Predictions / Football / USA. Major League Soccer / Austin vs San Diego

Prediction Audit: Austin vs San Diego Prediction, Odds & AI Betting Tips

Austin Austin
Sep 27, 2026 - 00:30
3 1.09
3 1.38
xG Accuracy: 38%
San Diego San Diego

The model missed the final outcome (Draw 3–3).

The model had projected San Diego at 41.8%, but the full-time result went the other way.

Tracked markets vs full-time result

Prediction grade F

Each row compares the pre-match model lean to the full-time result.

  • Market Prediction Result Outcome
  • Over / Under 2.5 Under 2.5 Over 2.5 (6 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 San Diego Draw ✖ Incorrect
  • Correct Score Insights 1-1, 0-1, 1-0, 1-2, 0-0 3-3 ✖ Incorrect

Model vs Closing Market

Moderate Disagreement

The closing market prices Austin higher than the statistical model.

Largest probability gap: Austin -8.7 pp

Outcome Model Closing Market Difference Signal
Austin 28.0% 36.7% -8.7 pp Market Higher
Draw 30.1% 26.4% +3.8 pp Aligned
San Diego 41.8% 37.0% +4.9 pp Aligned

The closing market estimates Austin's win probability at 36.7%, compared with the model's estimate of 28.0%, a difference of 8.7 percentage points. This highlights a disagreement between the model and market consensus, without indicating which view is ultimately correct.

Model probabilities are generated from the statistical xG model using a Poisson distribution. Closing market probabilities are derived from consensus closing 1X2 odds after margin removal. Values represent implied probabilities rather than betting recommendations. Closing snapshot: PRE1.

After full time, the result was Draw 3–3.

Market Assessment

The market and model broadly agree on Austin. The remaining divergence may reflect differences in team-strength assumptions rather than a directional disagreement.

  • Monitor line movement before kickoff — not a betting recommendation.

Post Match Insights

What worked

  • Both Teams To Score (Yes) matched the full-time result

What failed

  • Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (6 goals)
  • 1X2: model leaned San Diego; match finished Draw

Market lesson

The closing market differed from the model on San Diego by 8.7 percentage points (50.5% vs model 41.8%) — in this case the market view proved closer.

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Prediction Timeline

How this prediction moved from forecast to full-time review.

  1. Sep 27, 2026 · 19:06 UTC Forecast generated
    • Model 1X2 · Austin 28.0% · Draw 30.1% · San Diego 41.9%
    • xG · Austin 1.09 — San Diego 1.38
  2. Sep 27, 2026 · 00:00 UTC Opening odds snapshot PRE30
    • 1X2 odds · Austin 2.64 · Draw 3.67 · San Diego 2.62
    • Implied 1X2 · Austin 36.7% · Draw 26.4% · San Diego 37.0%
    • Bookmaker · Pinnacle
  3. Sep 27, 2026 · 00:29 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Austin 2.64 · Draw 3.67 · San Diego 2.62
    • Implied 1X2 · Austin 36.7% · Draw 26.4% · San Diego 37.0%
    • Bookmaker · Pinnacle
  4. Sep 27, 2026 · 00:30 UTC Kickoff
  5. FT Full-time result Draw · 3–3
  6. FT Prediction missed 1X2 lean did not match full-time result
  7. Archived Prediction review

Historical Snapshot

Frozen at kickoff — the model output as it stood before the match started.

Historical verdict: Observe
Historical Decision No Primary Bet
Outcome Missed
Pre-match metrics (historical context)
Prediction Reliability 75/100 · High
  • Validation: Pass
Evidence ★★★★★
  • No strong statistical edge
  • Direction agrees with model lean
  • Validation passed
Pricing proximity (inverse gap) 56/100
Betting Confidence 78/100

Validation Report

Immutable Snapshot

Prediction Time: Sep 21, 2026 · 00:46 UTC Snapshot ID: dp-10078400

Closing Odds 2.64
AI Fair Odds —
CLV Pending
Final Result Draw · Austin 3–3 San Diego
Prediction ✖ Missed
Decision Grade F

Model Performance

This prediction contributes to:

  • Primary Bets ROI (180d): -100.0%

Review FAQ

How accurate was the prediction?
This page grades directional markets (1X2, Over/Under 2.5, BTTS) against the full-time result. The prediction grade reflects how many of those tracked markets matched reality.
What does xG Accuracy measure?
xG Accuracy compares the model's pre-match expected-goals profile to the actual scoreline — not whether every market hit. A strong directional review can coexist with a moderate xG accuracy score.
Why wasn't the exact score predicted?
Correct-score outcomes are low-probability tails even when the model reads the match profile well. We highlight top score bins for context; missing the exact line does not invalidate a directional review.
Does this improve the AI record?
Each finished match is logged in our validation pipeline. Aggregated hit rates and CLV studies are published separately — this page is the per-match audit trail.

