Solihull Moors vs Boston United: match statistics

Solihull Moors have a clear advantage in chance creation: on average 12.36 shots and 5.41 shots on target per home match versus Boston’s 9.77 shots and 3.59 on target away, and their home xG is 1.62 compared to the visitors’ 1.29 xG. This translates to an average of 2.00 goals for the home side at home versus 1.59 for Boston away, with equal shot conversion of 16%. In the last 5 matches Solihull scored 5 and conceded only 2 (averaging 1.6 points per match), while Boston scored 5 and conceded 5 (averaging 1.2 points), indicating better defensive form for the hosts. Although Solihull concede 1.77 goals on average at home and Boston 1.59 away, Solihull’s higher clean sheet rate (23% vs 9%) and a positive home goal difference (+5) favor the hosts. Considering the advantage in chance creation and more favorable recent metrics, I forecast a home win with approximately 60% probability, 25% for a draw and 15% for an away win.

I forecast BTTS - Yes because seasonal indicators show 50% for Solihull and 73% for Boston (average ~62%) versus a league BTTS average of 56%. Both teams concede relatively frequently: Solihull 1.41 and Boston 1.59 goals per match, with xG at 1.42 (Solihull) and 1.29 (Boston) in attack and 1.39 and 1.63 in defense, indicating an exchange of scoring opportunities. However, the last 5 matches reduce confidence — Solihull has only 20% BTTS in the last 5 and concedes on average 0.4 goals, while Boston has 60% BTTS and concedes 1.0 goal in the last 5, partially offsetting the season trend. Additionally, Boston is more efficient with shots (conversion 16% vs 10% for Solihull) despite fewer on-target attempts (3.59 vs 4.23), so I assign 66% to BTTS and 34% to No Goal.

Based on xG: Solihull has xG_for 1.42 and Boston’s defense xG_against 1.63 — the average yields about 1.53 expected goals for the home side; Boston has xG_for 1.29 versus Solihull’s xG_against 1.39, giving roughly 1.34 expected goals, totaling 2.87 goals. A Poisson model with λ=2.87 estimates P(over 1.5) ≈78%, P(over 2.5) ≈55% and P(over 3.5) ≈32%, so the strongest signal is over 1.5. Seasonal stats support this direction: over 0.5 occurred in 91% of Solihull’s matches and 95% of Boston’s, while over 1.5 occurred in 68% and 86% of their matches, increasing the likelihood of at least 2 goals. Boston averages 1.59 goals per season and has 73% BTTS rate, which further supports the over, although note Solihull have conceded only 0.4 goals on average in their last 5 matches (2 conceded in 5), which limits the appeal of higher lines.

Half-time statistics indicate a high probability of a draw: hosts were level at the break in 59% of matches, visitors in 41%, averaging roughly a 50% chance of a half-time draw. After the break the visitors perform better — the winning share in the second half is 32% for visitors versus 23% for hosts, and the average points gained after the break are 1.27 for visitors vs 1.05 for home teams. Visitors are also more efficient (shot conversion 16% vs 10%) and have a higher average goals by half-time 0.77 versus 0.41 for hosts, which increases the likelihood of breaking the deadlock. Additionally, xG shows nominal parity: xG_for 1.42 for hosts vs 1.29 for visitors, so technical advantage is minimal, but the visitors’ higher efficiency is material. For the full match the visitors hold a slight edge in seasonal results (win rate 32% vs 27% and season average goals 3.18 vs 2.41), therefore I select the scenario draw at half-time and visitors to win (X/2) as the strongest signal.