Team competitions

Run retrospectives on visible Jira work, not guesswork.

SeeCodes turns recent Jira-visible activity into a retrospective board that highlights contributors, solved tasks, and a weighted relative-effort score across logic, architecture, and UI/spec signals.
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On this page

6 sections

A retrospective board built around visible Jira work

The Team Competition view brings together visible completed work, issue context, and contribution signals in one Jira-native retrospective page.

Built for retrospectives and team reflection

This view is designed to help teams look back on completed work with more shared context. Effort is a subjective, team-friendly index intended to support healthier retrospectives, coaching, and planning conversations.

Project page / SeeCodes

Team Competition

AI-generated retrospective board
Current + Last SprintLast MonthLast Half Year
Sort: EffortRefresh Metrics

Visible contributors

3

Jira visibility enforced

Visible solved tasks

11

Done tasks in scope

Relative effort produced

764

Composite effort units in scope

Monthly baseline

100

Typical visible completed task

AI-generated retrospective board

“Effort” is a productivity-oriented composite of active minutes, changed LOC, files changed, and architecture / logic / UI-specification signals.

Raw effort = active minutes × 1.8 + LOC changed × 0.18 + files changed × 3, plus bonuses for architecture (+24), logic (+14), and UI / specification (+10). A relative score of 100 means roughly “typical for this month.”

Visible tasks in scope: 22. Hidden by Jira visibility: 3. Activity records in scope: 144. Relative task effort is normalized so that 100 behaves like a monthly index baseline, not like an hour target or a payroll number.

Competition Board

Sort by relative effort, solved tasks, logic, architecture, UI/spec, or contributor name.

#1

Avery Chen

5 solved • 7 contributed tasks

ArchitectureLogic
Team share41%

Relative effort 312 • Active minutes 184 • LOC changed 1210 • Files changed 47 • Avg solved-task effort 128

#2

Priya Shah

4 solved • 6 contributed tasks

LogicUI / Spec
Team share33%

Relative effort 254 • Active minutes 162 • LOC changed 990 • Files changed 39 • Avg solved-task effort 117

#3

Jordan Miles

3 solved • 5 contributed tasks

UI / SpecLogic
Team share26%

Relative effort 198 • Active minutes 141 • LOC changed 740 • Files changed 31 • Avg solved-task effort 109

Solved Task Board

Top solved tasks in scope with relative effort and transparent raw-effort drivers.

PLAT-221

Rework auth token rotation

Effort 142Raw 102.43 contributorsStory
Architecture +24Logic +14

Assignee Avery Chen • 23 active min • 78 LOC changed • 3 files changed

PAY-84

Stabilize billing guardrails

Effort 124Raw 89.62 contributorsBug
Logic +14UI / Spec +10

Assignee Priya Shah • 22 active min • 78 LOC changed • 4 files changed

WEB-97

Refine logout and error messaging

Effort 108Raw 78.22 contributorsTask
Logic +14UI / Spec +10

Assignee Jordan Miles • 19 active min • 61 LOC changed • 3 files changed

How the page reads effectiveness from effort

Strictly speaking, the formula measures relative effort. The page becomes a useful effectiveness view only when that score is read next to solved work and average solved-task effort.

Important nuance: the formula is an effort proxy

On this page, what people casually call “effectiveness” is really a combination of relative effort, solved work, and task difficulty. The formula itself is intentionally subjective and is designed to support healthier retrospectives, not to claim a universal truth about individual productivity. The literature links immediately below explain why the page combines activity, churn, diffusion, normalization, and semantic bonuses.
Current scoring logic
Raw effort = active minutes × 1.8
           + LOC changed × 0.18
           + files changed × 3
           + architecture signal ? 24 : 0
           + logic signal ? 14 : 0
           + UI / spec signal ? 10 : 0

Relative score ≈ raw effort / monthly typical raw effort × 100
100 ≈ "typical for this month"

The strongest scientific case is for the choice of inputs and for the monthly normalization step. The exact coefficients are still product heuristics chosen so that time, churn, diffusion, and semantically heavier work all stay visible in one interpretable index. The next section makes those research links explicit.

