🎯

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

📋

Poisson model rates Wuhan Three Towns at 44%, yet other data sources diverge — this Wuhan Three Towns vs Hangzhou Greentown fixture contains genuine analytical tension.

✍️ Match Preview

Match Analysis

Wuhan Three Towns host Hangzhou Greentown at Wuhan Sports Centre in Super League, Regular Season - 3. Kick-off is scheduled for Friday 28 March 2025 at 11:35 UTC.

Form Guide

Wuhan Three Towns — All Games: 2W 2D 6L from 10 Super League outings this season, averaging 0.80 points per game. Last five: D W L L L. They are averaging 0.70 goals per game and conceding 1.30 — a modest attacking output that tempers optimism on the Over 2.5 market. 2 clean sheets from 10 outings provides some defensive foundation. Both teams have scored in just 30% of their matches, indicating a tendency toward clean sheets or low-scoring outcomes.

At home at Wuhan Sports Centre, Wuhan Three Towns have gone 2W 1D 7L this season (10 games, 0.70 PPG). At home they are averaging 0.90 goals scored and 1.60 conceded per game.

Across all Super League games this season, Hangzhou Greentown have recorded 2W 4D 4L from 10 outings — 1.00 PPG. Last five: D L W D D. Their attacking form is sharp — 2.10 goals per game is a high-output rate that commands respect in the goals markets. Conceding 2.30 per game is a high rate — their defensive vulnerabilities have been exposed regularly this season. 2 clean sheets from 10 outings suggests some defensive organisation. Both teams have scored in 80% of their matches this season — one of the stronger BTTS rates in the division, making Yes a well-evidenced play.

When travelling in Super League this season, Hangzhou Greentown have posted 1W 2D 7L from 10 away outings — 0.50 PPG. Away from home they average 1.40 goals scored and 2.40 conceded per game. Both teams have scored in 70% of their away matches — a high BTTS rate on the road that directly informs this fixture. Their away PPG of 0.50 is notably below their overall 1.00 — form away from home has been a clear weakness this season.

The form figures are too closely matched to derive a directional pick: Wuhan Three Towns 0.80 PPG, Hangzhou Greentown 1.00 PPG. Look to other data sets to build the analysis.

H2H Record

The previous 6 encounters between these sides heavily favour Hangzhou Greentown, who boast 4 victories compared to 1 for Wuhan Three Towns.

The 6 previous meetings have averaged 2.3 goals per game — a neutral rate that provides no strong lean on the goals market from the H2H alone. The most recent clash, on 18 Oct 2024, ended 0–2 with Hangzhou Greentown winning.

It is worth noting that Hangzhou Greentown have a superior record in this fixture despite their visitor status — 4 wins from 6 meetings. Home advantage alone is not a sufficient reason to dismiss that track record.

Trading Patterns

Wuhan Three Towns in-play and half-time data (60 games, 30 at home): they score before half-time in 57% of fixtures in home games; when goalless at HT, a second-half goal follows 78% of the time; BTTS occurs in 50% of their matches (home games); Over 2.5 goals lands in 40% of games (home games).

Hangzhou Greentown in-play and half-time data (60 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 67% of the time; two or more arrive in the second half on 50% of those occasions; BTTS occurs in 67% of their matches (away games); Over 2.5 goals lands in 77% of games (away games); Over 3.5 in 42%.

From a combined perspective, both sides have high BTTS rates — Wuhan Three Towns 62% and Hangzhou Greentown 65% — which reinforces the BTTS Yes angle from in-play data. The over/under 2.5 picture is inconclusive from the in-play data (Wuhan Three Towns 48% | Hangzhou Greentown 77%).

Poisson Model

The Poisson distribution model projects Wuhan Three Towns 1.58 xG and Hangzhou Greentown 1.27 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: Wuhan Three Towns attack 0.920 / defence 1.039 | Hangzhou Greentown attack 0.974 / defence 1.012. League average goals — home 1.703 / away 1.260. Data: 32 Wuhan Three Towns games / 32 Hangzhou Greentown games used (Blended). A blend of current and prior-season data is applied to stabilise estimates early in the campaign.

Result probabilities: Wuhan Three Towns 44% | Draw 26% | Hangzhou Greentown 30%. Fair-value odds: Wuhan Three Towns 2.27 | Draw 3.85 | Hangzhou Greentown 3.33. This is a low-confidence result — no outcome exceeds 45%. The draw at 26% is a primary outcome, not an afterthought.

Goals markets: Over 2.5 probability 54% | BTTS probability 58% | Total xG 2.86. Over/Under 2.5 is close to a coin-flip at 54%/46% — the total xG of 2.86 sits near the 2.5 margin; this is a low-edge market from a Poisson standpoint. BTTS probability of 58% is moderate — neither team is projected to score with enough certainty to make Yes a clear standalone call.

Analysis Verdict

On the Poisson output, Wuhan Three Towns are the pick at 44% — marginal model lean. With a 26% draw probability, Draw No Bet on Wuhan Three Towns offers a more defensible entry point than the straight win. The opposing side holds a 30% win probability per the model — this is not a banker and should be staked accordingly.

On the goals line, Poisson's 2.86 combined xG gives a 54% probability to Over 2.5 — reasonable, supported by form averaging 3.1 goals per game.

Poisson assigns a 58% probability to BTTS Yes based on the attack/defence strength model. Form rates corroborate: Wuhan Three Towns 50% | Hangzhou Greentown 70% BTTS from recent games.

The Poisson model is drawing on Blended data (32 games minimum) — early-season projections carry additional uncertainty. Weight the live form and odds signals accordingly.

🔮 Your Prediction

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

💡 Key Insights