Cricket Data Journalism: From a 44-Match Notebook to a Structural Narrative

I began with 44 matches, a Rangpur notebook, and a suspicion of easy numbers. As a data journalist specializing in cricket and football, I rely on evidence rather than assumed facts. In 2026, at Rangpur Stadium, I hand-coded all 44 matches of the Bangladesh Premier League football season. I discovered that 61% of Abahani Limited Dhaka's open-play goals originated in the left half-space, a pattern no local reporter had named at the time. The Data Monk identity traces back to my first paid byline. In 2026, using approximately 1,200 shot coordinates from open sources, I built an xG model in Google Sheets for the Russia World Cup. Croatia's three consecutive extra-time matches became my test case. In the England semifinal, the team covered 143.6 km, the highest in the tournament. A Dhaka football site published my 3,000-word breakdown and paid me 4,000 Taka. Data journalism is a career, not a hobby. This realization drove me toward professional development. Empty stadiums taught me that crowd absence is a measurable variable. In 2026, after coding 83 Bundesliga matches, I found that the home win rate dropped from 43.3% to 33.3%. In data journalism, the key is the structural process. The model concept dictates that every data point has a source and a measurement method. Understanding the assumptions behind easy numbers is crucial. In sports, metrics like strike rates, rankings, and home advantage depend on specific underlying assumptions. Through data and documentation, we see the structural machinery behind sports. Empty seats, schedule density, and performance are part of the plan. I view match reports not as stories to be told, but as evidence to be tested. The notebook of 44 matches is merely a start, but following its path allows us to build a new perspective on sports.