ECSS Paris 2023: OP-AP25
INTRODUCTION: Despite the growing availability of tracking data, quantifying off-ball defensive performance remains a conceptual challenge in football analytics. Passive contributions through positioning and anticipation are hard to capture in prevalent event-based frameworks. To address this gap, we propose a two-step framework that first quantifies off-ball defensive actions using a set of dedicated metrics and subsequently integrates these metrics into a network-based representation to analyse individual and collective defensive behaviour. METHODS: We propose a novel set of metrics that quantify defensive involvement and responsibility based on the offensive passing actions of the opposition. Involvement is based on players’ proximity to opponent passes, while responsibility reflects the expected involvement derived from roles within dynamically detected formations. Players’ defensive behaviours are classified as either faults (being outplayed) or contributions (stopping the opponent from progression) based on the value of each offensive pass (Expected Threat). The validity and robustness of the model are evaluated on a cross-gender and cross-competition data set spanning 516 matches. Based on this involvement-responsibility framework, Defensive Valued Networks (DVNs) are created at both the player and team level. Player-level DVNs represent the passing interactions a defender is involved in, while team-level DVNs model shared defensive responsibility between players during opposition actions. A total of 64 matches from the Men’s World Cup 2022 provided by PFF FC are used. Network metrics (total degree and network density) are correlated with measures of defensive success (goals and shots against) to examine the effectiveness of our approach. RESULTS: All four types of metrics (involvement, responsibility, fault and contribution) show higher validity (~1SD) in a combined index of noisy proxies than traditional metrics. For example, a central defender’s market value is much more strongly associated with their fault (competition-averaged r = -0.423) than with their number of interceptions (r = 0.074). Network metrics derived from fault-only DVNs show significant correlations with conceded shots for both player and team level network: Player’s network r = 0.233 (p < 0.01) and total degree r = 0.345 (p < 0.001); team network density r = 0.294 (p < 0.001) and total degree r = 0.311 (p < 0.001). If contribution is considered alone, significant correlations only exist in the shared team-level network, (density r = 0.364, p < 0.001; total degree r =0.388, p < 0.001). CONCLUSION: We propose involvement- and responsibility-based metrics to quantify off-ball defensive performance. When cast into a network representation, it can be used to quantify the value attributed to each individual and the team's defensive cooperation. These finding demonstrates that the presented metrics can help clubs determine which players contribute towards positive match outcomes through effective off-ball defending.
Read CV Runqing MaECSS Paris 2023: OP-AP25
INTRODUCTION: Youth of the same chronological age may differ substantially in biological maturity, making it essential to account for maturation when comparing physical performances within age groups, as maturity influences the development of specific fitness components. Several predictive equations have been proposed to estimate maturation status, either via age at peak at height velocity (APHV) or predicted adult height (PAH). This study examined the concordance among commonly used predictive methods for classifying players by maturation status and, to the best of our knowledge, investigated for the first time whether differences in physical performance are more closely related to maturation or chronological age. METHODS: A total of 192 youth male soccer players (U14=55, U15=55, U16=43, U17=39) were assessed. Measurements included anthropometry, sprint performance (10 m and 30 m), countermovement jump (CMJ), and endurance capacity (Yo-Yo Intermittent Recovery Test, YYIRT1). APHV was estimated using Mirwald, Moore, Moore2, and Fransen methods, while PAH was calculated using the Khamis-Roche method. Players were classified by maturation status using both conservative and less conservative criteria. Agreement among APHV estimates was assessed using Bland-Altman analysis, and classification agreement was evaluated with Fleiss’ Kappa. The impact of maturation on performance was examined using two-step linear regression models to assess changes in explained variance (R²). RESULTS: All predictive methods yielded significantly different APHV estimates (p < 0.05); regression slopes were significant in all comparisons, indicating increasing disagreement at higher APHV values. Classification agreement was weak under both criteria (Kappa = 0.521 and 0.549). For the 10 m and 30 m sprint performance, inclusion of maturation status significantly improved R² in most cases (ΔR² = 0.025-0.118), indicating that maturation explained an additional 2–11% of variance. For YYIRT1, maturation did not improve models beyond chronological age. For CMJ, the contribution of maturation was limited, with only four methods showing a significant increase in R². CONCLUSION: Accounting for biological maturation independently of chronological age is essential, as failing to do so confounds their respective effect. Maturation influences performance unevenly, showing stronger associations with sprint ability than with strength or endurance, highlighting the need for further targeted research.
Read CV Stefano AmatoriECSS Paris 2023: OP-AP25
INTRODUCTION: This study investigated distances covered during linear sprints to reach absolute (20.0 km.h-1, 25.0 km.h-1) and relative sprinting thresholds (70%, 80%, 90% of Maximal Sprinting Speed (MSS)) and MSS itself in highly trained male U12 to U17 youth soccer players. We examined through mediation analysis whether age-related effects on these distances operate primarily through increases in MSS or involve additional developmental factors. METHODS: We collected data from 110 male youth soccer players using a cross-sectional design. Players performed two 40m linear sprints with the velocity continuously measured with a radar gun. Raw velocity-time data were smoothed using cubic splines. Generalised additive models examined relationships between MSS (range: 23.5 km.h-1 to 34.1 km.h-1) and distances to sprinting thresholds, and between chronological age and distances to sprinting thresholds. Mediation analysis quantified the extent to which age effects on distances were transmitted through MSS (indirect effects) versus age-independent pathways (direct effects) for absolute sprinting thresholds. RESULTS: With increasing MSS, distances to 20.0 km.h-1 decreased from 7.0 m to 3.5 m, to 25.0 km.h-1 from 23.7 m to 7.0 m, to 70% MSS increased from 3.5 m to 5.9 m, to 80% MSS from 5.8 m to 9.4 m, to 90% MSS from 9.9 m to 15.9 m, and to MSS from 24.0 m to 36.3 m. With increasing age, corresponding values were 6.4 m to 3.8 m, 19.7 m to 7.7 m, 3.9 m to 5.3 m, 6.3 m to 8.4 m, 10.7 m to 14.3 m, and 26.2 m to 34.4 m. Mediation analysis revealed that age effects on absolute sprinting thresholds operated primarily through MSS, while distances to MSS involved both MSS-dependent and MSS-independent pathways. CONCLUSION: We investigated distances to absolute (20.0 km.h-1, 25.0 km.h-1) and relative sprinting thresholds (70%, 80%, 90% of MSS) and MSS itself in U12 to U17 youth soccer players. Distances to absolute sprinting thresholds decreased with increasing MSS and age, while distances to relative sprinting thresholds and MSS increased. Mediation analysis revealed that age effects on distances to 20.0 km.h-1 and 25.0 km.h-1 operated almost entirely through increases in MSS, whereas distance to MSS was influenced by both MSS increases and MSS-independent developmental factors. Practitioners can use these age- and MSS-specific distance benchmarks to individualise sprint prescription in team sessions, individual training, and rehabilitation contexts.
Read CV Ludwig RufECSS Paris 2023: OP-AP25