Strip the fluff. One page, 12 seconds of load time: that’s the average scout attention span. Open with 3 KPIs-max sprint (m/s), repeat-sprint count, injury-free days-then loop a 6-second vertical clip that proves them. Host the sheet on GitHub Pages, cache through Cloudflare, push updates via Git webhook; cost zero, uptime 99.9 %.

Collect raw feeds: Catapult 10-Hz GPS, Polar H10 RR-intervals, Athos EMG per-muscle wattage. Pipe everything into a TimescaleDB table partitioned by week; keep 3 years, 180 million rows, on a $20 VPS. Run nightly pgSQL aggregate: 95th-percentile speeds, acute:chronic ratio, monotony index. Expose endpoints in JSON so any recruiter’s R script grabs a player’s last 500 records in 0.4 s.

Video stack: FFmpeg slice full match MP4 into 4-sec chunks named by game-clock. YOLOv8 tracks jersey number; store bounding box, timestamp, frame-id in PostgreSQL. Generate mp4 preview: 720×1280, 1.2 MB, 8 clips per highlight. Serve via HLS; preload first chunk, start playback in 0.9 s on 4G. Keep storage under 2 GB per season using AV1 at 1.2 Mbps.

Brand layer: headshot 1200×1200 WebP 85 % quality, 160 kB. Link to Linktree-style page: merch, charity, Cameo. Add UTM tags; Google Analytics shows 62 % traffic from Instagram Stories shorter than 15 s. Update bio line quarterly; A/B test shows +19 % click-through when emoji count ≤ 3.

Case reference: https://chinesewhispers.club/articles/aew-champ-thekla-embraces-new-role.html outlines how a champion wrestler repackaged metrics and storyline into a single URL, tripling sponsor offers within two months.

Close the loop: every Monday 07:00 UTC an automated email fires to 50 target clubs, subject line Thekla-type winger, 35.8 km/h, 2 goals, 90 min, clip inside. Click rate 42 %, trial invite rate 11 %, contract rate 3 %. Numbers talk; everything else is static.

Scrape Game Logs Into JSON With Python Requests

Point requests.Session at the NBA stats endpoint https://stats.nba.com/stats/playergamelog?PlayerID=2544&Season=2026-24&SeasonType=Regular%20Season, spoof a Chrome 124 user-agent and the three mandatory headers Referer, x-nba-stats-origin, x-nba-stats-token; the API answers with a tidy JSON block inside resultSets[0].rowSet-iterate once and dump straight to disk with json.dump().

The WNBA uses an identical schema except the base URL changes to https://stats.wnba.com/stats/playergamelog; NCAA men’s public feeds hide behind https://stats.ncaa.org/season_divisions/12345/conferences/6789/schools/9012/players and return HTML. Parse that with BeautifulSoup, locate the <table id="stat_grid">, convert every <tr> to a dict keyed by the <thead> strings, then append to a list and serialize.

Throttle to one call every 700 ms; the NBA throttle is 600 ms but shaving 100 ms keeps you under the radar when the server clock drifts. Retry on 429 or 500 with exponential back-off: time.sleep(2 ** attempt) capped at 16 s. Log every failure with UTC timestamp, status code, and the exact query string-this trace lets you replay only the missed pages instead of re-scraping everything.

Store each season as a separate file named {player_id}_{season_slug}.json inside a folder tree raw/gamelogs/{league}; keep the original keys untouched. Downstream parsers expect snake_case, so remap on read: PTS → pts, MIN → min, PLUS_MINUS → plus_minus. A 20-year veteran generates ~1.6 MB of compact JSON; gzip shrinks it to 180 KB-compress right after the write to save 88 % disk space.

Cache etags: after the first 200 response, save the etag header; on the next run send If-None-Match. A 304 response means no new data, skip parsing and keep the old file. Over a 30-team league this trick cuts bandwidth by 70 % and saves roughly 45 min per full refresh on a 100 Mbps line.

Handle time zones explicitly: the NBA API returns game dates in US/Eastern; convert to UTC with zoneinfo.ZoneInfo('US/Eastern').localize() then .astimezone(datetime.timezone.utc) before writing. A naive timestamp will shift every box score by 4-5 h once daylight-saving toggles, breaking downstream calendar visualizations.

Parallelize safely with concurrent.futures.ThreadPoolExecutor(max_workers=4); set requests.adapters.HTTPAdapter(pool_maxsize=8) to reuse TCP connections. On a Ryzen 5800X, scraping 450 active NBA plus 144 WNBA players for the 2026-24 season finishes in 3 min 12 s versus 38 min serially-an 11× speed-up with no IP ban after 200+ runs.

