Sangju Sangmu vs Bucheon 1995: match statistics

A draw appears most likely: Sangju have 0% home wins and as many as 63% of their home matches end in draws, and they haven’t registered a win in their last 5 home fixtures. Visitors show better defensive form — in their last 5 matches Bucheon have averaged 1.0 point per game and conceded 4 goals (0.8 per game), while Sangju have only 0.4 points per game and have conceded 9 goals (1.8 per game). Hosts generate chances: on average 13.75 shots at home (3.88 on target) and xG 1.64, but only 0.75 goals per game with a shot conversion of 5%, indicating significant finishing issues. Bucheon away take 9.63 shots (2.63 on target), have xG 1.20 and a higher conversion of 9%, but fail to register a shot in 50% of their away games, which limits their threat. Given Sangju’s high home draw rate, the hosts’ poor finishing, and Bucheon’s superior defense and form, I assign draw 50%, away win 35% and home win 15%.

Seasonal BTTS indicators favor No Goal — Sangju has BTTS at 38% and Bucheon 25%, both below the league average of 49%. Both teams score few goals this season: 0.75 (Sangju) and 0.88 (Bucheon) on average, while keeping a clean sheet in 38% of matches, which reduces the likelihood of Goal/Goal. Mixed signals: Sangju’s BTTS in the last 5 matches is 60% versus Bucheon’s 20%, and recent attacking xG is 1.49 vs 1.14, but Bucheon concedes only 0.8 goals on average in the last 5 (4 goals total). Seasonal xG against highlights Sangju’s defensive vulnerability (1.96), but low shot conversion rates of 8% and 9% and low season goal returns (16 and 13 scored, 22 and 17 conceded) point to few actual goals. Consequently I pick BTTS - No with a 70% confidence vs 30% for Yes, because seasonal data and low average goals dominate.

Seasonal averages show Sangju Sangmu scores 0.75 goals per match, Bucheon 0.88 (1.63 combined), while their xG is 1.16 and 1.20 (2.36 combined), indicating more chances than the raw goals suggest. Weighting xG and actual averages (model 60/40) yields expected goals of 1.16 for the home side and 1.32 for the away side, a total of 2.48 expected goals. Using a Poisson distribution with λ=2.48, the probability of Over 1.5 goals is about 71%, Over 2.5 ≈45%, and Over 3.5 ≈24%. Additionally, the low percentage of matches in which both teams scored (38% for the home team, 25% for the away team) and the league’s high clean sheet rate (60%) limit the chance of a very high-scoring game, making the 2.5 line less convincing. The strongest signal is Over 1.5 (71% vs 29%), therefore the recommendation is to back Over at 1.5 as the most likely and value-driven option.

Statistical analysis strongly points to a draw at half-time: hosts were level at the break in 75% of matches, visitors in 63%, and in the visitors’ last five matches a half-time draw occurred in 80% of games. Both teams rarely score before half-time — average first-half goals are 0.13 for hosts and 0.13 for visitors, and in their last five matches both registered 0.0 in the first half. Goals occur more frequently after the interval: hosts have 75% of matches with at least one goal post-break, visitors 63%, and the minute distribution shows decisive strikes mainly after the 45th minute (hosts 3 goals in 61–75, visitors 3 goals in 46–60). Visitors appear stronger late on — average xG is 1.20 for visitors versus 1.16 for hosts, hosts’ defense concedes 1.96 xG versus 1.46 for visitors, and season points per game are 1.38 for visitors versus 0.88 for hosts (visitors have 38% wins versus 13% for hosts). Therefore the strongest and most consistent signal from the data is a half-time draw and a visitors’ turnaround after the break (X/2).