🎯

Fixture360 only lists fixtures from a curated set of leagues, chosen specifically to keep our prediction model accurate.

📋

Poisson model rates Pisa at 41%, yet other data sources diverge — this Hellas Verona vs Pisa fixture contains genuine analytical tension.

✍️ Match Preview

Match Analysis

Hellas Verona host Pisa at Stadio Marcantonio Bentegodi in Serie A, Regular Season - 24. Kick-off is scheduled for Friday 6 February 2026 at 19:45 UTC.

Recent Form

Across all Serie A games this season, Hellas Verona have gone 2W 2D 6L from 10 outings — a 0.80 PPG return. Last five: L L D L L. They are averaging 1.00 goals per game and conceding 2.10 — a modest attacking output that tempers optimism on the Over 2.5 market. However, 2.10 goals conceded per game is a concern — opposing attacks have exploited them regularly. 1 clean sheet from 10 outings provides some defensive foundation. This season is still relatively young for Hellas Verona, so this record blends games from this season and last.

Hellas Verona at Stadio Marcantonio Bentegodi this season: 1W 2D 7L from 10 home games — 0.50 PPG on home soil. At home they are averaging 1.10 goals scored and 1.90 conceded per game. Both teams have scored in 70% of their home games — an outstanding BTTS rate that is a key data point for this fixture.

Looking at all fixtures this season, Pisa stand at 0W 4D 6L from 10 Serie A matches — 0.40 PPG. Last five: L D D L L. Their scoring rate of 0.90 per game is modest, conceding 2.20 — not a side to back heavily on the Over 2.5 from their form alone. Conceding 2.20 per game is a high rate — their defensive vulnerabilities have been exposed regularly this season. Both teams have scored in 60% of their games — a noteworthy BTTS rate that supports the Yes market. This season is still relatively young for Pisa, so this record blends games from this season and last.

On the road, Pisa have gone 0W 6D 4L from 10 away fixtures this term (0.60 PPG). Away from home they average 1.50 goals scored and 2.50 conceded per game. Both teams have scored in 80% of their away matches — a high BTTS rate on the road that directly informs this fixture.

The form comparison is too close to call — 0.80 PPG (Hellas Verona) versus 0.40 (Pisa). When the figures are this level, no pick can be reliably derived from recent results alone.

Both teams score in over 70% of each side's relevant games (using home/away splits). At that combined rate, BTTS Yes is as well-evidenced as it gets — the data strongly backs two-way scoring.

Head to Head

There is little to separate the sides historically. From 1 previous meetings, Hellas Verona have won 0, Pisa 0, with 1 draws — a balanced ledger that offers no obvious directional steer on its own.

The last 1 meetings have been tight affairs, averaging just 0.0 goals per game. That low-scoring pattern is a meaningful historical input for the Under 2.5 market. The most recent clash, on 18 Oct 2025, ended 0–0 with a draw.

With a balanced win record and just 0.0 goals per game historically, the H2H points toward a tight, low-scoring contest. Under 2.5 goals has the strongest statistical grounding from the fixture history regardless of which side wins.

In-Play Data

Hellas Verona trading profile (61 games, 30 at home): they score before half-time in 70% of fixtures in home games; when goalless at HT, a second-half goal follows 65% of the time; BTTS occurs in 47% of their matches (home games); Over 2.5 goals lands in 60% of games (home games); they fail to score in 46% of games.

Pisa trading profile (61 games, 30 at away): they score before half-time in 80% of fixtures in away games; when goalless at HT, a second-half goal follows 71% of the time; BTTS occurs in 60% of their matches (away games); Over 2.5 goals lands in 53% of games (away games); they fail to score in 33% of games.

BTTS rates are mixed from the trading data — Hellas Verona 46% versus Pisa 52%; no strong lean in either direction. The over/under 2.5 picture is inconclusive from the in-play data (Hellas Verona 52% | Pisa 51%).

Poisson Model

The Poisson distribution model projects Hellas Verona 1.75 xG and Pisa 1.85 xG. These expected goals figures are the primary input: they feed a goal-probability matrix across all scoreline combinations to derive the result and market probabilities below. Underlying strength ratings: Hellas Verona attack 0.880 / defence 1.437 | Pisa attack 1.104 / defence 1.605. League average goals — home 1.243 / away 1.168. Pisa bring a strong defensive rating of 1.605 — this is suppressing Hellas Verona's projected output below what their attack strength alone would suggest. Data: 61 Hellas Verona games / 23 Pisa games used (CurrentSeason).

Result probabilities: Hellas Verona 37% | Draw 22% | Pisa 41%. Fair-value odds: Hellas Verona 2.70 | Draw 4.55 | Pisa 2.44. This is a low-confidence result — no outcome exceeds 45%. The draw at 22% is a primary outcome, not an afterthought.

Goals markets: Over 2.5 probability 70% | BTTS probability 70% | Total xG 3.61. Over 2.5 is the model's clear signal at 70% — a total xG of 3.61 means the expected score alone exceeds the 2.5 threshold, making the Over not just likely but structurally supported by both sides' attack/defence profiles. BTTS Yes at 70% reflects that both xG figures (1.75 / 1.85) are above the threshold where scoring blanks become the primary outcome — both sides are projected to find the net.

Summary & Verdict

The Poisson model's primary lean is Pisa at 41% — marginal model lean. With a 22% draw probability, Draw No Bet on Pisa offers a more defensible entry point than the straight win. The opposing side holds a 37% win probability per the model — this is not a banker and should be staked accordingly.

The Poisson model projects 3.61 xG in total — Over 2.5 at 70% — reasonable confidence, supported by form averaging 3.5 goals per game.

On the BTTS market, Poisson puts 70% on Yes. Form rates corroborate: Hellas Verona 70% | Pisa 80% BTTS from recent games.

The outsider holds a 37% win probability per the model — this is not a banker and should not be treated as one. Proportionate stakes are appropriate given the genuine contest the data projects.

🔮 Your Prediction

Sign in to submit your prediction and see how it compares to the rest of the community.

💡 Key Insights