Elia Caprile’s goals prevented at home and away
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Against his share of what his opponents usually allowed goalkeepers, Elia Caprile was 0.87 goals over at home and 0.14 goals under away in 2026-27, 4 games against 6. The plain averages are 0.87 and -0.14, a difference of 1.00.
4 games at home is still too small a sample for the statistics to say much, but maybe you can find something if you squint.
goals prevented a game over expected
at home
+0.87
away
−0.14
-101
A +1.00 difference in goals prevented over expected at home vs away is a bit unusual: shuffle which games were at home and it comes up 14 times in 100.
plain
0.87
-0.14
-101
A +1.00 difference in goals prevented at home vs away is a bit unusual: shuffle which games were at home and it comes up 14 times in 100.
The games
at homeaway
| Side | Date | Game | Result | Prevented | Expected |
|---|---|---|---|---|---|
| Aug 22, 2026 | @ Parma | W | 0.21 | -0.00 | |
| Aug 30, 2026 | vs Internazionale | L | 0.93 | -0.00 | |
| Sep 7, 2026 | vs Lecce | W | 0.99 | -0.00 | |
| Sep 12, 2026 | @ Atalanta | W | 0.70 | 0.00 | |
| Sep 19, 2026 | @ Udinese | W | 1.31 | 0.00 | |
| Oct 11, 2026 | vs Juventus | T | 1.33 | 0.00 | |
| Oct 19, 2026 | @ Monza | L | -1.96 | -0.01 | |
| Oct 24, 2026 | vs Bologna | L | 0.21 | -0.00 | |
| Oct 27, 2026 | @ AS Roma | L | -0.69 | 0.00 | |
| Nov 1, 2026 | @ Lazio | L | -0.39 | 0.01 |
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.