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FC Bayern München

Prediction Intelligence
FC Bayern München

Live data for professional portfolio management, trading and predictions.

Position
1.
Points
83
Goal diff
+81
Akte Bayern — Club-Dossier FC Bayern München
Intelligence
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At a glance

Live data for professional portfolio management, trading and predictions.

Bundesliga Table

Bundesliga table matchday 32
# Club P W D L GF GA GD Pts
1 Bayern 32 26 5 1 116 35 +81 83
2 BVB 32 20 7 5 65 32 +33 67
3 Leipzig 32 19 5 8 63 42 +21 62
4 Leverkusen 32 17 7 8 66 43 +23 58
5 Stuttgart 32 17 7 8 66 46 +20 58
6 Hoffenheim 32 17 7 8 64 48 +16 58
7 Freiburg 32 12 8 12 45 53 -8 44
8 Eintracht 32 11 10 11 57 60 -3 43
9 Augsburg 32 11 7 14 42 56 -14 40
10 Mainz 32 9 10 13 41 50 -9 37
11 Gladbach 32 8 11 13 37 50 -13 35
12 HSV 32 8 10 14 36 51 -15 34
13 Union 32 8 9 15 37 57 -20 33
14 Koeln 32 7 11 14 47 55 -8 32
15 Werder 32 8 8 16 37 57 -20 32
16 Wolfsburg 32 6 8 18 42 67 -25 26
17 St. Pauli 32 6 8 18 27 55 -28 26
18 Heidenheim 32 5 8 19 38 69 -31 23

Top Scorers

Bundesliga Top Scorers Season 25646

  1. 1
    Harry Kane
    Harry Kane
    Bayern · 32
    33 Goals
  2. 2
    Deniz Undav
    Deniz Undav
    Stuttgart · 29
    18 Goals
  3. 3
    Patrik Schick
    Patrik Schick
    Leverkusen · 30
    16 Goals
  4. 4
    Luis Díaz
    Luis Díaz
    Bayern · 29
    15 Goals
  5. 5
    Serhou Guirassy
    Serhou Guirassy
    BVB · 30
    15 Goals
# Player Club Goals
6 Michael Olise Bayern 14
7 Andrej Kramaric Hoffenheim 14
8 Christoph Baumgartner Leipzig 13
9 Yan Diomande Leipzig 12
10 Said El Mala Koeln 12

Pinnacle Oracle

Form & Momentum

Bayern — Form & Home/Away

Last 5
WWWWD
Home
41 Pts
Record: 13-2-1 Goals: 63:18
Away
42 Pts
Record: 13-3-0 Goals: 53:17

Last result: Draw. Last 5 form: W-W-W-W-D.

The form of the last five matches is the most important leading indicator for short-term bets. A team on a three-match win streak is significantly underpriced when the odds movement hasn't yet caught up with the momentum. The Pinnacle Oracle weights this form at roughly 30 percent against table position (40 percent), home/away splits (20 percent) and opponent strength (10 percent).

Assists & Card Ranking

Bundesliga Top Assists

  1. 1
    Michael Olise
    Michael Olise
    Bayern · 24
    19 Assists
  2. 2
    Julian Ryerson
    Julian Ryerson
    BVB · 28
    14 Assists
  3. 3
    Luis Díaz
    Luis Díaz
    Bayern · 29
    13 Assists
  4. 4
    Jamie Leweling
    Jamie Leweling
    Stuttgart · 25
    9 Assists
  5. 5
    Andrej Ilic
    Andrej Ilic
    Union · 26
    9 Assists
# Player Club Assists
6 Bazoumana Touré Hoffenheim 9
7 Farès Chaïbi Eintracht 9
8 Fisnik Asllani Hoffenheim 8
9 Konrad Laimer Bayern 8
10 Christoph Baumgartner Leipzig 8

Bundesliga Card Ranking (Yellow + Red×3)

  1. 1
    Dominik Kohr
    Dominik Kohr
    Mainz · 32
    10 2
  2. 2
    Eric Martel
    Eric Martel
    Koeln · 24
    11 1
  3. 3
    Niklas Stark
    Niklas Stark
    Werder · 31
    6 2
  4. 4
    Marco Friedl
    Marco Friedl
    Werder · 28
    9 1
  5. 5
    Rocco Reitz
    Rocco Reitz
    Gladbach · 23
    8 1
# Player Club Y R Total
6 Nicolai Remberg HSV 11 0 11
7 Johan Manzambi Freiburg 4 2 6
8 Miro Muheim HSV 7 1 8
9 Moritz Jenz Wolfsburg 7 1 8
10 Wouter Burger Hoffenheim 7 1 8

Statistical Splits BETA

What actually moves Bayern's result — and what's myth. Bootstrap confidence intervals from 68 matches of the Kompany-Ära.

