Predict the season

Level 2: two to three weeks, real architecture. Use with: first-sql, train-a-model, chart-the-truth. Resume line when done: Built {project_name}, a season analytics site with a prediction model scored against real results — live at {url}

Brief

Can you predict how your team's remaining games go, and prove you called it before kickoff? Build a full-season dataset for a team you actually follow, answer real analytics questions with SQL, then train a simple model that predicts upcoming results. Publish each prediction with a date before the game, score it after, and show the running comparison against a dumb baseline on a live site.

Personalize

• Your team. School team, local club, the league you follow, an esports roster. You need to care enough to know when the data looks wrong. Public league results are fine to collect.
• Your prediction target. Win or lose, over or under a points line, whether your striker scores. Pick one target and write it down before you build anything.
• Your baseline. "Always pick the home team" or "always pick the team higher in the table." The model only matters if it beats this.

Milestones

1. Write the prediction question in the README: exactly what is predicted, and the moment a prediction is frozen (before kickoff, timestamped).
2. Build the dataset: every completed game this season in SQLite, with date, opponent, venue, score, and any per-game stat you can get for every single game.
3. Sanity-check with queries: your totals reproduce the league table. Fix mismatches before going further.
4. Answer three analytics questions you actually wonder about, each as a SQL query plus a chart.
5. Compute the baseline's accuracy over the completed games. This number goes on the site first.
6. Train a simple model (logistic regression or even a hand-tuned scoring rule) on the early season, test it on later games it never saw. Report both numbers.
7. Put the site live: dataset, charts, baseline accuracy, model accuracy on held-out games.
8. Freeze and publish predictions for the next upcoming games, each with a visible timestamp.
9. Score predictions as results come in. Update the running tally: model vs baseline vs reality.
10. After at least five scored predictions, write the honest verdict and a limitations section: sample size, what the model cannot see (injuries, weather, lineup changes).

Done means

• Live site with at least five predictions that were published before the game and scored after, timestamps visible
• Baseline accuracy stated on the page next to model accuracy, on the same games
• Model evaluated on games it was not trained on, and the split is explained in your words
• If the baseline wins, the page says so plainly
• Limitations section written by you, including sample size
• Dataset queryable: the SQL file and database are in the repo