Standing on the shoulders of giants

An immense amount and quality of work is done every day the world over by a vast range of sports scientists, both in research and on the field. It is a truism to say that we – and Quanter – would be nowhere without them and their contributions to the knowledge base on sport performance and injury/illness risk. In recognition of their efforts, we dedicate our first blog post to those giants on whose shoulders we stand.

So, this post outlines the core influences on both the Quanter app and all the work we do to support the development of team sports. The concepts and methods we use are at the cutting edge of sports technology and applied research. At the same time, they drive at holism and simplicity, rather than isolating factors of athletic performance. These are the paradigms in which we operate, and the inspirations of researchers and practitioners whom we follow.


Athlete Subjective Self-Reporting

Anna Saw et al., 2015
Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures
British Journal of Sports Medicine

Data & Method

  • Systematic review of 56 studies on subjective and objective monitoring methods about athlete well-being and acute & chronic training load


  • Changes in acute and chronic training load have a high impact on athletes’ reported perceptions about their wellbeing (e.g. stress, fatigue)
  • Both subjective and objective measures an important part of the coaches’ toolbox
  • “subjective measures are more responsive to training than objective measures”


Training Load & Illness

Carl Foster, 1998
Monitoring training in athletes with reference to overtraining syndrome
Medicine & Science in Sports & Exercise

Data & Method

  • Analysis of data on training load (sRPEs, monotony, strain) and illnesses of 25 competitive athletes


  • High correlation between illnesses and the exceeding of individual-specific thresholds in training load measures, especially strain
  • “modest immuno-suppression during periods of heavy training (or high levels of other stressors)… renders the individual more susceptible to infection”


Training Load & Injury

Tim Gabbett, 2010
The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes
Journal of Strength and Conditioning Research

Data & Method

  • Collects and analyses training load and injury data of 91 professional rugby league players over 4 years.


  • Strong correlation between higher training loads and injuries at a rate which varies at different stages of the season

Tim Gabbett, 2016
The training-injury prevention paradox: should athletes be training smarter and harder?
British Journal of Sports Medicine

Data & Method

  • Assesses how injury risk is associated with the relationship between Acute TL (average daily TL over last week) and Chronic TL (average daily TL over last 3-6 weeks).


  • Uncovers the association between increased injury risk and overloads in the acute:chronic ratio of sRPE.
  • Demonstrates that acute:chronic underloads can also increase injury risk.



As influential research is newly published or uncovered, and which we take into our hearts and minds, we do our best to summarise them here.