Stuttgart vs Hamburger SV: match statistics

Stuttgart is the clear favorite: at home they win 71% of matches and keep a clean sheet in 50% of games; this season they have 16 wins and 56 goals, while Hamburger sit 12th with only 15% away wins, 7 victories and 32 goals overall. The hosts generate more attacking threat — on average 15.71 shots and 5.79 shots on target at home versus 11.08 shots and 4.38 shots on target for the visitors away, with a conversion rate of 10% vs 8%. The advantage is confirmed by chance quality: Stuttgart’s home xG is 1.73 with 1.26 xG conceded, while Hamburger have 1.23 xG away with 1.76 xG against; average goals are 1.57 for hosts versus 0.85 for guests away. In the short term Stuttgart collects 1.2 points per game in the last 5 matches (2 wins, 3 losses), Hamburger 1.0 point (1 win, 2 draws, 2 losses); additionally Hamburg fail to score in 38% of away matches compared to 21% of Stuttgart’s home games without a goal. Considering the superiority in possession (58% vs 47%), the higher quantity and quality of chances and better attacking and defensive metrics, I tip the home win.

Season-long Stuttgart has BTTS in 67% of matches, Hamburg in 54%, and the league average is 60%, providing a baseline near 60% for a both-teams-to-score scenario. The last 5 matches, however, show divergence: Stuttgart has only 40% BTTS (avg 1.4 scored and 1.6 conceded), while Hamburg has 80% BTTS (avg 1.2 scored and 1.4 conceded). Stuttgart at home generates on average 13.33 shots, 4.73 on target and xG 1.45 and has scored 56 goals this season, whereas Hamburg has season xG 1.23 but concedes on average 1.77 goals (xG against 1.76) and has conceded 41 goals; this suggests Hamburg’s defense is vulnerable to conceding. A counterpoint is that Hamburg scores only 0.85 goals season-long and failed to score in 38% of matches, which limits confidence in a 'Yes' BTTS. Considering season-long 67% vs 54%, Hamburg’s strong 80% BTTS in the last 5, and Stuttgart’s xG and shot averages, I favor the 'Yes' scenario with a probability of 62% versus 38% for 'No'.

Stuttgart has an xG of 1.45 and scores on average 1.40 goals per match, registering 13.33 shots with 4.73 on target and an 11% conversion, while Hamburg away posts an xG of 1.23, averages 11.08 shots and an 8% conversion. Hamburg concede 1.77 goals on average away (xG against 1.76), and Stuttgart’s xG against is 1.46, which by simple averaging yields a forecast of 1.61 goals for the home side ((1.45+1.76)/2). For the visitors I expect about 1.35 goals ((1.23+1.46)/2), so the total expected goals is 2.96. Applying a Poisson distribution with λ=2.96, the probability of at least 2 goals is approximately 80%, which aligns with seasonal over 1.5 rates: Stuttgart 80% and Hamburg 69%. Over 2.5 has a moderate probability (~57%, seasonal 47% and 54%), and over 3.5 is less likely (~34%, seasonal 27–31%), therefore the strongest and most reliable signal from the data is over 1.5.

The most likely HT/FT scenario is a draw-draw (X/X) at roughly 23%, because draws at half-time occur in 47% of home matches and matches finish level in 40% of cases, providing a strong signal for maintaining the draw. Stuttgart, however, holds an offensive edge—scoring on average 1.4 goals per game versus Hamburg’s 0.85 and posting a higher xG (1.45 vs 1.23)—thus the half-time draw and home win (X/1) receives the second-highest weight at 11%. At the break the home side averages 0.67 goals and the visitors 0.31, while Hamburg concedes 1.77 goals per game and 0.77 by half-time, indicating the hosts have a realistic chance to decide the match after the interval. Additionally, Stuttgart has better shot conversion (11% vs 8%) and more on-target attempts on average (4.73 vs 4.38), increasing the probability of a decisive second-half goal. The model’s HT/FT distribution is: 1/1 19%, 1/X 6%, 1/2 2%, X/1 11%, X/X 23%, X/2 13%, 2/1 3%, 2/X 11%, 2/2 12%, which reflects the strongest signal on X/X alongside supporting home-team statistics.