How does FootballMoney predict this match?

Our AI model analyzes historical team performance, ELO ratings, recent form, injuries, and statistical metrics to generate probability estimates for Home Win, Draw, and Away Win.

What is Model Confidence vs Prediction Probability?

Prediction probability (e.g., 41% Home Win) is the chance of a specific outcome. Model Confidence measures how certain the AI is about the ranking of outcomes — even with low probabilities, the model can be highly confident about which outcome is most likely.

How does the Prediction Timeline work?

After a match finishes, we reconstruct how the AI's probability estimates changed throughout the game, identifying key turning points where the model adjusted its prediction based on in-game events.

Portland Timbers vs San Diego

Thu, Aug 20, 2026 10:30 AM

AI Verdict

Home Win — 42% probability ★☆☆☆☆

K. Velde (Portland Timbers) — Injury default
J. Mosquera (Portland Timbers) — Injury default
Z. McGraw (Portland Timbers) — Injury Back

Confidence: Uncertain · Updated: Thu, Aug 20, 2026 10:30 AM

AI Prediction

Predicted outcome: Home Win (42% probability)

Home Win: 42%
Draw: 22%
Away Win: 36%

Most likely scores: 1-1 (12%), 1-0 (11%), 0-1 (11%)

Model Confidence

Confidence Rating: Uncertain (18%)

Very close race — model is uncertain about the ranking

Prediction Evolution (15 snapshots)

17:10 — Home 0% / Draw 0% / Away 0%
17:10 — Home 0% / Draw 0% / Away 0%
17:10 — Home 0% / Draw 0% / Away 0%
17:10 — Home 0% / Draw 0% / Away 0%
18:56 — Home 38% / Draw 24% / Away 39% (Δ Home +37.2%, Draw +23.5%, Away +38.3%)
18:56 — Home 38% / Draw 24% / Away 39%
18:56 — Home 38% / Draw 24% / Away 39%
18:56 — Home 38% / Draw 24% / Away 39%
18:56 — Home 38% / Draw 24% / Away 39%
18:56 — Home 38% / Draw 24% / Away 39%
08:07 — Home 38% / Draw 23% / Away 39% (Δ Home +0.3%, Draw -0.3%)
08:07 — Home 38% / Draw 23% / Away 39%
08:07 — Home 38% / Draw 23% / Away 39%
08:45 — Home 38% / Draw 23% / Away 39%
08:45 — Home 38% / Draw 23% / Away 39%

Advanced Details

Match Signals

🩹 K. Velde (Portland Timbers) — Injury default ✅ confirmed
🩹 J. Mosquera (Portland Timbers) — Injury default ✅ confirmed
🩹 Z. McGraw (Portland Timbers) — Injury Back ✅ confirmed [Impact: Home -2%, Draw +1%, Away +1%]
🩹 G. Guerra (Portland Timbers) — Injury default ✅ confirmed [Impact: Home -2%, Draw +1%, Away +1%]
🩹 O. Fernandez (Portland Timbers) — Injury default ✅ confirmed [Impact: Home -2%, Draw +1%, Away +1%]
🩹 L. Morgan (San Diego) — Minor Missing Fixture ✅ confirmed [Impact: Home +1%, Away -1%]
🩹 J. Tverskov (San Diego) — Minor Missing Fixture ✅ confirmed [Impact: Home +1%, Away -1%]
🩹 P. Sisniega (San Diego) — Minor Missing Fixture ✅ confirmed [Impact: Home +1%, Away -1%]
🩹 A. Reyes (San Diego) — Minor Missing Fixture ✅ confirmed [Impact: Home +1%, Away -1%]
🩹 W. Kumado (San Diego) — Minor Missing Fixture ✅ confirmed [Impact: Home +1%, Away -1%]
🩹 A. Godoy (San Diego) — Minor Missing Fixture ✅ confirmed [Impact: Home +1%, Away -1%]

Frequently Asked Questions

Is Home Win really likely to win?: Home Win has a 42% edge — the model sees a slight advantage but recognizes the matchup is competitive. Draw (22%) and the other outcome are both realistic possibilities.
What is the biggest factor affecting this prediction?: K. Velde (Portland Timbers) — Injury default
How confident is the model in this ranking (Uncertain, 18%)?: Model Confidence measures how certain the AI is about the order of outcomes, not any single probability. This is a toss-up — the model struggles to separate the outcomes confidently.
How does the AI prediction model work?: Our ensemble combines Gradient Boosting and Random Forest models trained on historical match data, ELO team ratings, recent form, and statistical metrics. Score distributions use Poisson-based simulations for the most likely scorelines.

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