3. Liga21°0,1 mm20 km/h

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    Viktoria KölnViktoria KölnPossession control
    41
    Preussen MünsterPreussen MünsterCounter-attacks & crosses
    The model's lean: 1 · Viktoria Köln (50%)Best value by the model: Under 0,5 @ 20.00 (+28 %)

    Viktoria KölnPreussen Münster · 3. Liga

    1 · Viktoria Köln 50%X 25%25% Preussen Münster · 2

    Analysis: Viktoria KölnPreussen Münster

    Marcus Johansson · · Analysis from the match model · How the predictions work

    TEXT: Viktoria Köln enters this clash with a solid 50% probability of winning, according to model projections. Their possession-based style contrasts sharply with Preussen Münster's counter-attacking approach, setting the stage for a tactical battle. Despite trailing their visitors by a point in the standings, the hosts have outperformed in expected points and goals metrics, suggesting underlying strength not fully captured by their current league position.

    The most likely outcomes—a 1-1 draw or a narrow 1-0 victory for Viktoria—highlight a game that seems poised to teeter on a knife-edge. Expected goals paint a picture of Köln having more than a slight edge at 1.72 compared to Münster's 1.09. This indicates that while Münster might snatch a draw or capitalize on a counter, the game’s flow could favor the home side if they manage to assert their possession game effectively.

    Köln's ability to convert possession into quality chances, as reflected in their superior xG and xGA figures, aligns with the model's lean towards them. Their defense appears more resilient compared to Münster's, with a lower expected goals against per match at 1.5. In contrast, Münster concedes an average of 2.0 xGA, potentially leaving them vulnerable to Köln's pressing game, especially if they fail to transition swiftly into their counter-attacking style.

    The over 2.5 goals market shows a slight edge, projected at 53%. Given both sides' tendencies—Köln's ability to create chances and Münster's counter-attacking threat—there's a realistic scenario where goals could flow. However, the first goal might dictate the pace, either opening the game or leading to a more cautious approach.

    For punters, the standout value lies with Viktoria Köln securing a win, as the underlying stats suggest the hosts have a firm foothold in this matchup to justify a stake.

    Viktoria KölnViktoria Köln
    Form WLDLW
    League ranking xG #2xGA #7xT #15xP/match #6Points #12
    Key players
    • David OttoLWxG 0.74 · xT -0.04
    • Tim KlossCBxG 0.00 · xT 0.09
    • Taylan DumanCMxG 0.54 · xT 0.02
    Preussen MünsterPreussen Münster
    Form WD
    League ranking xG #14xGA #16xT #19xP/match #13Points #5
    Key players
    • Montell NdikomRWxG 0.99 · xT 0.08
    • Felix HiglFxG 0.04 · xT 0.00
    • Ryan JohanssonCAMxG 0.15 · xT 0.00
    Final score
    41
    The model called the outcome
    Predicted probabilities: Viktoria Köln 50% · Draw 25% · Preussen Münster 25%

    Match prediction: Viktoria KölnPreussen Münster

    Predicted score matrix

    Preussen Münster
    Viktoria Köln
    0
    1
    2
    3
    4+
    0
    0–06.4%
    0–16.3%
    0–23.6%
    0–31.3%
    0–4+0.4%
    1
    1–010.1%
    1–111.6%
    1–26.2%
    1–32.2%
    1–4+0.8%
    2
    2–08.9%
    2–19.7%
    2–25.3%
    2–31.9%
    2–4+0.7%
    3
    3–05.1%
    3–15.6%
    3–23.0%
    3–31.1%
    3–4+0.4%
    4+
    4+–03.2%
    4+–13.5%
    4+–21.9%
    4+–30.7%
    4+–4+0.2%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-112%
    2. 2.1-010%
    3. 3.2-110%
    4. 4.2-09%
    5. 5.0-06%
    Expected goals
    1,721,09
    Both teams to score
    55%

    Over/under goals

    Expected goals: 2,8
    Under 2,547%
    @2.63EV+23%
    Over 2,553%
    @1.70

    Ready-made bet suggestions

    More combos & build your own

    01 · 2 legs

    Safe

    • Viktoria Köln to win50%
    • Over 1.5 goals77%

    Combined probability

    42%

    Fair odds

    2.40

    Two legs pulling the same way — the probability is computed exactly from the score matrix, not multiplied.

