J1 LeagueSanga Stadium by Kyocera, Kyoto26°1,1 mm5 km/h

    Läs på svenska
    Kyoto SangaKyoto SangaPossession control
    vs
    Avispa FukuokaAvispa FukuokaBalanced & physical
    The model's lean: 1 · Kyoto Sanga (41%)

    Kyoto SangaAvispa Fukuoka · J1 League

    1 · Kyoto Sanga 41%X 29%30% Avispa Fukuoka · 2

    Analysis: Kyoto SangaAvispa Fukuoka

    Oscar Nilsson · · Model-assisted analysis, fact-checked · How the predictions work

    Kyoto Sanga and Avispa Fukuoka have been close rivals in their recent matchups, with each team finding ways to edge the other out in the six games since 2023. The hosts have won twice while Fukuoka has claimed victory in three encounters, leaving one match drawn. Despite this, it's the visitors who hold the slight upper hand in terms of points and expected points, 3 to 1 and 5.1 to 2.9 respectively. However, the current model leans towards the home side with a 41% probability of victory, suggesting that Kyoto's possession-oriented style may exploit Fukuoka's physicality.

    Current form shows Sanga struggling to convert expected goals into actual points; they average 0.98 xG per match but allow 1.4 xGA, suggesting a vulnerability defensively. In contrast, Fukuoka manages a more balanced 1.1 xG and 0.86 xGA, which has translated to slightly better outcomes on the pitch. The expected goals for the match — 1.34 for Kyoto against 1.07 for Fukuoka — imply a tight contest, with the most likely scorelines being 1-1 and 1-0.

    The weather's forecast of 12.9 mm of rain could dampen scoring opportunities, which aligns with the model's 43% probability for over 2.5 goals. Yet, historical goal averages of 3.0 per meeting between these two suggest otherwise. This tension makes the over 2.5 goals market particularly interesting, especially given the 5% value identified at odds of 2.42 with Betsafe, Betsson, and Nordicbet.

    Given the statistical landscape and potential weather impact, a 1-1 draw seems the most plausible outcome. But for those inclined to take on a bit more risk, backing the over 2.5 goals at the value odds could be worth a look.

    Kyoto SangaKyoto Sanga
    Form WWLDL
    League ranking xG #17xGA #14xT #4xP/match #16Points #18
    Key players
    • Rafael Elias da Silva BatagliaFxG 0.46 · xT 0.00
    • Shinnosuke FukudaRWBxG 0.07 · xT 0.40
    • Yoshinori SuzukiCBxG 0.00 · xT 0.07
    Avispa FukuokaAvispa Fukuoka
    Form WLLWL
    League ranking xG #14xGA #5xT #15xP/match #5Points #12
    Key players
    • Werik Silva PintoFxG 0.23 · xT 0.10
    • Tomoya MikiCMxG 0.18 · xT 0.06
    • Kokoro MaedaCDMxG 0.04 · xT 0.03

    Match prediction: Kyoto SangaAvispa Fukuoka

    Predicted score matrix

    Avispa Fukuoka
    Kyoto Sanga
    0
    1
    2
    3
    4+
    0
    0–09.4%
    0–19.2%
    0–25.1%
    0–31.8%
    0–4+0.6%
    1
    1–011.7%
    1–113.3%
    1–26.9%
    1–32.4%
    1–4+0.8%
    2
    2–08.1%
    2–18.6%
    2–24.6%
    2–31.6%
    2–4+0.5%
    3
    3–03.6%
    3–13.9%
    3–22.1%
    3–30.7%
    3–4+0.2%
    4+
    4+–01.6%
    4+–11.7%
    4+–20.9%
    4+–30.3%
    4+–4+0.1%
    Probability per final score. Rows = home goals, columns = away goals.

    Most likely scorelines

    1. 1.1-113%
    2. 2.1-012%
    3. 3.0-09%
    4. 4.0-19%
    5. 5.2-19%
    Expected goals
    1,341,07
    Both teams to score
    49%

    Over/under goals

    Expected goals: 2,4
    Under 2,557%
    @1.49
    Over 2,543%
    @2.42EV+5%

    Ready-made bet suggestions

    More combos & build your own

    01 · 2 legs

    Safe

    • Double chance Kyoto Sanga or draw (1X)71%
    • Under 3.5 goals78%

    Combined probability

    55%

    Fair odds

    1.83

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

    02 · 3 legs

    Balanced

    • Double chance Kyoto Sanga or draw (1X)71%
    • Under 2.5 goals57%
    • Rafael Elias da Silva Bataglia to score29%

    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 score49%
    • Over 2.5 goals43%
    • Rafael Elias da Silva Bataglia to score29%
    • João Pedro Mendes Santos to be booked20%

    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+).

    • 1 · Kyoto Sanga41% probability@2.60PinnacleEV+7%reference odds
    • Over 2.543% probability@2.42BetsafeEV+5%reference odds
    • Over 2.543% probability@2.42BetssonEV+5%reference odds
    Bookmaker1 · Kyoto SangaX2 · Avispa Fukuoka
    Betsafe
    EV+ 4%
    2.52
    2.922.90
    Betsson
    EV+ 4%
    2.52
    2.922.90
    Nordicbet
    EV+ 4%
    2.52
    2.922.90
    Pinnacle
    EV+ 7%
    2.60
    3.062.93

    Odds updated 27 Aug, 10:40

    Odds movement

    10 %
    39 %34 %

    2.612.60

    X0 %
    34 %29 %

    3.053.06

    20 %
    35 %30 %

    2.922.93

    Pinnacle · 3 recorded price levels · 22/08/2026 → 26/08/2026 · fixed scale 5 percentage points

    Statistics

    Head-to-head

    6 matches
    Kyoto Sanga 2Draw 1Avispa Fukuoka 3
    Goals: 99 (⌀ 3)

    Head-to-head based on J1 League data since 2023.

