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Mallorca v Las Palmas — Prediction, Odds & Stats

Sunday 11 October 2026 · ESP-La Liga 2 · 2627

La Liga results-only Prediction generated

AI prediction

ai-blend v1.0 daily
Mallorca win
46.5%
model 46.5%
Draw
26.8%
model 25.8%
Las Palmas win
26.8%
model 27.8%

Calibrated: the model's number corrected by how often calls at that confidence have landed, out of sample (3,799 settled matches in the thinnest band), then normalised. The smaller figure is the raw model output.

Top 5 scorelines

  1. 1-0 12.3%
  2. 1-1 12.2%
  3. 2-1 9.0%
  4. 0-1 9.0%
  5. 2-0 8.6%

Goals

Over 2.545.4% Under 2.554.6%
BTTS yes49.3% BTTS no50.7%
Mallorca 1+76.4% Mallorca 2+42.3%
Las Palmas 1+64.9% Las Palmas 2+28.2%

Expected goals 1.44 – 1.05. Every market above is read off one scoreline distribution.

Footing

AI confidence
60/100
Data quality
80/100
Model agreement
98%
★ Best pick
Nothing on a measured band prices above 1.18 here.
★ Most likely to land
Nothing on a measured band prices above 1.18 here.

Positive factors

  • Form is good: 1.80 points a match from the last five.
  • Their last eleven was closer to full strength: Las Palmas were missing 1.08 expected goal involvements a match against 0.48.

Risk factors

  • No specific risk flags beyond the usual variance of a football match.
Why the model thinks this

The price

Mallorca have an estimated 46.5% win probability, against 26.8% for Las Palmas and 26.8% for the draw. Expected goals from the ensemble are 1.44 for Mallorca and 1.05 for Las Palmas.

Why Mallorca

Form is good: 1.80 points a match from the last five. Their last eleven was closer to full strength: Las Palmas were missing 1.08 expected goal involvements a match against 0.48.

Why the draw is live

The draw sits at 26.8%. Draws run at 27% in this competition.

Why Las Palmas can win

Las Palmas carry the stronger rating, 1573 Elo against 1485. Form is good: 2.00 points a match from the last five.

Key players

Mallorca's leading contributors by expected goal involvements a match: Vedat Muriqi (0.60), Zito (0.22), Sergi Darder (0.20). Las Palmas's leading contributors by expected goal involvements a match: Fabio Silva (0.42), Oliver McBurnie (0.26), Alberto Moleiro (0.21).

Availability

1 of Mallorca's top three contributors did not start their last match. 3 of Las Palmas's top three contributors did not start their last match.

The matchup, as far as the data reaches

Mallorca: 58% possession, 14.0 shots a match, 10.6 faced, 0.14 xG a match from set pieces. Las Palmas: 56% possession, 8.8 shots a match, 12.2 faced, 0.28 xG a match from set pieces.

Inputs last refreshed: matches 2026-09-05 22:20 · team_stats 2026-09-05 09:41 · availability 2026-09-05 21:03 · prediction 2026-09-05 18:39 ·

Mallorca are the model's most likely outcome at 46.5%. The likeliest scoreline is 1-0 at 12.3%. Confidence 60/100, data quality 80/100. These are probabilities, not certainties: the less likely outcome happens as often as its number says.

Written from the numbers the model saw, not by a language model; nothing here is a statistic the snapshot does not hold. How predictions work.

Was this pick made in advance?
A model whose name ends -oos is out of sample: it was fitted only on matches played before this one, so its pick is a genuine forecast rather than hindsight. Anything without that suffix was fitted on all available data.

What the chances say

Mallorca are conceding fewer than expected — 3.1 goals saved beyond what the chances suggest.

LWWDLWDLLW last 10 scored 14 from 14.8 xG conceded 12 from 15.1 xG

Las Palmas have scored 8 from 9.8 expected and conceded 14 from 15.3 — both ends in line with the chances, so the recent record is a fair reflection.

DLWWLLLLLL last 10 scored 8 from 9.8 xG conceded 14 from 15.3 xG

Finishing swings far more than chance creation, so a gap this size usually narrows. Over ten matches it is suggestive, not settled — a genuinely elite finisher beats expected goals for years.

What the model said

ModelHome / Draw / Away Expected goalsLikeliest score O2.5BTTS
dc-xg 1.36–0.93 1-0 14% 40% 45%

A model whose name ends -oos was produced by the walk-forward backtest: it was fitted only on matches played before this one, so the pick is genuinely out of sample.

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On this page

Match

What this page is for

One fixture in full. The model's probability for every market it prices, the prices stored against them, the teams expected to feature, and for a match already played, the shot map and the xG timeline.

How to read the shot map

Every shot is placed where it was taken and sized by its chance of being scored. A cluster of small marks from distance and a clean sheet is a side that was busy without ever threatening; two big marks and a defeat is a side that had the better of it and lost anyway. Both happen constantly, and both are invisible in the score.

How to read the xG timeline

The steps are chances as they arrived, so the shape shows who was on top and when. A flat line that jumps once near the end is a smash and grab.

Things to watch

The stored prediction is the one made before the match, kept deliberately so it can be judged afterwards. It is not re-fitted with hindsight.

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