Kongsvinger vs Odd: match statistics

I predict a home win (1); Kongsvinger at home average 2.33 goals per match (average xG 1.72) with 6 SOT and 13.33 shots overall, with a finishing rate of 18%, while Odd away average 2.00 goals per match (xG 1.44) with 4.67 SOT and 11 shots overall at the same 18% conversion. In the last 5 matches the hosts have scored 10 goals and have a 3-0-2 record (form 'wdwdw'), and the visitors have scored 8 goals with a 3-1-1 record (form 'dwwwl'), translating to PPG 2.2 for Kongsvinger versus 2.0 for Odd. Both teams have similar defensive efficiency — on average 0.67 goals conceded per match and 33% clean sheets — so the decisive factor will be the quality and number of goal-scoring opportunities, where the hosts are favoured (higher xG and more SOT). Kongsvinger also have 16 goals scored this season versus Odd's 15 and remain unbeaten at home (Win% 100), while Odd controls possession well too (63% away), which can make it harder to dictate the game but does not negate Kongsvinger’s advantage in directly finishing chances. Based on these numbers I assign a 55% probability to a home win, 25% to a draw and 20% to an Odd win.

The BTTS - Yes scenario appears more likely as Kongsvinger scores an average of 2.25 goals per match and Odd 2.00, against a league average of 3.55 goals per match. In the last 5 fixtures Kongsvinger had BTTS in 80% (4/5) and Odd in 60% (3/5), indicating both teams in current form often both score and concede. xG indicates chance creation: xG for is 1.91 for Kongsvinger and 1.44 for Odd, while xG against is 1.34 and 1.18, so both sides generate over 1 expected goal and have exposure to conceded chances. Although Kongsvinger has 50% clean sheets and Odd concedes only 0.67 goals on average this season, Odd’s last 5 matches show 1.2 goals conceded and xG against 1.18, suggesting less defensive solidity recently. Overall, considering season BTTS 50% vs 67%, form 80% vs 60%, and high xG and goal averages, I assign 72% to 'yes' and 28% to 'no', therefore recommendation: Yes (BTTS - Yes).

The xG-based model gives 1.55 xG for Kongsvinger ((1.91 + 1.18)/2 = 1.55) and 1.39 xG for Odd ((1.44 + 1.34)/2 = 1.39)), totaling 2.94 expected goals. From a Poisson distribution with a mean of 2.94 there is ~79% probability of at least 2 goals, ~56% for at least 3 goals and ~34% for 4 or more. Seasonal data support an offensive profile: Kongsvinger have 75% of matches over 1.5 and 75% over 2.5, Odd have 67% in both categories; hosts score on average 2.25 goals per game, visitors 2.00, with average shots 17 vs 11 and shots on target 6.25 vs 4.67, and BTTS is 50% for Kongsvinger and 67% for Odd. Based on this the strongest signal is the Over 1.5 (line 1.5, over) — it carries the highest probability (≈79%) and is confirmed by seasonal over/BTTS percentages.

Kongsvinger led at the break in 4 of 4 home matches (100%) and scores on average 1.5 goals by half-time, while Odd averages 0.33 goals by half-time, indicating a clear home start. However, Odd on the road averages 3.0 points per match versus Kongsvinger’s 1.75 at home, and in second halves Odd scores on average 1.67 goals and concedes none, with 9 points and 100% wins in second halves. Although Kongsvinger’s xG is 1.91 versus Odd’s 1.44, the visitors have higher shot efficiency (18% vs 13%), and the hosts concede on average 1.0 goal after the break and have a -1.0 goal difference in second halves, increasing the risk of losing their lead. Summing up, the combination of Kongsvinger’s 100% half-time leads (1.5 HT on average) and Odd’s dominance in second halves (1.67 goals 2H, 100% 2H wins) suggests a scenario where the home side leads at the break and Odd completes a turnaround after half-time. Therefore I pick HT/FT 1/2 as the best selection and assign it the highest probability (42%) while acknowledging smaller chances for the 1/1 variant.