How to read a score of 100

  • 100 ≈ a typical visible completed task in the current month.
  • 120 means roughly 20% above that month’s visible baseline, not 20% 'better' than another team in another repository.
  • 60 means lighter than that month’s typical visible completed task, not low value or weak performance.

Where “effectiveness” actually shows up

  • Solved count shows whether effort is turning into finished work.
  • Average solved-task effort shows whether someone is closing lighter or heavier completed tasks.
  • Dominant-area labels show whether contribution skewed toward architecture, logic, or UI/spec work.

Active minutes × 1.8

Minutes with detected work activity give the score a focused-work component, but the page explicitly frames them as a proxy for retrospective context rather than payroll time.

Changed LOC × 0.18

Code churn captures implementation magnitude. The lower coefficient prevents raw line volume from overpowering every other signal on the board.

Files changed × 3

Cross-file changes usually imply broader reasoning, more coordination, and more places where side effects can appear, so the model gives diffusion visible weight.

Semantic bonuses

Architecture (+24), logic (+14), and UI / specification (+10) bonuses stop the model from pretending that every edit has the same blast radius or product meaning.

Why teams can trust the shape of this model

This score is grounded in well-established ideas from developer productivity, software engineering, and architecture research. The research is strongest on the ingredients and the need for context, while the exact weights remain product-tuned for clarity and usability.

Research-backed direction. Product-shaped scoring.

The strongest support from the literature is not for one magical coefficient set. It is for the overall shape of the model: contribution is multidimensional, context matters, and meaningful work shows up through more than one signal. Our weights are product choices, tuned to keep the score readable, balanced, and useful in real retrospectives.

Great work is bigger than one signal

Developer-productivity research keeps landing on the same point: strong contribution is multidimensional. That is why this model combines activity, change size, diffusion, and semantic signals instead of pretending one number can explain everything by itself.

Meaningful change should count

Research on code churn shows that change volume can reflect real implementation weight. That makes changed LOC a useful input here, not as a vanity metric, but as one visible part of how much work a completed change likely carried.

Broad changes usually carry more load

Studies on software-change risk repeatedly show that work spread across more files or subsystems is harder to reason about and easier to miss in review. That is why the model gives cross-file diffusion visible weight.

System-shaping work deserves extra visibility

Architecture and specification work often create cost, coordination, and downstream impact that raw structural counts miss. The bonus signals help the board recognize work that changes system shape, logic complexity, or product-definition clarity.

What the research world keeps pointing to

  • Developer productivity is multidimensional, so no single activity metric should define contribution.
  • Observable telemetry is useful, but it works best when read in context rather than as a universal truth.
  • Code churn helps represent implementation magnitude when interpreted as relative change weight.
  • Files touched and change diffusion are strong signals for broader, riskier, harder-to-review work.
  • Architecture and specification signals matter because semantic impact can exceed what raw counts show.
  • Normalization matters because teams need a readable month-relative baseline, not an absolute productivity grade.

How strong teams use the score well

  • Use the score to guide retrospectives, not to replace judgment.
  • Compare people only inside similar scope, visibility, and time windows.
  • Read effort next to solved work, review quality, and task difficulty.
  • Treat 100 as a month-relative baseline, not as a target for human worth.
  • Never turn the score into surveillance, payroll logic, or a one-number performance system.

Where teams use it

This page works best as a retrospective and planning aid, especially when a team wants to celebrate contribution without losing nuance.
  • Run retrospectives with a shared view of solved work, contributor mix, and relative task effort.
  • Celebrate specialists and generalists without forcing the conversation into raw ticket counts.
  • Spot work that needed multiple contributors or carried unusually high relative effort.
  • Find follow-up topics for pairing, knowledge sharing, or planning quality in the next sprint.

Best used for retrospectives and coaching

This board is ideal for looking back, spotting patterns, and celebrating contribution. It should support discussion and learning, not replace human judgment with a single score.