Genoa – Frosinone
For the Serie A fixture between Genoa and Frosinone, the 11Stat model assigns Genoa a 8% chance of winning, with the draw at 18% and Frosinone at 74%. The model's leading outcome is an Frosinone win, supported by 3 of 4 independent engines and a confidence score of 6.6 out of 10. These figures are calibrated probability estimates, not certainties — they describe how likely each result is, never a guaranteed outcome.
What are the outcome probabilities for Genoa vs Frosinone?
Ahead of this Serie A meeting, the 11Stat model distributes the three-way outcome as follows: Genoa win 8%, draw 18%, Frosinone win 74%. Because the three probabilities always sum to 100%, each figure can be read directly as the model's estimated likelihood of that result — a 8% rating means the model expects Genoa to win roughly that often if this exact match were replayed many times. No single number here implies certainty; the gap between the three values is what signals how one-sided or open the model considers the contest.
| Home | Draw | Away |
|---|---|---|
| %8 | %18 | %74 |
the 11Stat model assigns Genoa a 8% probability of beating Frosinone in Serie A.
the 11Stat model rates the draw at 18% and an Frosinone win at 74% for this fixture.
3 of 4 independent engines in the 11Stat model point to an Frosinone win, producing a confidence score of 6.6/10.
How do xG and recent form shape the model's read?
Underlying these percentages is each side's attacking and defensive profile, expressed through expected goals (xG). The 11Stat model weighs how many quality chances Genoa and Frosinone have been creating and conceding in recent matches, adjusts for opponent strength and venue, and blends in short-term form momentum.
How many independent engines agree on this match?
The 11Stat model is not a single algorithm. It runs 4 independent engines — including Poisson and Dixon-Coles goal models, an Elo-and-form engine, and an advanced bivariate simulation layer — and compares their outputs before publishing anything. For Genoa vs Frosinone, 3 of 4 engines converge on an Frosinone win, which feeds directly into the overall confidence score of 6.6/10. Strong cross-engine agreement raises confidence; disagreement between engines is treated as a warning sign that the match carries more uncertainty than the headline probabilities alone suggest.
What does a confidence score of 6.6/10 actually mean?
The confidence score summarizes how much internal evidence supports the model's leading outcome — it reflects engine consensus, data completeness, and how well-calibrated the model has been in comparable Serie A fixtures. A score of 6.6/10 is a measure of analytical conviction, not a promise about the result: even high-confidence projections lose regularly, exactly as their probabilities imply. 11Stat publishes this score so readers can distinguish matches where the models broadly agree from matches where the projection rests on thinner or conflicting evidence.
How does the 11Stat model calculate these probabilities?
Each probability is produced by simulating the match thousands of times. Goal-based engines estimate scoring rates for Genoa and Frosinone from xG, historical goal data, and venue effects, then run Monte-Carlo simulations across every plausible scoreline to derive win, draw, and loss frequencies. A separate Elo-and-form engine cross-checks the result from a team-strength perspective. Finally, a calibration layer — continuously validated against thousands of settled fixtures — adjusts the raw outputs so that, over time, matches rated at 8% genuinely resolve that way about 8% of the time. The output is a probability estimate for analytical and research purposes, not advice of any kind.
Frequently Asked Questions
Who does the model favor in Genoa vs Frosinone?
The 11Stat model's leading outcome is an Frosinone win, with Genoa rated at 8%, the draw at 18%, and Frosinone at 74%. This is a probabilistic assessment, not a predicted certainty.
How confident is the 11Stat model in this projection?
The confidence score is 6.6/10, based on agreement from 3 of 4 independent engines plus data quality and historical calibration for Serie A. Higher scores mean stronger internal consensus, not a guaranteed result.
Can the 11Stat model guarantee the result of Genoa vs Frosinone?
No. Football outcomes are inherently uncertain, and even a 8% rating means the other results still happen a meaningful share of the time. The model quantifies likelihood; it never promises an outcome and its output is not advice.
What data does the 11Stat model use for this analysis?
It combines expected goals (xG), historical scorelines, team-strength ratings, recent form, and venue effects, processed through 4 independent engines and Monte-Carlo simulation, with a calibration layer validated against thousands of settled matches.