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Predicted Workloads Vs Actual Reality in Professional Sports Teams


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Predicted workloads versus actual reality, why are these both such important variables for professional sports teams to monitor?

Planning and tracking workload units over a day, a week or a monthly period allows coaches to compare what actually occurred with what they would have liked to have occurred. It allows coaches to reflect upon their their training practices and principles, particularly in the lead up to crucial competition as outlined in the case example below.

Predictive workloads are a valuable yet inexpensive tool which do not take a lot of time to complete. It is a process that is hugely reliable within the literature and there are plenty of studies available on this method of managing workload.

Case Example

Jim works for a professional rugby club. Here are his comments on predicted workloads.

“Traditionally we collected RPE but didn’t do workload predictions – we just collected it and looked at it. What we would do then in the lead up to competition, particularly during a crucial week (in terms of competition) was to decrease the duration of practice time by 25-30% in the hopes that we were then ready to peak at end of week – but more often than not we were flat on the day of big games.

So when we started to predict our workload we noticed that even though our duration of practice was reduced by 20-30% our intensity was incredibly high. I suppose this is understandable as all our athletes wanted to play in the big games and so were giving it their all in practice. In the end we were probably doing 20% more workload, than a normal week even though we had them on the field for less amount of time. Predicting our workloads allowed us to actually quantify exactly that (workload units), we could see it happening, in real time, during that actual week. If we overshot the runway on a Monday by 20-30% we can tell our coaches that if they continue on this trend, at end of week our players will be flat. What we found was that by using predicted workloads it allowed us to control decisions we were making on workload on a daily basis and give that feedback to the coaches on how we felt we should be preparing the athletes”.

Workload Balance

The workload balance is defined by the point where cumulative stress results in maximal gains (fitness/strength etc.) and minimal loss (stress/fatigue/injury). Workloads sustained by athletes can vary across sports and across sub-domains of training within sports (agility, fitness, gym, pitch etc.). Thus monitoring workload in practice typically employs a system of arbitrary units (Time x RPE) to allow comparative analysis of the overall training load for athletes which allows coaches and support staff to periodise training, recovery and adaptation appropriately. This process has been further simplified with the ability to setup predicted workloads using athlete monitoring software.

Each training stress contributes varying relative significance to an athlete’s overall training load accumulation with game loads being most intense, cross training and weights training adding the next most intense load with field and conditioning training contributing less intense loads. However field training loads have been significantly associated with both contact and non-contact field training injuries, while strength and power loads are significantly associated with injuries sustained in strength and power activities. It is therefore vital that each training stress is both individually monitored and planned ahead of time in order to more accurately assess an athlete’s risk of injury in a given training block and to allow for more accurate planning of future training sessions based on individual session loads.

In-season, as the amount of 1–2 weekly load or previous to current week increment in load increases, so does the risk of injury in elite players. To reduce the risk of injury, derived training and game load values of weekly loads and previous week-to-week load changes should be individually monitored. Larger one weekly, two weekly and changes from previous to current week loads measured in arbitrary units were associated with increased injury risk, particularly during in-season training phases. This once again highlights the importance of not only assessing players stress and workloads on an individual basis but also being able to plan these workloads ahead of teamed based on factors like age, injury history and previous to current workloads.

Converting arbitrary units to percentage of total workloads; changes greater than 130% in season and 200% preseason were considered acute on a weekly incremental basis. With larger increments players are at increased risk of injury due to both game and training load. By being aware of these kinds of figures, that impact on injury incidence, it makes it easier for coaches and members of the backroom team to plan their session and weekly workloads ahead of time and make more informed decisions based on these planned figures.

So why not set up your predicted workloads? It will enable you and your team to immediately analyse your actual workload patterns against your planned loads. Profiler gives you real-time workload analysis allowing you make informed decisions, and positively impact your team’s performance and injury profile.


  • Gabbett, T. J. and Jenkins, D. G. (2011) Relationship between training load and injury in professional rugby league players, Journal of Science and Medicine in Sport, 14, 204–209.
  • Rogalski, B., Dawson, B., Heasman, J. and Gabbett, T. J. (2013) Training and game loads and injury risk in elite Australian footballers, Journal of Science and Medicine in Sport, 16, 6.
  • Gabbett, T. J. and Ullah, S. (2012). Relationships between training load, injury, and fitness in sub-elite collision sport athletes, Journal of Strength and Conditioning Research, 26(4), 953-60.
  • Piggott, B. (2008) The relationship between training load and incidence of injury and illness over a pre-season at an Australian Football League Club, (unpublished thesis M.A.), Edith Cowan University



  • Case Study
  • Player Performance Analysis
  • Rugby
  • Sports Load Management

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