# NBA Value Bets vs Live Kalshi Prices

**Use case:** 

Compares the model's playoff probability for each NBA team against the live price of its Kalshi contract, and reports the edge, the expected value net of fees, and a quarter-Kelly position size capped per position and in total. Both sides of every contract are priced, so an overpriced favourite shows up as a chance to sell. Value calls stay locked until the season is ten games old.

## Input

```json
{
  "iterations": 20000,
  "archiveToNamedDataset": "",
  "regressionGames": 20,
  "pythagoreanWeight": 0.65,
  "homeAdvantage": 0.32,
  "strengthUncertainty": 0.22,
  "priorCarryover": 0.6,
  "seasonLength": 82,
  "minGamesPlayedForValue": 10,
  "includeMarketComparison": true,
  "includePlayInMarkets": false,
  "includeChampionshipMarket": false,
  "edgeThreshold": 0.05,
  "feeRate": 0.07,
  "bankroll": 1000,
  "maxPerPositionPct": 5,
  "maxTotalExposurePct": 25
}
```

## Output

```json
{
  "team": {
    "label": "Team"
  },
  "marketTicker": {
    "label": "Kalshi ticker"
  },
  "marketSide": {
    "label": "Side"
  },
  "marketPrice": {
    "label": "Price"
  },
  "fairPlayoffProbability": {
    "label": "Model %"
  },
  "edge": {
    "label": "Edge"
  },
  "expectedValuePerContract": {
    "label": "EV / contract"
  },
  "call": {
    "label": "Call"
  },
  "suggestedContracts": {
    "label": "Contracts"
  },
  "suggestedStake": {
    "label": "Stake"
  }
}
```

## About this Actor

This example demonstrates how to use [NBA Playoff Odds API — Monte Carlo Simulator & Value Bets](https://apify.com/commodus67/nba-playoff-odds-monte-carlo.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/commodus67/nba-playoff-odds-monte-carlo.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
This Task's input is already configured above — use it as-is rather than inventing a new one.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/commodus67/nba-playoff-odds-monte-carlo.md

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).