When xG Meets Fan Bias: How the Pacers’ Playoff Dreams Were Calculated (and Why They Still Miss Turner)

When xG Meets Fan Bias: How the Pacers’ Playoff Dreams Were Calculated (and Why They Still Miss Turner)

As a data wizard from East London, I’ve watched the Pacers’ obsession with Turner fade under luxury tax math. This isn’t folklore—it’s regression. When his 30M contract collided with 16M in tax penalties, the numbers didn’t lie. The Bayes model saw it before the fans did. Now, with new data, opportunity emerges: not instinct, but algorithm. This is how analytics breaks sports玄学.
Can we really trust AI predictions in football? 5 overlooked risk signals from a Bayesian model

Can we really trust AI predictions in football? 5 overlooked risk signals from a Bayesian model

As someone who built a Bayesian football win predictor at a UK sports analytics startup, I’ve seen how algorithms misread human intuition. Data doesn’t lie—but the people interpreting it often do. This piece exposes five silent risks in AI-driven match forecasting, rooted in my Croydon upbringing and the quiet tension between code and humanity.
When xG Meets Fan Bias: How Data Exposed the NBA’s Illogical Trade Myths

When xG Meets Fan Bias: How Data Exposed the NBA’s Illogical Trade Myths

As a data巫师 from East London, I used Bayesian models to dismantle the fan-driven myths around NBA trades. This isn’t about gut feelings—it’s about expected goals (xG), rebound efficiency, and salary-to-performance ratios that no traditional scout would dare touch. Let the numbers speak.