Preston Swigart contributed to DurhamAcademy/ChocoChipScouting by building and refining a robust frontend for scouting analytics, focusing on UI/UX improvements, data visualization, and stability. He implemented new features such as match visualization and chat integration using Vue.js, TypeScript, and Chart.js, while also addressing accessibility and persistent state management. Preston enhanced the reliability of team metrics by correcting sorting logic and improved maintainability through code cleanup and testing scaffolding. His work included debugging complex UI states, refining dark mode, and ensuring accurate data presentation, resulting in a more scalable, accessible, and production-ready application for the 2025 season.
April 2025: DurhamAcademy/ChocoChipScouting focused on stabilizing the Teams table UX by correcting sorting behavior and cleaning up logging. Implemented precise numeric sorting for numeric strings with varying decimal places and reverted an unintended change that affected the Accuracy column sorting. These changes improve data reliability for team metrics, reduce user confusion, and streamline maintenance.
April 2025: DurhamAcademy/ChocoChipScouting focused on stabilizing the Teams table UX by correcting sorting behavior and cleaning up logging. Implemented precise numeric sorting for numeric strings with varying decimal places and reverted an unintended change that affected the Accuracy column sorting. These changes improve data reliability for team metrics, reduce user confusion, and streamline maintenance.
March 2025 performance summary for DurhamAcademy/ChocoChipScouting focused on delivering measurable business value through UI/UX improvements, reliability fixes, and readiness for production deployment. The month featured key frontend feature delivery, targeted bug fixes, and foundational testing/QA work that increases release confidence and scalability of the product. Key outcomes include visible UI/visual improvements, a new chat feature, and a stronger codebase with cleaner architecture and testing scaffolding. Demonstrated technologies and skills include Vue/Nuxt frontend work, UI/UX design, code cleanup/refactor, and server-side testing readiness that support faster, safer shipping.
March 2025 performance summary for DurhamAcademy/ChocoChipScouting focused on delivering measurable business value through UI/UX improvements, reliability fixes, and readiness for production deployment. The month featured key frontend feature delivery, targeted bug fixes, and foundational testing/QA work that increases release confidence and scalability of the product. Key outcomes include visible UI/visual improvements, a new chat feature, and a stronger codebase with cleaner architecture and testing scaffolding. Demonstrated technologies and skills include Vue/Nuxt frontend work, UI/UX design, code cleanup/refactor, and server-side testing readiness that support faster, safer shipping.
February 2025 — DurhamAcademy/ChocoChipScouting monthly performance snapshot. Focused on delivering stability, accessibility, and scalable UX while strengthening testing and deployment practices. Key features delivered include accessibility improvements, persistent save-progress, stability enhancements, and UI/code quality improvements. Major bug fixes covered endgame options, matches page, Nuxtimg rendering, and dashboard issues, contributing to a more reliable, maintainable product with clearer debugging paths.
February 2025 — DurhamAcademy/ChocoChipScouting monthly performance snapshot. Focused on delivering stability, accessibility, and scalable UX while strengthening testing and deployment practices. Key features delivered include accessibility improvements, persistent save-progress, stability enhancements, and UI/code quality improvements. Major bug fixes covered endgame options, matches page, Nuxtimg rendering, and dashboard issues, contributing to a more reliable, maintainable product with clearer debugging paths.
January 2025 (DurhamAcademy/ChocoChipScouting): Delivered a major upgrade to the scouting data model and analytics stack for the 2025 season, including Coral Level scoring, differentiation of autonomous/teleoperated phases, and extended match-level data capture (Algae in Processor/Net) with updated bindings and UI. Implemented Rich Match Visualization integrated into the team detail page for deeper analytics. Updated event options to support 2025 events and mappings, and performed UI polish across scouting pages for improved usability. Fixed core sorting logic on the team index to ensure accurate reef-related ranking. Overall, these changes improve data fidelity, enable faster, data-driven decisions, and establish a scalable frontend for the 2025 season.
January 2025 (DurhamAcademy/ChocoChipScouting): Delivered a major upgrade to the scouting data model and analytics stack for the 2025 season, including Coral Level scoring, differentiation of autonomous/teleoperated phases, and extended match-level data capture (Algae in Processor/Net) with updated bindings and UI. Implemented Rich Match Visualization integrated into the team detail page for deeper analytics. Updated event options to support 2025 events and mappings, and performed UI polish across scouting pages for improved usability. Fixed core sorting logic on the team index to ensure accurate reef-related ranking. Overall, these changes improve data fidelity, enable faster, data-driven decisions, and establish a scalable frontend for the 2025 season.
In December 2024, work on DurhamAcademy/ChocoChipScouting focused on stabilizing the dashboard UI in no-data scenarios. The primary deliverable was a bug fix to ensure the dashboard renders the image correctly when no events are scheduled, accompanied by a refactor of the image container to improve rendering and visual consistency in no-data states. This work reduces UI glitches in edge cases and enhances maintainability of the dashboard components.
In December 2024, work on DurhamAcademy/ChocoChipScouting focused on stabilizing the dashboard UI in no-data scenarios. The primary deliverable was a bug fix to ensure the dashboard renders the image correctly when no events are scheduled, accompanied by a refactor of the image container to improve rendering and visual consistency in no-data states. This work reduces UI glitches in edge cases and enhances maintainability of the dashboard components.
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