2026 season by coaching era

2026 · Independent analysis · public data (FotMob / Sofascore) · snapshot as of round 19, 2026

Problem

São Paulo changed head coach three times in a single 2026 campaign — Crespo, then Machado, then Dorival. The league table gave a misleading read of each spell: one coach's results flattered the football, another's buried it. The task was to separate results from underlying performance and answer the decision-relevant question honestly — what actually changed in the team, and is the current downturn real or noise?

Approach

• Build the right yardstick. I built expected-goals (xG) and expected-points (xPTS) models — a Poisson model on match xG — from per-match data consolidated across FotMob and Sofascore.
• Benchmark fairly. Every metric is ranked against all 20 Série A clubs, direction-aware, so "good" always means the same thing.
• Split by era. The season is partitioned by coaching tenure, so each coach's football is measured on its own terms — while being honest that two of the three samples are small (four matches) and therefore directional.

What it produced

• An interactive dashboard: era cards, points-vs-xPTS bars, a style radar, xG for / against, and a direction-aware league-rank heatmap — plus three at-a-glance SVG one-pagers (below).
• A clear, defensible headline insight: the table was understating the team — the process pointed to ~1.34 expected points per game under Dorival while results delivered 0.25, a finishing-and-luck gap rather than a footballing collapse.
• A recommendation framed for a decision-maker: prioritise final-third conversion over a structural overhaul, and firm up the small samples before anchoring personnel calls to them.