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አማርኛ Sample profile (60 matches)
Tables →These are raw rates for the sample shown, not predictions. They are useful as a sanity check against the totals and match-odds prices you are being offered — a league running well under its usual goal rate is worth noticing before backing overs.
Latest scores
How our Ligue 1 picks settled
Full record →Graded automatically against these same scores, flat 1-unit stakes, losers included.
Reading Ligue 1 results without fooling yourself
Results pages invite pattern-hunting, and most patterns found in twenty matches are noise. The reliable content here is the aggregate: this sample produced 2.82 goals per game with home sides winning 40% of the time. Compare those to the prices on offer for the next round — if a bookmaker’s totals line implies a goal rate far from the competition’s actual one, that gap is at least worth a second look.
What does not work is backing a team because it won its last three, or fading one because it lost them. Markets price recent results immediately and usually over-price them, which is why our model weighs two seasons of scoring rates with time decay instead of chasing streaks. Use these scores to check the market’s assumptions, not to build a system from them.
Ligue 1 FAQ
Where do these Ligue 1 results come from?
From the same football-data snapshot that feeds our model and league tables, refreshed twice a day. The timestamp at the top of the page shows when it was last pulled.
How far back do the results go?
This page shows the most recent 60 matches; our dataset holds 349 finished Ligue 1 games in total, and the full history behind any specific pairing is on its head-to-head page.
Can I use recent results to predict the next round?
Only carefully. Short runs of results are mostly noise, and the market has already priced in anything obvious. Aggregate profiles — goals per game, home-win rate over a large sample — carry more signal than any five-match streak, which is why our model weighs two seasons of rates rather than recent form.