How data is used to analyse football matches

Raw results are a small and noisy sample. Shot-based measures stabilise far faster, which is why expected goals and shot quality are standard inputs while possession and corners are mostly descriptive.

• Expected goals for and against: the workhorse, stable within a handful of matches.
• Venue split: home and away scoring rates differ enough to model separately.
• Recency weighting: recent form matters, but weighting it too heavily overfits.
• Availability: confirmed absences of high-usage players move a match materially.
• Schedule: fixture congestion and travel have small, measurable effects.

A standard approach estimates each side's expected goals in this fixture, then treats goals as a counting process to derive a distribution over scorelines. Summing the relevant cells gives 1X2, both teams to score and over/under probabilities from the same underlying model.

BetBuddy blends a venue-split Poisson model with an Elo-style strength rating and recent-form adjustment, then requires a freshness check so that a team with stale or thin current-season data is excluded rather than guessed at.

P(home win) = Σ P(home goals = i) × P(away goals = j) for all i > j

The market is the strongest single forecaster available, so the model's job is not to beat it everywhere but to find the specific fixtures where it disagrees for a reason. Prices are de-margined before comparison, and the gap must clear a minimum edge before a selection qualifies.

A model that is never scored against outcomes is an opinion. Calibration measures whether things predicted at 30% happen about 30% of the time; Brier score and log loss summarise it in one number; closing line value checks whether the market moved towards the model.

Betting carries financial risk and no staking plan removes it. Historical performance does not guarantee future results, and BetBuddy runs in paper mode: stakes are simulated and no bookmaker account is connected.

Useful football models are built from a small number of stable inputs rather than a long list of weakly predictive ones. Venue-split scoring and conceding rates, recent form weighted towards recent matches, schedule congestion and confirmed absences carry most of the signal available before kick-off.

Each input needs a freshness rule. A team rating built from matches played eight months ago describes a squad that may no longer exist, so BetBuddy enforces a maximum team-data age and a minimum number of current-season matches before a fixture is eligible for analysis at all.

• Split home and away rates; combining them hides a real and persistent effect.
• Weight recent matches more heavily, but not so heavily that one result dominates.
• Treat missing or stale data as a reason to skip a fixture, not to guess.

The fastest way to build a model that backtests beautifully and fails live is to let information from after the decision point leak into the inputs. Final results, post-match statistics and closing prices are all invisible at the moment a real bet is placed.

BetBuddy stores a snapshot of the exact inputs available at analysis time and grades every prediction against that snapshot. Rebuilding a historical estimate later uses the stored snapshot, never the current state of the database.