Totals Research: Building Your Database

## The Data You Need Totals betting quality depends entirely on the quality of your goals data and the depth of contextual factors. Building a totals-specific database takes time but compounds significantly. ## Required Data Fields For every match in your target leagues: - Date, competition, home team, away team - Full-time score (goals by each team) - Half-time score - Expected goals (home and away) - Pre-match total line (opening and closing) - Weather data (temperature, wind, precipitation) - Referee assigned - Match significance score (relegation, title implications, nothing at stake) - Key absences (leading goal scorer, defensive anchor) ## Analysing Contextual Factors in Totals Once you have 3+ seasons of data with contextual annotations, test each factor independently: For each factor (e.g. "high wind"): - Filter all matches with high wind (> 15 mph) - Compare actual average goals to pre-match total line - Calculate whether Under hit at higher than 50% rate - Calculate the average CLV of Under bets in these conditions If a factor consistently produces >52% Under hit rate across 200+ qualifying matches: it is a real contextual effect worth systematically applying. ## The Calibration Test for Totals Models Split your historical data into deciles by your model's expected goals estimate (0–1.5, 1.5–2.0, 2.0–2.5, 2.5–3.0, 3.0+). For each decile, calculate: - Average actual goals - Average model prediction If average actual goals matches average prediction within each decile, your model is well-calibrated for totals. If not, identify the systematic bias and correct it.