Cap.Space is my sports analytics platform. It tracks salary caps, contracts, and performance across the NFL, NBA, MLB, NHL, WNBA, MLS, and the top soccer leagues, then runs its own models to tell you what a player is actually worth against what he costs.
| BKNBrooklyn Nets | $16.8M |
| MEMMemphis Grizzlies | $3.7M |
| UTAUtah Jazz | −$13.1M |
| MILMilwaukee Bucks | −$19.0M |
| CHACharlotte Hornets | −$19.5M |
| WASWashington Wizards | −$23.3M |
Analysts, agents, front offices, and the kind of fan who has opinions about dead cap. Every tool runs on the same live database.
Build trades that actually clear the CBA. The cap math updates as you go, so you find out immediately whether a deal is legal or fantasy.
Five-year cap projections for every team. Dead money, active allocations, and the space a front office will really have when it matters.
Market comparison for any deal. See where a contract sits against comparable players at the same position, priced by the model.
Model an extension before it happens. Change the years and the guarantees and watch the cap consequences ripple forward.
Head to head on salary and performance. The comparison people usually make with vibes, made with numbers instead.
Real-time space for every team in every league covered, updated as contracts move, so the sheet is never a week behind the news.
The ratings on Cap.Space aren't vibes. They come from a pipeline that turns raw play data into player values, and every number below is real.
34M+ data points across 90+ tables: play grades, box scores, contracts, wages, and market values from every league, cleaned and joined into one warehouse.
Up to 40 engineered features per position group, standardized so players only get compared to the job they actually do. Nearly 34,000 player seasons scored.
A separate cross-validated regression for each position: 11 NFL groups and 6 soccer groups, each with its own replacement-level baseline. Out-of-sample R² runs as high as 0.97.
League-specific wage curves translate ratings into an implied salary. Set that against the real contract and you get surplus value: what a player is worth versus what he costs.
| Player | Pos | Model rating | Cap hit | Implied value | Surplus |
|---|---|---|---|---|---|
| Byron Young | ED | 86.0 | $6.0M | $38.5M | +$36.0M |
| Puka Nacua | WR | 96.0 | $5.8M | $36.2M | +$32.8M |
| Drake Maye | QB | 79.4 | $10.0M | $34.7M | +$30.6M |
| Bo Nix | QB | 76.2 | $5.1M | $27.4M | +$24.9M |
| Matthew Stafford | QB | 87.7 | $48.3M | $55.9M | +$23.7M |
Cap.Space is not a dashboard bolted onto someone else's data. The pipelines, the models, the interface, and the deployment are all mine, which is the same thing I offer clients.
Ingestion from many sources with entity resolution across leagues, so the same player lines up no matter which provider named him.
Per-position regularized regressions with cross-validated tuning and replacement-level baselines, plus wage curves fit per league.
A data-dense interface that stays fast and readable, because the best model in the world is useless if nobody can navigate it.
The pipeline that prices athletes can price talent, contracts, or inventory in your business. That work runs through TFC Business Solutions.
See TFC Business Solutions