Research background and hypothesis. Europeans have won Olympic and world gold medals since modern indoor men’s handball became an international sport (1938) and an Olympic sport (1972) (www. ihf.com). Nevertheless, no research has been carried out in order to find out the tendencies in European modern men’s handball. Research aim of this study was to illustrate discriminant indicators of sport performance between winners and losers in European men’s modern handball match activities. Research methods. Data sets were gathered from the European Handball Federation (EHF) website (http://www.eurohandball.com) covering the five European Men’s Handball Championships (EMHC): 2002, 2004, 2006, 2008 and 2010 (n = 239 matches). Each match was classified as successful and non-successful for each team, and then the number of analysed matches doubled to 478. We examined 28 variables of sport performance, but only 15 key indicators where significant discriminant between winners and losers at least in one EMHC was shown. Research results. Winners scored more goals (p < 0.01, p < 0.001), were better in total attack (p < 0.001) and positional attack (p < 0.01, p < 0.001), performed more efficiently in shooting total (p < 0.001) and shooting from long distance (p < 0.01, p < 0.05), and goalkeepers saved more throws (p < 0.05, p < 0.001).Discussion and conclusions. Winning and losing teams played in the same pattern (p > 0.05). In many cases the separate match-play was determined by using temporal model of playing style. The phenomenon is that teams scored more goals in the second half than in the first one despite the fact that players’ activities decreased in the second half. Sport performance profile in European modern handball can generate a useful database.Keywords: sports games, performance analysis, elite athletes.

Background: The aims of this study were 1) to investigate the seasonal dynamics of training load, neuromuscular status, athlete mental state, and physical performance indicators and 2) to determine how these variables and training content relate to match performance and outcomes in semi-professional female handball players. Additionally, this study examined the practical application of monitoring-informed periodization using a structured microcycle model. Methods: A longitudinal observational study was conducted across the entire competitive season (193 days, 159 training sessions, 34 matches) of semi-professional female handball players. The internal training load was quantified using the session rating of perceived exertion multiplied by the session duration (min), allowing the calculation of the total weekly load, monotony, strain, and acute:chronic workload ratio. Neuromuscular status was assessed using the countermovement jump (CMJ), whereas the athlete’s mental state was evaluated using a five-component well-being questionnaire. Physical performance indicators (strength and intermittent endurance) and sport-specific performance (throwing velocity) were also assessed. The match performance was analyzed using notational analysis. The training content was categorized into physical, technical, tactical, integral, recovery, warm-up, and theoretical components. Results: Major performance findings demonstrated significant improvements in intermittent endurance and strength endurance, whereas CMJ remained unchanged throughout the season. The internal load varied systematically across microcycles, with higher monotony and strain during the competitive phases. Match outcome was primarily associated with defensive performance variables, particularly first-half defensive efficiency (r_pb = 0.680, p = 0.011) and goalkeeper effectiveness (r_pb = 0.576, p = 0.039). Acute training content, especially tactical ( r = 0.54) and integral training ( r = 0.48), demonstrated the strongest relationship with match outcome. Conclusion: Performance in semi-professional female handball players is best understood as an integrated system in which the training load (including total weekly load, monotony, strain, and acute:chronic workload ratio), athlete well-being, and tactical preparation interact dynamically. Match success was primarily associated with defensive efficiency and short-term sport-specific preparation. These findings support monitoring-informed, periodization approach based on a structured microcycle model (retraining, one match, and two match competition microcycles), demonstrating that effective performance development can be achieved without a traditional long preparatory phase when training loads are coherently structured throughout the seasons.

Long-term, single-club datasets linking fitness and match performance are rare in women’s handball. This retrospective study profiled fitness and game-quality indicators across 14 seasons (2010–2022, 2026; N = 45; 8–17 players/season). Anthropometric and specific-fitness values (height 173.90 ± 5.53 cm, body mass 68.28 ± 8.08 kg, jump-throw velocity 79.94 ± 6.83 km/h, countermovement jump 43.46 ± 4.84 cm, VO₂max 48.01 ± 3.61 mL/kg/min) were sub-elite. Shot efficiency (55.74 ± 6.00%) approached elite values (58.90 ± 7.60%). Backs were older, more experienced, and higher-scoring than Wings and Pivots. Universal players matched Backs’ output despite being younger and less experienced. Experience correlated with goals per match (r = 0.51, p = 0.001), positive actions (r = 0.44, p = 0.005), and action balance (r = 0.47, p = 0.003). Throwing velocity and sprint/agility speed showed similar associations. Endurance, strength, and coordination/dribble deficit showed none. Fitness indicators improved across 2010–2022, but a single season after near-complete roster turnover departed markedly from this trend. Trends should not be extrapolated to a changed roster without retesting. A small set of trainable, sport-specific qualities, not general fitness, best predicts match performance. Findings provide normative benchmarks and training priorities for coaches.

Monitoring the muscle mechanical properties and functions of female athletes throughout their training season is relevant to understand the relationships between these factors and to predict noncontact injuries, which are prevalent among female athletes. The first aim of this study was to determine whether female handball players’ passive stiffness of the hamstring muscles is associated with hamstring extensibility, strength of knee flexors and extensors, and lower limb stiffness. Additionally, the study monitored fluctuations in these factors over 25 weeks. The study utilized an isokinetic dynamometer to record hamstring passive stiffness, extensibility, and hamstring and quadriceps strength of 18 young handball players. Lower limb stiffness was determined from a countermovement vertical jump conducted on a force plate. The countermovement jump involved the calculation of the peak force during the eccentric phase and the mean force during the concentric phase. The results showed a positive correlation between hamstring passive stiffness and lower limb stiffness ( r = 0.660, p < 0.01), knee flexion and extension strength ( r = 0.592, p < 0.01 and r = 0.497, p < 0.05, respectively), and eccentric peak force ( r = 0.587, p < 0.01) during jumping. The strength of knee extensors increased significantly after 6 weeks, and hamstring stiffness after 12 weeks of training. In conclusion, the increased hamstring stiffness following training did not match other factors associated with injury risk. Therefore, preventing multifactorial injury risk requires a comprehensive approach, and monitoring one factor alone is insufficient to predict noncontact injuries in female handball players.

Long-term, single-club datasets linking fitness and match performance are rare in women's handball. This retrospective study profiled fitness and game-quality indicators across 14 seasons (2010–2022, 2026; N = 45; 8–17 players/season). Anthropometric and specific-fitness values (height 173.90 ± 5.53 cm, body mass 68.28 ± 8.08 kg, jump-throw velocity 79.94 ± 6.83 km/h, countermovement jump 43.46 ± 4.84 cm, VO₂max 48.01 ± 3.61 mL/kg/min) were sub-elite. Shot efficiency (55.74 ± 6.00%) approached elite values (58.90 ± 7.60%). Backs were older, more experienced, and higher-scoring than Wings and Pivots. Universal players matched Backs' output despite being younger and less experienced. Experience correlated with goals per match (r = 0.51, p = 0.001), positive actions (r = 0.44, p = 0.005), and action balance (r = 0.47, p = 0.003). Throwing velocity and sprint/agility speed showed similar associations. Endurance, strength, and coordination/dribble deficit showed none. Fitness indicators improved across 2010–2022, but a single season after near-complete roster turnover departed markedly from this trend. Trends should not be extrapolated to a changed roster without re-testing. A small set of trainable, sport-specific qualities, not general fitness, best predicts match performance. Findings provide normative benchmarks and training priorities for coaches.