Haka vs JäPS: match statistics

JäPS holds a clear edge over the last 5 matches: 8 goals scored and only 1 conceded, a record of 3 wins and 2 draws and an average of 2.2 points per game. Haka in the same period scored 7 goals, conceded 3 and has 2 wins and 3 draws with an average of 1.8 points. In away stats JäPS averages 3 shots per game and 2 on target, whereas Haka’s home stats show 0 average shots and 0 shots on target this season, indicating an advantage for the visitors in chance creation. League position also favors JäPS: 4th versus Haka’s 8th, and defensively JäPS have conceded only 1 goal in the last 5 matches versus Haka’s 3. In xG terms JäPS has Away xG For 0.49 and xG Against 1.89, which indicates a moderate number of expected chances (0.49) and some defensive risk (1.89), but considering 8 vs 7 goals and the 2.2 vs 1.8 points averages, I project a JäPS win with a relatively low scoreline.

Statistical analysis leads me to conclude that a No Goal scenario is more likely: I assign 70% to 'no' and 30% to 'yes'. JäPS away has a very limited offense — away offensive xG 0.49 with only 3.0 shots and 2.0 shots on target per match and a 0.0% conversion rate, which significantly reduces the visitors' chance of scoring. Haka creates substantially more chances (home xG 1.24, 10.0 shots and 4.0 shots on target) and faces a team that concedes average away xG of 1.89, so the hosts are favored to score while Haka’s xG against is low (0.48). Additionally, seasonal and league indicators support No BTTS — the seasonal BTTS percentage for both teams is 0%, the league has only 38% BTTS and 85% of matches with a clean sheet expected, which favors a no-both-teams-to-score outcome. The last 5 matches provide mixed signals (Haka BTTS 60% vs JäPS 20%, last 5: Haka 1.4 goals and 0.6 conceded, JäPS 1.6 and 0.2), but the strongest signal remains the visitors’ poor finishing and low xG, hence the 'no' pick.

The xG-based model gives Haka 1.57 and JäPS 0.48 (calculated as averages: home xG_for 1.24 vs away xG_against 1.89 -> 1.57; and away xG_for 0.49 vs home xG_against 0.48 -> 0.48), resulting in a total of 2.05 expected goals. In the last 5 matches Haka had 1.64 xG and scored 7 goals (1.4 per match), while JäPS had 1.63 xG and scored 8 goals (1.6 per match), conceding 3 and 1 goals respectively. Haka averages 10 shots and 4 on target per match versus JäPS’s 3 shots and 2 on target, indicating a home advantage in chance creation. A Poisson model with λ=2.05 yields probabilities of over 1.5 ≈61%, over 2.5 ≈34% and over 3.5 ≈15%, therefore the strongest signal is over 1.5.

I selected 1/1 as the most likely HT/FT outcome because the home team leads at the break in 100% of their home matches (leadingAtHTPercentage_home = 100.0) and averages 1.0 goals in the first half (scoredAVGHT_home = 1.0), with their sole home goal so far occurring in the 31–45 minute window (goals_scored_min_31_to_45_home = 1.0). Additionally, the home side wins all their home games (winPercentage_home = 100.0) and collects an average of 3.0 points per home match this season (seasonPPG_home = 3.0), while the visitors have winPercentage_away = 0.0 and seasonScoredAVG_away = 0.0. The disparity in chance creation is reflected in xG (xG_for_avg_home = 1.24 vs xG_for_avg_away = 0.49), total shots (10.0 vs 3.0) and shots on target (4.0 vs 2.0), which supports a home goal before halftime. Furthermore, the hosts concede virtually no goals at home (seasonConcededAVG_home = 0.0) while the visitors carry a high xG against (xG_against_avg_away = 1.89), so the risk of a turnaround by JäPS is minimal. Based on these figures I assign a 72% probability to the 1/1 scenario, allocating the remaining 28% mainly to 1/X and X/1 as alternative, less likely outcomes.