Mission
Support competitive performance
Translate raw match and training data into clear insights that can support tactical review, player development, and evidence-based decision making.
AI · Tracking Data · Match Intelligence
AU Women’s Soccer Analytics Center
A research and analytics hub for Auburn women’s soccer, combining match data, player tracking, machine learning, and performance insights to support tactical understanding, player development, and data-driven decision making.
Built for coaches, analysts, students, and collaborators interested in sports analytics, performance modeling, and applied AI in women’s soccer.
Expected Goal Trend
+18%
Press Recovery Rate
62%
Tracking Coverage
95%
AI Reports Generated
24
About the Center
This site is designed as a central place to present Auburn women’s soccer analytics work, including performance analysis, player evaluation, match intelligence, and AI-supported research projects.
Research
Bridge sports and AI
Build a platform where sports analytics, statistics, and machine learning come together through practical, interpretable applications in women’s soccer.
Education
Create hands-on student opportunities
Involve students in real-world data workflows, visualization, modeling, and reporting for applied learning in modern sports data science.
Research Focus
The center can grow around several complementary analytics directions, from basic match reporting to advanced AI-based modeling.
Match & Team Performance
- Possession structure and territorial control
- Shot quality and chance creation
- Pressing and defensive recovery patterns
- Passing networks and build-up sequences
Player Analytics
- Role-based player profiles
- Movement intensity and workload
- Spatial tendencies and heatmaps
- Development trends across time
AI & Predictive Modeling
- Event prediction and sequence modeling
- Player tracking and trajectory analysis
- Automated report generation
- Video-to-insight pipelines
Data Pipeline
A simple, credible structure for the center is to show how raw data becomes actionable insight.
1. Collect
Match video, event logs, player tracking, training data, and performance summaries.
2. Clean
Standardize timestamps, align player identifiers, remove noise, and validate data quality.
3. Model
Estimate metrics, detect patterns, build predictive models, and compare player or team profiles.
4. Visualize
Create heatmaps, trend charts, dashboards, match reports, and role-based summaries.
5. Act
Deliver interpretable insights for coaches, staff, students, and research collaborators.
Example Insights
These are placeholder visual sections showing the kind of outputs your center could feature on the site.
Attacking Heatmap
Illustrate where the team creates pressure, receives the ball, or generates shots.
Team Performance Snapshot
Compare recent match metrics across selected performance dimensions.
Projects
Start simple. These project cards are intentionally realistic for a first version of the website.
Current
Match Report Automation
Develop repeatable post-match summaries with key metrics, event timelines, and player notes.
In Progress
Player Movement Profiling
Quantify player roles using spatial behavior, intensity measures, and recurring tactical patterns.
Future
AI-Assisted Video Analytics
Explore semi-automated pipelines that connect game video with tracking and event-level insights.
Team
Replace these placeholders with your real lab members, students, collaborators, and coaching contacts.
Director / Faculty Lead
Statistics, AI, sports analytics, and applied modeling.
Student Analysts
Data cleaning, visualization, match coding, and report generation.
Collaborators
Coaches, performance staff, computer vision researchers, and domain experts.
Build this into your real center website
This first version is intentionally clean and easy to edit. You can now replace the placeholder text with your own team description, add real figures, connect project links, and publish it as a GitHub Pages site.