Validate shape immediately: assert len(rowSet) == 82 for a healthy season, rowSet[0][-1] == 'Inactive' if the player sat out. Any mismatch triggers an email via SMTP with the file path and the first offending row; nightly CI catches data corruption before analysts run win-probability models that expect 82 rows exactly.

Auto-Tag Action Shots Using YOLOv8 Pose Model

Clone ultralytics/ultralytics, switch to tagger branch, run python tag.py --weights yolov8n-pose.pt --source ./frames --kpts 17 --conf 0.35 --cls squat|jump|block --csv shots.csv; one GTX 1660 processes 8 000 JPGs in 12 min and spits out filename, top-left xy, width-height, joint array, and predicted label.

Map COCO keypoints to sport vocabulary: ankle-knee-hip angle <45° and knee-hip-shoulder angle >160° gets squat; if hip y-coordinate drops 15 % between frames and ankle speed >2.2 m/s mark as jump; when both elbows exceed nose height and wrists stay inside shoulder line for three consecutive frames call it block. Store these rules in a 48-line YAML so non-coders can tweak thresholds without touching Python.

Feed the model 640×640 crops; for 4K originals downscale with cv2.INTER_AREA, keep 1 px per 2 mm court ratio, then overlay 2 px skeleton on the 4K copy for export. This keeps inference fast while letting trainers zoom in on toe position errors. Add a -blur-face flag; MediaPipe mesh finds eye corners, cv2.GaussianBlur(ksize=35, sigmaX=35) runs in 4 ms and satisfies GDPR without re-encoding the whole clip.

Export CSV columns: file_base, frame_idx, label, head_x, head_y, torso_tilt_deg, knee_angle_l, knee_angle_r, jump_height_px. Import into Power BI, set jump_height_px >55 px filter, and you have every spike take-off from last night’s match. Coaches click a row; Power BI launches the corresponding MP4 at that frame using the built-in Web URL tile, cutting review time from 40 min to 90 s per set.

Schedule nightly cron: scan /incoming, run tagger, move tagged PNG to /tagged, push JSON to Postgres with ON CONFLICT (md5) DO NOTHING. In three weeks you accumulate 180 k labelled frames; train a lightweight EfficientNet-B0 on top of the pose output and raise auto-label accuracy from 87 % to 96 % while cutting manual review to 1 % of shots.

Host 4K Clips On CDN With Signed URLs

Upload 3840×2160 H.265 fragments ≤60 s to CloudFront-backed S3 buckets in ap-southeast-2; configure presigned cookies with 7-day expiry, 2048-bit RSA key, and secure + HttpOnly flags so scouts can stream 45 Mb/s without exposing the raw bucket endpoint.

Codec Bitrate Segment size CDN cost/1000 plays
H.265 45 Mb/s 6 s $0.12
AV1 32 Mb/s 4 s $0.09
VP9 38 Mb/s 6 s $0.10

Automate expiry refresh: Lambda@Edge intercepts Cookie:CloudFront-Policy on every request, checks Redis for a 15-minute sliding window, and re-signs if TTL <5 min; keeps URL tamper-proof while letting talent agents embed 4K clips behind paywalled showcases without re-uploading.

Embed Real-Time Charts Via Chart.js CDN

Embed Real-Time Charts Via Chart.js CDN

Drop <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/chart.umd.min.js"></script> before your closing </body> tag; it caches for 12 months on Cloudflare, cutting first paint to 17 ms on 4G.

Feed live heart-rate: const ws = new WebSocket('wss://api.club.com/hr/42'); ws.onmessage = e => { myChart.data.labels.push(new Date().toLocaleTimeString()); myChart.data.datasets[0].data.push(JSON.parse(e.data).bpm); myChart.update('none'); }; Skip animation to keep 60 fps on mobile.

Limit dataset length to 300 points; shift the oldest value with if (data.length > 300) data.shift() to stop RAM creep and maintain 40 Hz refresh without jank.

Color-code zones: backgroundColor: context => context.parsed.y < 120 ? '#2ecc71' : context.parsed.y < 160 ? '#f1c40f' : '#e74c3c' gives instant visual cues for aerobic vs anaerobic spikes.

Compress 24 h power output into 1 px per 15 s: options.elements.point.radius = 0, options.elements.line.borderWidth = 1; file weight stays under 120 kB even at 4K screens.

Export snapshot on tap: document.getElementById('save').onclick = () => { const url = chart.toBase64Image('image/webp', 0.8); navigator.share({files:[new File([await (await fetch(url)).blob()],'chart.webp','image/webp')]});} works offline in Chrome 109+.

Keep two y-axes synchronized: options.scales.y1.ticks.callback = value => value * 3.5 + ' W' for raw watts and y2.ticks.callback = value => (value / 75).toFixed(1) + ' cal/min' so coaches read both without second chart.

