Badminton Data Audit: From the Srikanth Sheet to the Satwik-Chirag Pair, the Story of an Open Ledger

I started the Srikanth sheet at sixteen, and I never really closed it. In 2026, when Kidambi Srikanth won four BWF Super Series titles — Indonesia, Australia, Denmark and France — I hand-coded twelve televised matches into 1,146 rallies and 4,872 shots. The sheet tracked net-kill conversion, average rally length and error clusters. I shared it with 43 members of my school badminton club; 37 replied with corrections. The result: in finals, Srikanth won 58.4 percent of rallies past ten shots. Raw smash speed alone does not win matches. It was my first proof that a fan community can audit a data story, provided you show them the columns. That habit is the foundation of my work today. The school library had no wifi, so I learned xG one column at a time. During the 2026 Russia World Cup I built a 64-match xG workbook using 169 goals and open shot-location data. That same event-data discipline went into the 2026 Commonwealth Games women's singles final, where Saina Nehwal beat P.V. Sindhu 21-18, 21-10. I coded 70 rallies and 412 shots and found Saina won 68 percent of rallies lasting eight shots or more. The media was selling a Saina-Sindhu feud; I wanted a bigger sample. That blog drew 128 comments across three fan forums, and since then my rule is fixed: before writing any rivalry angle, run a rally-length check, quote at least two fan-forum reactions, and label every tactical claim as sample-limited. Now consider Bangkok 2026, where India beat 14-time champions Indonesia 3-0 for their first Thomas Cup title. Most recaps treated the men's doubles rubber — Satwiksairaj Rankireddy and Chirag Shetty — as a warm-up. I charted its three games: 142 rallies, 611 shots, and 47 percent of Chirag's net kills coming after Satwik's flat serve. That is not coincidence; it is a pattern. I posted nine charts and asked doubles fans to check them; the thread drew 1,200 impressions and three coach corrections. That is my method: explain how the model was built, in public, so readers can verify it themselves. But data has limits, and I must state them plainly. Any sheet, whether 1,146 rallies or 611 shots, tells only the story of the matches the analyst watched. I keep Srikanth's 2026 title runs in that sheet, but I do not infer from it that his April 2026 World No. 1 ranking was the model's product. Correlation and causation are different things. Srikanth's rise to World No. 1 in April 2026 is a historical fact, and so is the later dip in form. But I do not have minute-by-minute data on workload, coaching changes and media attention, so I will not say 'the data proved it.' I will say only that what the sheet shows demands interpretation, and interpretation requires information from outside the court — calendars, injury history, coaching continuity. This is where doubles equity comes in, and here I must break a double standard. When Satwik-Chirag reached World No. 1 in men's doubles, the echo in Indian coverage was not at the level singles received. I am not saying singles deserves less coverage; I am saying like-for-like comparison matters. If we headline a 58.4 percent rally-win figure for singles, then a 47 percent serve-linked net-kill figure for doubles deserves the same microscope. That is not reader opinion; it is a coverage rule. And the rule matters more in team events like the Thomas Cup, where the doubles rubber decides the tie. In Bangkok, India's 3-0 included a doubles opener. Calling it a warm-up misreads the structure of the match. There is one more chapter in this data ledger I never closed: the Premier Badminton League. In 2026, at nineteen, I scraped the PBL auction and final season — seven teams, 41 auction buys, and 312 fan comments on Bengaluru Raptors' second straight title. I matched bid prices to fan sentiment and found the loudest complaints were about playing time, not salaries. The auction sheet is where loyalty goes to get a price tag. Then COVID emptied arenas; I logged 312 closed-door fan posts and watched the PBL fold after five seasons. There were 312 of us in that stadium, and I still remember the sound. My crowd-loss file became a crude map of what the league never built: local, recurring, data-literate fan rituals. I do not treat a league collapse as a spreadsheet autopsy. I lead with fan-loss evidence, disclose scraped-sample limits, and pair every business number with a supporter consequence. When a league dies, I write what ritual disappeared, not only which owner lost money. But here too I must resist my own sentiment. I pair PBL memories with calendar, ownership and team structure, otherwise it becomes nostalgia, not analysis. The question now is what comes next for this open ledger. Indian badminton's next cycle is changing in both singles and doubles. When I build my next model, I will log doubles rubbers separately, make serve-linked net kills an independent column, and state the sample size with every chart. Before I believe a number, I make it survive a bad notebook page. And if you, the reader, spot an error in my sheet, write to me — because without community audit, this ledger is incomplete.