Split Group A Group B Δ ppg 95% CI p-value Significance
Home games vs. away games Home 2.59 ppg · n=34 Away 2.44 ppg · n=34 +0.15 [-0.29, 0.59] 0.56 ⚪
Versus top-6 opponents vs. rest of the league Vs top 6 2.40 ppg · n=20 Vs rest 2.56 ppg · n=48 -0.16 [-0.65, 0.30] 0.52 ⚪
With vs. without Joshua Kimmich in the starting XI With Joshua Kimmich 2.55 ppg · n=56 Without Joshua Kimmich 2.33 ppg · n=12 +0.22 [-0.42, 0.95] 0.57 🟡
With vs. without Harry Kane in the starting XI With Harry Kane 2.60 ppg · n=53 Without Harry Kane 2.20 ppg · n=15 +0.40 [-0.19, 1.07] 0.20 🟡
With vs. without Michael Olise in the starting XI With Michael Olise 2.49 ppg · n=51 Without Michael Olise 2.59 ppg · n=17 -0.10 [-0.57, 0.43] 0.70 ⚪
With vs. without Min-jae Kim in the starting XI With Min-jae Kim 2.52 ppg · n=46 Without Min-jae Kim 2.50 ppg · n=22 +0.02 [-0.45, 0.52] 0.98 ⚪
With vs. without Manuel Neuer in the starting XI With Manuel Neuer 2.68 ppg · n=44 Without Manuel Neuer 2.21 ppg · n=24 +0.47 [-0.03, 0.98] 0.06 🟡
Heavy week (after UCL/intl. break) vs. normal week Heavy week 2.24 ppg · n=38 Normal week 2.87 ppg · n=30 -0.63 [-1.01, -0.23] 0.00 🟢
After UCL midweek vs. without UCL before After UCL 2.03 ppg · n=30 No UCL 2.90 ppg · n=38 -0.86 [-1.30, -0.44] 0.00 🟢
Full strength (0 absences) vs. 2+ key-player absences 0 absences 2.33 ppg · n=15 2+ absences 2.32 ppg · n=25 +0.01 [-0.64, 0.68] 1.00 ⚪

Reading: 🟢 statistically significant · 🟡 indicative (sample or effect too small) · ⚪ no effect detectable · ⬜ untested

ppg = points per game (3 for a win, 1 for a draw, 0 for a loss). Δ ppg = difference in ppg between the two groups. 95% CI = bootstrap confidence interval (10,000 resamples). p-value < 0.05 = statistically significant at n ≥ 20.

Methodology: Single-Regime-Analyse (nur Kompany-Ära). xG fehlt im Plan und ist nicht enthalten. Bootstrap-CIs statt parametrischer Tests.
Not in dataset: xG, PPDA, Distance Covered

Myth Check BETA

What fans believe — and what the data says. Every myth is tested against real match data.

Indicative

"Without Kane, nothing works"

Indikativ: Mit 2.604 ppg, ohne 2.2 ppg — Trend deutlich (p≈0.2046), Stichprobe für klares 🟢 zu klein.

Prediction relevance: Kein Adjustment nötig.

Refuted

"Bayern struggles against top-6 opponents"

Gegen Top 6: 2.4 ppg · gegen Rest: 2.563 ppg (Δ -0.163).

Prediction relevance: Top-6-Gegner haben keinen messbaren Sondereffekt.

Confirmed

"Midweek UCL games cost points"

Bestätigt: Nach CL-Spielen holt Bayern nur 2.033 ppg, ohne CL-Belastung 2.895 ppg (Δ -0.862, p=0). Stärkster Effekt im Datensatz.

Prediction relevance: Adjustment -28.73pp wenn Bayern aus einer CL-Woche kommt. Pinnacle preist diesen Effekt vermutlich nicht voll ein.

Refuted

"Home games are different"

Heim: 2.588 ppg · Auswärts: 2.441 ppg (Δ 0.147).

Prediction relevance: Heimvorteil ist nicht überdurchschnittlich.

Match clusters (K-Means + PCA) BETA

62 Bayern Bundesliga matches from the Kompany era in 10-dimensional feature vectors (possession, shots, passes, goals...). K-Means identifies 4 match types, PCA projects to 2D for visualization.

Matches in PC1/PC2 space

Each point = one match. Color = result. Hover for details.

