Monitoring external load in elite male handball players depending on playing positions

Abstract

Monitoring workload is critical for elite training and competition, as well as preventing potential sports injuries. The assessment of external load in team sports has been provided with new technologies that help coaches to individualize training and optimize their team’s playing system. In this study we characterized the physical demands of an elite handball team during an entire sports season. Novel data are reported for each playing position of this highly strenuous body-contact team sport. Sixteen world top players (5 wings, 2 centre backs, 6 backs, 3 line players) were equipped with a local positioning system (WIMU PRO) during fourteen official Spanish first league matches. Playing time, total distance covered at different running speeds, and acceleration variables were monitored. During a handball match, wings cover the greater distance by high-speed running (> 5.0 m·s -1 ): 410.3 ± 193.2 m, and by sprint (> 6.7 m·s -1 ): 98.0 ± 75.4 m. Centre backs perform the following playing position that supports the highest speed intensities during the matches: high-speed running: 243.2 ± 130.2 m; sprint: 62.0 ± 54.2 m. Centre backs also register the largest number of high-intensity decelerations (n = 142.7 ± 59.5) compared to wings (n = 112.9 ± 56.0), backs (n = 105.2 ± 49.2) and line players: 99.6 ± 28.9). This study provides helpful information for professional coaches and their technical staff to optimize training load and individualize the physical demands of their elite male handball players depending on each playing position.

Physical Demands during Official Competitions in Elite Handball: A Systematic Review

“…Over the past two decades, the global positioning system (GPS) has been developed to understand, with greater accuracy and precision, the physical demands of outdoor team sports (i.e., football or rugby) [ 14 ]. However, this method cannot be used for indoor sports because the GPS signal is blocked [ 15 , 16 ]. Recently, with the purpose of overcoming this inconvenience, different companies (e.g., Wimu TM , Catapult TM and Kinexon TM ) developed a local positioning system (LPS) with ultra-wideband technology (UWB) [ 17 ] to track and analyse indoor team sports (i.e., basketball, handball, futsal, netball or ice hockey) [ 16 , 18 , 19 , 20 ].…”

Contextualizing Physical Data in Professional Handball: Using Local Positioning Systems to Automatically Define Defensive Organizations

“…For instance, a 0–6 defensive organization is usually implemented as it is very stable and robust against offensive perturbation. By extension while looking at the match demands in regard of the defensive organization, the players’ physical activity in terms of distance covered at different speed intensity does differ between players’ roles as expected [ 1 , 4 ], but also can differ between the consideration of the defensive organization (i.e., between the original intention at the start of the defensive phase or the actual geographical organization of the players), specifically when using a 1–5 or 3–3 defensive organization. In this case, adding the defensive context to filter the global match demands allows to quantify the cost of the defense, for instance, the wingers will show higher demands (i.e., walk, jog and run) when the team is defending in 1–5 compared to a 0–6.…”

“…Starting from the raw data from the local positioning system, multiple steps were necessary to get a clear view of the match context, for instance removing the players who stand on the bench is a key step necessary prior to defining any match information (e.g., handball defensive or offensive activity), whereas this step is usually not considered when looking at the global match demands of the players [ 4 ]. Indeed, handball is a sport with unlimited, and high rotations between players (i.e., players come in and out of the game very easily and frequently), similar to ice hockey, and while they usually sit on the bench when they are out therefore not drastically impacting the match demands numbers (i.e., no change in sprinting distance covered, etc…), those rotations do impact the detection of the defensive/offensive organization [ 4 ]. Defining the context of the player’s activity, specifically when using sensors (i.e., without any information on the players from the opposition team nor on the ball location) necessitates a drastically deeper processing of the data than quantifying the global match demands.…”

“…Playing roles influence largely match demands [ 1 , 4 ] found that center backs and wings (4040 ± 1007 m and 3903 ± 1224 m respectively) cover largely more distance than the other players. Wings and center backs were more involved in HSR action both in distance (410 ± 193 m and 243 ± 130 respectively) and in number (39.6 ± 18.2 and 34.9 ± 18.1).…”

“…A growing number of elite teams are equipped with a local positioning system ( Tracking , s. d.) and many studies used this technology to study match demands [ 4 , 5 , 9 ]. Automatic detection of the defensive formation could be very useful both to help coaches to design adapted training content and in a research context.…”