DOI: 10.5061/dryad.sf7m0cgh6
title: Accelerometer-based network analysis in female soccer: performance levels

description: This dataset examines the complexity of network structures in professional
 and collegiate women’s soccer teams using directed network analysis based
 on tri-axial acceleration data. The study involved one professional team
 and one university-level team, with data collected from matches during
 their respective seasons. Directed network analysis identified dyads and
 triads, representing cooperative interactions among players, while
 movement entropy quantified the influence of individual movements within
 the team. Network diversity, defined as the variability in activation
 probabilities of dyads and triads, was calculated to evaluate the tactical
 dynamics and cooperative behaviors of the teams. Data were collected using
 GNSS devices equipped with tri-axial accelerometers, ensuring precise
 measurement of movement intensity. The findings provide insights into the
 structural and functional differences in team coordination between
 professional and collegiate levels. The dataset is anonymized and adheres
 to ethical guidelines, enabling reproducibility and further exploration of
 team dynamics in sports science.
description: Participants Prior to participant
 recruitment, we calculated the minimum required number of matches using
 G*Power 3.1.9.4 (Heinrich Heine Universität Düsseldorf, Germany). This
 study employs a two-way analysis of variance (ANOVA) to primarily examine
 the interaction effects between the period of the match (the first half
 and second half of the match) and three team groups (professional teams
 during the first half of the season, professional teams during the second
 half of the season, and collegiate teams). Thus, the calculation for the
 F-test with ANOVA was conducted a priori, given an effect size of 0.40, an
 α error probability of 0.05, a power of 0.80, and a numerator df of 2 with
 six groups. The effect size (0.40) for this analysis was set based on
 findings from a previous study that examined changes in team coordination
 states during matches and reported a large effect size (η² = 0.240 to
 0.263) for differences influenced by the level of the opposing team. The
 total required sample size was calculated as 64 matches across six groups,
 with 11 matches per group. Based on this analysis, we
 recruited one professional-level women’s soccer team and one
 university-level women’s soccer team, both playing 11 matches per season.
 However, data were missing for four matches in the first half of the
 season and three matches in the second half for the professional team.
 Similarly, four matches were missing for the university team. Ultimately,
 data were collected from seven matches in the first half of the season
 (hereafter referred to as Pro1) and eight matches in the second half of
 the season (Pro2) for the professional team. Pro1 included 17 players,
 while Pro2 included 21 players. For the university-level team (hereafter
 referred to as Amateur), data were collected from seven matches in the
 second half of the season, with a total of 21 players included in the
 analysis. The selection of teams ensured data
 consistency and allowed for in-depth comparisons of competitive
 characteristics and tactical features at each level. Focusing on a single
 team per level minimized variability in tactical approaches and playing
 styles, enhancing result comparability. All participants provided written
 informed consent prior to data collection. The study adhered to the
 principles outlined in the Declaration of Helsinki and was approved by the
 Ethics Committee of Waseda University (Approval Number: 2023-044). Data
 were anonymized to ensure confidentiality, and all acceleration data were
 securely stored to prevent unauthorized access.
 Measurement Methods Each player wore a
 Global Navigation Satellite System (GNSS) device equipped with a triaxial
 accelerometer (PlayerTek, Catapult, AUS) on their upper back (bottom of
 the neck) to record acceleration data during matches. Data collection
 spanned from immediately before match entry until the end of play or
 substitution. Recorded data were segmented into 45-minute halves,
 excluding stoppage time. For substitutions, data were split to include
 only active periods for each player. For example, if Player A was
 substituted by Player B in the 25th minute of the second half, Player A’s
 data for the first half and the first 25 minutes of the second half were
 analyzed, while Player B’s data from the 25th to the 45th minute of the
 second half were included. Network Analysis
 Methods <strong>Analysis of Dyads and
 Triads</strong> In directed network analysis,
 nodes represented players (sources and targets of connections), while
 edges denoted directed connections. A dyad consisted of two nodes and one
 edge, whereas a triad involved three nodes with directed edges among them.
 For example, if four nodes (A, B, C, D) were connected, and an additional
 edge linked B and D, the network would contain two triads and five
 dyads. This analysis followed procedures similar to
 those described by Tanaka et al. (2021). Dyads and triads were identified
 by computing movement entropy based on acceleration signals captured in
 the same spatial and temporal context. All analyses were performed using a
 directed network analysis system (Hitachi Ltd., Tokyo, Japan).
 Calculation of Movement Entropy Movement
 entropy quantifies the influence of one player’s movements on another. The
 calculation involved three steps: 
 Acceleration data were collected at 10 Hz and converted into
 movement intensity scores. Intensity data were
 normalized using histograms based on Sturges’ formula.
