Statistik / Bola Sepak / Romania. Liga I / Universitatea Cluj vs Arges Pitesti

Universitatea Cluj vs Arges Pitesti Statistics & Analysis

Universitatea Cluj Universitatea Cluj
May 02, 2026 - 17:30
1 1.15
0 0.89
xG Accuracy: 75%
Arges Pitesti Arges Pitesti
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Tracked markets vs full-time result

Each row compares the model’s highlighted side (or lean) to what happened at full time.

  • Market Prediction Result Outcome
  • Lebih / Kurang 2.5 Kurang 2.5 Kurang 2.5 (1 goals) ✔ Correct
  • Kedua-dua Pasukan Menjaringkan Gol BTTS Tidak Tidak ✔ Correct
  • 1X2 Universitatea Cluj Universitatea Cluj ✔ Correct
  • Cerapan skor tepat 1-0 1-0 ✔ Correct

Taklimat perlawanan AI

Ringkasan perlawanan AI

Quick read on how the model reads this matchup.

  • League: Liga I
  • Fixture: Universitatea Cluj vs Arges Pitesti
  • Kickoff: 2026-05-02 15:00:00
  • 1X2 (model): Home 40.0% · Draw 33.6% · Away 26.5%
  • xG (showing): Universitatea Cluj 1.15 — Arges Pitesti 0.89 (total xG ≈ 2.04)
  • Primary / headline line (Betting Primary Pick when shown): Under 2.5 goals
  • Model: 66.6% · Implied: 57.4% · Probability edge: +9.2 pts · Est. EV: +9.9%
  • BTTS (model): Yes 42.0% · No 58.0%
  • Correct score (top bin): 1-0 (15.0%)

Use the cards for tiering; this text only restates the same inputs in narrative form.

1X2 can look balanced even when side markets show clearer structure.

Taruhan terbaik dan sebab

Primary pick from the decision engine: Under 2.5 goals.

Model probability is compared to implied probability from odds to highlight a probability edge; EV uses the same model probability with the best decimal price tracked.

When several markets sit near +EV, keep stakes small — correlation means edges do not add cleanly.

FAQ

Who has the edge in the match-winner market?

Use the 1X2 model percentages in the summary and the 1X2 market card: the side with the highest model % is the model lean, but check EV — a lean can still be -EV after prices.

What is the best-supported line in this snapshot?

Match the hero card above: if it says “Betting Primary Pick”, that leg cleared primary rules; if it says “Best +EV (tracked markets)”, it is the strongest +EV line that did not meet stricter Primary thresholds. The bullets below repeat the same model %, implied %, edge (pts), and EV % as that card.

Why might 1X2 look unattractive while totals do not?

Tight 1X2 prices often embed a fair three-way split, so EV on match-winner can sit negative even when Over/Under or BTTS still diverges from the model — compare the 1X2 row on the market cards to O/U and BTTS.

Safer market than correct score?

Markets with more liquidity and smoother prices (often 1X2 or O/U 2.5 from many books) are usually easier to reason about than long-tail correct-score prices; still read EV on each leg.

Faktor risiko

  • Price movement: implied probabilities and EV move with odds.
  • Sample / data gaps: low-information leagues widen forecast bands.
  • In-play state: goals and red cards are not modelled here.
  • Scoreline variance: the most likely scoreline is still usually a low absolute probability outcome (often well below 20%).

Metodologi

  • Inputs: Same structured facts bundle as the public prediction page (xG / Poisson snapshot, market EV where available, decision engine v2).
  • Narrative: Template sentence library with fixture-stable selection (no per-request LLM for this block).
  • Compliance: Educational framing only; not personalised advice.

Terakhir dikemas kini

May 01, 2026 (UTC)

Bagaimana untuk menggunakan ini
  • Tanpa baris utama, bandingkan baris +EV pada kad pasaran di bawah (bukan hanya 1X2).
  • Jangan parlay banyak picks tepi nipis bersama-sama;tepi tidak menambah dengan pasti.
  • Anggap pukulan panjang sebagai permainan pilihan, bersaiz tinggi sahaja.

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Langgan Sekarang
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Liga I Liga I — Kedudukan
# PASUKAN MP M S K PTS
1 Universitatea Craiova 9 6 1 2 49
2 Universitatea Cluj 9 6 0 3 45
3 CFR 1907 Cluj 9 4 3 2 42
4 Dinamo Bucuresti 9 3 3 3 38
5 Rapid 9 1 2 6 33
6 Arges Pitesti 9 1 3 5 31
7 FCSB 30 13 7 10 46
8 Uta Arad 30 11 10 9 43
9 FC Botosani 30 11 9 10 42
10 Oţelul 30 11 8 11 41
11 Farul Constanta 30 10 7 13 37
12 Petrolul Ploiesti 30 7 11 12 32
13 Csikszereda 30 8 8 14 32
14 Unirea Slobozia 30 7 4 19 25
15 AFC Hermannstadt 30 5 8 17 23
16 Metaloglobus 30 2 6 22 12
# PASUKAN MP GS GC +/- PTS
1 FCSB 30 48 40 +8 46
2 Oţelul 30 39 32 +7 41
3 Farul Constanta 30 39 37 +2 37
4 Uta Arad 30 39 44 -5 43
5 FC Botosani 30 37 29 +8 42
6 Csikszereda 30 30 58 -28 32
7 AFC Hermannstadt 30 29 50 -21 23
8 Unirea Slobozia 30 27 46 -19 25
9 Metaloglobus 30 25 66 -41 12
10 Petrolul Ploiesti 30 24 31 -7 32
11 Universitatea Craiova 9 12 6 +6 49
12 Universitatea Cluj 9 12 10 +2 45
13 Dinamo Bucuresti 9 12 11 +1 38
14 Rapid 9 8 14 -6 33
15 CFR 1907 Cluj 9 7 6 +1 42
16 Arges Pitesti 9 5 9 -4 31
# PASUKAN MP xG xGC +/- PTS
1 FCSB 30 50.6 28.2 +22.4 46
2 Dinamo Bucuresti 9 43.7 24.3 +19.4 38
3 Universitatea Craiova 9 37.5 24.4 +13.1 49
4 Oţelul 30 41.3 33.1 +8.2 41
5 Farul Constanta 30 37.8 33.2 +4.6 37
6 CFR 1907 Cluj 9 34.8 32.4 +2.4 42
7 Rapid 9 35.3 33.2 +2.1 33
8 FC Botosani 30 33.9 32.2 +1.7 42
9 Universitatea Cluj 9 32.0 30.8 +1.2 45
10 Arges Pitesti 9 26.0 25.8 +0.2 31
11 AFC Hermannstadt 30 29.8 31.9 -2.1 23
12 Petrolul Ploiesti 30 28.9 34.5 -5.6 32
13 Uta Arad 30 33.5 42.0 -8.5 43
14 Unirea Slobozia 30 26.9 39.8 -12.9 25
15 Metaloglobus 30 23.1 42.4 -19.3 12
16 Csikszereda 30 22.9 49.8 -26.9 32
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