Hamburger SV vs Borussia Mönchengladbach

Hamburger SV
#15 · 9 pts
vs
Scheduled
Borussia Mönchengladbach
#18 · 4 pts

AI prediction · locked November 4, 2026 10:42 pm

HSV 51%Draw 22%M'gladbach 27%
Over 2.567%
Both score66%
Model goals2.01–1.41

Model pick 2–1

Most likely scores

2–1 · 9.3%1–1 · 9.2%2–2 · 6.6%2–0 · 6.6%1–2 · 6.5%1–0 · 6.5%

Why

The model expects HSV to score 2.01 goals: league home average 2.12 × HSV home attack 0.80 × M'gladbach away defence 1.18.

It expects M'gladbach to score 1.41 goals: league away average 1.38 × M'gladbach away attack 0.88 × HSV home defence 1.17.

Based on 3 home games for HSV, 4 away games for M'gladbach and 72 finished matches in the competition.

Attack above 1.00 means scoring more than the league average; defence above 1.00 means conceding more than average (weaker).

Most likely outcome: HSV (51%). This is an estimate, not a certainty.

What the AIs predict

Every AI got exactly the same data and locked its pick before kickoff.

Claude2–1Locked
HSV 42% · draw 27% · M'gladbach 31% · over 2.5 55%

Hamburger SV has a 2-0-1 home record and a stronger home scoring rate than its away form. Gladbach has won only once away and concedes 2.75 per game in this competition. Both sides are weak defensively, so a narrow home win with a goal at each end is the most likely outcome.

ChatGPT2–1Locked
HSV 44% · draw 27% · M'gladbach 29% · over 2.5 56%

Hamburg are slightly stronger at home, while Gladbach have conceded 22 in 8 matches and 12 in 4 away games. Both teams have recent scoring form, but Hamburg’s home record suggests a narrow home win.

AI reasoning is generated by each model from the data shown on this page and may contain mistakes.

Form & stats this competition season

HSVM'gladbach
Table position#15 · 9 pts#18 · 4 pts
Last 5 (newest first)LLWWWDWLLL
Home W-D-L (goals)2-0-1 (3:6)0-1-3 (7:10)
Away W-D-L (goals)1-0-4 (4:13)1-0-3 (4:12)
Goals per game at home (for/against)1.00 / 2.001.75 / 2.50
Goals per game away (for/against)0.80 / 2.601.00 / 3.00
xGn/an/a

xG is not available from the current data source (football-data.org). Competition averages: 2.12 home and 1.38 away goals per match (72 finished matches).

Schedule, form & head-to-head

HSVM'gladbach
Days since last match5.16.2
Matches in last 14 days21
Next match+15.9 d: @ Bremen (BL1)+14.8 d: vs Dortmund (BL1)
Points per game, last 5 (all comps)1.800.80
Clean sheets / failed to score (this comp)1 / 4 (8)1 / 3 (8)
Over 2.5 / both scored (this comp)5 / 3 (8)7 / 5 (8)

HSV – recent matches, all competitions: L @ Frankfurt 1–3 · L @ SC Paderborn 0–1 · W vs Stuttgart 1–0 · W @ Hoffenheim 3–2 · W vs 1. FC Köln 2–1 · L @ RB Leipzig 0–5

M'gladbach – recent matches, all competitions: D vs SC Paderborn 0–0 · W @ Stuttgart 3–2 · L vs Hoffenheim 1–2 · L @ 1. FC Köln 1–2 · L vs Mainz 3–4 · L @ Freiburg 0–5

Head-to-head

Last 2 meetings: HSV 0 wins, 2 draws, M'gladbach 0 wins · 0.00 goals per game.

  • HSV 0–0 M'gladbach Bundesliga · 17 Jan 2026
  • M'gladbach 0–0 HSV Bundesliga · 24 Aug 2025

Only the six tracked competitions count; domestic cups are not included.

Model assumptions & limitations

Assumptions

  • Goals for each team follow independent Poisson distributions.
  • Expected goals (λ) = league average home/away goals × attack strength × opponent defence strength.
  • Strengths use only this competition and season: home form for the home side, away form for the away side. If either team has too few home/away games, overall form (all games) is used instead and this is stated in the explanation.
  • Strengths are shrunk towards the league average when few games have been played.
  • Predictions are for the score after regular time (90 minutes plus stoppage time).
  • The model's tipped score is the one with the highest expected points under the game's scoring system, not necessarily the most likely single score.

Limitations

  • No injuries, suspensions, line-ups, transfers, rotation or motivation are considered.
  • No xG or shot data is used – the current data source does not provide it.
  • Results in other competitions (e.g. domestic league for Champions League teams) are not included.
  • Independent Poisson models tend to slightly underestimate draws.
  • Probabilities are estimates with considerable uncertainty, not facts or guarantees.

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