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NFL Win Probability Calculator

nfl_win_probability
Read-only

Turn an NFL point spread and game total into win probability, projected score, cover probability, and over/under probability — using the PredictionMarketsPicks scoring-margin model. Provide the spread (home-favored = negative, e.g. -6.5) and optional total, OR two team abbreviations to auto-derive the spread from the power ratings. Given two teams it also returns OUR OWN game total (model_total) and the probability the game goes over the total you passed (model_over_pct), and it credits NO home-field advantage on the nine international neutral-site games — basis names the venue when it applies. Free, no key. Use for "NFL win probability from the spread", "what does a -7 spread mean", "who wins Chiefs vs 49ers", "NFL score prediction", "what total does your model project", "is this game at a neutral site".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalNoGame total (over/under points) — this is the LINE you want tested, not our projection. Defaults to the fitted league baseline (NFL_MODEL_CONSTANTS.TOTAL_BASELINE) if omitted. Our own projected total comes back as `model_total` regardless of what you pass here (teams mode only).
spreadNoPoint spread from the home team's perspective — home favored is NEGATIVE (e.g. -6.5). Provide this OR homeTeam+awayTeam.
awayTeamNoAway team — same formats as homeTeam (SF, 49ers, San Francisco 49ers). Used with homeTeam.
homeTeamNoHome team — abbrev (KC), nickname (Chiefs), full name (Kansas City Chiefs) or city (Kansas City) all work. Auto-derives the spread from PMP power ratings.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / total / description
      Previous value: -"Game total (over/under points) — this is the LINE you want tested, not our projection. Defaults to the league baseline (44) if omitted. Our own projected total comes back as `model_total` regardless of what you pass here (teams mode only)."New value: +"Game total (over/under points) — this is the LINE you want tested, not our projection. Defaults to the fitted league baseline (NFL_MODEL_CONSTANTS.TOTAL_BASELINE) if omitted. Our own projected total comes back as `model_total` regardless of what you pass here (teams mode only)."
  2. Changed1 schema field changed
    • changedInput schema / properties / total / description
      Previous value: -"Game total (over/under points). Defaults to the league baseline (44) if omitted."New value: +"Game total (over/under points) — this is the LINE you want tested, not our projection. Defaults to the league baseline (44) if omitted. Our own projected total comes back as `model_total` regardless of what you pass here (teams mode only)."
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it read-only, and the description adds meaningful behavioral details: the spread sign convention, that total is the tested line rather than the projection, that model_total is returned in teams mode, no home-field advantage on nine international neutral-site games, and that no API key is required. This exceeds the annotation baseline.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded: core purpose first, then input modes, then special behaviors and use cases. It is longer than strictly necessary, but every sentence adds either parameter semantics, output context, or invocation guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only calculator with no output schema, the description covers all needed inputs, the outputs an agent can expect (win probability, cover probability, over/under, model_total, model_over_pct, basis), edge cases like neutral-site games, and example queries. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description goes beyond it: it clarifies spread sign from home perspective, explains the total parameter is the line to test rather than the model projection, notes the league-baseline default, and lists accepted team name formats. This materially improves correct parameter usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: turning an NFL point spread and game total into win probability, projected score, cover probability, and over/under probability. It names the underlying model and clearly distinguishes the tool from siblings like nfl_power_ratings by focusing on game-level probability outputs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives concrete input modes (spread+total OR two team abbreviations) and a 'Use for' list with natural-language queries, so an agent knows when to invoke it. It does not explicitly contrast it with sibling tools or state when not to use it, but the use cases provide clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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