    Key facts

    • A high-scoring fixture: 3.0 goals per meeting on average.
    xG & xGA per match — 3-game rolling average

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

    Kyoto Sanga
    Avispa Fukuoka
    When goals are scored and conceded 2026
    Kyoto Sanga (47)
    Avispa Fukuoka (54)
    Pass networks

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

    Kyoto Sanga
    Shinnosuke Fukuda: 155 passningar, xT 1.1FukudaJoão Pedro Mendes Santos: 134 passningar, xT 0.46SantosWeverton Silva de Andrade: 121 passningar, xT 0.04AndradeYoshinori Suzuki: 109 passningar, xT 0.13SuzukiKyo Sato: 108 passningar, xT 0.36SatoSung-Jun Yoon: 96 passningar, xT 0.06YoonGakuji Ota: 78 passningar, xT 0.1OtaRafael Elias da Silva Bataglia: 55 passningar, xT -0.11BatagliaRen Kato: 42 passningar, xT 0.07KatoKodai Nagata: 41 passningar, xT 0.06NagataTaiki Hirato: 40 passningar, xT 0.08Hirato
    Avispa Fukuoka
    Yuma Tsujioka: 93 passningar, xT 0.29TsujiokaTomoya Miki: 90 passningar, xT 0.21MikiTeppei Oka: 81 passningar, xT 0.26OkaKokoro Maeda: 75 passningar, xT 0.05MaedaYu Hashimoto: 50 passningar, xT 0.27HashimotoTakumi Nagaishi: 45 passningar, xT 0.22NagaishiKazuki Fujimoto: 45 passningar, xT -0.02FujimotoKennedy Mikuni: 42 passningar, xT 0.03MikuniKeiya Shiihashi: 41 passningar, xT -0.04ShiihashiTatsuki Nara: 40 passningar, xT 0.04NaraYota Maejima: 39 passningar, xT 0.08Maejima
    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.

    Kyoto Sanga
    Shinnosuke Fukuda: 155 passningar, xT 1.1FukudaJoão Pedro Mendes Santos: 134 passningar, xT 0.46SantosWeverton Silva de Andrade: 121 passningar, xT 0.04AndradeYoshinori Suzuki: 109 passningar, xT 0.13SuzukiKyo Sato: 108 passningar, xT 0.36SatoSung-Jun Yoon: 96 passningar, xT 0.06YoonGakuji Ota: 78 passningar, xT 0.1OtaRafael Elias da Silva Bataglia: 55 passningar, xT -0.11BatagliaRen Kato: 42 passningar, xT 0.07KatoKodai Nagata: 41 passningar, xT 0.06NagataTaiki Hirato: 40 passningar, xT 0.08Hirato
    Avispa Fukuoka
    Yuma Tsujioka: 93 passningar, xT 0.29TsujiokaTomoya Miki: 90 passningar, xT 0.21MikiTeppei Oka: 81 passningar, xT 0.26OkaKokoro Maeda: 75 passningar, xT 0.05MaedaYu Hashimoto: 50 passningar, xT 0.27HashimotoTakumi Nagaishi: 45 passningar, xT 0.22NagaishiKazuki Fujimoto: 45 passningar, xT -0.02FujimotoKennedy Mikuni: 42 passningar, xT 0.03MikuniKeiya Shiihashi: 41 passningar, xT -0.04ShiihashiTatsuki Nara: 40 passningar, xT 0.04NaraYota Maejima: 39 passningar, xT 0.08Maejima

    The teams in numbers

    PerformanceKyoto SangaAvispa Fukuoka
    Points13
    xPoints2.95.1
    xG per match0.981.1
    xGA per match1.40.86
    xG within 8s of winning the ball0.290.14
    xGA within 8s of losing the ball0.460.11
    Playing styleKyoto SangaAvispa Fukuoka
    Build-up efficiency0.320.33
    Field tilt0.60.43
    xT per match1.30.75
    xTA per match1.20.66
    Won balls, offensive half2031
    Pressing intensity0.220.22
    Pressing efficiency0.290.28
    Pressing efficiency, offensive half0.240.25
    Entries into the box per match1512
    Entries into the box against1610
    Pass completion %0.760.67
    Pass completion % under pressure0.720.66
    Passes per match395255
    Passes against per match326413
    Switches of play per match30.516.4
    Long balls per match2929
    Set piecesKyoto SangaAvispa Fukuoka
    xG from free kicks0.020.02
    Corners per match25
    Corners against per match6.71.7
    xG per corner00.01
    xGA per corner against0.020.01
    First touch, offensive corners %0.50.47
    First touch, defensive corners %0.60.6
    OtherKyoto SangaAvispa Fukuoka
    Throw-in control0.720.8
    The goalkeepersGakuji Ota (Kyoto Sanga)Takumi Nagaishi (Avispa Fukuoka)
    Saves106
    Save %59%67%
    xG prevented29%36%
    Claims0 (0%)0 (0%)

    Frequently asked questions

    Who wins Kyoto Sanga vs Avispa Fukuoka according to our model?

    The model gives Kyoto Sanga a 41% win probability. Full 1X2 picture: Kyoto Sanga 41%, draw 29%, Avispa Fukuoka 30%.

    What is the most likely scoreline?

    The model's most likely final score is 1-1 at 13% probability.

    Over or under 2.5 goals?

    The model rates over 2.5 goals at 43% and under at 57%.

    Will both teams score?

    The probability of both teams scoring is 49% 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.

    More matches in the league

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