Swedish Ettan17°24 km/hUpdated

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    Kristianstad FCKristianstad FCCounter-attacks & crosses
    vs
    The model's lean: 2 · Ängelholm (43%)
    ÄngelholmÄngelholmLow block & direct

    Kristianstad FC – Ängelholm · Swedish Ettan

    1 · Kristianstad FC 30%X 27%43% Ängelholm · 2
    Kristianstad FCKristianstad FC
    Form DDLDL
    League ranking xG #30xGA #15xT #27xP/match #25Points #27
    Key players
    • Svante SvedinLWxG 0.11 · xT 0.28
    • Omar DamphaFxG 0.29 · xT 0.03
    • Paolo LiljaFxG 0.26 · xT 0.08
    ÄngelholmÄngelholm
    Form DWLDD
    League ranking xG #26xGA #9xT #17xP/match #14Points #19
    Key players
    • Vilgot CarlssonFxG 0.34 · xT 0.1
    • Oliver Stojanovic FredinFxG 0.12 · xT 0.57
    • Anton NilssonRWxG 0.26 · xT 0.14

    Match prediction: Kristianstad FC – Ängelholm

    Predicted score matrix

    Ängelholm →
    Kristianstad FC →
    0
    1
    2
    3
    4+
    0
    0–06.5%
    0–18.9%
    0–26.9%
    0–33.5%
    0–4+1.8%
    1
    1–07.6%
    1–112.2%
    1–28.9%
    1–34.5%
    1–4+2.3%
    2
    2–05.1%
    2–17.6%
    2–25.7%
    2–32.9%
    2–4+1.5%
    3
    3–02.2%
    3–13.3%
    3–22.5%
    3–31.2%
    3–4+0.6%
    4+
    4+–00.9%
    4+–11.4%
    4+–21.0%
    4+–30.5%
    4+–4+0.3%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-112%
    2. 2.0-19%
    3. 3.1-29%
    4. 4.2-18%
    5. 5.1-08%
    Expected goals
    1,28–1,50
    Both teams to score
    56%

    Over/under goals

    Expected goals: 2,8
    Under 2,547%
    –
    Over 2,553%
    –

    Odds & value

    No odds available yet.

    Analysis: Kristianstad FC – Ängelholm

    Oscar Nilsson · · How the predictions work

    The numbers here speak of a tight contest, yet they tip the scales towards Ängelholms FF. With a 43% probability of victory according to the model, they have a discernible edge over Kristianstad FC's 30%. It's a clash of styles that often leads to intriguing outcomes: Kristianstad FC’s reliance on counter-attacks and crosses meets Ängelholms FF’s disciplined low defence and direct play. Though Kristianstad emerged victorious in their only meeting since 2023, the broader metrics suggest a different story could unfold this time.

    Kristianstad FC finds themselves lagging slightly behind in the league standings and statistical expectations. With 14 points compared to Ängelholms FF’s 17, they also trail in expected points and goals per match. This numerical disadvantage is compounded by an expected goals tally of 1.28 against them in this match, hinting at a tough defensive test. Moreover, their style of play might not fare well against Ängelholms FF’s practical approach, potentially limiting their effectiveness on the counter.

    Ängelholms FF steps onto the pitch with a subtle upper hand, not just in points, but in the analytical realm of xG and xGA too. Their expected goals at 1.50 per match suggest an ability to create more quality opportunities. While it's worth noting their slightly weaker statistical defence at 1.2 xGA, it hardly poses a significant hindrance given Kristianstad’s modest offensive averages. The most likely scorelines of 1-1, 0-1, and 1-2 all favour scenarios where Ängelholms FF either holds their ground or capitalises on Kristianstad’s defensive frailties.

    Over 2.5 goals in this match sits at 53%, and with both teams likely to find the back of the net at a 56% probability, goals could indeed be on the agenda. A keen eye might spot value in backing Ängelholms FF at 2.30 with Bet365, considering their statistical edge and the model’s lean. The prediction leans towards a 1-2 scoreline, with Ängelholms FF exploiting moments of vulnerability in Kristianstad’s setup, and the over 2.5 goals market emerging as an enticing proposition.

