How to manage player load in a packed schedule?

It provides a quick overview of the current load-profile of each player and the ratio-estimate gives an insight into the relationship of the load-demands in the last 7 days in relation to the chronic workload of the past 28 days, which can then be used to evaluate whether a given player is ready to participate in a full session or in need of recovery. It provides coaches with an indication of whether the load of the players is increasing or decreasing too rapidly and it’s a tool to ensure continuity in load from week to week. Something that’s crucial to reduce the risk of injuries.

Keeping players fit and available is an imperative predisposition for securing competitiveness for each team. With great physical demands being placed on players from continuous participation in one or two games per week, there is a high need for tools to effectively monitor the balance between imposed load and needed recovery. Accompanying performance optimization with recovery strategies should be considered vital, due to the great economic burden as players are withdrawn from game participation due to injury (1), while teams with greater player availability typically performs better (2).

One such tool is the Acute Chronic Workload-ratio (ACWR), which provides a load-ratio, based on the load of the previous 7 (acute) and 28 (chronic) days. Based on the acute and chronic demands, the workload ratio is derived. The model provides the practitioner with an indication of whether the load demands during a week are impacted of workloads either increasing or declining too rapidly, which subsequently could be an indication of either over- or under-loading. The ACWR can furthermore assist in ensuring continuity in imposed workloads from week to week, as rapid changes in training demands are associated with an elevated risk of sustaining an injury (3, 4).

In the graph, three players are in the optimal range and fit to train, while one player exceeds the optimal interval and could have an elevated risk of overtraining. In that way, the Acute Chronic Workload gives coaches a quick overview of the players’ preparedness for training every day.

Importance of monitoring load-continuity

With high-level teams periodically participating in weeks with 2 matches due to cup games or international tournaments, a subsequent rise of injury-risk becomes evident. Earlier investigations revealed that elevated fixture congestions are associated with an increased risk of injuries (5, 6), as it inevitably increases the load-demands imposed on the players, hence emphasizing the elevated need for recovery. Conversely, players that are continuously exposed to lower load-demands, could also be in an elevated risk of injury, potentially due to under-preparedness (7). Securing that players chronically are exposed to higher loads, could potentially have a “protective” effect against injuries (8), given that recovery strategies are planned appropriately in between high exposure sessions.

How it works and recommended use

To further assist coaches in the process of load management, the Acute Chronic-feature has been added to the app, providing quick accessibility of the current load-profile of each player. Here, the ratio-estimate gives an insight into the relationship of the most recent load-demands (7 days)in relation to the chronic workload (28 days), which can then be used to evaluate whether a given player are ready to participate in a full session or in need of recovery. Adding more granularity into the process of load management, the practitioner could benefit from also including wellness-questionnaires, and see the current state of perceived fatigue, muscle soreness and sleep quality[1].

As seen in Figure 1, each players workload-profile are highlighted, with three players located in the optimal range, while one player exceeds the optimal interval. The overview enables coaches to get a quick insight in to the players preparedness for training on the specific day.

For example, “Player 1” whose ratio-estimation exceeds the optimal zone, could be in an elevated risk of overtraining due to a marked rise in the acute workload compared the chronic workload (+47), while the remaining three players are fit to train. Parameters influencing the elevated ratio estimate could be identified as elevated game-participation following an injury, which consequently elevates the acute load.

[1] This feature is included in the next release.

‍Ratio Intervals

·       > 0.75: Workloads are below the optimal zone (potential risk of sustaining an injury).

·       0.75 – 1.25: Optimal zone.

·       > 1.25: Workload exceeding the optimal zone (higher potential risk of sustaining an injury).

Literature

1.     Eliakim, E, Morgulev, E, Lidor, R,Meckel, Y. (2020). Estimation of injury costs: financial damage of English Premier League teams’ underachievement due to injuries.

2.     Hägglund, M, Waldén, M, Hedevik, Het al. (2013). Injuries affectteam performance negatively in professional football: An 11-year follow-up ofthe UEFA Champions League injury study 

3.     Nilsson, T, Börjesson, M,Lundblad, M et al. (2023). Injury incidence in male elite youth football players is associated with preceding levels and changes in training load. 

4.     Malone, S,Owen, A, Newton, M et al. (2016). The acute:chonic workload ratio in relation to injury risk in professional soccer. Journal of Science and Medicine in Sport, 20(6), p. 561-565.

5.     Dupont, G, Nedelec, M, McCall, Aet al. (2010). Effect of 2 soccermatches in a week on physical performance and injury rate. The AmericanJournal of Sports Medicine, 38(9).

6.     Page, RM, Field, A, Langley, B etal. (2013). The Effects of Fixture Congestion on Injury in Professional Male Soccer: A Systematic Review. Sports Medicine, 53, p. 667-685. 

7.     Gabett, T.(2016). The training—injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50, p. 273-280. 

8.     Hulin, T,Gabett, TJ, Lawson, DW, Caputi, P, Sampson, JA. (2016). The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players.