Backtesting

Q: What is backtesting?

A: Backtesting is the process of testing strategy rules with historical data. In football betting, it usually requires historical matches, historical odds, match results, and betting rules. The purpose is to simulate what would have happened if the strategy had been executed strictly at that time.

Q: Why does a football quant strategy need backtesting?

A: A short winning streak proves very little. Only when the same rule is tested across enough historical matches can we begin to judge whether the result was luck or whether there may be a repeatable probability edge. Backtesting is the step that turns a quantitative strategy from an idea into a system.

Q: What basic data does backtesting require?

A: At minimum, it needs four types of data: match time, match result, the odds available at the time of betting, and the strategy trigger conditions. If the goal is to study the capital curve, the test also needs staking rules such as fixed stake, fixed percentage, or the Kelly formula.

Q: What are the most common backtesting mistakes?

A: The most common mistake is using future information. Examples include using post-match data to decide whether a pre-match bet should have been placed, or replacing real buyable odds with opening odds, closing odds, or average odds. Another common mistake is looking only at total profit while ignoring maximum drawdown, losing streaks, and sample size.

Q: How is football betting backtesting different from stock backtesting?

A: Stock backtesting usually deals with continuous price series. Football betting backtesting deals with discrete match events. A football match has a clear settlement time and result, while odds may change before kickoff. The backtest must define exactly which timestamp's odds are used; otherwise, the result is distorted.

Q: Where does backtesting sit in this site's knowledge graph?

A: Backtesting is the strategy validation layer. It connects quantitative strategy, odds, and future Python implementations. Later example pages can provide match samples, while implementation pages can show how to reproduce the backtest process in Python.