INFO
Mechanism that allows obtaining an adjusted forecast for the completion of a particular task based on an analysis of deviations in forecasting similar tasks
Team Scoring 2024 is a web-based task forecasting and estimation system that applies probability theory to predict task completion times based on historical employee performance data. The application implements Evidence-Based Scheduling principles to provide adjusted forecasts by analyzing deviations in past task estimates, enabling more predictable project delivery timelines.
This tool should give us a complete understanding of when the release is most likely to happen. See also
The system addresses the challenge of unreliable task estimation by tracking actual completion times against initial estimates. For each employee, it builds a statistical model of their estimation accuracy across tasks of varying complexity, producing three forecast scenarios:
Original employee estimate
Probability-adjusted based on historical accuracy
Pessimistic scenario based on historical maximums
INFO
The forecasting algorithm analyzes similar completed tasks (filtered by complexity rating 1-5) and calculates probability distributions of estimate-to-actual ratios. This produces a confidence curve showing likelihood of completion at various future dates.
The TJob type represents the fundamental unit of work in the system. It combines task metadata, forecasting information, activity logs, roadmap milestones, and hierarchical relationships.
The system provides a chronological record of work performed on jobs through structured log entries that can contain text, progress metrics, checklists, and external links. The activity timeline aggregates logs across jobs to provide a unified view of recent work.
Checklists provide structured task tracking, allowing users to break down log entries into discrete, actionable items with completion tracking and ordering capabilities.
Jobs can be linked together to form hierarchical structures where parent jobs represent projects containing child jobs as sub-tasks.
Jobs are assigned to employees through the TForecast structure embedded in each job.
The Business Time Configuration system provides a user interface for defining and managing custom weekly work schedules stored in browser localStorage. These configurations define which hours are considered "business time" for each day of the week, enabling time-based analytics and forecasting calculations. The system supports multiple named configurations with full CRUD operations, validation, and edit controls.
The Business Time Configuration page allows users to create, edit, and manage multiple weekly schedule templates. Each configuration specifies working hours for each day of the week in HH:MM:SS format, supporting multiple time ranges per day (e.g., split shifts). The system validates all time inputs and persists configurations to localStorage for use across sessions.
Based on the codebase analysis, the project demonstrates several scalability-focused architectural patterns and optimizations.
The project implements client-side scalability through Web Workers for parallel processing, pagination systems for large datasets, and optimized build configurations with chunk splitting. It's designed as a single-user application with performance optimizations for handling complex forecasting calculations and large job datasets.
Parallel Processing with Web Workers The application uses multiple dedicated Web Workers to offload heavy computations from the main thread:
withRootMW.js:129-160types.js:99-107types.js:117-125types.js:72-80withRootMW.js:143-150withRootMW.js:151-160calc.v4.js:73-89The Vite configuration implements several performance optimizations:
useSearchBasicWorker.ts:1-35The architecture is optimized for single-user client-side performance rather than multi-user server scalability. The system handles scalability through:
The project appears designed as a desktop/web application for individual team leads or project managers rather than a multi-tenant SaaS platform. The scalability focus is on handling large datasets and complex calculations within browser constraints rather than concurrent user scaling.
In progress...