How much would you spend building this?
A calibrated, multi-sport prediction model is not a weekend project. Set your engineer rate and sports coverage to see the itemized cost of building what we already provide via API.
US senior ML engineer: ~$150/hr loaded. Senior quant: ~$200-400/hr.
NBA, NFL, MLB, NHL, NCAAMB, Soccer, Tennis, esports = 11. Each adds ~40 hrs tuning.
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Why building is harder than it looks
The cost estimate above is generous. The hidden costs that kill most DIY projects:
- Calibration discipline. Raw XGBoost outputs are miscalibrated — a 70% confidence pick actually wins ~65%. Fixing this with isotonic regression on a held-out slice adds 40+ hours you didn't budget for.
- Look-ahead bias. Most DIY sports models silently leak future information in team-priors features. Catching this requires backtest discipline that takes months to internalize.
- Multi-sport tuning. Basketball K=20, NFL K=20 HFA=55, MLB K=4, NHL K=8. Each sport needs separate calibration. Single-sport models don't generalize.
- Live recalibration. Models drift. A production system needs monitoring that detects ECE drift weekly. That's a month of infrastructure work alone.
- Ongoing maintenance. Every sport season start: re-train. Roster changes: re-tune. API deprecations: rewrite. ~10 hours/month perpetually.
Our blog covers the real build process in depth, including where we've broken models and how we've fixed them. If you want to build yourself, those posts are a free starting point. If you want the working system, the API is ready now.
Methodology
Build-cost estimates based on our own development hours across 11 sports. ELO tuning: ~40 hrs/sport. Base infrastructure (XGBoost + isotonic + backtest harness + feature pipeline + monitoring + API): ~280 hrs, amortized across sports. First-year maintenance: engineer hours + $500/mo data subscription (ESPN-free tier works; commercial feeds run $1k-10k/mo) + $200/mo hosting. Numbers are directional — actual costs vary 2-3× up or down based on team experience with sports-ML specifically.