In the dynamic world of competitive robotics, especially within the FIRST Robotics Competition (FRC), teams are increasingly relying on data to guide their strategies, scouting, and performance evaluations. One platform that has dramatically shifted the landscape of data usage in this domain is Statbotics. Known for its precision, accessibility, and analytical depth, Statbotics has become an essential tool for robotics teams aiming to compete at the highest levels.

The Genesis of Statbotics

Statbotics was born out of a need for a better, more accurate method of understanding and leveraging robotics competition data. Its creator, a former high school robotics participant and software engineer, recognized a gap in the tools available to FRC teams. While scouting and data collection were already common practices, the process was largely manual and inconsistent across teams. Statbotics was developed to automate, streamline, and enhance how teams analyze match performance and opponent capabilities.

What began as a side project quickly evolved into a comprehensive open-source platform for robotics data analytics. It introduced new ways of interpreting match data, providing both historical insights and real-time updates. Over the years, the platform has undergone significant updates, expanding its features and refining its algorithms.

Core Features and Tools

Statbotics offers a suite of tools designed to provide insights into team and match performance. Central to its functionality is the EPA (Expected Points Added) model—a predictive metric that evaluates how much a team contributes to their alliance’s final score on average. Unlike traditional metrics that often fail to account for external variables, EPA offers a more nuanced, normalized view of team performance.

1. Expected Points Added (EPA)

EPA is the heart of Statbotics. This statistic is more than just a simple average; it’s a sophisticated prediction model that adjusts for factors like alliance partners, opposition strength, and match context. By converting performance into point-based ratings, it allows teams to be compared on a standardized scale.

• Component EPA: This breaks down a team’s performance into key game phases such as autonomous mode, teleoperation, and endgame. Teams can analyze which aspects of gameplay they excel at or need improvement in.

• Overall EPA: A comprehensive score that encapsulates a team’s average performance across all metrics and matches.

• Year-to-Year Comparisons: EPA scores are normalized across seasons, allowing users to track long-term team progress and consistency.

2. Match Predictions

One of the most powerful features is the match prediction engine. By using live and historical data, the platform can accurately forecast the outcome of matches, taking into account the combined EPA scores of all participating teams. This is useful not just for strategy but also for fans and event organizers.

3. Team Insights and Trends

Teams can access detailed profiles that include EPA over time, match histories, event participation, and ranking point statistics. These insights help drive strategic decisions, such as alliance picks or identifying key competitors during playoffs.

4. Event Analytics

For each competition, Statbotics provides breakdowns of event-wide statistics, such as average EPA scores, team performance distributions, and win probabilities. This makes it easier to gauge the competitiveness of an event and prepare accordingly.

Technical Architecture

Statbotics runs on a high-performance backend designed for real-time data processing. It integrates with publicly available robotics data APIs and databases, continuously syncing and analyzing new match results.

• Backend Infrastructure: Built on a scalable framework that supports complex statistical calculations and can handle large data volumes during major events.

• Frontend Interface: A clean and intuitive web interface makes it easy for users to explore data. Features like filters, charts, and sortable tables offer interactive ways to interpret information.

• APIs and Data Access: Developers and analysts can use REST or Python APIs to pull data for custom dashboards, internal scouting apps, or educational purposes.

Transforming Scouting and Strategy

Traditionally, scouting in robotics competitions involved teams collecting manual data about robot performance during matches. While this method is still valuable, Statbotics provides a data-driven supplement or alternative that increases accuracy and efficiency.

Teams can use EPA metrics to pre-scout competitors before events, saving time and improving alliance selections. Coaches can adjust in-match strategies based on anticipated performance metrics. Statbotics also helps in identifying sleeper teams—those whose win-loss records may not reflect their true potential due to weak alliance pairings.

By centralizing data and offering predictive capabilities, Statbotics allows for more informed decision-making, which can be a competitive advantage in a game where margins for error are slim.

Supporting Education and Learning

In addition to supporting FRC teams, Statbotics contributes to STEM education by providing students with a platform to learn about statistics, predictive modeling, and data science. Educators and mentors use the platform to teach students how data can inform engineering and design decisions. The visualizations and analysis tools make abstract mathematical concepts more concrete and relevant.

Moreover, Statbotics encourages open-source contributions and provides documentation to help students and developers understand how its system works. This exposure not only aids in their robotics pursuits but also builds transferable skills in software development, data analysis, and project management.

Community and Collaboration

One of the reasons for Statbotics’ success is its strong community support. Users are encouraged to suggest improvements, report bugs, and contribute code. This collaborative model has helped the platform evolve rapidly and stay closely aligned with the needs of the FRC community.

Frequent updates and community polls ensure that Statbotics remains agile. Whether it’s adjusting how a game-specific feature is calculated or introducing a new form of match visualization, the platform is shaped by its users.

Forums, discussion boards, and repositories provide ample space for engagement, and many FRC teams share how they’ve integrated Statbotics into their workflows.

The Competitive Edge

In high-level competition, where many robots are finely tuned and teams are well-practiced, the difference often comes down to strategy. That’s where Statbotics offers a critical edge. By identifying strengths, weaknesses, and performance trends across the field, it allows teams to make tactical decisions that can swing a match’s outcome.

For instance, a team may use EPA to decide whether to focus on defensive play, targeting the opponent’s strongest robot based on performance metrics. Alternatively, alliance captains can use the platform to pick partners that balance out their own capabilities or specialize in endgame tasks.

This kind of real-time, evidence-based planning is increasingly becoming the standard in robotics competitions, and Statbotics is at the forefront of this movement.

Looking Ahead

Statbotics continues to grow, both in functionality and adoption. Future developments may include deeper machine learning integrations, enhanced scouting app compatibility, and expanded analytics for off-season and non-FRC competitions. There is also potential for integrating sensor and vision data for even richer performance modeling.

As robotics continues to blend engineering with artificial intelligence, platforms like Statbotics will play a vital role in shaping how teams train, plan, and execute.