아직 감지된 업데이트가 없습니다. GitHub 릴리스가 나오면 여기에 보입니다.
최근 완료된 8시간 회차 사이 GitHub 스타 증가량 순
Use Zwift Click, Zwift Play, Shimano Di2, or any Bluetooth controller with MyWhoosh, Rouvy, Zwift, and more. Free to try on iOS, Android, Windows, and macOS.
개인 계정의 공개 저장소 소유자는 GitHub로 로그인해 운영자를 확인하고 소개를 직접 관리할 수 있습니다. 관리자 승인은 필요하지 않습니다.
로그인한 뒤 확인 파일을 만들어 이 저장소의 기본 브랜치에 30분 안에 커밋해 주세요. 다른 브랜치나 포크의 파일은 인정하지 않습니다. 파일을 올린 뒤 아래에서 확인하면 관리자 승인 없이 관리할 수 있습니다. 자격을 유지하려면 파일을 남겨 두세요.
로그인이 필요하면 로그인 화면으로 이동합니다. 로그인 후 이곳으로 돌아와 원하는 확인 버튼을 다시 눌러 주세요.
프로젝트 폴더에서 /nomorevibe verify를 실행하면 소유권을 확인하고 소개를 직접 관리할 수 있습니다.
1분 만에 확인하기메이커가 쓴 소개는 위의 한 줄이 전부입니다. 더 자세한 내용은 저장소 README에서 읽을 수 있습니다. 저장소 README ↗
README 전체를 불러오는 중입니다.
README 전문 보기 ↗A daily GitHub Action pulls box scores with hoopR, builds every team's stats by window (YTD, L3, Home, Away, Prior) plus league averages, writes them to web/public/data/, and commits. The commit triggers a Vercel redeploy of the Next.js site.
The pipeline does no model math. Projections come from the Model sheet in web/model.json: a spreadsheet laid out like Sam's Team v Team tab, edited on the site's Model page and run for every game.
Full spec: docs/NBA_MODEL_SPEC.md.
pipeline/
config.R season types, league-average and rate methods
stats.R the 41 stat definitions (numerator / denominator per team-game)
fetch.R hoopR box scores + schedule, ESPN fallback for games missing from the bulk file
team_games.R one row per team per game with opponent stats joined, possessions
windows.R YTD / L3 / H/A / Prior team values and league averages as of a date
export.R per-date slate JSON (games + stat inputs)
stats_export.R raw stat tables (CSV + JSON)
run.R entry point
tests/ unit tests (run_tests.R)
web/ Next.js site (reads web/public/data and web/model.json at build time)
.github/workflows/daily.yml
Every run writes the model's inputs to web/public/data/stats/ (overwritten daily, history in git):
| File | Contents |
|---|---|
team_stats.csv | One row per team per window (YTD, L3, Home, Away, Prior) with games played and all 41 stats |
league_averages.csv | League average per window (mean of the 30 teams) |
team_games.csv | Every team-game this season and last: box score, opponent box, possessions, per-game stats |
slate.csv | Today's games and tip times (UTC) |
On the live site they're at /data/stats/<file>. The Team stats page lists them with a ready-made
=IMPORTDATA("…") formula for Google Sheets. Rates are decimals (0.523 = 52.3%).
Requires R 4.4+ with hoopR dplyr tibble jsonlite testthat, and Node 20+.
Rscript pipeline/tests/run_tests.R # unit tests
Rscript pipeline/run.R # today's slate (Eastern date)
Rscript pipeline/run.R --date=2026-10-23 # one date
Rscript pipeline/run.R --backfill # every slate this season through today
cd web && npm install && npm run dev # http://localhost:3000
run.R skips (with a warning) any slate where a team has no stats.
web, and keep the default Next.js settings. Every push to main redeploys.The Model page is a spreadsheet laid out like the Team v Team tab: score and stat line at the top, then YTD / L3 / H/A / Prior / Weighted rows for each stat, with Home, Away, Weight, League Average, Difference, Adjusted and Expected columns. Pick the home and away teams and every stat cell fills in.
=[Home YTD Points/Game], =[Away H/A EFG%], =[League Home Points/Game], =[Home Team].
H/A = the home team's home games / the away team's away games. -- = no games in that window.A1, $D$8, ranges B13:B16, + - * / ^ &, comparisons,
SUMPRODUCT, SUM, AVERAGE, MIN, MAX, ROUND, ROUNDUP, ROUNDDOWN, MROUND, ABS, IF, IFERROR.-- is 0 inside SUMPRODUCT/SUM and #VALUE! in plain arithmetic.Edits are kept in your browser as a draft. To publish: Download model.json, then upload it to the
web/ folder on GitHub and commit. Vercel redeploys and every visitor sees the new projections.
npx tsx scripts/build-model-template.ts (in web/) rebuilds the starting template, overwriting model.json.
Possessions use 0.475 x FTA and seasons include play-in and playoff games, both to match TeamRankings.
Still open: whether rate stats within a window should be
ratio of totals (current) or average of game rates (pipeline/config.R).
web/scripts/tune-weights.ts picks each stage's weights by backtest over the last three seasons (every
YTD/L3/H/A/Prior combination, scored on margin + total error, checked leave-one-season-out). The daily
workflow re-tunes on Mondays. Weighted rows skip windows with no games instead of counting them as 0.web/scripts/confidence.ts re-projects every game since 2023-24 through the published sheet with
point-in-time stats, joins sportsbook lines (pipeline/cache/lines.csv, via ESPN), and fits three models,
each validated leave-one-season-out (a model that doesn't beat a flat guess shows its base rate):
public/data/confidence_games.json (every slate game) and the report behind the Confidence page.
Research scripts: research*.ts (what was tested and rejected)./tracking): every finished game by team, home/away, model favourite/underdog and spread size
(ML W-L, covers vs the model or the sportsbook); precedents for each matchup on game pages, slate cards and the
Model page. Tested as a confidence feature (research6.ts): situational records do not predict future covers
(they slightly reverse), so they are shown but not used in the confidence %.pipeline/cache/inputs_<season>.ndjson.gz; rebuild them with
Rscript pipeline/backtest_inputs.R 2024 2025 2026 after changing stat definitions.pipeline/injuries.R): BPM-style, from Hollinger Game Score per 100 possessions scaled to
team net rating, shrunk toward replacement (-2), point-in-time.pipeline/injury_reports.py, the last report
at least an hour before tip, Out/Doubtful); today uses ESPN's injury report. Sheet references:
[Home Injury Value | BPM | Minutes | Count | List | DTD]. The Injuries section's adjustment (method 1, value lost
x 0.4; method 2 = BPM/2 each way; 0 = off) feeds the score cells.[Home Lineup Strength] = available players' ratings x projected minutes. The
section blends the sheet's margin 30% toward the player-based margin.pipeline/darko.py archives DARKO's free daily board to pipeline/cache/darko/ (no history is
published), so DARKO can be backtested once snapshots accumulate. EPM's terms prohibit copying its data.docs/SHEET_FORMULAS.md has the spreadsheet's formulas (reverse-engineered from docs/sheet_export_lal_bkn.csv) for reference when rebuilding them on the Model page.