    02 · 3 legs

    Balanced

    • Viktoria Köln to win50%
    • Over 2.5 goals53%
    • Jakob Sachse to score26%

    Combined probability

    Fair odds

    The whole slip is priced jointly via 20,000 simulated matches — the scorer interacts with result and total goals, never naive multiplication.

    03 · 4 legs

    Bold

    • Both teams to score55%
    • Over 2.5 goals53%
    • Jakob Sachse to score26%
    • Rico Preißinger to be booked24%

    Combined probability

    Fair odds

    The goal-fest: both teams score with at least three goals, plus the match's most likely scorer and card candidate — all priced jointly via 20,000 simulated matches.

    Odds & value

    The model's value spots

    Bets where the model's probability beats what the odds imply (EV+).

    • Under 0.56% probability@20.00Paddy PowerEV+28%reference odds
    • Under 1.523% probability@5.50Paddy PowerEV+25%reference odds
    • Under 2.547% probability@2.63Paddy PowerEV+23%reference odds
    Bookmaker1 · Viktoria KölnX2 · Preussen Münster
    Paddy Power
    EV+ 10%
    2.20
    3.752.50
    Pinnacle
    EV+ 5%
    2.10
    3.643.29

    Odds updated 30 Aug, 08:30

    Odds movement

    1-1 %
    47 %42 %

    2.122.10

    X0 %
    28 %23 %

    3.633.64

    2+7 %
    32 %27 %

    3.083.29

    Over 2,5+1 %
    58 %53 %

    1.691.70

    Pinnacle · 9 recorded price levels · 26/08/2026 → 30/08/2026 · fixed scale 5 percentage points

    Statistics

    xG & xGA per match — 3-game rolling average

    Solid line = created (xG), dashed = conceded (xGA).

    Viktoria Köln
    Preussen Münster
    When goals are scored and conceded 2026
    Viktoria Köln (56)
    Preussen Münster (42)
    Pass networks

    Last 5 matches. Circle size = pass volume, line width = combinations between players. Attacking left to right.

    Viktoria Köln
    Tim Kloss: 142 passningar, xT 0.17KlossTobias Eisenhuth: 141 passningar, xT 0.31EisenhuthArne Schulz: 95 passningar, xT 0.15SchulzMeiko Sponsel: 87 passningar, xT 0.08SponselNiklas Swider: 87 passningar, xT 0.11SwiderTaylan Duman: 61 passningar, xT 0.04DumanKilian Jakob: 49 passningar, xT 0.05JakobLucas Wolf: 36 passningar, xT -0.01WolfDavid Otto: 32 passningar, xT 0OttoEric Babacar Gueye: 21 passningar, xT 0.04GueyeNiklas Castelle: 13 passningar, xT -0.18Castelle
    Preussen Münster
    Rico Preißinger: 71 passningar, xT 0.04PreißingerNiko Koulis: 66 passningar, xT 0.06KoulisTim-Justin Dietrich: 62 passningar, xT 0.14DietrichMorten Jens Behrens: 60 passningar, xT 0.14BehrensWesley Adeh: 40 passningar, xT 0.04AdehCharalambos Makridis: 38 passningar, xT 0.01MakridisRyan Johansson: 36 passningar, xT -0.03JohanssonLucas Christopher Zeller: 30 passningar, xT 0.01ZellerMontell Ndikom: 27 passningar, xT 0.1NdikomFelix Higl: 24 passningar, xT 0HiglLouis Philipp Kolbe: 22 passningar, xT 0.14Kolbe
    xT networks

    The same network weighted by offensive threat (xT): circle size = threat created by the player, line width = threat of the combinations. Last 5 matches, attacking left to right.

