Football Match Probability Calculator (Independent Poisson Model)

x402

Football Match Probability Calculator (Independent Poisson Model) is a paid API for AI agents from gofiber.hr, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-22).

Calculates football match outcome probabilities (home win, draw, away win, BTTS, over/under 2.5, score matrix) from expected goals (xG) values using an independent Poisson model

Price / call$0.02

Seller volume →—

Success—

StatusUnknown

How this service works

GOFIBER d.o.o. iz Petrinje izvodi elektroinstalacije, strukturno mrežno kabliranje, videonadzor i WAGO PLC automatizaciju u Sisačko-moslavačkoj županiji i šire. Brza ponuda.

Output

Returns a JSON object containing match market probabilities: home_win, draw, away_win (summing to 1), btts_yes/no, over_2.5/under_2.5, and a score_matrix of individual scoreline probabilities (e.g. '1-0': 0.123). Also includes model metadata: model name, method, execution time, truncation mass, score_matrix_mass, verification status, tool version, and a result SHA256 hash for integrity checking.

Example use cases

Pre-match betting market analysis

“I have home xG of 2.1 and away xG of 0.9 for tonight's match — can you run the independent Poisson model and give me the home win, draw, away win probabilities plus whether both teams are likely to score?”

Score matrix for fantasy football lineup

“For a match where the home team is expected to score 1.5 goals and the away team 1.2 goals, give me the full scoreline probability matrix up to 5 goals using the Poisson model so I can see which exact scores are most likely.”

Over/under 2.5 goals probability check

“Can you compute the over 2.5 and under 2.5 goals probabilities for a game with home xG of 1.3 and away xG of 1.6, using the independent Poisson method with max goals set to 10?”

Technical details

Endpoint

POSThttps://gofiber.hr/x402/math/football-probability?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a mathematically rigorous, Poisson-based probability distribution over football match outcomes from expected goals inputs. It is ideal for sports betting analysis, fantasy football, or match modeling pipelines where you need both aggregate markets (home/draw/away, BTTS, over/under) and granular scoreline probabilities in a single call. Prefer it over simpler win-probability APIs when you need the full score matrix or when xG values are your primary input signal.

Side effects

home_xg or away_xg <= 0 (exclusiveMinimum violated) — returns validation error
max_goals > 15 or score_matrix_max_goals > 15 — returns validation error
Non-numeric xG values — returns schema validation error
Payment not attached or insufficient — returns HTTP 402
Internal computation error — may return error status with job_id for debugging

Input

Schema · Request body

Field	Type	Description
model	string
away_xg	number
home_xg	number
max_goals	integer
score_matrix_max_goals	integer

Output

Schema · Response

{
  "type": "json",
  "example": {
    "job_id": "3f2a9c8e7b6d5a4f3e2d1c0b9a8f7e6d5c4b3a2f1e0d9c8b7a6f5e4d3c2b1a09",
    "result": {
      "tool": "sports_probability",
      "result": {
        "draw": 0.2598475202403483,
        "model": "independent-poisson",
        "method": "poisson_exact",
        "metric": "match_markets",
        "btts_no": 0.5089246834,
        "away_win": 0.2522070740684634,
        "btts_yes": 0.4910753166,
        "home_win": 0.4879454056911883,
        "over_2_5": 0.4561865786,
        "under_2_5": 0.5438134214,
        "score_matrix": {
          "0-0": 0.0820850447,
          "0-1": 0.0820850447,
          "0-2": 0.0410425224,
          "0-3": 0.0136808408,
          "0-4": 0.0034202102,
          "0-5": 0.000684042,
          "1-0": 0.1231275671,
          "1-1": 0.1231275671,
          "1-2": 0.0615637836,
          "1-3": 0.0205212612,
          "1-4": 0.0051303153,
          "1-5": 0.0010260631,
          "2-0": 0.0923456753,
          "2-1": 0.0923456753,
          "2-2": 0.0461728377,
          "2-3": 0.0153909459,
          "2-4": 0.0038477365,
          "2-5": 0.0007695473,
          "3-0": 0.0461728377,
          "3-1": 0.0461728377,
          "3-2": 0.0230864188,
          "3-3": 0.0076954729,
          "3-4": 0.0019238682,
          "3-5": 0.0003847736,
          "4-0": 0.0173148141,
          "4-1": 0.0173148141,
          "4-2": 0.0086574071,
          "4-3": 0.0028858024,
          "4-4": 0.0007214506,
          "4-5": 0.0001442901,
          "5-0": 0.0051944442,
          "5-1": 0.0051944442,
          "5-2": 0.0025972221,
          "5-3": 0.0008657407,
          "5-4": 0.0002164352,
          "5-5": 0.000043287
        },
        "truncation_mass": 5.618e-7,
        "score_matrix_mass": 0.9949530409
      },
      "status": "ok",
      "rendered": null,
      "verified": true,
      "warnings": [],
      "request_id": "27c18c6b0a714152b16575b4e3c85b43",
      "execution_ms": 5.154,
      "input_sha256": "3aea24f640ed8aaea6ffe8f09bc219f015aed100e0242366bf02bcae7527c765",
      "tool_version": {
        "tool": "sports_probability",
        "digest": "b57001ea80408f9ae57b853e34562d1a50ef201d7639a94cd770cdebd1143085",
        "params": {
          "metric": "match_markets",
          "max_goals": 10,
          "score_matrix_max_goals": 5
        },
        "environment": {
          "blas": "scipy-openblas",
          "python": "3.12.13",
          "system": "Linux",
          "machine": "x86_64",
          "libraries": {
            "numpy": "2.2.1",
            "scipy": "1.15.0",
            "sympy": "1.14.0",
            "ortools": "9.11.4210",
            "networkx": "3.4.2"
          },
          "blas_version": "0.3.28",
          "implementation": "CPython"
        },
        "tool_impl_version": "1.0.0"
      },
      "verification": {
        "comparison": "numerical_equivalence"
      },
      "result_sha256": "ea053b598e68bf86c868ec50042b335cd5bc13adf5728f2ac601c660ceb684ae",
      "schema_version": 1,
      "non_finite_fields": [],
      "non_finite_detected": false
    },
    "status": "FULFILLED",
    "result_url": "https://gofiber.hr/x402/jobs/3f2a9c8e7b6d5a4f3e2d1c0b9a8f7e6d5c4b3a2f1e0d9c8b7a6f5e4d3c2b1a09/result",
    "status_url": "https://gofiber.hr/x402/jobs/3f2a9c8e7b6d5a4f3e2d1c0b9a8f7e6d5c4b3a2f1e0d9c8b7a6f5e4d3c2b1a09"
  }
}

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