Italy
Where each Serie Ateam is most likely to finish, from 1,000 Monte Carlo simulations of every remaining fixture. Each team's per-match scoring rate to date drives the Poisson model that samples goals for every unplayed match; the table aggregates position frequencies and average final points across all simulations.
Read the methodology in our model transparency page. For top-scorer race projections see Serie A top scorer probabilities.
| # | Team | xPts Avg | xPts Mod | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 87.5 | 89 | 100.0 | ||||||||||
| 2 | 74.8 | 76 | 84.2 | 11.3 | 4.5 | ||||||||
| 3 | 71.9 | 73 | 4.9 | 32.8 | 35.4 | 20.0 | 6.9 | ||||||
| 4 | 71.7 | 73 | 10.9 | 42.0 | 19.9 | 19.7 | 7.5 | ||||||
| 5 | 69.7 | 71 | 9.3 | 25.0 | 35.8 | 29.9 | |||||||
| 6 | 69.5 | 71 | 4.6 | 15.2 | 24.5 | 55.7 | |||||||
| 7 | 59.4 | 61 | 100.0 | ||||||||||
| 8 | 56.3 | 55 | 100.0 | ||||||||||
| 9 | 53.0 | 54 | 85.4 | 13.0 | |||||||||
| 10 | 50.9 | 50 | 8.6 | 52.6 | |||||||||
| 11 | 50.3 | 49 | 6.0 | 34.4 | |||||||||
| 12 | 45.2 | 44 | |||||||||||
| 13 | 43.4 | 45 | |||||||||||
| 14 | 42.2 | 41 | |||||||||||
| 15 | 42.4 | 41 | |||||||||||
| 16 | 40.8 | 40 | |||||||||||
| 17 | 36.5 | 38 | |||||||||||
| 18 | 35.1 | 34 | |||||||||||
| 19 | 22.0 | 21 | |||||||||||
| 20 | 18.7 | 18 |