Team Velocity Metrics Are Lying

DevTeam Alpha's sprint velocity looks great on paper (up 25% over 6 sprints) but delivery complaints and bug reopens are rising. The manager must reason about what the metric actually measures and where it's being gamed or misleading — before leadership rewards the team on a false signal.

# Aplly.xyz Case Study Submission

## Title
Team Velocity Metrics Are Lying

## Type
Data Analytics

## Difficulty
Intermediate

## Estimated Time
50 minutes

## Overview
DevTeam Alpha's sprint velocity looks great on paper (up 25% over 6 sprints) but delivery complaints and bug reopens are rising. The manager must reason about what the metric actually measures and where it's being gamed or misleading — before leadership rewards the team on a false signal.

## Case Details

Function Focus: Metric validity reasoning, critical data judgment, Goodhart's Law application

Scenario:
Leadership wants to publicly reward the team for "improved velocity." The manager suspects the number is misleading and needs to produce a written diagnosis before that happens — using cross-metric reasoning by hand, not just eyeballing the velocity chart.

Dataset Structure:
- Sprint, Story Points Completed, Ticket Count, Average Ticket Size, Bug Reopen Rate, Stakeholder Satisfaction Score

Tasks:
1. By hand, identify what could cause velocity to rise while quality/satisfaction falls (e.g., point inflation, breaking large tickets into many small ones to inflate throughput)
2. Cross-check velocity against the other 3 metrics to find the specific inconsistency in the data
3. Write the actual explanation you would give leadership, correcting the naive "velocity = productivity" narrative
4. Propose one better metric or metric combination that would have caught this earlier

Expected Output:
Written diagnosis identifying the gaming pattern + corrected narrative for leadership + one proposed alternative metric with justification.

Evaluation Criteria:
Correct identification of the specific gaming pattern present in the data, quality and practicality of the alternative metric proposal, and clarity of the explanation for a non-technical leadership audience.

## Data Sources

| Sprint | Story Points | Ticket Count | Avg Ticket Size | Bug Reopen Rate | Satisfaction |
|---|---|---|---|---|---|
| 1 | 40 | 20 | 2.0 | 5% | 8.2 |
| 2 | 42 | 21 | 2.0 | 6% | 8.0 |
| 3 | 45 | 25 | 1.8 | 8% | 7.5 |
| 4 | 48 | 30 | 1.6 | 11% | 7.0 |
| 5 | 50 | 34 | 1.5 | 14% | 6.5 |
| 6 | 50 | 36 | 1.4 | 16% | 6.2 |

(Diagnostic pattern: ticket count is rising faster than story points, average ticket size is shrinking — consistent with tickets being split into smaller pieces to farm completed-ticket counts and inflate apparent throughput, while bug reopen rate and satisfaction both trend the opposite direction of "velocity.")

## Solution Frameworks
Metric cross-validation, Goodhart's Law application, leading vs. lagging indicator analysis

## Solver Guidance & Tutorials
Link to: "Why Single Metrics Lie: A Manager's Guide" tutorial

## What You'll Learn
- Healthy skepticism toward any single headline metric
- Diagnosing metric gaming from cross-metric patterns
- Communicating a nuanced, uncomfortable finding upward to leadership clearly

## Tags
metrics, team performance, analytical reasoning, Goodhart's Law, data analytics

## Registration Links
- Register as Solver
- Register as Evaluator

| Sprint | Story Points | Ticket Count | Avg Ticket Size | Bug Reopen Rate | Satisfaction |
|---|---|---|---|---|---|
| 1 | 40 | 20 | 2.0 | 5% | 8.2 |
| 2 | 42 | 21 | 2.0 | 6% | 8.0 |
| 3 | 45 | 25 | 1.8 | 8% | 7.5 |
| 4 | 48 | 30 | 1.6 | 11% | 7.0 |
| 5 | 50 | 34 | 1.5 | 14% | 6.5 |
| 6 | 50 | 36 | 1.4 | 16% | 6.2 |

(Diagnostic pattern: ticket count is rising faster than story points, average ticket size is shrinking — consistent with tickets being split into smaller pieces to farm completed-ticket counts and inflate apparent throughput, while bug reopen rate and satisfaction both trend the opposite direction of "velocity.")

Metric cross-validation, Goodhart's Law application, leading vs. lagging indicator analysis