite young athlete, understanding growth and maturation and why they matter, the relative age effect and the junior-to-senior transition, performance progression and realistic expectations, and the prevention and management of youth injuries and illness — including data from recent Youth Olympic Games.

A thank you to the CYSM, ISEH and the audience

I want to thank the Center for Youth Sports Medicine and the Institute of Sport, Exercise and Health for hosting me and for the care they put into convening this event. The ISEH has been a genuine centre of excellence for sport and exercise medicine since its creation as a legacy of the 2012 London Olympic Games, and its commitment to the health of young athletes is exactly the kind of leadership this field needs. I was equally grateful for the audience — the questions, the engagement and the obvious dedication of so many practitioners to getting this right. Youth sport sits at the intersection of health, development and performance, and it is genuinely encouraging to see so much interest in protecting the young people at the centre of it.

Bringing athletics data to life: a new (experimental) project

One recurring frustration in this area is that the data needed to put a young athlete’s results into context — how the best in the world actually developed over time — is hard to access and harder to compare against. So I have been building something to help coaches interested in Athletics.

I am sharing here an early look at the Athletics Performance Tracker, an AI-assisted project that brings athletics results databases to life and makes them accessible to everyone. The aim is to let coaches, athletes, parents and researchers explore how performance develops with age and compare a young athlete’s results against those of top-ranked athletes.

It currently covers around 30 World Athletics events with thousands of athletes and tens of thousands of performances, and includes:

  • Development curves showing how the world’s best progress from age 12 to 40, with mean values and confidence intervals across the top 10, 20, 50 or 100 athletes.
  • Year-on-year analysis of absolute and percentage improvements for individual athletes or whole cohorts.
  • World top lists for every event, with comparisons to the previous year.

You can explore it here: athletics-tracker.fly.dev.

An important caveat: this is very much an experimental project and still under active development. It is intended for research and exploration, it is not affiliated with World Athletics, and features and data will continue to change. I would welcome feedback as it evolves.

In summary

Early success is not a requirement for success as an adult. Assessing maturity status is key to interpreting training and test results. Training should be progressive, varied and developmentally appropriate, and we need to understand injury patterns — and load — far better than we currently do, with more work needed on young female athletes in particular. Above all, health and longevity in sport should always come before performance at a young age. If we get that balance right, we give more young athletes the chance to fulfil their potential — and to stay healthy doing it.

Data and Dashboards Part 2

Following up on my previous post on sports technology I have been using and data visualisation/analysis platforms, I want to share more information about various data visualisation options I have come across recently.

Anybody involved in sport at any level is now recording some data in training and/or competition thanks to the smartwatches most people wear, mobile phones and related apps and wearable technologies such as rings and bracelets capable of recording various aspects of performance.

In recent months, the Oura ring received a lot of attention due to its implementation in the NBA bubble. The ring is capable of measuring activity, sleep and heart rate variability by means of pulse oximetry. You can read the PhD thesis of Dr Hannu Kinnunen here. I met Hannu years ago when working on a project with Polar on the RS800 and he always had some creative ideas about wearable technology and algorithm development, so I am very happy to see his product getting so much attention. I don’t wear rings, but it is definitively on my list to try it.

The other wearable receiving a lot of attention is the whoop strap. Similar technology in a bracelet format. Validation studies are starting to be published, and it seems that Whoop is reasonable in measuring sleep as compared to polysomnography. It seems to be quite accurate also in assessing heart rate and respiratory rate.

Thanks to improvements in data processing of mobile phones and quality of sensors placed in them, there has been also an increase in the development of apps capable of assessing ‘readiness’ to train by measuring heart rate variability parameters. As a long term user of the HRV4 training app, I can say that this simple tool developed by Dr Marco Altini is fantastic. Pretty accurate as indicated by validation studies and now well used in the field (see an example here and one here) it provides good quality data in a simple manner also with the possibility to monitor different athletes with the coach app. Marco has really done a great job with this app, and the data generated are useful to drive programmes also with athletes coached remotely. His latest work (the Heart Rate Variability Logger app) to estimate the aerobic threshold non-invasively has been recently featured in the British Journal of Sports Medicine. I have not downloaded this app yet, but will do soon as I plan to run more and want to use the data to drive my program, hoping that the calf muscles behave.

I have tracked morning Heart Rate data for a number of years now, and can say that for me it is a way to track training and non training stress very well. Alterations in morning Heart Rate and HRV indices are affected by many factors, however form my own personal experience, I know that when HR is high and RMSSD is low, there is something brewing and I need to put the foot off the pedal. In 2017, Xiao Li and her colleagues at the Snyder Lab at Stanford University published a paper showing that tracking heart rate among other physiological signals in daily life can give warning of sickness onset. It’s a great paper, based on a careful examination of data from over 250,000 daily measurements among 43 people. Fascinating paper which shows how, thanks to technology, we are moving towards the ability to be able to truly personalise health and training interventions also form remote by having relevant data to use. There are now a number of studies recruiting individuals worldwide to share their wearable data to understand more about flu and COVID-19 symptoms. One of them is here https://quantifiedflu.org and it is using data from a number of wearable technology. If you are interested, have a look at the page and take part!

On a personal level I am very interested in using my scientific training to answer personal questions, and I really like this framework recently proposed by Gary Wolf and Martijn De Groot which was based on a previous attempt by Li et al. more than 10 years ago (see picture below).

A Stage-Based Model of Personal Informatics Systems by Ian Li, Anind Dey, and Jodi Forlizzi

As indicated in my previous post, one of the challenges to the use of multiple technology platforms is the ability to put all the data in the same place and be able to visualise them to make inferences. I have shared some examples before, but what is truly missing is the ability to simply visualise everything you measure without using time consuming processes involving downloading of data in .csv format and/or complex API connections, hours of R-coding and expertise in various domains. Thankfully, there are some free solutions appearing which are promising and can provide simple ways to integrate data.

The first one I want to talk about is the Habit Dashboard. This personal health analytics platform integrates data from multiple apps and allows the user to access a comprehensive view. Both the graphic and tabular formats are good and data streams sync very easily.

There are also alternatives like building your own dashboard with Google (see how to import Strava data in Google forms here), Grafana (link here) and Power BI (link here).

Last but not least, an excellent tool developed by John Peters in collaboration with Prof. Stephen Seiler to be able to analyse endurance training sessions and competitions. EnDuRA (Endurance Durability and Repeatability Analyser) can be found at http://endura.fit and you can import Garmin data (FIT and TCX format activity files either as .FIT.TCX.FIT.GZ). And if you want to read more about the concept of ‘Durability’, this recent review is a must read for anybody working with endurance athletes.