    Statistics

    Head-to-head

    1 matches
    Kristianstad FC 1Draw 0Ängelholm 0
    Goals: 1–0 (⌀ 1)

    Head-to-head based on Ettan data since 2023.

    xG & xGA per match — 3-game rolling average

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

    Kristianstad FC
    Ängelholm
    When goals are scored and conceded — 2026
    Kristianstad FC (16–24)
    Ängelholm (24–24)
    Pass networks

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

    Kristianstad FC
    Svante Svedin: 170 passningar, xT 1.65SvedinLinus Svensson: 152 passningar, xT 0.09SvenssonWilliam Klevendal: 140 passningar, xT 0.36KlevendalAlbert Ejupi: 137 passningar, xT 0.23EjupiKasper Alsén: 117 passningar, xT 0.21AlsénViktor Agardius: 104 passningar, xT 0.3AgardiusAndré Kamp: 97 passningar, xT 0.18KampVilmer Lindberg: 90 passningar, xT 0.2LindbergSamuel Wikström: 69 passningar, xT 0.19WikströmOtto Larsson Wågert: 64 passningar, xT 0.35WågertOmar Dampha: 59 passningar, xT 0.27Dampha
    Ängelholm
    Benjamin Tannus: 218 passningar, xT 0.56TannusOliver Stojanovic Fredin: 192 passningar, xT 2.78FredinDaniel Bergman: 166 passningar, xT 0.12BergmanEric Vedholm: 138 passningar, xT 0.36VedholmMohammad Ahmadi: 135 passningar, xT 0.39AhmadiVilgot Carlsson: 92 passningar, xT 0.42CarlssonJacob Svensson: 91 passningar, xT 0.09SvenssonAnton Nilsson: 86 passningar, xT 0.6NilssonWilliam Sonntag: 72 passningar, xT 0.02SonntagRobin Streifert: 63 passningar, xT 0.37StreifertMelker Tågsjö: 59 passningar, xT 0.35Tågsjö
    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.

    Kristianstad FC
    Svante Svedin: 170 passningar, xT 1.65SvedinLinus Svensson: 152 passningar, xT 0.09SvenssonWilliam Klevendal: 140 passningar, xT 0.36KlevendalAlbert Ejupi: 137 passningar, xT 0.23EjupiKasper Alsén: 117 passningar, xT 0.21AlsénViktor Agardius: 104 passningar, xT 0.3AgardiusAndré Kamp: 97 passningar, xT 0.18KampVilmer Lindberg: 90 passningar, xT 0.2LindbergSamuel Wikström: 69 passningar, xT 0.19WikströmOtto Larsson Wågert: 64 passningar, xT 0.35WågertOmar Dampha: 59 passningar, xT 0.27Dampha
    Ängelholm
    Benjamin Tannus: 218 passningar, xT 0.56TannusOliver Stojanovic Fredin: 192 passningar, xT 2.78FredinDaniel Bergman: 166 passningar, xT 0.12BergmanEric Vedholm: 138 passningar, xT 0.36VedholmMohammad Ahmadi: 135 passningar, xT 0.39AhmadiVilgot Carlsson: 92 passningar, xT 0.42CarlssonJacob Svensson: 91 passningar, xT 0.09SvenssonAnton Nilsson: 86 passningar, xT 0.6NilssonWilliam Sonntag: 72 passningar, xT 0.02SonntagRobin Streifert: 63 passningar, xT 0.37StreifertMelker Tågsjö: 59 passningar, xT 0.35Tågsjö

    The teams in numbers

    PerformanceKristianstad FCÄngelholm
    Points1417
    xPoints18.521.9
    xG per match11.1
    xGA per match1.31.2
    xG within 8s of winning the ball0.20.19
    xGA within 8s of losing the ball0.250.24
    Playing styleKristianstad FCÄngelholm
    Build-up efficiency0.30.35
    Field tilt0.410.48
    xT per match1.21.4
    xTA per match1.61.4
    Won balls, offensive half3229
    Pressing intensity0.510.47
    Pressing efficiency0.250.28
    Pressing efficiency, offensive half0.230.28
    Entries into the box per match1211
    Entries into the box against1513
    Pass completion %0.760.75
    Pass completion % under pressure0.70.7
    Passes per match325307
    Passes against per match386389
    Switches of play per match20.218.4
    Long balls per match3831
    Set piecesKristianstad FCÄngelholm
    xG from free kicks0.120.16
    Corners per match5.14.2
    Corners against per match4.64.2
    xG per corner0.020.03
    xGA per corner against0.020.01
    First touch, offensive corners %0.430.23
    First touch, defensive corners %0.620.6
    OtherKristianstad FCÄngelholm
    Throw-in control0.530.51
    The goalkeepersOtto Larsson Wågert (Kristianstad FC)Robin Streifert (Ängelholm)
    Saves4640
    Save %66%63%
    xG prevented44%26%
    Claims91 (96%)115 (98%)

    Frequently asked questions

    Who wins Kristianstad FC vs Ängelholm according to our model?

    The model gives Ängelholm a 43% win probability. Full 1X2 picture: Kristianstad FC 30%, draw 27%, Ängelholm 43%.

    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 56% according to the model.

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