Super Nova vs Grobiņa: match statistics

Grobiņa concedes an average of 1.6 goals per away match and has conceded 4 goals in the last 5 fixtures, while Super Nova concedes 2.2 goals at home and has shipped 11 goals in their last 5 — a clear defensive advantage for the visitors. Offensively the hosts are more efficient: Super Nova scores 1.1 goals per home game, averages 8.3 shots (3.4 on target) and a 13% conversion rate, whereas Grobiņa on the road averages 10.1 shots (3.3 on target) but only 0.6 goals and a 6% conversion. Looking at xG, the visitors perform better — Grobiņa’s average away xG is 1.24 vs Super Nova’s 1.08 home xG, and over the last 5 matches the visitors average 1.4 points per game compared to the hosts’ 1.2. Additionally Super Nova’s home metrics are worrying: hosts win only 10% of home matches and have 0% clean sheets, while Grobiņa wins 20% of away games and keeps 20% clean sheets, which increases the visitors’ chances. Therefore I forecast an away win; probabilities: home 20%, draw 25%, away 55%.

Super Nova scores on average 0.75 and concedes 1.42 per match, while Grobiņa scores 0.6 and concedes 1.6, indicating both teams are prone to conceding. Season BTTS rates are 33% (Super Nova) and 40% (Grobiņa), but in the last 5 matches Super Nova had BTTS in 80% and Grobiņa only 20%, giving a mixed signal. A critical factor is Super Nova conceding 2.2 goals on average in the last 5 matches versus Grobiņa’s 0.8, so the hosts are more likely to allow opposition goals. Recent xG are 1.37 each, with season xG 1.05 vs 1.24; with average shots 8.08 and 10.1 and shots on target 2.67 and 3.3, both sides create chances. Considering the league BTTS level of 57%, low clean sheet percentages of 25% and 20%, and high season goals conceded (35 and 33), I project BTTS - Yes with a 60% probability versus 40% No.

The xG-based model forecasts 1.19 goals for Super Nova (xG 1.05 vs Grobiņa defense 1.33) and 1.52 for Grobiņa (xG 1.24 vs Super Nova defense 1.79), totaling 2.71 goals. With that total, a Poisson distribution yields about a 75% chance of at least 2 goals (Over 1.5), about 51% for at least 3 goals (Over 2.5) and only around 29% for 4+ goals (Over 3.5). Seasonal figures provide additional support: Over 1.5 occurred in 58% of Super Nova’s matches and 70% of Grobiņa’s matches, and both teams concede frequently (Super Nova 35, Grobiņa 33). Grobiņa averages 10.1 shots and 3.3 on target per game, while Super Nova has a higher conversion rate (9% vs 6%), which increases the realistic chance of converting opportunities. As a result, the strongest signal is Over 1.5 (recommended); Over 2.5 is only marginally favored, and Over 3.5 remains risky.

A draw at half-time is the most likely: the hosts were level at the break in 58% of matches, and a model using HT averages (Super Nova 0.42 goals vs Grobiņa 0.40) gives about a 52% chance of a half-time draw (0-0 ~44% and 1-1 ~7%). In the second half the hosts have slightly higher expected goals (second half ≈0.76 for Super Nova vs ≈0.61 for Grobiņa, calculated as FT–HT), so from half-time draws the match most often stays drawn or tips in favor of the home side. In the full HT/FT distribution the strongest single signals are X/X (~20%), then 1/1 (~19%) and X/1 (~18%), while 2/2 is around 16%. The stats are mixed: Grobiņa has higher xG (1.24 vs 1.05) and more shots (10.1 vs 8.08), but Super Nova has better conversion (9% vs 6%) and many half-time draws (58%), so the away advantage is limited. Considering these numbers, I recommend HT/FT: X/X as the most likely outcome.