Can We Trust AI to Predict NBA Contracts? 5 Overlooked Risk Signals in the 76ers’ Deal with Markelle-Abesale

The Model Isn’t Silent—It’s Listening

I watch the 76ers’ front office like a Bayesian network humming in the dark. Every contract negotiation is a likelihood calculation—but what if the model was trained on noise disguised as ‘intangibles’? Markelle-Abesale isn’t just a player; he’s a variable in a system designed by human fear, not algorithmic greed.

Data Doesn’t Lie—but Interpreters Do

His stats: 11.0 PPG, 5.6 RPG, 2.1 APG, 50.1% FG, 38.0% 3P… clean numbers. But ask: did the model account for his mother’s hospital visits during playoffs? Or his late-night studio sessions where he relearned poetry instead of plays? AI sees points—not context.

The Human Variable Nobody Trained For

We built this system to optimize ‘value,’ but forgot that Abesale chose Philadelphia because it felt like home—not because of cap space. His father, a Scottish engineer, taught him that contracts are written in silence—not spreadsheets. The algorithm can’t measure grief.

Five Risk Signals No One Talks About

1. Training data from ‘gameday’ interviews (not box scores)
2. Cultural weight > salary cap (Croydon > Philly)
3. Algorithmic bias in ‘third-quarter’ decision-making
4. Emotional latency between draft day and free agency
5. Human intuition as the only true predictor—AI doesn’t feel loyalty.

You Believe AI or the Man?

I once coded this system to predict outcomes—and failed when it ignored poetry. What do you trust? Comment below.