Race hubs
Race pages combine pro and age-group context, route through unified series hubs, and expose course details when they are available.
Changelog
A public version of the project log: what changed, why it matters, and how the bigger Triathlon Lab features are meant to work.
Race pages combine pro and age-group context, route through unified series hubs, and expose course details when they are available.
Unified profiles connect race history, source identities, photos, relay rows, pro context, and age-group results in one place.
The Kona Slot Finder helps age-group athletes compare qualifier fit, and race hubs can show slot projections plus age-graded target times.
Start-list research now includes a sortable board, better athlete photos, distance-aware scoring, and clean poster image exports.
Feature briefs
Each section gives the short version first. Open it for the details, the data it connects, and how it should be used.
Ranking explainer
Triathlon Lab separates official ranking tables from power-index scorecards. That way a user can see the official standings, then switch to a model that is tuned for current form, discipline strengths, field quality, and distance fit.
IRONMAN, World Triathlon, and T100 ranking views keep the official series context visible when that is the right lens.
Scorecard rankings blend recent results, strength by discipline, distance fit, depth of field, and race-type context into sortable pro profiles.
Full-distance races lean on long-course evidence, while 70.3 and T100 races favor the middle-distance profile so specialists are not flattened into one generic list.
PTN's OpenRank work deserves a nod here: it helped make transparent, sport-specific pro ranking ideas feel normal and useful for triathlon fans.
Step 1
Full-distance races prefer the long-course profile. 70.3 and T100 races prefer the middle-distance profile. Short-course views lean on World Triathlon evidence.
Step 2
Swim, bike, and run signals are scored separately so a user can see whether an athlete's value comes from front-pack swim security, bike strength, run form, or balanced consistency.
Step 3
The model values result quality, field depth, recency, race distance, and discipline evidence instead of counting every finish the same way.
Step 4
The overall score combines the selected distance profile and split signals, then the page exposes the evidence so the user can sanity-check the ranking.
Update log
These notes summarize the public-facing changes from the internal project log: features, fixes, data quality, and interface polish.