Our End-to-End Analytics Workflow

# Our End-to-End Analytics Workflow Every successful analytics project follows a clear, repeatable process. Here's how we work with clients from initial discovery through long-term support. ## Phase 1: Discovery & Requirements Before writing any code or building any models, we invest time understanding your operation: - **Current State Assessment**: What data do you already collect? What tools do you use? Where are the gaps? - **Stakeholder Interviews**: We speak with coaches, scouts, front office staff to understand different perspectives and needs - **Goal Definition**: Clear, measurable objectives—not just "better analytics" but specific outcomes like reducing scouting coverage gaps or improving power play efficiency by X% - **Timeline & Budget Alignment**: Realistic expectations around what can be delivered and when **Typical Duration**: 1-2 weeks ## Phase 2: Data Pipeline Setup Modern analytics requires clean, reliable data flows: - **Data Source Integration**: Connect to video tracking systems, league APIs, internal databases - **Automated Collection**: Set up scheduled ingestion so data stays current without manual work - **Quality Validation**: Automated checks for missing data, outliers, inconsistencies - **Secure Storage**: Cloud infrastructure with proper access controls and backup systems **Key Deliverable**: A robust data pipeline that runs automatically, feeding fresh data to all downstream analytics ## Phase 3: Model Development & Validation This is where analytics gets built: - **Feature Engineering**: Transform raw event data into meaningful metrics (zone entry success rates, shot quality, transition efficiency) - **Model Training**: Develop expected goals models, player similarity algorithms, projection systems tailored to your needs - **Historical Backtesting**: Validate models against past seasons to ensure they actually work - **Bias Checks**: Test for league-specific quirks, sample size issues, confounding variables **Typical Duration**: 3-6 weeks depending on complexity ## Phase 4: Visualization & Integration Analytics only matters if people actually use it: - **Dashboard Design**: Custom dashboards focused on daily workflows—not just pretty charts, but actionable interfaces - **Training Sessions**: Teach staff how to interpret metrics, when to trust the numbers vs trust intuition - **Workflow Integration**: Embed analytics into existing processes (pre-game prep, scouting reports, trade evaluations) - **Documentation**: Clear explanations of what each metric means and when to use it **Key Principle**: We don't hand over a black box. Everyone understands what the models do and why. ## Phase 5: Ongoing Support & Iteration Analytics isn't a one-time delivery: - **Regular Check-ins**: Weekly or biweekly sync to review insights, answer questions, refine approach - **Model Updates**: Adjust for rule changes, new data sources, evolving strategy trends - **Ad-Hoc Analysis**: Quick-turnaround answers for specific questions (trade deadline evaluations, opponent scouting) - **Seasonal Reviews**: Formal retrospectives to assess what worked, what didn't, how to improve **Support Models**: Retainer-based (monthly hours) or project-based (specific deliverables) ## Why This Process Works - **Collaborative, Not Prescriptive**: We adapt to your existing systems rather than forcing you into ours - **Iterative Delivery**: You see progress every week, not after months of development - **Focus on Adoption**: The best model in the world is worthless if no one uses it - **Transparent Communication**: Regular updates, clear timelines, no surprises ## Internal Links Learn more about [our services](/services) or [get in touch](/contact) to discuss your specific needs. ## Conclusion Hockey analytics isn't about building the fanciest model—it's about delivering insights that actually change decisions. Our workflow ensures every project starts with clear goals, builds reliable infrastructure, and ends with tools people use every day.