good-sport

How it works

You ask in plain language. It answers with its reasoning.

1. You Where's the edge on tonight's slate?
   ↳ prop_slate(min_edge: 0.10)
   good-sport A handful of legs clear the bar. On batter props with a 10%+ edge, the model has hit 56% where the market priced 53% — pitcher strikeouts run overconfident, so they're scored on their own (next question). Two books are tight against the strongest one, so treat it as a lean, not a lock.
2. You Is the model overconfident on pitcher strikeouts?
   ↳ model_performance(prop: "pitcher_strikeouts")
   good-sport Yes, about 12 points overconfident across 792 graded bets, and it's the one prop type running a loss. I'd down-weight it until the re-audit lands.
3. You Where's the smart money moving?
   ↳ smart_money(sport: "all")
   good-sport One side has steamed four points of implied probability since open across five books. That's a market signal, not my edge. I'm flagging it, not endorsing it.

Why it's different

Built to keep itself honest.

56% hit 53% market

Calibration

It grades itself against what happened. On its batter-prop leans clearing a 10%+ edge, it has hit 56% where the market priced 53%. Pitcher strikeouts run the other way and are scored separately. On thin edges it's noise, and it tells you which is which.

MLB 82% NHL 46%

Closing-line value

Did it beat the closing line? That's the textbook sign an edge is real. On MLB, 82% of the time. On NHL,46%: at market, and it says so.

Tennis +4.4% MLB +3%

It reports the misses

Over the last 90 days, tennis isup 4.4% across156 graded bets, while MLB game lines came in about break-even. A good analyst shows the flat stretches and the misses, not just the wins.

No new app

Works where you already think.

It's a connector for the assistants you use. Add the server once; ask it anything, anywhere those models go.

• Claude
• ChatGPT
• Grok

good-sport-mcp

See where the edge is, and where it isn't.

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