How do we figure out which teams are strongest and most likely to win any given match? Our model considers two main approaches. Goal-based models simulate how many goals each side will score, then work out the result from there. Outcome-based models skip the goals and directly estimate the probability of a win, draw, or loss. Both have real strengths, and understanding how they work is key to understanding the probabilities we publish.
How we make predictions
How We Rate Team Strength
摘要 + 示例 · 完整文档共 1,977 字
摘要
示例
The two modelling traditions
Goal-process models treat scoring as a stochastic process — typically Poisson — where each team has a latent attack/defence rate. Results are simulated by sampling goals.
Outcome-prediction models skip the goal process and directly output P(home / draw / away) — or P(home win by ≥ k) and P(total ≥ n) for derived targets — from features. Discriminative classifiers, ratings systems, and regression models all sit here.
The two traditions complement each other. Goal models give you a coherent joint distribution over scores (good for derived targets: BTTS, exact-score grid, goal-differential lines), at the cost of strong distributional assumptions. Discriminative models often beat goal models on raw 1X2 log-loss but can't produce a coherent joint distribution for derived targets.
完整文档
Pro Pass
想阅读完整文档？
How We Rate Team Strength 共 1,977 字。购买 Pass 可解锁此文档及所有研究笔记全文, 以及逐场概率、四模型对比和逐场战术分析。
24h self-service refund·No subscription, no auto-renewal·Access through 31 Dec 2026. See refund policy.