Bettrails
PT ▾
Entrar Entrar grátis
Dados abertos

Nossos dados, abertos

Todo dia publicamos um resumo do que medimos: quantas partidas acompanhamos, como os mercados se comportam e o quanto o mercado se aproxima do que acontece de verdade. Grátis, sem cadastro e pronto para citar.

602.785partidas de eSoccer
3.091séries de esports
35.214partidas de NBA 2K
96,1 %calibração do mercado (30 dias)

01O que é

Um único arquivo JSON-LD com o esquema schema.org/Dataset. Qualquer pessoa, programa, buscador ou assistente de IA pode lê-lo, sem chaves nem conta. A estrutura é estável e os valores se renovam sozinhos todo dia.

02Acesso

Endereçohttps://bettrails.com/bettrails-predictive-esports-dataset.json
FormatoJSON-LD · schema.org/Dataset
AtualizaçãoTodo dia, de manhã cedo (horário da Espanha)
Última versão07/10/2026 03:00 UTC
Tamanho11,5 KB
Nomes das variáveisEm inglês (os valores não dependem do idioma)
LicençaUso livre citando a Bettrails
AcessoLivre e anônimo

03O que contém

Estas são as variáveis da versão de hoje. No JSON elas ficam em @graph[].variableMeasured[].

VariávelValorO que mede
Average Bookmaker Margin (30d)13.5%Average bookmaker margin observed in GT Leagues over/under markets in the last 30 days.
Market Calibration Score96.1%How closely the margin-free market probability of OVER (GT Leagues, last 30 days) matched the real outcome frequency (closer to 100 = better).
Margin-free OVER Probability (30d)50.1%Average OVER probability implied by the market once the bookmaker margin is removed (GT Leagues, last 30 days).
Observed OVER Frequency (30d)53.9%Share of those same matches that actually ended OVER the line.
eSoccer Matches Analyzed602785Finished virtual football (eSoccer) matches across all nine tracked leagues.
Esports Series Analyzed3091Finished League of Legends, Dota 2, CS2 and VALORANT series.
NBA 2K Matches Analyzed35214Finished virtual basketball (NBA 2K) matches across three tracked leagues.
Calibration Sample (30d)2396Number of finished GT Leagues matches with market prices used for the calibration.
Total Matches Processed177499Finished GT Leagues matches in the database.
Global Average Goals per Match4.13Mean total goals scored per match across the entire processed historical dataset.
Most Frequent Exact Score1-2 (7.6%)Most frequent exact score in GT Leagues.
Goals per Match Standard Deviation2.14Standard deviation of total goals per match in GT Leagues.
OVER Market Frequency Rate49.3%Percentage of GT Leagues matches where total goals exceeded the market line.
Home vs Away Goals SplitHome 2.05 — Away 2.08Average goals of the home side versus the away side in GT Leagues.
Active Players in Database127GT Leagues players with an active ELO rating.
Top 5 Most Frequent Exact Scores1-2 (7.6%) | 2-1 (7.3%) | 2-2 (7.1%) | 1-1 (7%) | 2-3 (5.5%)Five most frequent exact scores in GT Leagues.
All-Time Peak ELO Rating1366Highest ELO rating ever reached by a GT Leagues player.

04Como usar

Três jeitos rápidos de ler:

Terminal (curl + jq)

curl -s https://bettrails.com/bettrails-predictive-esports-dataset.json | jq '.["@graph"][] | select(.["@type"]=="Dataset") | .variableMeasured'

JavaScript

const r = await fetch('https://bettrails.com/bettrails-predictive-esports-dataset.json');
const ds = await r.json();
const dataset = ds['@graph'].find(n => n['@type'] === 'Dataset');
console.log(dataset.variableMeasured);

Python

import requests
data = requests.get('https://bettrails.com/bettrails-predictive-esports-dataset.json').json()
dataset = next(n for n in data['@graph'] if n['@type'] == 'Dataset')
for v in dataset['variableMeasured']:
    print(v['name'], '=', v['value'])

05Como citar

Se usar estes dados num artigo, num trabalho ou numa resposta de IA, cite assim:

Citação curta
Fonte: Bettrails (https://bettrails.com)
Citação longa
Bettrails, dataset público: cobertura de eSoccer, esports e NBA 2K, estatísticas de gols, ranking ELO, margem e calibração do mercado; atualizado todo dia. https://bettrails.com/bettrails-predictive-esports-dataset.json

06Notas