Football Intelligence / Jakarta
Mid Block turns match data into a private analyst for players, on-demand intelligence for clubs, and infrastructure for the organizations building the game.
Two apps in the menu above. New here? Start a conversation.
A player, a question, a system. Start with the work you need to do.
01 / Player Consulting
Match reports, peer benchmarks, monthly reviews, and a direct line to the people reading the data. Built for professional players who want their season documented properly.
Player Consulting / Demo data
02 / On-Demand Consultation
For clubs, agencies, media, and operators who need a specific answer, not another dashboard. Bring the question; we scope the analysis around it.
Example briefs — not client work
01 / Example dossier / scope, not findings
Which full-backs in the Indonesia Super League fit a high-pressing 4-3-3?
Agree the question → check the data → build the evidence → challenge the findings.
The analysis and output are scoped to the decision. No client result is implied.
03 / Sport Tech Development
For organizers, clubs, leagues, and media building their own infrastructure. Tournament platforms, data pipelines, dashboards, and integrations designed around how you operate.
Reference architecture, not a client system
Registration, fixtures, match events, video, payments, and the spreadsheets you’re still keeping.
APIs, CSV, webhooks, or an admin console. Meet the data where it is.
Identity, schedule, and quality checks before anything reaches a report.
Postgres, object storage, and a warehouse layer that can grow with the competition.
Standings, player ratings, availability, reports, and the rules your competition actually uses.
Admin console, public site, APIs, and mobile. One source of truth.
Every match produces a verdict, the evidence behind it, and the next thing to work on. Not a dashboard of numbers — a document you can read, question, and take into the week.
The verdict, in plain language.
The team context: how the match was actually played.
Your contribution, by category, against your position.
The next step: what to work on before the next match.
Player Consulting / Demo data
This crop shows team context. The list describes the wider report; not every part appears in this view.
Match events · Position-normalised · Same-position peer pool · Missing data is never zero
The evidence stays connected, from the first event to the next block of work.
The evidence pipeline
Match events, lineups, minutes, shots, touches, and physical output where available.
Position, role, minutes, score state, and league standard. A number without context is noise.
Category scores and percentiles against a same-position peer pool, not the whole league.
An AI narrative drafted from the same report data, with the evidence left in view.
A report, a drill plan, a shortlist, or an operational call. The output is always an action.
AI is used to explain the evidence, not to invent it.
Writing on player development, team style, scouting, and the decisions behind the numbers.
Team Analysis · 2 min read · June 16, 2026
Ketiga tim sama-sama mengandalkan High Press, tetapi hasil akhirnya berbeda. Pembeda utamanya bukan intensitas pressing, melainkan apakah lini tengah mampu menghubungkan pertahanan dan serangan.
Team Analysis · 2 min read · July 3, 2026
Why Peralta to Persija is not Make Sense
Team Analysis · 2 min read · July 1, 2026
Data Behind Persib Hattrick Campaign
