Match prediction·USA: MLS·Saturday 01 Aug, 23:30 UTC

Red Bull New York vs Orlando City SC

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 sits at 85% because Red Bull's 2.18 xG dominates Orlando's 1.52 in a fixture built for multiple tallies.

📊 Win probability
50.3% home24.9% draw24.8% away
📈 xG chain — base → adjustments → final goal estimate
Red Bull New YorkstepOrlando City SC
1.93Base xG · rolling 26-match1.61
× 1.13Home advantage · Home× 0.94
no injury adjustment — full squads available per FotMob
2.18Final goal estimate — what the model uses1.52

New York Red Bulls average 4.6 corners per game at home this season (23 competitive matches). Orlando City average 4.5 corners per game away from home this season (25 competitive matches).

🎯 Scoreline distribution · chance of each scoremodel estimate
0
1
2
3
4
5
6
0
2
4
3
1
1
1
5
8
6
3
1
2
6
9
7
3
1
3
4
6
5
2
1
4
2
4
3
1
1
5
1
2
1
1
6
1
home windrawaway win
Most likely outcomes
Over 1.5 goals
85%
Orlando City SC or draw
28%

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

More predictions for these teams

New York Red Bulls vs Orlando City — Head-to-Head

This is the first meeting Bawler has on record between New York Red Bulls and Orlando City — 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: New York Red Bulls 50% · draw 25% · Orlando City 25%.

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

⚡ Full-time recap available
Red Bull New York 32 Orlando City SC
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.