Challenge Approach Features Roadmap
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TAG Running

True Age Grading for the devoted runner.

Standard age grading compares you to world records — an impossibly high bar that makes most recreational runners feel like they're permanently failing. TAG Running takes a different approach: benchmark yourself against your local Parkrun course records instead. A fairer, more honest read on where you actually stand.

Role

UX & Product Designer

Type

Solo / Self-initiated

Platform

Web app

Status

Live

TAG Running interface

The challenge

What wasn't working.

Before building anything, I looked at what already existed. The tools were there — but they were designed around the wrong people. Researching the existing age grading landscape made the problem immediately clear.

What already existed

Existing age grading calculator — input form
Typical input form — no context, no visual hierarchy
Existing age grading result — 25.64% against world records
The result: 25.64%. Compared to a world record. No context for what that means.

01

Limited visibility and usability

The tools that existed were hard to find, harder to read, and built without any consideration of user experience. Outdated HTML forms with no visual hierarchy — outputs that even a technically confident runner would struggle to interpret.

02

Elite comparisons, recreational runners

Traditional age grading tables compare performance to world-record pace. For recreational Parkrun runners, this produces scores so low — often below 30% — that they feel meaningless. The tools were designed by and for competitive athletes, then handed to everyone else.

03

Course difficulty disconnect

Every Parkrun course is different. Hills, terrain, surface — they all affect finishing times significantly. Grading a runner on a hilly course against the same world-record standard as someone on a flat one produces a fundamentally unfair result.

04

Opaque, unexplained scoring

A raw percentage with no context. Existing tools showed runners a number and stopped there. No plain-language explanation of what it meant, how it compared to others at their venue, or what they'd need to do to improve it.

The approach

Four decisions that shaped it.

1

User discovery

Before wireframing anything, I looked at who was actually searching for this kind of tool. Parkrun attracts recreational runners — people running on Saturday mornings for community and personal challenge, not competition. The data was revealing: women represent over 40% of Parkrun participants, but were almost entirely absent from age grading discussions and existing tool audiences. The tools had defaulted to "male first" in every input, every example, every result. That shaped a specific UX decision early.

WAVA age grading calculator — male-first default
Research — male-first defaults throughout
Older WAVA calculator interface
The oldest tools — functional but completely inaccessible

Design decision

No default gender selection. Runners choose their category before the form submits — an intentional friction point that treats every user as equally likely, rather than treating one as the assumed case.

2

Visual storytelling

Raw percentages are abstract. Adding 0.1% to your age grade is technically an improvement — but it doesn't feel like one. The decision was to translate the result into something visual: a dynamic progress chart that fills as your time approaches the course record. The end of the bar isn't an arbitrary benchmark — it's the fastest person your age has run that exact course. Someone real. Someone local.

Design decision

The chart doesn't start at zero — it starts at a "reasonable recreational runner" baseline, so the bar feels meaningful from the first input rather than overwhelming. Seeing a bar at 60% full feels very different to reading "60%" in a table.

3

Scoring over percentages

Traditional grading gives you 42.73%. TAG gives you a score out of 1000. The same information, reframed. A higher integer grows more visibly than a decimal-point percentage — and with a ceiling at 1000 rather than 100%, there's more psychological headroom to improve. The scoring system borrows from gaming: the goal post moves, but it always feels reachable. Going from 412 to 438 in a month reads like progress. Going from 41.2% to 43.8% reads like rounding error.

Design decision

The maximum is 1000, but displayed scores are rounded to the nearest integer — no decimals. This keeps the output clean and avoids false precision. A runner who improves by 3 seconds should see a meaningful jump in their score, not a 0.12% nudge.

4

UI anchored in the ecosystem

Parkrun's visual identity is red and green. TAG's colour system deliberately echoes it — not to mimic, but to anchor. Runners arriving from Parkrun should feel immediately at home. The typography is clean, the layout single-column, and the only bright accent on any result page is the progress bar. Everything else recedes so the number — and the sentence that explains it — can do the work.

Design decision

Every result includes a single plain-language sentence — written as UX copy, not a tooltip. "You're running faster than 68% of people your age at this course." That sentence carries more weight than any number, and it required more careful writing than any component in the UI.

What's in it

Key features.

Course-specific benchmarking

Your grade is calculated against records set at your specific Parkrun course by runners in your age category — not global tables, not world records. The benchmark is local and real.

Personal growth insights

Run the same course twice and compare your TAG scores. The question isn't just where you are — it's where you're going. The tool is built around longitudinal improvement, not a one-time snapshot.

Visualised goal-setting

The progress chart shows exactly how much time you'd need to drop to move your score meaningfully — translating abstract grading into a concrete target time. A goal you can train towards.

UX writing built in

Every result includes a plain-language interpretation — a sentence that turns a score into a story. Written to encourage rather than inform, because recreational runners need context, not data dumps.

What's next

On the roadmap.

Data expansion

Extending course record coverage beyond the current UK dataset — more courses, more age categories, and handling edge cases like multi-terrain venues where the same course changes character between events.

Training integration

Connecting your TAG score to actionable training guidance — if you're at 620 and want to hit 700, what weekly sessions would get you there? Bridging the gap between grading and coaching.

Local club integration

Sharing grades within a running club context — a club leaderboard, collective improvement tracking, and milestone celebrations that make individual progress feel communal.

Social comparison

Opt-in grade sharing — compare your progress with friends running different courses, different age categories. The same competitive instinct that drives Parkrun PB culture, applied to fair comparison.

Edge case testing

Junior age grades, first-timers with no comparison baseline, courses with limited records — the grading logic needs to handle incomplete data gracefully and communicate uncertainty without undermining the result.

Next project

Wembley