PC1 — Offensive dominance PC2 — Efficiency VfL Wolfsburg (A) · 3:2 · 08/25/24 SC Freiburg (H) · 2:0 · 09/01/24 Holstein Kiel (A) · 6:1 · 09/14/24 Werder Bremen (A) · 5:0 · 09/21/24 Bayer 04 Leverkusen (H) · 1:1 · 09/28/24 Eintracht Frankfurt (A) · 3:3 · 10/06/24 VfB Stuttgart (H) · 4:0 · 10/19/24 VfL Bochum 1848 (A) · 5:0 · 10/27/24 FC Union Berlin (H) · 3:0 · 11/02/24 St. Pauli (A) · 1:0 · 11/09/24 FC Augsburg (H) · 3:0 · 11/22/24 Borussia Dortmund (A) · 1:1 · 11/30/24 Heidenheim (H) · 4:2 · 12/07/24 FSV Mainz 05 (A) · 1:2 · 12/14/24 RB Leipzig (H) · 5:1 · 12/20/24 Borussia Mönchengladbach (A) · 1:0 · 01/11/25 TSG Hoffenheim (H) · 5:0 · 01/15/25 VfL Wolfsburg (H) · 3:2 · 01/18/25 SC Freiburg (A) · 2:1 · 01/25/25 Holstein Kiel (H) · 4:3 · 02/01/25 Werder Bremen (H) · 3:0 · 02/07/25 Bayer 04 Leverkusen (A) · 0:0 · 02/15/25 Eintracht Frankfurt (H) · 4:0 · 02/23/25 VfB Stuttgart (A) · 3:1 · 02/28/25 VfL Bochum 1848 (H) · 2:3 · 03/08/25 FC Union Berlin (A) · 1:1 · 03/15/25 St. Pauli (H) · 3:2 · 03/29/25 FC Augsburg (A) · 3:1 · 04/04/25 Borussia Dortmund (H) · 2:2 · 04/12/25 Heidenheim (A) · 4:0 · 04/19/25 FSV Mainz 05 (H) · 3:0 · 04/26/25 RB Leipzig (A) · 3:3 · 05/03/25 Borussia Mönchengladbach (H) · 2:0 · 05/10/25 TSG Hoffenheim (A) · 4:0 · 05/17/25 RB Leipzig (H) · 6:0 · 08/22/25 Hamburger SV (H) · 5:0 · 09/13/25 FC Augsburg (A) · 3:2 · 08/30/25 TSG Hoffenheim (A) · 4:1 · 09/20/25 Werder Bremen (H) · 4:0 · 09/26/25 Eintracht Frankfurt (A) · 3:0 · 10/04/25 Borussia Dortmund (H) · 2:1 · 10/18/25 Borussia Mönchengladbach (A) · 3:0 · 10/25/25 Bayer 04 Leverkusen (H) · 3:0 · 11/01/25 FC Union Berlin (A) · 2:2 · 11/08/25 SC Freiburg (H) · 6:2 · 11/22/25 St. Pauli (H) · 3:1 · 11/29/25 VfB Stuttgart (A) · 5:0 · 12/06/25 FSV Mainz 05 (H) · 2:2 · 12/14/25 Heidenheim (A) · 4:0 · 12/21/25 VfL Wolfsburg (H) · 8:1 · 01/11/26 FC Köln (A) · 3:1 · 01/14/26 RB Leipzig (A) · 5:1 · 01/17/26 FC Augsburg (H) · 1:2 · 01/24/26 Hamburger SV (A) · 2:2 · 01/31/26 TSG Hoffenheim (H) · 5:1 · 02/08/26 Werder Bremen (A) · 3:0 · 02/14/26 Eintracht Frankfurt (H) · 3:2 · 02/21/26 Borussia Dortmund (A) · 3:2 · 02/28/26 Borussia Mönchengladbach (H) · 4:1 · 03/06/26 Bayer 04 Leverkusen (A) · 1:1 · 03/14/26 FC Union Berlin (H) · 4:0 · 03/21/26 SC Freiburg (A) · 3:2 · 04/04/26 Demolition Working win Narrow win Dominance
Win Draw Loss

Cluster overview

Match type n Wins Draws Losses Win % Poss. Shots Goals Conceded
Demolition 17 17 0 0 100% 63.4 20.4 4.6 0.6
Dominance 16 14 2 0 87.5% 75.4 23.4 3.8 0.8
Working win 13 9 3 1 69.2% 60.8 12.8 2.1 1
Narrow win 16 8 6 2 50% 69.8 18.3 2.1 1.4

Methodology: Explorative Cluster-Analyse, n=62 / 10 Features = grenzwertig. xG fehlt im SportMonks Plan. K-Means k=4 hardcoded, Cluster-Labels heuristisch nach Centroid-Eigenschaften.

What the data doesn't say

Table, form and odds show the status quo. They say nothing about whether a coach is on the verge of being sacked, a key player is injured, or the board is internally under pressure. This is exactly where the Predictions page comes in: there season markets (Polymarket), transfer rumours and schedule strength feed into the assessment — factors that don't show up in any standard statistic.

The FC Bayern München File in turn provides the historical context: which crises has the club survived, which not. Anyone moving money on Bundesliga markets needs all three layers — hard stats, forward markets and institutional memory.

Related

The data shows the status quo. What does this mean for the season?