 Movement entropy was calculated to evaluate how much one
 player’s movements predicted another’s, with results ranging from 0 (no
 influence) to 1 (strong influence). A
 basic model was used to simplify the analysis, considering only the
 previous movements of both players (k=l=1k = l = 1k=l=1). The entropy
 value represented the degree of uncertainty reduction when predicting
 Player B’s next movement based on Player A’s current movement. This method
 provided a clear measure of directional influence between players in the
 network. <strong>Complexity of Network
 Structure</strong> In this study, we adopted a
 method to calculate information entropy based on the occurrence
 probabilities of Dyads (2 nodes) and Triads (3 nodes) and evaluate the
 difference from the maximum information entropy. This information entropy
 serves as a measure of the complexity of the network structure and is
 defined as "network diversity." Specifically, the complexity is
 calculated using the ratio of the maximum entropy (Hmax​) when nodes are
 randomly activated with equal probability to the actual entropy (H) based
 on observed activation probabilities. A value closer to 1 indicates the
 presence of fixed cliques (Dyads or Triads), while a value closer to 0
 reflects a more even distribution.
description: # Accelerometer-based network analysis in female soccer: performance
 levels
 [https://doi.org/10.5061/dryad.sf7m0cgh6](https://doi.org/10.5061/dryad.sf7m0cgh6) ## Description of the data and file structure #### Dataset Overview This dataset investigates the complexity of network structures in professional and collegiate women’s soccer teams, focusing on cooperative interactions and tactical dynamics. Data were collected during matches using GNSS devices equipped with tri-axial accelerometers, providing precise measurements of player movements and interactions. #### Data Content The dataset includes: 1. **Player Movement Data**: Tri-axial acceleration data recorded at 10 Hz for all players during each match. Data capture spans from match entry to exit, with segmentation into 45-minute halves. Stoppage time is excluded, and substitutions are handled by segmenting data accordingly. 2. **Network Analysis Results**: Quantitative results of directed network analysis, including: * Number of dyads (two-player interactions) and triads (three-player interactions). * Movement entropy values representing the influence of individual movements on others. * Network diversity metrics evaluating the variability in activation probabilities of dyads and triads. 3. **Team-Specific Details**: Information categorized by team and match period: * Professional team (Pro1: first half of the season; Pro2: second half of the season). * University-level team (Amateur: second half of the season). #### Data Collection Details * **Devices**: PlayerTek GNSS devices with tri-axial accelerometers. * **Frequency**: 10 Hz. * **Analysis Software**: Directed network analysis was conducted using a proprietary system developed by Hitachi Ltd. #### Ethical Compliance The dataset adheres to ethical guidelines, including anonymization to protect participant confidentiality. Written informed consent was obtained from all participants, and the study was approved by the Ethics Committee of Waseda University (Approval Number: 2023-044). #### Potential Applications This dataset provides valuable insights into: * Differences in team coordination and tactical behaviors between professional and collegiate soccer teams. * Quantitative evaluation of cooperative interactions using movement entropy and network diversity metrics. The dataset is suitable for reproducibility studies and further research into team dynamics in sports science. ### Files and variables #### File: Data_set_Dyad＆Triad_in_Football_ver4.xlsx ### Files and Variables 1. **Sheet1: Dyad** * Contains information on the number of dyadic interactions (two-player connections) during matches, segmented by team and match half. **Column** * Category (amateur/Pro-1st season/Pro-2nd season) * 1st/2nd half * Number of Dyad [number] 2. **Sheet2: Triad** * Records the number of triadic interactions (three-player connections) during matches, segmented by team and match half. **Column** * Category (amateur/Pro-1st season/Pro-2nd season) * 1st/2nd half * Number of Dyad [number] 3. **Sheet3: Diversity** * Includes diversity metrics for dyads and triads, capturing the variability in their activation probabilities during matches. **Column** * Type of network (Dyad/Triad) * Category (amateur/Pro-1st season/Pro-2nd season) * 1st/2nd half * Diversity [AU] 4. **Sheet4: Direction_Position** * Contains directional analysis results, including positional roles, thresholds for directional influence, and the corresponding network index (NW). **Column** * Category (amateur/Pro-1st season/Pro-2nd season) * Position (DF/MF/FW) * Value of network index [AU] * Network category (&lt;1.0 or ≥1.0) # Code/software ##### Analysis Software 1. **Network Analysis System** * A proprietary directed network analysis system developed by Hitachi Ltd. (Tokyo, Japan) was used to calculate dyads, triads, and movement entropy. ## Data Quality * All sheets are complete with no missing values or headers. * The dataset is ready for analysis and requires no preprocessing for structural issues. ## Notes for Use * Data within each sheet should be filtered appropriately based on the purpose of analysis. * Ethical considerations should be observed. ## Access information NA ## Update History * **2025-01-07**: Initial dataset release with no outstanding issues. * **2025-08-14**: Removed the worksheet titled *"Non-contact Injury"* from the dataset.