Performance is affected by a wide variety of factors; the external environment and psycho- physiological/social stressors within an athlete’s life impact greatly on both training and competitive performance. It has proven challenging for coaches to systematically capture these extrinsic non tangible factors and conditions to ensure we focus on an athlete centric approach to coaching including a mix of wellbeing, capability and performance. This paper proposes a set of variables, condition mapping and practical connections to improving coaching.


Measuring and managing training methods to track the loading of athletes has been the charge of coaches for eons, be it simply by time, distance, intensity, TRIMP, or TSS. We track it, model it, discuss it, and make important decisions upon it.

Each coach has their preferred method; one that is tried and tested, and for them and their athlete’s it works? From this data, they can relate training to performance, – correlate training to competition outcomes.However, there is currently a poor understanding of which metrics are most important as lead indicators of performance, and it is performance that is the ultimate indicator of physical and psychological well-being and the athletes’ current state readiness for optimal performance.

But what if the coach is unable to create a consistent match between training to competition outcomes? What if the outcome is different to what they expect?

  • What other reason may exist?
  • Are they missing something or not giving it enough weight?
  • Could they as coach foresee a problem?

Possibly not, as we as coaches have become very good at collecting and analysing training load metrics, and there are a myriad of software solutions to help us. Or possibly there are factors we know about, but do not bring within the scope of “key data”.

What we are not necessarily collecting in a structured form, is athlete data relating to motivation, anxiety, sleep, nutrition, hydration, fatigue or the athlete’s perception of training load, arguably factors that have a significant impact upon performance. Subjective measures for routine (daily) athlete monitoring are relatively cheap and simple to implement compared to objective measures, they have also been identified as being more sensitive and consistent.

Coaching can be defined as the process of “assisting an athlete to achieve the best possible level of performance” (Woodman, 1993), how can we do this if we are not considering and then measuring, tracking and analysing the full scope of factors and data that contribute to the athlete achieving their best possible outcome.


While we recognise but don’t always understand or respond to health and wellbeing factors, it is argued that our correlation between training and performance outcomes will always be a partial correlation, subject to higher levels of uncertainty as a projection of future performance.

Training imposes stress, which shifts an athlete’s wellbeing, both physical and psychological, along a continuum that progresses from acute exercise stimulus to acute fatigue to overreaching and finally overtraining syndrome.

Adapted from: Fry and Kraemer, Sports Medicine 23:106- 129, 1997

Although overreaching is able to be integrated within a periodised training plan, progression towards over training is detrimental. Athletes need to be closely monitored to ensure training stimulate the desired effect on performance and well-being. The collection of subjective measures has been identified as particularly important for the ongoing monitoring of progression, because the advancement of overtraining syndrome may be gradual and less easily identified than in situations of acute overload. The ability for coaches to be able to measure and analyse athlete health metrics, including external stressors, overtime is vitally important to the successful coaching and management of athletes.

The health and wellbeing metrics we are currently collecting and analyzing as part of our study and extension of the coaching data set, include:

  1. Motivation
  2. Anxiety
  3. Sleep
  4. Nutrition
  5. Hydration
  6. Fatigue
  7. Training Load.

The data collected for each factor is subjective to each athlete, they rate each factor on a five (5) point scale dependent upon how they feel. Are they feeling motivated about their sport and training? Are they feeling anxious? Are they getting enough sleep? Are they making good nutritional choices? Are they re-hydrating? Are they feeling fatigued? How do they feel (perceived training load) about the level of their current training load?

Subjective measures of mood disturbance, perceived anxiety, training load and recovery have been identified by researchers as being pf particular importance as they have superior sensitivity and consistency when compared to objective measures.

Research has indicated that there is a positive relationship between VO2max and subjective wellbeing indicating that subjective measures reflect an athlete’s ability to perform a sustained maximal effort, reflecting an athlete’s psychological readiness to perform.


Recently, we have been working with a high performance level swim squad. We collected seven (7) key performance metrics from July (2016) to October (2016). utilising our athlete monitoring system (AMS) application. This gathers subjective information from athletes to derive behaviour understanding – modification if required – and leads to performance improvement. The app is downloaded onto the athlete’s phone and takes around thirty seconds each day for them to enter the data. It is a simple, intuitive interface and provides a simple effective approach that doesn’t overwhelm the athlete or the coach.

The AMS is ‘athlete-centric’ helping them to develop self-awareness, encourage creative thinking and emotional intelligence as well as developing ownership and responsibility to address complementary aspects of their life. It helps them develop a sense of self awareness that also develops a platform and ownership for their own success.
Each day the athlete is asked to rate the following questions on a five (5) point scale. The information collected is individual and subjective to each athlete.