    Viktoria Köln
    Tim Kloss: 142 passningar, xT 0.17KlossTobias Eisenhuth: 141 passningar, xT 0.31EisenhuthArne Schulz: 95 passningar, xT 0.15SchulzMeiko Sponsel: 87 passningar, xT 0.08SponselNiklas Swider: 87 passningar, xT 0.11SwiderTaylan Duman: 61 passningar, xT 0.04DumanKilian Jakob: 49 passningar, xT 0.05JakobLucas Wolf: 36 passningar, xT -0.01WolfDavid Otto: 32 passningar, xT 0OttoEric Babacar Gueye: 21 passningar, xT 0.04GueyeNiklas Castelle: 13 passningar, xT -0.18Castelle
    Preussen Münster
    Rico Preißinger: 71 passningar, xT 0.04PreißingerNiko Koulis: 66 passningar, xT 0.06KoulisTim-Justin Dietrich: 62 passningar, xT 0.14DietrichMorten Jens Behrens: 60 passningar, xT 0.14BehrensWesley Adeh: 40 passningar, xT 0.04AdehCharalambos Makridis: 38 passningar, xT 0.01MakridisRyan Johansson: 36 passningar, xT -0.03JohanssonLucas Christopher Zeller: 30 passningar, xT 0.01ZellerMontell Ndikom: 27 passningar, xT 0.1NdikomFelix Higl: 24 passningar, xT 0HiglLouis Philipp Kolbe: 22 passningar, xT 0.14Kolbe

    The teams in numbers

    PerformanceViktoria KölnPreussen Münster
    Points34
    xPoints3.42.3
    xG per match2.51.3
    xGA per match1.52
    xG within 8s of winning the ball0.150.1
    xGA within 8s of losing the ball0.180.41
    Playing styleViktoria KölnPreussen Münster
    Build-up efficiency0.30.26
    Field tilt0.490.21
    xT per match0.810.42
    xTA per match0.781.1
    Won balls, offensive half3118
    Pressing intensity0.20.23
    Pressing efficiency0.310.33
    Pressing efficiency, offensive half0.240.26
    Entries into the box per match113
    Entries into the box against916
    Pass completion %0.80.67
    Pass completion % under pressure0.730.72
    Passes per match410255
    Passes against per match332354
    Switches of play per match20.65
    Long balls per match3823
    Set piecesViktoria KölnPreussen Münster
    xG from free kicks00
    Corners per match53
    Corners against per match4.55.5
    xG per corner0.060.01
    xGA per corner against0.010.04
    First touch, offensive corners %0.40.67
    First touch, defensive corners %0.670.55
    OtherViktoria KölnPreussen Münster
    Throw-in control0.680.6
    The goalkeepersArne Schulz (Viktoria Köln)Morten Jens Behrens (Preussen Münster)
    Saves57
    Save %46%78%
    xG prevented34%51%
    Claims0 (0%)0 (0%)

    Teams & more matches this round

    Frequently asked questions

    Who wins Viktoria Köln vs Preussen Münster according to our model?

    The model gives Viktoria Köln a 50% win probability. Full 1X2 picture: Viktoria Köln 50%, draw 25%, Preussen Münster 25%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 12% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 53% and under at 47%.

    Will both teams score?

    The probability of both teams scoring is 55% according to the model.

    Why are some legs locked in the bet builder?

    The same rules as the bookmakers’ builders: legs that contradict or already follow from your slip cannot be combined — for example correct score 2–1 with under 2.5 goals. The difference is that we don’t look the rules up in a table; we compute them straight from the score matrix.

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    Predictions come from PlaymakerAI's match model and reflect the model's assessment — not betting advice. Gamble responsibly.