Workflow Hub

I enjoy researching football accumulators, but I did not want a workflow that simply asks AI for “the best bets.” I wanted a repeatable process that starts with evidence, makes uncertainty visible, and is allowed to say “pass.” I started building it on 4 August 2026. The result is a private Football Lab covering the Premier League, Championship, and League One. It is for personal research and entertainment only—not a public tips service, income claim, or automated betting system. The workflow pulls public football data into a private, traceable store, then cleans, reconciles, and blends it before analysis: - football-data.co.uk: 7,420 normal-context matches from 2021–22 to 2025–26, including results, basic statistics, referees, and historical odds. I excluded COVID-affected 2020–21. - Fixture Download: An initial 2026–27 schedule snapshot containing 1,484 fixtures across the three divisions. - Premier League public match feed: A five-season layer covering referees, cards, event timing, added time, and 469 penalty kicks split into scored, saved, and missed. - Official Premier League Transfer Watch and BBC Sport: A source ledger for squad movement. - Official EFL appointment pages: Timestamped Championship and League One weekend checks. Schedules, appointments, and transfers retain their source and capture time instead of becoming untraceable web snippets. Step-by-step: 1. I validate fixture identity, duplicates, dates, missing fields, and team-name mismatches before modelling. I then reconcile the sources into a common club and fixture record. A tidy report built on a broken fixture list is still wrong. 2. I build separate Elo and Poisson baselines that turn historical team performance and home advantage into expected goals and home/draw/away probabilities. The divisions remain separate, so Championship form is not quietly treated as Premier League form. Each fixture is predicted before its result updates the model, preventing hindsight from creeping in. I backtested the baseline against 1,484 completed 2025–26 fixtures to establish an honest benchmark rather than claim a magic model. 3. I add context that the baseline cannot see alone. When an official referee appointment is confirmed, I timestamp it and match it to the fixture. The Lab can then show competition-specific cards, dismissals, and—where Premier League evidence exists—penalty-kick and added-time patterns. The question is not whether a referee picks a winner, but whether the match environment looks more volatile or the sample is too thin to support a useful conclusion. Unknown or changed appointments remain neutral. 4. I maintain a private append-only ledger of source-backed squad movement and label every club as established, promoted, relegated, or limited history. A signing does not automatically improve a probability, and an old-division record is not treated as identical new-division form. Until those effects earn a tested role in the model, they widen uncertainty or rule out a fragile fixture. 5. I begin with the complete fixture board rather than a short list of favourites. For every game, I combine baseline probabilities, expected goals, team context, confirmed squad changes, referee environment where evidence exists, and unresolved live checks. I write a plain-English match story explaining what the baseline sees, what could make the fixture fragile, and whether the sensible outcome is candidate, watch, or pass. 6. I preserve the full board in a private Weekend Sheet, along with the model read and reason, uncertainty flags, and the small number of research candidates that survive the checks. There can be up to seven candidates, but seven is never a quota: three strong games means three, and none means pass. 7. Before results, I record each run’s data cutoff, model version, and referee-status snapshot. After the round, a separate debrief compares the original probabilities and swerves with what happened, checks whether the flags caught fragile fixtures, and identifies one bounded improvement. The Lab remains in private paper-run mode while a recurring live results source, appointment capture, and market checks earn their own evidence gates. AI helps turn public-source data into an inspectable research workflow. It makes uncertainty visible and treats “pass” as just as valid as a confident call.

As a hobby/non-work-related creative outlet, I've started to write and create YouTube videos about retro football (soccer) stories. Part of the visuals are vintage stickers of the players mentioned. This used to be a time-intensive Google/eBay search when I was looking for 20+ players at a time. So I used Claude to build a local "Sticker Search" tool. It lets me enter the player, season, and team, find their sticker from that year, and download it to my working folder with one click. It's made my hobby way more fun and sped up the tiresome bit. P.S. I used Billy's guide on Manus to create the animated logo for the videos! Step-by-step: 1. I listed the inputs I needed for each search: player, team, and season. 2. I used Claude to build the local Sticker Search interface and search logic. 3. I added recent-search shortcuts so I could repeat or refine searches quickly. 4. I made each result downloadable with one click to my working folder. 5. I used Billy's Manus guide to create the animated logo for the videos.

This World Cup 2026 poetry generator scraped the web for match information and called LLMs to help users generate match-specific poems. 1. A daily schedule triggered web scraping to collect match information. 2. When a user requested a poem, the generator used that match information to create a new match-specific poem with help from LLMs.