  • How would you rate your current training load? 1 = Very Light / 5 = Very Hard
  • How would you rate the quality of your nutrition choices today? 1 = poor choices / 5 = excellent choices
  • How would you rate your current level of anxiety and or stress? 1 = no stress / 5 = very stressed
  • How would you rate your current level of motivation? 1 = low motivation / high motivation
  • How would you rate your level of fatigue today? 1 = No Fatigue/Feeling Great / 5 = Very Fatigued/Tired
  • How would you rate your level of hydration today? 1 = dehydrated / 5 fully hydrated
  • How would you rate your quality of sleep for last night? 1 = Low (broken or little sleep) / 5 = High (deep sleep, 8 or more hours)

This enables us to track each metric, and provide that information to the coaches to aid them in their decision making. The charts that we are currently utilising are powerful and based on the premise of Chronic and Acute Training Load, key metrics utilised by Training Peaks (www.trainingpeaks.com). We have adapted the model for our own purposes – Chronic Performance Chart (CPC) and Acute Performance Chart (APC).

The data is presented as a time series utilising a moving-average (MA) model. This is a common approach for modelling univariate time series (each factor is modelled independently). The moving- average model specifies that the output variable is linearly dependent on the current and various past values of a stochastic (imperfectly predictable) term. This allows for a smoothing of the data to allow for analysis and predictions of trends.

The Chronic Performance Chart (CPC), is an MA Model of the last 30 days of training and the Acute Performance Chart (APC) is an MA Model of previous 14 days. The APC provides the coach with a more detailed picture of the athlete’s subjective rating of the seven (7) factors.

The following charts reflect the daily performance metrics of three (3) athletes over the period July 2016 to Sept 2016; they have all experienced the same training and competition schedule. This time period relates to the final three months of their competitive season, preceding the national competition at the end of September.

Athlete A

Nutrition (NUT), Hydration (HYD), Sleep (SLP), Training Load (TLD), Fatigue (FTG), Motivation (MTV) and Anxiousness (ANX).


  • Motivation has trended downwards, matched by a reduction in Training Load (actual or perceived).
  • Anxiousness, whilst rated low, has been trending slightly upwards
  • Fatigue spiked in the middle of September, but quickly dissipated.
  • Nutrition and Hydration are steady.

Athlete B

Nutrition (NUT), Hydration (HYD), Sleep (SLP), Training Load (TLD), Fatigue (FTG), Motivation (MTV) and Anxiousness (ANX).

Nutrition (NUT), Hydration (HYD), Sleep (SLP), Training Load (TLD), Fatigue (FTG), Motivation (MTV) and Anxiousness (ANX).


  • Sleep has steadily declined.
  • Training Load dropped in mid-September and Anxiousness slightly climbed at this point.
  • Fatigue spiked in the middle of September, but has reduced with the reduction in Training Load.
  • Nutrition has had a slight decline from July to October, whilst Hydration has improved.

Athlete C

Nutrition (NUT), Hydration (HYD), Sleep (SLP), Training Load (TLD), Fatigue (FTG), Motivation (MTV) and Anxiousness (ANX).

Nutrition (NUT), Hydration (HYD), Sleep (SLP), Training Load (TLD), Fatigue (FTG), Motivation (MTV) and Anxiousness (ANX).


  • Motivation has steadily climbed.
  • Training Load has steadily declined.
  • Sleep and Nutrition have remained steady.
  • Fatigue began to decline with the reduction in training load, however even though Training load continued a downward trend, Fatigue has increased since the beginning of September.
  • Anxiousness peaked at the end of July, after which there was a gradual decline, however this has also begun to increase since mid-September.

How to use the charts – How do I respond as a coach?

Athlete monitoring is not limited to either subjective or objective measures, instead the coach can use them to complement each other, to build a more refined picture of their athletes. The coach is able to overlay each of these charts with actual training load data, time trial and competition results, which the coach can the use to work with the athlete to achieve greater success. It is only with the coaches input that the charts and underlying data become meaningful, for the coach provides the context for the picture we are creating. We can see that while training load has decreased for all three athletes, this has resulted in an increase in motivation for Athletes B and C but a decrease for Athlete A. This may be a key point for the coach to provide an intervention with Athlete A, why is motivation decreasing? What can the coach do to stem the tide?

The collection and analysis of this information means the coach is no longer flying somewhat blind, flying on observation, not instruments, with only a small picture of overall athlete condition through which to attempt to understand, and help an athlete achieve success. The coach can now make more informed decisions, and implement interventions before problems reach a precipice.

In the case study, the athletes coach has been able to access the daily performance metrics, and has made alterations to training sessions based upon the motivation (MTV) metric. By monitoring the motivation of the athletes in her squad, and when there was a noted change, the coach adapted the session to increase the engagement of the athletes with their training.


The inclusion of this additional health and wellbeing information has a number of athlete and coach benefits, it can:

  • Increase athlete’s mindfulness of factors important in improving their performance
  • Help to ensure the athlete takes responsibility for factors impacting their own performance improvement
  • Provide greater depth of understanding of the athlete for the coach
  • Develop a capacity for the coach to foresee issues before they impact upon performance
  • Ensure the coach is able to understand how training affects each athlete in regards to the captured metrics.

It provides the coach with a more comprehensive understanding than achieved by general observation of ad-hoc questions in relation to factors that complement the technical aspects of coaching.

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