3. Liga26°14 km/hUpdated

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    Alemannia AachenAlemannia AachenPossession control
    vs
    The model's lean: 1 · Alemannia Aachen (40%)
    VerlVerlPossession control

    Alemannia AachenVerl · 3. Liga

    1 · Alemannia Aachen 40%X 27%33% Verl · 2
    Alemannia AachenAlemannia Aachen
    Form WWDWW
    League ranking xG #15xGA #13xT #15xP/match #12Points #7
    Key players
    • Mika SchroersFxG 0.25 · xT 0.13
    • Jonas OehmichenLWxG 0.04 · xT 0.18
    • Lars GindorfCAMxG 0.6 · xT 0.21
    VerlVerl
    Form WLWLW
    League ranking xG #12xGA #3xT #7xP/match #18Points #6
    Key players
    • Berkan TazLWxG 0.38 · xT 0.16
    • Fynn OttoCBxG 0.07 · xT 0.15
    • Timur GayretCMxG 0.11 · xT 0.18

    Match prediction: Alemannia AachenVerl

    Predicted score matrix

    Verl
    Alemannia Aachen
    0
    1
    2
    3
    4+
    0
    0–05.1%
    0–16.8%
    0–25.3%
    0–32.6%
    0–4+1.3%
    1
    1–07.2%
    1–111.4%
    1–28.2%
    1–34.0%
    1–4+2.1%
    2
    2–05.8%
    2–18.6%
    2–26.4%
    2–33.1%
    2–4+1.6%
    3
    3–03.0%
    3–14.5%
    3–23.3%
    3–31.6%
    3–4+0.8%
    4+
    4+–01.7%
    4+–12.5%
    4+–21.8%
    4+–30.9%
    4+–4+0.5%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-111%
    2. 2.2-19%
    3. 3.1-28%
    4. 4.1-07%
    5. 5.0-17%
    Expected goals
    1,561,48
    Both teams to score
    61%

    Over/under goals

    Expected goals: 3,0
    Under 2,541%
    Over 2,559%

    Odds & value

    The model's value spots

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

    • Under 3.564% probability@1.71PinnacleEV+9%
    • 2 · Verl33% probability@3.20PinnacleEV+7%
    • X · Draw27% probability@3.94PinnacleEV+5%
    Bookmaker1 · Alemannia AachenX2 · Verl
    Pinnacle2.00
    EV+ 5%
    3.94
    EV+ 7%
    3.20

    Odds updated 4 Aug, 09:04

    Analysis: Alemannia AachenVerl

    Oscar Nilsson · · How the predictions work

    Twelve matches unbeaten: that's the impressive run Alemannia Aachen are riding into their clash with Verl. It's not just the results but the manner of this streak, marked by an 18-match scoring spree, reflecting a squad finely attuned to finding the back of the net. Yet, Verl are no strangers to resilience, possessing a similar penchant for controlling possession. Both sides mirror each other’s tactical approach, setting the stage for a midfield battle where control will be fiercely contested.

    Alemannia Aachen, despite their unbeaten streak, have a mixed record against Verl in recent encounters, showing one win, one draw, and two losses since 2024. This suggests that Verl knows a thing or two about unsettling their opponents, particularly when both teams are likely to see an equal share of the ball. The model leans towards Alemannia Aachen, giving them a 40% chance of victory, but with such a narrow edge, Verl's 33% chance shouldn’t be dismissed lightly.

    Numbers paint a picture of a tightly contested match: Alemannia Aachen have an expected goals (xG) figure of 1.56, barely edging out Verl’s 1.48. However, Verl's superior defense (xGA at 1.3 vs. Aachen’s 1.7) might be the crucial factor. The most probable scoreline, 1-1, reflects the balance of strengths and weaknesses these teams possess. With 59% odds for over 2.5 goals and 61% for both teams to score, the match promises a flow that could tilt either way.

    Verl at odds of 3.2 with Pinnacle carries a value lean, although it's a cautious one with just a 7% edge according to the model. Yet, for those eyeing a more measured approach, the under 3.5 goals at 1.709, also with Pinnacle, offers a solid 9% edge. While the spectacle of goals might entice, the smarter play leans towards a controlled affair.

    Statistics

    Head-to-head

    4 matches
    Alemannia Aachen 1Draw 1Verl 2
    Goals: 66 (⌀ 3)

    Head-to-head based on 3. Liga data since 2024.

