Wolfsburg v Werder Bremen — Prediction, Odds & Stats
Sunday 6 September 2026 · FIFA:2z7257m7hj58zuxcjrsg4erzc · 2627
FIFA:2z7257m7hj58zuxcjrsg4erzc
results-only Prediction generated
AI prediction
ai-blend v1.0 dailyCalibrated: the model's number corrected by how often calls at that confidence have landed, out of sample (372 settled matches in the thinnest band), then normalised. The smaller figure is the raw model output.
Top 5 scorelines
- 1-1 11.3%
- 1-0 10.4%
- 2-1 9.7%
- 2-0 9.0%
- 0-1 6.5%
Goals
| Over 2.5 | 53.3% | Under 2.5 | 46.7% |
| BTTS yes | 54.4% | BTTS no | 45.6% |
| Wolfsburg 1+ | 82.2% | Wolfsburg 2+ | 51.5% |
| Werder Bremen 1+ | 66.1% | Werder Bremen 2+ | 29.5% |
Expected goals 1.73 – 1.08. Every market above is read off one scoreline distribution.
Footing
Positive factors
- Wolfsburg have created 1.88 xG a match over their last five while Werder Bremen have allowed 2.14.
- At home Wolfsburg average 2.13 xG a match.
- They have had 30 days' rest to Werder Bremen's 1.
Risk factors
- Data quality is 45/100, so this price rests on less than the model would like.
Why the model thinks this
The price
Wolfsburg have an estimated 54.7% win probability, against 22.6% for the draw and 22.6% for Werder Bremen. Expected goals from the ensemble are 1.73 for Wolfsburg and 1.08 for Werder Bremen.
Why Wolfsburg
Wolfsburg have created 1.88 xG a match over their last five while Werder Bremen have allowed 2.14. At home Wolfsburg average 2.13 xG a match. They have had 30 days' rest to Werder Bremen's 1.
Why the draw is live
The draw sits at 22.6%.
Why Werder Bremen can win
Werder Bremen carry the stronger rating, 1432 Elo against 1402. Werder Bremen have created 0.93 xG a match over their last five while Wolfsburg have allowed 1.81. Away from home Werder Bremen average 0.93 xG a match.
Key players
Wolfsburg's leading contributors by expected goal involvements a match: Christian Eriksen (0.40), Mohammed Amoura (0.40), Dzenan Pejcinovic (0.32). Werder Bremen's leading contributors by expected goal involvements a match: Romano Schmid (0.44), Jens Stage (0.30), Niclas Füllkrug (0.28).
Availability
1 of Wolfsburg's top three contributors did not start their last match. 2 of Werder Bremen's top three contributors did not start their last match. 55% of Werder Bremen's last eleven are new to the club this window.
The matchup, as far as the data reaches
Wolfsburg: 50% possession, 13.2 shots a match, 12.0 faced, 0.43 xG a match from set pieces. Werder Bremen: 45% possession, 11.2 shots a match, 17.8 faced, 0.16 xG a match from set pieces.
Inputs last refreshed: matches 2026-09-06 07:42 · team_stats 2026-09-06 06:08 · availability 2026-09-06 07:53 · prediction 2026-09-05 18:39 ·
Wolfsburg are the model's most likely outcome at 54.7%. The likeliest scoreline is 1-1 at 11.3%. Confidence 29/100, data quality 45/100. These are probabilities, not certainties: the less likely outcome happens as often as its number says.
Written from the numbers the model saw, not by a language model; nothing here is a statistic the snapshot does not hold. How predictions work.
Was this pick made in advance?
-oos is out of sample: it was fitted only on matches
played before this one, so its pick is a genuine forecast rather than hindsight.
Anything without that suffix was fitted on all available data.What the chances say
Wolfsburg are finishing sharply below their chances — 6.8 goals fewer than expected across 10 matches, despite creating the openings. They are also conceding fewer than expected — 2.2 goals saved beyond what the chances suggest.
Werder Bremen are finishing above their chances — 2.7 goals more than expected across 10 matches, which usually cools off. They are also conceding fewer than expected — 3.4 goals saved beyond what the chances suggest.
Finishing swings far more than chance creation, so a gap this size usually narrows. Over ten matches it is suggestive, not settled — a genuinely elite finisher beats expected goals for years.
What the model said
| Model | Home / Draw / Away | Expected goals | Likeliest score | O2.5 | BTTS |
|---|---|---|---|---|---|
| ai-blend | 1.73–1.08 | 0% | 0% | 0% |
A model whose name ends -oos was produced by the walk-forward backtest:
it was fitted only on matches played before this one, so the pick is genuinely out of sample.