What Predicted Performance — and What Actually Stuck
The single most decision-useful finding of the 2026 Calcutta project. Built from 2024 + 2025 auction prices joined to actual match-play results. All numbers re-derived from the real data.
The question
The handicap and the betting money are a prediction of how a team will do. The actual match-play points are the result. So: did the billing predict the score, and does beating your billing stick to anyone? (E.g., a team billed for 20 points that scores 25 "beat its billing" by 5.)
1. The billing barely predicts anything
Within a flight (6 teams, ~150 points split among them, so the average team scores 25), the auction price explains only 3–5% of the variance in points (R² = 0.03 in 2024, 0.05 in 2025). Each full step up the price ladder is worth just +0.5 points. The real spread of finishes is ~16 to ~34.
So "billed for 20, scored 25" is the norm, not the exception — the prediction is nearly powerless, and the residual (what you score over your billing) is almost the entire story. And that residual is mostly luck: net better-ball match play off the low handicap is designed to equalize ability.
2. At the team level, beating your billing does NOT stick
Year-over-year correlation of team residuals: r = −0.23 — actually negative. The team that overperformed last year tends to regress the next. "They crushed it in 2025" is not a reason to buy them in 2026. This is why no team-based betting strategy survived the backtest.
2025's biggest over- and under-performers (price → predicted points → actual):
| Team | Billed | Scored | Beat billing by |
|---|---|---|---|
| Keister + Knapp | 24 | 32.5 | +8.3 |
| Gelinas + Chafin | 25 | 33.0 | +8.3 |
| Kulik + DiCicco | 24 | 32.0 | +7.8 |
| Armstrong + Ballard | 27 | 34.5 | +7.2 |
| Nodar + Heslep | 24 | 30.5 | +6.5 |
| Pearson + Ferguson | 25 | 16.0 | −9.3 |
| Mulick + Thilmany | 25 | 16.5 | −8.2 |
3. But a handful of INDIVIDUALS beat their billing every year — and they're our buy card
Split teams into players and ask who outscored their billing in both 2024 and 2025. A short, sticky list falls out — and it overlaps almost perfectly with the teams we'd already flagged through completely different methods:
| Player | 2024 (over billing) | 2025 (over billing) | On the buy card? |
|---|---|---|---|
| Estes | +6.7 | +6.2 | yes, Wood + Estes |
| Keister | +4.9 | +8.3 | yes, Knapp + Keister |
| Copeland | +4.1 | +4.2 | yes, Wright + Copeland |
| Bachstein | +4.1 | +4.8 | (Hancock + Bachstein) |
| Flammia | +4.1 | +6.2 | — |
The market reprices teams every year (partners change, last year's hero reverts), but a few individuals quietly outscore their card season after season.
4. Why this matters: three independent methods, same names
This residual test is purely statistical — it knows nothing about who plays golf. Yet the names it surfaces (Estes, Copeland, Keister) are the same players we independently flagged via: - Human intel — an eyewitness sandbagger read (Copeland), insider knowledge. - The model + prior results — Wood + Estes, Knapp + Keister winning flights.
When an eyewitness, a simulation, and a blind 2-year residual test all point at the same handful of players, that's no longer a backtest artifact — it's signal.
Summary
- Money predicts the floor, not the ceiling. The cheapest third of teams finish dead last ~40% of the time, stable across both years (priciest third: ~19%). The market reliably spots duds; it's a coin flip on champions.
- Don't bet on teams that overperformed — bet on the individuals who reliably outscore their billing. The edge is at the player level, not the team level.
- The one-liner: The market prices the card. We price the player.
Honest caveat
Two years of price data and a short list — some names on it are partly luck (with ~120 players, a few will beat their billing twice by chance). What makes it credible is the cross-method convergence, not the residual test alone. Treat it as a strong lean, not a certainty.