    Key facts

    • Alemannia Aachen are unbeaten in their last 12 league matches.
    • Alemannia Aachen have scored in 18 consecutive league matches.
    xG & xGA per match — 3-game rolling average

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

    Alemannia Aachen
    Verl
    When goals are scored and conceded 2025
    Alemannia Aachen (7657)
    Verl (8248)
    Pass networks

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

    Alemannia Aachen
    Danilo Wiebe: 183 passningar, xT 0.31WiebePetros Bagalianis: 163 passningar, xT 0.23BagalianisJonas Oehmichen: 145 passningar, xT 1.36OehmichenMehdi Loune: 144 passningar, xT 0.51LouneJoel Miguel da Silva Kiala: 118 passningar, xT 0.22KialaFotios Pseftis: 88 passningar, xT 0.02PseftisMika Schroers: 85 passningar, xT 0.43SchroersMatti Wagner: 78 passningar, xT 0.21WagnerPierre Nadjombe: 63 passningar, xT 0.39NadjombeLars Gindorf: 58 passningar, xT 0.63GindorfFaton Ademi: 55 passningar, xT 0.04Ademi
    Verl
    Martin Ens: 370 passningar, xT 0.44EnsFynn Otto: 313 passningar, xT 0.45OttoNiko Kijewski: 305 passningar, xT 0.23KijewskiOualid Mhamdi: 271 passningar, xT 0.55MhamdiBerkan Taz: 237 passningar, xT 0.83TazPhilipp Schulze: 193 passningar, xT 0.06SchulzeAlmin Mešanović: 175 passningar, xT 0.59MešanovićFabian Wessig: 137 passningar, xT 0.21WessigJoshua Chima Eze: 107 passningar, xT 0.04EzeYari Otto: 103 passningar, xT 0.01OttoJulian Stark: 97 passningar, xT 0.17Stark
    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.

    Alemannia Aachen
    Danilo Wiebe: 183 passningar, xT 0.31WiebePetros Bagalianis: 163 passningar, xT 0.23BagalianisJonas Oehmichen: 145 passningar, xT 1.36OehmichenMehdi Loune: 144 passningar, xT 0.51LouneJoel Miguel da Silva Kiala: 118 passningar, xT 0.22KialaFotios Pseftis: 88 passningar, xT 0.02PseftisMika Schroers: 85 passningar, xT 0.43SchroersMatti Wagner: 78 passningar, xT 0.21WagnerPierre Nadjombe: 63 passningar, xT 0.39NadjombeLars Gindorf: 58 passningar, xT 0.63GindorfFaton Ademi: 55 passningar, xT 0.04Ademi
    Verl
    Martin Ens: 370 passningar, xT 0.44EnsFynn Otto: 313 passningar, xT 0.45OttoNiko Kijewski: 305 passningar, xT 0.23KijewskiOualid Mhamdi: 271 passningar, xT 0.55MhamdiBerkan Taz: 237 passningar, xT 0.83TazPhilipp Schulze: 193 passningar, xT 0.06SchulzeAlmin Mešanović: 175 passningar, xT 0.59MešanovićFabian Wessig: 137 passningar, xT 0.21WessigJoshua Chima Eze: 107 passningar, xT 0.04EzeYari Otto: 103 passningar, xT 0.01OttoJulian Stark: 97 passningar, xT 0.17Stark

    The teams in numbers

    PerformanceAlemannia AachenVerl
    Points6464
    xPoints52.343.3
    xG per match1.51.5
    xGA per match1.71.3
    xG within 8s of winning the ball0.350.24
    xGA within 8s of losing the ball0.210.23
    Playing styleAlemannia AachenVerl
    Build-up efficiency0.30.29
    Field tilt0.50.67
    xT per match0.981.1
    xTA per match0.990.77
    Won balls, offensive half2725
    Pressing intensity0.220.22
    Pressing efficiency0.310.36
    Pressing efficiency, offensive half0.360.39
    Entries into the box per match1215
    Entries into the box against1210
    Pass completion %0.70.82
    Pass completion % under pressure0.660.78
    Passes per match265530
    Passes against per match345223
    Switches of play per match14.123.9
    Long balls per match2631
    Set piecesAlemannia AachenVerl
    xG from free kicks0.060.07
    Corners per match4.56.2
    Corners against per match5.24.1
    xG per corner0.040.04
    xGA per corner against0.080.07
    First touch, offensive corners %0.430.5
    First touch, defensive corners %0.540.57
    OtherAlemannia AachenVerl
    Throw-in control0.670.78
    The goalkeepersJan Olschowsky (Alemannia Aachen)Philipp Schulze (Verl)
    Saves6094
    Save %63%67%
    xG prevented49%47%
    Claims0 (0%)0 (0%)

    Frequently asked questions

    Who wins Alemannia Aachen vs Verl according to our model?

    The model gives Alemannia Aachen a 40% win probability. Full 1X2 picture: Alemannia Aachen 40%, draw 27%, Verl 33%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 11% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 59% and under at 41%.

    Will both teams score?

    The probability of both teams scoring is 61% 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.