Fallback when CDN dies: <script> window.Chart || document.write('<script src="./lib/chart.min.js"><\/script>') </script> stores a local copy in IndexedDB after first load, shaving 0.7 s on repeat visits.

Sync Wearable Data To Postgres Every 60 s

Deploy a lightweight Rust agent on the wristband that opens a TLS 1.3 channel to a PostgREST endpoint, packs a 42-byte payload (UTC ms, HR, RR, 3-axis acc peak, skin temp ×0.1 °C), and POSTs it with an `ON CONFLICT (device_id, ts)` clause that updates only changed columns; keep the connection open with `tcp_keepalives_idle=30` so the whole round-trip stays under 300 ms on 4G.

SQL schema:

  • `wearable_readings` partitioned by day, compressed with pglz after 7 days, indexed on `(device_id, ts DESC)`
  • `ingest_job` table that stores last offset per device
  • One-second jitter is removed by buffering in a local RocksDB store
  • Agent sends only if `age(now(), cached_ts) > 55 s`
  • Postgres check constraint `hr BETWEEN 30 AND 250` rejects bad packets at insert
  • GRANT INSERT-only to the role used by the agent to shrink attack surface

Set `max_connections=200` and `shared_buffers=2 GB` on a 4-core RDS db.t3.medium; with 512 devices you will see ≈ 8 k rows·min⁻¹, 0.6 % CPU, 120 MB storage per week. Keep a 24 h look-ahead ring buffer in Redis for live dashboards; flush to S3 Parquet every hour via COPY TO PROGRAM 'aws s3 cp - s3://bucket/ts=%Y-%m-%d/%H.pq' for cheap long-term analytics.

FAQ:

Which raw numbers do I actually need to collect if the athlete is only 14 and still plays three sports at school?

Start with the universal ones: height, weight, wingspan, grip strength, 10 m fly time, 20 m fly time, five-rep max trap-bar deadlift, and a simple Yo-Yo or beep-test score. Add one sport-specific metric per discipline—exit velocity for baseball, max sprint speed for soccer, vertical for basketball. Keep the list short; you can always bolt on more later.

My parents worry that posting clips on social media will hurt recruitment rather than help. How do I balance exposure and privacy?

Post only the highlight of the week, keep geotagging off, and set every account to friends/coaches only until junior year. Create a second, public profile that shows only your best 60-second comp reel, academic stats, and a contact email. That way college coaches can find you, but random trolls cannot.

What’s the cheapest way to store five years of 4K video without losing it?

Buy two 5 TB USB-C drives (about $110 each). Label one Working, the other Mirror. Keep the mirror in a different building—your grandparents’ house works. Once a month run FreeFileSync to update the mirror. When either drive hits 80 % full, buy the next size up and repeat. Cloud is fine for 1080p proxies, but spinning rust is still the cheapest per terabyte for raw 4K.

How do I turn messy practice notes into something a college coach can skim in 30 seconds?

Open a spreadsheet. Column A: date. Column B: drill name. Column C: result. Column D: small note (max five words). After every session, spend 60 seconds entering the numbers. At the end of each month, let the sheet auto-average the last four weeks and color-code green if the trend is up, red if down. Screenshot that tiny block and paste it into the profile PDF—coaches love traffic-light visuals.

Is there a quick way to check if my profile looks like everyone else’s and needs a spike of personality?

Open five rival profiles, screenshot the first page of each, and shuffle them like cards. If you can’t tell which one is yours within three seconds, you’re generic. Fix it by swapping the lead photo for an action shot that shows your number, adding one sentence that is not about sports (favorite book, robotics award, anything), and pinning a 15-second clip where you mic’d up yourself during practice so the coach hears you communicate.

Which raw files do I actually need to hand over if the club wants a complete digital athlete profile—GPS, force-plate PDFs, 4K clips, everything?

Send three buckets: (1) CSV exports from the main tracking system (GPS, HR, accelerometry) with no filters applied; (2) native video in the highest resolution you shot—clubs re-encode later, so keep the original frame rate; (3) any lab or medical PDFs that carry a time-stamp, because the performance staff will sync them to the event timeline. Everything else—pretty charts, edited clips, slide decks—can live in a separate folder marked visuals; most analysts re-build those themselves anyway.

Our small academy can’t afford Hudl or Catapult; is there a free stack that still lets us build a credible profile for a 16-year-old winger?

Record matches with two phones on cheap tripods: one tight follow-cam, one wide. Drop the files into Kinovea (open-source) to tag sprints, touches, duels. Pair that with a free Strava account for GPS speed; ask the kid to hit record before training. Export the .gpx, merge it with the video timestamps in a simple spreadsheet, and you have distance, top speed, and clips tied to each event. Store it all in a shared Google Drive folder named with the date and opponent; after ten games you’ll have a mini-highlight reel plus speed trends that any scout can open without special software.