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UEFA Champions League · Score Research

08-05 02:00 CST · UEFA Champions League · Separate HT/FT models

Sa

Saint Gilloise

Home side / left market direction

Full-time first pick 1:1 Probability 18.4% · Standard score cluster Standard score cluster: 1:1 18.4%; 2:1 13.2%; 1:2 10.3%; 2:2 9.4% Half-time first pick 0:0 (62.1%)
Bo

Bodo Glimt

Away side / right market direction

Five-Factor Read

1. Market strength Saint Gilloise side rates higher

The handicap leans toward Saint Gilloise; the market prices the home side as stronger.

2. Form and pace 0:0

HT first pick is 0:0 (62.1%); HT draw probability is 65.9%, so the model still respects early caution.

3. AI Sidecar Read FT total 2.75

This match uses a DeepSeek AI sidecar factor. The AI signal only applies a small candidate-score reweighting and does not override the market/history main model by itself. AI note: 中立场微雨，主队攻击力偏弱但防守漏指数低，客队攻击强但防守同样稳固，双方节奏接近，总进球倾向中等偏低；市场主让平半但客队有反击能力，看好小球或平局，客队可能偷球。

4. Venue factor neutral / weather risk

Venue: 拉巴特竞技场. Neutral venue. Weather/temperature: 微雨, 25℃～26℃. Neither side has a clear host-country edge, so the venue read mainly removes normal home advantage and pushes the decision back to market, lineup and tournament context. Weather creates a disruption signal, so high-tempo attacking chains and blowout tails are treated more carefully. The totals line is high, but the weather-risk signal keeps unconditional open-game assumptions under review.

5. Sample and soft factors Strong sample

259 comparable matches; market bucket support is solid

Full-Time Score Distribution

1:1 18.4%
2:1 13.2%
1:2 10.3%
2:2 9.4%
3:1 7.8%
2:3 5.3%
3:2 4.9%
1:3 3.7%
FT market: side line 0.25 · total 2.75 · Bucket ah_ou:0.25/3.0
Score cluster: Standard score cluster: 1:1 18.4%; 2:1 13.2%; 1:2 10.3%; 2:2 9.4%

Half-Time Score Distribution

0:0 62.1%
1:0 15.0%
0:1 13.4%
1:1 3.7%
2:0 1.9%
0:2 1.8%
2:1 0.6%
1:2 0.6%
HT market: side line 0.00 · total 1.00
Saint Gilloise win40.0%
Draw31.1%
Bodo Glimt win28.9%
Saint Gilloise expected goals1.88
Bodo Glimt expected goals1.63
Under 3.553.3%
FT temperature0.95
Empirical weight76.4%
Sample size259
Europe 1X2home 41.0% / draw 27.1% / away 31.8%

Key Variables

First-goal path Saint Gilloise benefits most from opening the scoring; if HT stays 0:0, the FT distribution compresses toward lower scores.
Deciding factor Draw probability is 31.1%, so the exact-score set should keep draw and one-goal-margin scripts alive.
Risk point Under 3.5 is 53.3%; if the live total moves up materially, rerun the model.

Pre-Match Update · Market Movement

Published 2026-08-25 00:37:43 CST (started/finished about 478h 38m ago) | Snapshot window: pending -> 2026-08-25 00:37:43

First snapshotFT handicap 待补 · total 待补
Latest marketFT handicap 0.25 · total 2.75

Movement: FT handicap data pending; FT total data pending; HT handicap data pending; HT total data pending.

Pre-match note: Market snapshots are thin; refresh Feijing data again before publishing the final pre-match version.

Final Read: 1:1

Predicted side: Saint Gilloise win · Actual side: draw · side missed

Using the full-time handicap, totals, comparable market buckets and half-time market, the model's full-time first pick for Saint Gilloise vs Bodo Glimt is 1:1 (18.4%). Saint Gilloise side rates higher. This match uses a DeepSeek AI sidecar factor. The AI signal only applies a small candidate-score reweighting and does not override the market/history main model by itself. AI note: 中立场微雨，主队攻击力偏弱但防守漏指数低，客队攻击强但防守同样稳固，双方节奏接近，总进球倾向中等偏低；市场主让平半但客队有反击能力，看好小球或平局，客队可能偷球。 HT first pick is 0:0 (62.1%); HT draw probability is 65.9%, so the model still respects early caution. Exact-score outputs are probability rankings for pre-match research and review.

Risk note: exact scores are low-hit-rate events. The model output is a probability ranking, not a certainty; late market moves, lineups, red cards and early goals can reshape the distribution.

Sources

Feijing · fixtures, results, Asian handicap, totals, half-time markets, Europe 1X2 · Local history database · historical market lines, full-time scores, half-time scores · DeepSeek · AI sidecar context, bounded candidate reweighting only