Jan Oblak’s goals prevented at home and away
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Against his share of what his opponents usually allowed goalkeepers, Jan Oblak was 0.58 goals over at home and 0.18 goals over away in 2026-27, 6 games against 8. The plain averages are 0.58 and 0.18, a difference of 0.40.
goals prevented a game over expected
at home
+0.58
away
+0.18
-101
A +0.40 difference in goals prevented over expected at home vs away is about what you’d expect.
plain
0.58
0.18
-101
A +0.40 difference in goals prevented at home vs away is about what you’d expect.
The games
at homeaway
| Side | Date | Game | Result | Prevented | Expected |
|---|---|---|---|---|---|
| Aug 19, 2026 | vs Málaga | W | 0.05 | -0.00 | |
| Aug 23, 2026 | vs Villarreal | T | 1.24 | -0.00 | |
| Aug 29, 2026 | @ Sevilla | W | 0.45 | 0.00 | |
| Sep 5, 2026 | @ Athletic Club | L | -1.55 | -0.00 | |
| Sep 9, 2026 | @ Liverpool | L | -0.44 | 0.00 | |
| Sep 13, 2026 | @ Real Sociedad | W | 0.45 | 0.00 | |
| Sep 16, 2026 | vs Osasuna | W | 0.31 | -0.01 | |
| Sep 20, 2026 | vs Real Madrid | W | -0.48 | 0.00 | |
| Oct 10, 2026 | @ Alavés | W | 0.40 | -0.00 | |
| Oct 13, 2026 | vs Manchester United | W | 0.92 | -0.00 | |
| Oct 17, 2026 | @ Espanyol | W | 0.43 | 0.00 | |
| Oct 20, 2026 | @ VfB Stuttgart | T | 1.19 | -0.00 | |
| Oct 24, 2026 | vs Deportivo | W | 1.41 | -0.00 | |
| Oct 31, 2026 | @ Levante | W | 0.51 | 0.00 |
Method
- Expected goals are this site’s model of each shot’s chance, from where it was taken, whether it was a header, and how it was set up (a cross, a through ball, a headed pass, or no pass at all), fit on the 105,024 non-penalty shots of the five leagues and the Champions League since 2024-25 and within 3% of the goals scored in each season. Penalties are left out.
- Expected is what the opponent allowed goalkeepers in its other 2026-27 games, plus 0.00 for the home team (half the league’s home edge for goalkeepers that season), nothing at a neutral site, scaled to his share of his team’s goalkeepers’ goals prevented in his games, 100%.
- Each expected number is then fit to the season’s games: a game bears out about -1% of the difference the model predicts.
- The shuffle keeps the season’s shape: the games are cut into stretches, one a 10 games, where the dates leave the least variance, each game is read against its stretch, and the sides are shuffled only inside each stretch.