Workload efficiency: a new index for external and internal workload?

Contents of Research Review

1. Background & Objective
2. What They Did
3. What They Found
4. Practical Takeaways
5. Reviewer’s Comments
6. About the Reviewer
7. Comments

Background & Objective

The characterisation of physical performance in soccer matches using absolute parameters (e.g. distance covered) does not provide a comprehensive view of players load and performance.

Therefore, the authors aimed to develop and introduce a new index, known as ‘workload efficiency’, based on internal and external workload ratios. Additionally, they studied how this index was influenced by the training loads during the five days before the match.

What They Did

Fourteen players were monitored with global positioning system and heart rate sensors during forty-four on-field training sessions and sixteen competitive matches. Training loads were classified into five categories based on the number of days before game day (D-1 to D-5).

Match workload efficiency was calculated as the ratio between the equivalent distance (a measure of metabolic power; see HERE) and the modified training impulse (TRIMPmod; see HERE). External load was characterised by duration, total distance, equivalent distance, high speed (>14.4 km. h -1 ), very high speed (>19.8 km. h -1 ), sprinting (>25.2 km. h -1 ), running distances, as well as the number of medium (2.00–2.99 m. s²) and high (>3.0 m. s²) accelerations and the number of medium (-2.00– -2.99 m. s²) and high (<-3.0 m. s²) decelerations.

What They Found

The main findings of this study were:

• Internal and external load increased from D5 over D-4, with a peak on D-3 and decreased progressively on D-2 to D-1.
• Match workload efficiency was predicted by training loads recorded during the days before a match, yet only to a limited extent.
• The sprinting distance four and three days before a game and the total distance one day before a game seem beneficial for players’ physical efficiency in the upcoming game.
• High training loads and long training sessions one day before a game negatively influenced players’ workload efficiency.

Practical Takeaways

• Workload efficiency, an index combining external and internal load measurements was associated with the training loads in the pre-game days. This new index can provide additional information regarding physical performance of soccer player’s during competitive matches. Practitioners can determine workload efficiency in each game for each player to establish an individual profile and evaluate the performance and load of the player across the season.
• If available, using GPS technology can provide valuable information for practitioner’s on how to pace the training load before a competitive match. Combining several external parameters (including different categories of speed, acceleration, and deceleration) measured with GPS with internal load parameters quantified by a training impulse model is advised.
• Present findings suggest that for an optimal pre-game preparation, coaches should plan sprint training session during the week (3-4 days before the game) and additionally should avoid excessive training loads and long sessions one day before the game. Low-intensity running the day before the game can be included as it does not affect workload efficiency of the match.

Reviewer’s Comments

“This study suggested a new workload efficiency index, where the external load consisted of the equivalent distance and a modified training impulse. As the authors described, the equivalent distance “represents the distance that a player would have covered theoretically at a steady pace on grass using the total energy spent over the match or training”.

After calculating the match workload efficiency index, the authors analysed how different locomotive measures affected this index. Personally, I think that care must be taken when utilising such models, as they don’t account for possible heart rate responses to psychological factors (e.g. match-related stress, environmental factors (e.g. air temperature, humidity and height above sea level) or nutritional strategies (e.g. caffeine intake). Furthermore, the analysis used in this study provided a team average based model, neglecting important non-training related factors, such as playing position or player’s age.

As a practitioner, I always like to understand what is interesting, important, or a determinant for an athlete’s or team’s success. I believe this index is interesting, but still far off to being used in a way that will affect any athlete or team’s training schedule.”

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