Predictions are for informational purposes only. Always gamble responsibly and within your limits. Past performance does not guarantee future results.

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: Major League Soccer
  • Fixture: Austin vs San Diego
  • Kickoff: 2026-09-27 00:30:00
  • 1X2 (model): Home 45.0% · Draw 45.0% · Away 10.0%
  • xG (showing): Austin 1.09 — San Diego 1.38 (total xG ≈ 2.47)
  • Value headline: None — no positive EV could be estimated on tracked lines at current best prices (missing odds or thin book depth).
  • Structural leans (not bets): See the Over/Under and BTTS cards for any directional lean text.
  • BTTS (model): Yes N/A · No N/A
  • Correct score (top bin): N/A

When book depth is thin or odds are missing, EV may be unavailable even though the model still prefers one side on totals or BTTS — wait for cleaner prices or skip.

Prefer skipping to over-staking when the engine is honest about missing edge.

Historical Recommendation

Historical Decision: No Primary Bet

Outcome: Missed — Pre-match 1X2 lean did not match the full-time result.

Risk Factors Considered Before Kickoff

  • Price movement: implied probabilities and EV move with odds.
  • Sample / data gaps: low-information leagues widen forecast bands.
  • In-play state: goals and red cards are not modelled here.
  • Scoreline variance: the most likely scoreline is still usually a low absolute probability outcome (often well below 20%).

Last Updated

September 29, 2026 (UTC)

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Major League Soccer Major League Soccer — Standings
# TEAM MP W D L PTS
1 Vancouver Whitecaps 25 15 4 6 49
2 Houston Dynamo 26 13 5 8 44
3 St. Louis City 26 12 8 6 44
4 FC Dallas 26 12 8 6 44
5 San Jose Earthquakes 26 12 6 8 42
6 Los Angeles FC 27 11 8 8 41
7 Colorado Rapids 26 11 3 12 36
8 Los Angeles Galaxy 27 8 9 10 33
9 Portland Timbers 26 9 5 12 32
10 Seattle Sounders 25 8 8 9 32
11 San Diego 26 8 7 11 31
12 Austin 26 7 9 10 30
13 Real Salt Lake 26 8 5 13 29
14 Minnesota United FC 26 7 8 11 29
15 Sporting Kansas City 25 5 3 17 18
# TEAM MP GS GC +/- PTS
1 Vancouver Whitecaps 25 56 23 +33 49
2 FC Dallas 26 49 42 +7 44
3 Portland Timbers 26 47 48 -1 32
4 San Jose Earthquakes 26 46 38 +8 42
5 St. Louis City 26 45 36 +9 44
6 San Diego 26 44 44 0 31
7 Los Angeles FC 27 43 29 +14 41
8 Minnesota United FC 26 39 46 -7 29
9 Real Salt Lake 26 37 44 -7 29
10 Colorado Rapids 26 36 35 +1 36
11 Houston Dynamo 26 34 30 +4 44
12 Los Angeles Galaxy 27 34 42 -8 33
13 Austin 26 32 44 -12 30
14 Seattle Sounders 25 30 33 -3 32
15 Sporting Kansas City 25 29 63 -34 18
# TEAM MP xG xGC +/- PTS
1 Vancouver Whitecaps 25 15.4 7.0 +8.4 49
2 St. Louis City 26 16.2 8.0 +8.2 44
3 Colorado Rapids 26 12.7 7.5 +5.2 36
4 Portland Timbers 26 19.0 14.2 +4.8 32
5 Minnesota United FC 26 17.8 13.3 +4.5 29
6 San Diego 26 14.7 12.0 +2.7 31
7 San Jose Earthquakes 26 13.6 13.1 +0.5 42
8 Houston Dynamo 26 11.6 11.1 +0.5 44
9 Los Angeles FC 27 14.5 15.8 -1.3 41
10 FC Dallas 26 10.4 11.7 -1.3 44
11 Austin 26 9.5 13.7 -4.2 30
12 Seattle Sounders 25 10.6 15.4 -4.8 32
13 Real Salt Lake 26 14.2 19.2 -5.0 29
14 Sporting Kansas City 25 9.8 18.2 -8.4 18
15 Los Angeles Galaxy 27 11.2 21.1 -9.9 33
Major League Soccer USA. Major League Soccer Predictions