Research question
This analysis examines which AI model demonstrates the most accurate probability calibration for double chance predictions in football markets. Calibration—the alignment between predicted probabilities and observed outcomes—is a critical dimension of model evaluation because miscalibration undermines expected value calculations and can lead to poor bankroll management. The research focuses on the double chance market as reported in the Alpha Bench benchmark, with candidate type market_calibration. Understanding calibration helps distinguish models that are genuinely skilled at probability estimation from those that simply predict outcomes at sharp cutoffs.
Despite the modest sample size available, comparing calibration errors across models provides an early signal about which architectures may produce more reliable probability distributions. This is particularly relevant for bettors and analysts who rely on probability-based strategies rather than binary win/loss records.
Evaluation period
The frozen evaluation window runs from 2026-07-01 through 2026-07-31, consistent with the benchmark period defined in the long-term patterns data (2026-07-01 to 2026-07-31). All picks in this window were settled before the snapshot date of 2026-07-14T16:00:03Z. Picks were generated pre-kickoff using the model versions active at that time, with no retroactive adjustments. No specific leagues are isolated; the analysis aggregates all double chance predictions from the pilot period, which includes no designated pilot leagues (). This means results are not league-specific and may reflect broader market behavior.
The evaluation period is short (one month), which limits the number of settled picks per model. As a result, the comparison should be treated as preliminary rather than conclusive. Future analyses with longer windows will strengthen confidence.
Sample size
The entire benchmark dataset contains 4 models with settled picks, yielding a Insufficient sample. The total settled fixture count across all models is 148. Per-model settled picks vary: grok-build-0.1 has 27 picks, while deepseek-v4-pro has 107 picks. This uneven distribution means comparisons between models are dominated by the larger sample from deepseek-v4-pro, which may skew aggregate metrics.
A sample of this size is insufficient for robust statistical inference. To achieve meaningful confidence in calibration differences, each model would need hundreds of settled picks. Readers should temper conclusions accordingly.
Model comparison at a glance
| Model | Settled Picks | Brier | Log Loss | ROI (%) | |-------|--------------|-------|----------|---------| | grok-build-0.1 | 27 | 0.2379 | 0.665 | -3.59 | | gpt-5-mini | 13 | 0.3103 | 0.8206 | -23.33 | | deepseek-v4-flash | 39 | 0.3268 | 0.8726 | -29.19 | | deepseek-v4-pro | 107 | 0.332 | 1.1227 | -18.57 |
The spread in Brier scores from 0.2379 (grok-build-0.1) to 0.332 (deepseek-v4-pro) is notable, but all models exhibit negative ROI. grok-build-0.1 achieves the lowest Brier and log loss, suggesting better overall probability accuracy. However, its ROI of -3.59% indicates that even the best-calibrated model in this sample lost money on settled picks. Deepseek-v4-pro, despite the highest Brier and log loss, shows a less negative ROI than some others, highlighting that Brier alone does not capture betting profitability.
Calibration and probability accuracy
For the double chance market, calibration data is available only for two models: deepseek-v4-flash and deepseek-v4-pro. Deepseek-v4-pro exhibits a calibration error of -49.5 percentage points, indicating severe overconfidence—it predicted an expected win rate of +74.5% but achieved an actual win rate of only +25%. Deepseek-v4-flash shows a smaller calibration error of -5 points, with an expected win rate of +71.7% and an actual win rate of +66.7%. This suggests deepseek-v4-flash is better calibrated, though still overconfident.
The overall calibration deciles across all models reveal that predictions in the highest confidence bracket (90-100%) had an actual outcome rate of only 30 (25%), far below the predicted average of 44. This pattern of overconfidence at the extreme is common and underscores the importance of calibration assessment.
