The rise of data in Sports Administration

From the Moneyball effect to wearable IOT devices, the exponential creation of sports data is powering the next wave of athletic performance and sport administration.

From the predictive modelling of athlete performance, to the automation of match fixturing, sporting bodies are unlocking the secrets hidden within their vast and varied datasets to remain competitive.

The Solution


Thanks to AutoStat®, access to advanced analytics tools and techniques has never been easier.

AI and ML techniques are helping to unlock new insights and opportunities for sporting teams and governing bodies to ensure they remain competitive from athletic, financial and administrative perspectives. Organisations are deploying these techniques in athletic research, statistical analysis and administration management where solutions ranging from real-time dashboards to athletic performance modelling, enabling innovators and leaders in sports administration to unlock new efficiencies and guide data-driven decision making.

Sporting organisations are now able to interrogate their data with the same methods previously reserved for mathematicians, PhD data scientists and quantitative experts. From the grassroots football team, to the venue manager to the biostatistician, Sports analytics is entering a new era of data-driven insights and decision making to power the winners of tomorrow.

AutoStat® in Sports Administration

AutoStat® is optimizing and automating the scheduling of over 40,000 soccer matches across 1,600 venues in Victoria, assisting Football Victoria to meet the needs of thousands of stakeholders as it administers the largest grassroots sports organisation in Australia. Football Victoria selected AutoStat® as the only application capable of automating a task that previously required hundreds of hours in manual handling, while maximizing the needs and demands of its affiliate clubs, players, referees and spectators.

The code-free analytics capabilities of AutoStat® can allow your organisation to:

1. Model player and athlete performance using advanced statistical and machine-learning frameworks.
2. Optimize the scheduling of matches while adhering to hundreds of business and operational constraints.
3. Distribute athletic, administrative and operational insights via customizable dashboards and visualizations.
4. Build quantitative models of team/athlete performance by uploading and integrating match/game statistics, IOT device statistics (from fitness trackers, HR monitors) into the application.
5. Build predictive models of game-day attendance to maximize human resources, catering and security investment.
6. Connect disparate datasets to model and predict spectator movements and identify stadium bottlenecks and security issues.
7. Integrate predictive modelling and insights to existing software, systems and workflows.