BetSquare — Squared Analytical Precision for India's IPL Cricket Betting

▪️ What a squared IPL analytical model looks like — four squareness tests

Squareness Test 1 — Baseline consistency: A squared model produces exactly the same powerplay expected value for the same venue and pitch combination every time it is applied. If two applications of the same inputs produce different numbers, the model is not squared — it has inconsistency in the adjustment methodology. The BetSquare test: write down the Chepauk powerplay expected value for a good pitch before calculating it, then calculate it. Do the numbers match? If not, the baseline or adjustment is inconsistent and needs squaring.
Squareness Test 2 — Historical calibration check: A squared model's outputs should match observed historical session scores within a predictable range at least 85% of the time when no special conditions apply. A squared Chepauk good-pitch model that predicts 44.2 should produce actual scores in the 39–49 range (±5 from baseline) in approximately 68% of observations (1-sigma), and 36–52 (±8) in approximately 95%. If the actual distribution is systematically different, the baseline needs recalibration to be squared.
Squareness Test 3 — Adjustment additivity: In a squared model, adjustments combine correctly without double-counting. Chepauk baseline 44.2 + dry pitch −3.5 + weak batting −1 + batting first +1.2 = 40.9 adjusted. These adjustments should be verified as non-overlapping: the pitch adjustment and the batting-first adjustment shouldn't both incorporate the same "dry pitch causes slower scoring" effect (if they do, the same input is counted twice and the model is not squared).
Squareness Test 4 — Edge threshold calibration: A squared model's 5% threshold produces bets that actually outperform at the expected rate over 30+ observations. If the model is placing 5% deviation bets but accuracy over 50 bets is only 51% (barely above coinflip), the 5% threshold may need squaring — either the model is overestimating edge, or the threshold is set too low for the model's actual precision.