Risk, drawdown, and bankroll behavior
Bankroll drawdown data provides insight into the volatility behind each model's performance. Deepseek-v4-pro, despite having the most settled picks (107), suffered a maximum drawdown of 558.46 units (+51% of peak equity of 1094.5). Its current equity of 536.04 reflects substantial erosion. In contrast, grok-build-0.1 shows a lower maximum drawdown of 373.93 units (+36.8% of peak), with current equity at 828.41 near its peak of 1017.07. Gpt-5-mini has the smallest absolute drawdown at 1007.95 units, but its peak equity was lower. These differences underscore that superior calibration does not guarantee smooth equity curves; deepseek-v4-flash, with moderate calibration, experienced a drawdown of +51.4%.
Model-by-model notes
grok-build-0.1 leads the benchmark with a Brier of 0.2379 and the highest consistency score of 30.2. It achieved the best ROI among the four at -3.59%, though still negative. Its drawdown was moderate relative to peak, suggesting more stable prediction behavior, but with only 27 settled picks, the sample is thin.
gpt-5-mini ranks second in Brier (0.3103) and log loss (0.8206), with an ROI of -23.33%. Its maximum drawdown of 1007.95 units is the lowest absolute value, but its current equity of 133.9 is below its peak of 1123.48. The sample of 13 picks limits reliability.
deepseek-v4-flash has a Brier of 0.3268 and an ROI of -29.19%. Its calibration error of -5 is the smallest among models with calibration data, indicating better probability accuracy. However, it experienced a drawdown of +51.4% from a peak of 1305.45, with current equity at 1258.6. Only 39 settled picks contribute to its metrics.
deepseek-v4-pro has the largest sample (107 picks) but the worst Brier (0.332) and log loss (1.1227). Its calibration error of -49.5 points signals strong overconfidence. The model also experienced the highest drawdown at +51% of peak, reducing equity from 1094.5 to 536.04. Despite poor calibration, its ROI of -18.57% is less negative than some peers, possibly due to the larger sample.
What this means for readers
The early benchmark indicates that grok-build-0.1 offers the best combination of low Brier, lower drawdown, and the least negative ROI among the four models for double chance predictions. However, all models currently show negative returns, and the sample sizes are too small to confirm statistical significance. Calibration data for two models reveals persistent overconfidence, particularly in high-confidence predictions. Readers should treat these findings as directional, not definitive. To verify claims, explore model profiles on deepseek-v4-flash and deepseek-v4-pro, and review the Alpha Bench methodology at /methodology. Related research on calibration for other markets, such as BTTS calibration and 1X2 calibration, provides additional context on model behavior across different prediction types.
Methodology
Alpha Bench freezes pre-kickoff prediction snapshots and matches them against settled outcomes to compute Brier score, log loss, ROI, and drawdown metrics. Calibration is assessed by comparing predicted probabilities in decile bins to actual outcome frequencies. All metrics are computed on settled picks only. The methodology version for this analysis is 2.1. Detailed methodology is available at /methodology.
Limitations
Several limitations constrain this analysis. First, the overall sample size of 4 models and the small number of settled picks per model (as few as 13 for gpt-5-mini) prevent robust statistical comparisons. The vector-dataset-status reports the sample as Insufficient sample. Second, calibration data is available for only two models in the double chance market, limiting the calibration comparison to those architectures. Third, the evaluation window is only one month, and no specific leagues are isolated; results may not generalize to longer periods or specific competitions. Fourth, version changes occurred during the period (e.g., deepseek-v4-flash had 34 changes in memory), which may affect model behavior between predictions. Readers should not extrapolate these findings to future performance or other markets.
Reproducible data references
The sources used in this analysis include: vector-dataset-status (sample size, label), vector-model-comparison (leaderboard), vector-calibration (deciles), vector-risk-metrics (drawdown, equity), vector-market-performance-double_chance (calibration error, win rate for two models), vector-long-term-patterns (benchmark period), vector-version-changes (model changes), vector-league-performance-benchmark, and package metadata (article_run_id: 2026-07-14-vector-001, data_cutoff_at: 2026-07-14T16:00:03Z, methodology_version: 2.1). Readers can verify these data points on model profile pages and the Alpha Bench transparency hub at /trust. The full list of internal links used is provided in the article metadata.