When 1-1 Became the Default Setting

Running my Python script on Round 12’s fixtures felt like debugging stubborn code - six matches ended in 1-1 draws before we finally got some variance. Volta Redonda vs Avaí set the tone with a textbook ‘score early then defend poorly’ template (final xG: 1.2 vs 1.1 according to my model). The real outlier? That 94th minute equalizer from Criciúma against América-MG that made my regression models weep.

The Machine Learning Perspective

python

Sample from my predictive model

def predict_outcome(home_xG, away_xG):

return 'Draw' if abs(home_xG - away_xG) < 0.3 else 'Win'

Actual results: 50% accuracy this round (sigh)

Botafogo-SP’s 1-0 victory over Chapecoense proved my algorithm right for once - their defensive compaction (2.3 tackles/interceptions per shot faced) would make a blockchain look porous.

Standout Performers

• Goiás: Their comeback against Atlético Mineiro (2-1) featured two goals after the 80th minute. My fatigue index shows opponents’ defensive concentration drops 37% in final segments against them.
• Paraná Clube: That 2-1 win over Avaí wasn’t luck - their wingbacks created 78% of chances, exploiting Avaí’s chronic fullback weakness (a known bug since Matchday 3).

What the Numbers Don’t Show

The Amazonas FC vs Vila Nova clash (2-1) had more yellow cards (7) than shots on target (4). Sometimes data needs context: that torrential rain in Manaus turned the pitch into a physics-defying water park.

Upcoming Fixtures to Watch

My neural network flags:

1. CRB vs Vasco (July 5): A clash of aging squads where my ‘muscle fatigue’ metric predicts second-half collapses
2. The rematch between Avaí and Criciúma on July 10 - their first meeting produced more fouls than completed passes in the final third