Match prediction·USA: MLS·Wednesday 22 Jul, 23:30 UTC

Philadelphia Union vs Red Bull New York

Bawler weighs each team's recent scoring and defending, then adjusts for home advantage and injuries to work out how this match is likely to go. This page shows the full chain — every number we used.

Why the model calls it this way

Over 1.5 Goals lands at 84% because Philadelphia's 2.04 xG vastly outpaces Red Bull's 1.45, setting up a multi-goal affair led by striker Tai Baribo's 26% of team shot creation.

📊 Win probability
48.6% home26.1% draw25.3% away
📈 xG chain — base → adjustments → final goal estimate
Philadelphia UnionstepRed Bull New York
1.80Base xG · rolling 26-match1.54
× 1.13Home advantage · Home× 0.94
no injury adjustment — full squads available per FotMob
2.04Final goal estimate — what the model uses1.45

Philadelphia Union average 7.2 corners per game at home this season (27 competitive matches). New York Red Bulls average 4.1 corners per game away from home this season (20 competitive matches).

🎯 Scoreline distribution · chance of each scoremodel estimate
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9
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home windrawaway win
Most likely outcomes
Over 1.5 goals
84%
Red Bull New York or draw
31%

Model probabilities for this fixture, ordered by likelihood. Estimates from rolling expected goals — not guarantees.

More predictions for these teams

Philadelphia Union vs New York Red Bulls — Head-to-Head

This is the first meeting Bawler has on record between Philadelphia Union and New York Red Bulls — the model's ledger starts at model launch, so no earlier fixtures between these two sides have been logged and settled yet.

The model's verdict for this fixture: Philadelphia Union 49% · draw 26% · New York Red Bulls 25%.

Model probabilities are edge estimates for entertainment — not betting advice, and past performance does not guarantee future results. 18+.

⚡ Full-time recap available
Philadelphia Union 31 Red Bull New York
See how the model called it · call-by-call verdict · xG vs actual →
More match predictions ▶

Model estimates from rolling expected goals — not guaranteed outcomes. Past accuracy does not guarantee future results.