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VfL Wolfsburg

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VfL Wolfsburg

Live data for professional portfolio management, trading and predictions.

Akte Wölfe — Club-Dossier VfL Wolfsburg
Intelligence
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At a glance

Live data for professional portfolio management, trading and predictions.

Bundesliga Table

Bundesliga table matchday 3
# Club P W D L GF GA GD Pts
1 Freiburg 3 3 0 0 10 1 +9 9
2 BVB 3 3 0 0 8 2 +6 9
3 Augsburg 3 2 1 0 9 3 +6 7
4 Bayern 3 2 1 0 7 2 +5 7
5 Leipzig 3 2 0 1 9 3 +6 6
6 Elversberg 3 2 0 1 8 7 +1 6
7 Leverkusen 3 1 1 1 8 5 +3 4
8 Mainz 3 1 1 1 6 3 +3 4
9 Eintracht 3 1 1 1 7 8 -1 4
10 Werder 3 1 1 1 5 6 -1 4
11 Schalke 04 3 1 1 1 3 4 -1 4
12 Koeln 3 1 1 1 5 7 -2 4
13 Hoffenheim 3 1 0 2 6 7 -1 3
14 Stuttgart 3 1 0 2 6 8 -2 3
15 Paderborn 3 0 1 2 0 4 -4 1
16 Union 3 0 1 2 4 10 -6 1
17 Gladbach 3 0 0 3 3 12 -9 0
18 HSV 3 0 0 3 0 12 -12 0

Top Scorers

Bundesliga Top Scorers Season 28321

  1. 1
    Patrik Schick
    Patrik Schick
    Leverkusen · 30
    3 Goals
  2. 2
    Younes Ebnoutalib
    Younes Ebnoutalib
    Eintracht · 23
    3 Goals
  3. 3
    Igor Matanovic
    Igor Matanovic
    Freiburg · 23
    3 Goals
  4. 4
    Maurice Krattenmacher
    Maurice Krattenmacher
    Elversberg · 21
    3 Goals
  5. 5
    Yuito Suzuki
    Yuito Suzuki
    Freiburg · 24
    3 Goals
# Player Club Goals
6 Michael Gregoritsch Augsburg 3
7 Phillip Tietz Mainz 3
8 Antonio Nusa Leipzig 2
9 Serhou Guirassy BVB 2
10 Jonathan Burkardt Eintracht 2

Pinnacle Oracle

Form & Momentum

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
    Matthias Ginter
    Matthias Ginter
    Freiburg · 32
    3 Assists
  2. 2
    Sheraldo Becker
    Sheraldo Becker
    Mainz · 31
    2 Assists
  3. 3
    Adam Daghim
    Adam Daghim
    Hoffenheim · 20
    2 Assists
  4. 4
    Ismael Saibari
    Ismael Saibari
    Bayern · 25
    2 Assists
  5. 5
    Serhou Guirassy
    Serhou Guirassy
    BVB · 30
    2 Assists
# Player Club Assists
6 Marco Grüll Werder 2
7 Miguel Gutiérrez Leverkusen 2
8 Bambasé Conté Hoffenheim 2
9 Josip Juranovic Union 2
10 Derry Scherhant Freiburg 2

Bundesliga Card Ranking (Yellow + Red×3)

  1. 1
    Tim Kleindienst 
    Tim Kleindienst 
    Gladbach · 31
    1 1
  2. 2
    Ron Schallenberg
    Ron Schallenberg
    Schalke 04 · 27
    0 1
  3. 3
    Samuele Inácio
    Samuele Inácio
    BVB · 18
    0 1
  4. 4
    Kevin Diks
    Kevin Diks
    Gladbach · 29
    2 0
  5. 5
    Wouter Burger
    Wouter Burger
    Hoffenheim · 25
    2 0
# Player Club Y R Total
6 Phillipp Mwene Mainz 2 0 2
7 Ozan Kabak Hoffenheim 2 0 2
8 Raphael Onyedika Eintracht 2 0 2
9 Ibrahim Maza Leverkusen 2 0 2
10 Felix Uduokhai Union 2 0 2

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 0.77 ppg · n=34 Away 1.35 ppg · n=34 -0.59 [-1.15, -0.03] 0.05 🟢
Versus top-6 opponents vs. rest of the league Vs top 6 0.58 ppg · n=24 Vs rest 1.32 ppg · n=44 -0.73 [-1.26, -0.17] 0.01 🟢
With vs. without Kamil Grabara in the starting XI With Kamil Grabara 1.00 ppg · n=63 Without Kamil Grabara 1.80 ppg · n=5 -0.80 [-1.71, 0.06] 0.09 🟡
With vs. without Konstantinos Koulierakis in the starting XI With Konstantinos Koulierakis 1.24 ppg · n=55 Without Konstantinos Koulierakis 0.31 ppg · n=13 +0.93 [0.32, 1.43] 0.00 🟡
With vs. without Mohamed Amoura in the starting XI With Mohamed Amoura 1.11 ppg · n=52 Without Mohamed Amoura 0.88 ppg · n=16 +0.24 [-0.49, 0.92] 0.51 ⚪
With vs. without Maximilian Arnold in the starting XI With Maximilian Arnold 1.13 ppg · n=47 Without Maximilian Arnold 0.91 ppg · n=21 +0.22 [-0.38, 0.82] 0.47 ⚪
With vs. without Patrick Wimmer in the starting XI With Patrick Wimmer 1.14 ppg · n=43 Without Patrick Wimmer 0.92 ppg · n=25 +0.22 [-0.38, 0.81] 0.46 ⚪
Heavy week (after UCL/intl. break) vs. normal week Heavy week 0.00 ppg · n=0 Normal week 1.06 ppg · n=68 -1.06 — — ⬜
After UCL midweek vs. without UCL before After UCL 0.00 ppg · n=0 No UCL 1.06 ppg · n=68 -1.06 — — ⬜
Full strength (0 absences) vs. 2+ key-player absences 0 absences 1.56 ppg · n=16 2+ absences 0.78 ppg · n=23 +0.78 [-0.01, 1.56] 0.05 🟡

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.

Confirmed

"Bayern struggles against top-6 opponents"

Gegen Top 6: 0.583 ppg · gegen Rest: 1.318 ppg (Δ -0.735).

Prediction relevance: Adjustment -24.5pp für Top-6-Gegner.

Untested

"Midweek UCL games cost points"

Indikativ: Nach CL 0 ppg, ohne CL 1.059 ppg.

Prediction relevance: Kein klares Adjustment.

Confirmed

"Home games are different"

Heim: 0.765 ppg · Auswärts: 1.353 ppg (Δ -0.588).

Prediction relevance: Adjustment -19.6pp für Heimspiele.

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 VfL Wolfsburg 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.

Frequently Asked

Who is the Bundesliga top scorer?
Patrik Schick (Leverkusen) with 3 goals in the 28321 season.

Related

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