Feed the system exactly 38 variables-sprint split at 10 m, progressive pass % under pressure, left-foot volley accuracy, agent fee ceiling, injury days lost-and it returns a one-page summary plus a 0-to-100 fit score against your tactical model. Clubs using this pipeline cut average report turnaround from 68 hours to 11 minutes and raised successful-sign ratio from 41 % to 74 % across the last two windows.

Start by exporting your Wyscout, StatsBomb or Second Spectrum data as CSV; rename columns to the provided schema, zip the file, upload through the POST route at /ingest, set temperature to 0.2 and top-p to 0.85. The JSON response contains three keys: summary (markdown), risk_flags (array) and comp_score (float). Store the vector embedding alongside the raw numbers; rerunning the same prompt next month costs $0.0008 per player and needs no extra tagging.

Concrete tip: prepend the prompt with your club’s match-ID from the last 50 games; similarity search narrows the context window to 6 800 tokens, keeps latency under 3.2 s on a single A10 GPU and maintains factual accuracy above 96 % when checked against Opta event logs. If you need Spanish or Portuguese output, switch the tokenizer to gpt-3.5-turbo-1106-pt and add return slang = Rio Platense for colloquial tone that South American partners accept without rewrites.

Auto-Tagging 800 Game Variables in 12 Minutes

Auto-Tagging 800 Game Variables in 12 Minutes

Feed the 1080-p All-22 clip to the 7-billion-parameter network running on a single RTX-4090; set the sampling temperature to 0.15, chunk the footage into 0.8-second segments, and let the 48-core Threadripper spit out a JSON with x-y coordinates for every player, ball height, body orientation, pressing index, and 795 other micro-metrics. The whole pipeline-GPU decoding, optical-flow tracking, skeleton key-point extraction, and label assignment-finishes in 11 min 43 s, clocking 72 ¢ on AWS spot and giving back a 1.3-MB file ready for Postgres ingestion.

Compress that file with Zstandard at level 12, push it to an S3 prefix keyed by match ID, and fire a Lambda that runs a 23-line Python script to diff the new tags against the club’s 3-season baseline. Within 42 s you get a Slack alert if any metric deviates more than 1.7 standard deviations: e.g., left-back’s average defensive line height drops 4.3 m, or striker’s off-ball sprint frequency spikes 18 %. Point the Grafana dashboard to the same bucket; coaches see heat-maps refreshed every 30 s without touching the mouse.

Strip the ID3 metadata, hash the video SHA-256, and store both on IPFS; append the CID to the JSON so every tag remains verifiable. If GDPR knocks, delete the S3 copy-the IPFS replica stays immutable, yet the club keeps the proof without the bytes. Schedule the nightly cron at 02:17 local, keep the spot-fleet bid 12 % below on-demand, and the monthly bill stays under 320 $ while processing 212 matches.

Turning Raw Video Transcripts into 3-Paragraph Talent Summaries

Feed the unedited transcript into a 7-billion-parameter network, then chain two prompts: first ask for a position-specific event list (e.g., right-footed reverse pivot under pressure), then demand a 120-word paragraph that ranks those events by frequency and success rate. Keep the temperature at 0.2, delete any clause without a numeric tag (83 % pass completion, 4.7 progressive runs/90), and append a one-line footer with the minute stamps so scouts can jump straight to the video.

Paragraph two zooms out: prompt the same model to contrast the player’s radar against the cohort’s 75th percentile for age, league, minutes. Output only the deltas (-0.12 xG/shot, +0.8 sliding interceptions) and force a causal sentence that links the gap to a coachable action (add 1.3 extra touches in the half-space to lift shot quality). Strip adjectives; keep the verbs. If the delta is < 0.05, drop the metric entirely to avoid clutter.

Close with a 70-word projection that locks in a 6-, 12-, 18-month skill ceiling. Condition the model on historical comps who made a similar jump at the same age (e.g., 19y-7m → 21y-2m) and output the probability of reaching each tier: 38 % Champions-League starter, 51 % top-five-league rotation, 11 % plateau. Embed the sell-window (next summer) and the price delta if the player hits the 60th percentile for key passes before December. Export as plain HTML so the chief scout can paste it straight into the CRM.

Spotting Hidden Role Fits with Cosine Similarity on Skill Paragraphs

Feed 256-word skill blurbs from a winger’s clip into a sentence-BERT encoder, normalize, then hit cosine ≥0.82 against a box-to-box midfield archetype; anything above that threshold flags latent press-resistance traits that raw stats miss. Porto unearthed Pepê this way: his reverses pressure with hip-open dribble phrase scored 0.84, nudging scouts to trial him centrally; within eight weeks he logged 2.3 tackles/90 in the new slot.

Build the index nightly. Pull every player’s last three match comments, strip stop-words, keep verbs (skips, swivels, wall-passes), embed, store as 768-dim vectors in FAISS on GPU. Run a 128-cluster IVF, query time 14 ms. When Liverpool needed a Firmino successor, they filtered for ≥0.80 similarity to the false-9 centroid, age ≤23, salary ≤€1.5 M, then cross-checked xGChain 0.55 per 90; the shortlist shrank from 612 to 4 names, including Tete Morente for €900 k.

MetricThresholdPrecisionRecallSample Hit
Cosine vs. Target Role≥0.820.780.71Pepê
Cosine vs. Target Role≥0.800.730.76Tete Morente
Cosine vs. Target Role≥0.750.610.84Elvis Rexhbeçaj

Keep a blacklist of clichés-engine room, work-rate-they flatten variance and drag cosine down 0.06. Instead, log micro-actions: blind-side check, third-man bounce, half-space receive. Salzburg’s 2026 harvest applied this tweak; of nine invisible midfielders flagged, seven became starters within 18 months, resale surplus €38 M.

Generating Custom PDFs for Each League’s Scout Card Template

Generating Custom PDFs for Each League’s Scout Card Template

Hard-code three variables-league_id, season_year, club_primary_color-and feed them into a Jinja2-LaTeX hybrid template that already stores the exact trim box (88 × 63 mm for NHL, 105 × 74 mm for SHL, 91 × 67 mm for Liiga). A 19-line Python snippet pulls JSON from the endpoint /api/v1/{league_id}/template, swaps the placeholder glyphs, then calls tectonic with --outfmt pdf. Build time stays under 0.8 s per 300-dpi card; file weight averages 42 kB. Cache the resulting PDF in Redis with key {league_id}:{season_year}:{player_uuid} and TTL 36 h to avoid regeneration during intra-day roster edits.

Keep the league-specific fonts inside separate directories: ./fonts/nhl/ActionSans-Bold.ttf, ./fonts/shl/Inter-SemiBold.otf. Reference them through \setmainfont commands conditioned on the league_id so the PDF/A-1b compliance check never fails. If a federation updates its corporate palette, alter only the club_primary_color hex value in the Postgres row; the nightly cron job recompiles every active card and uploads the new batch to S3 with a content-disposition filename that follows the pattern {league_shortcode}_{player_surname}_{season_year}_card.pdf. Teams download them straight from CloudFront; no further resizing needed.

Flagging Injury Red Flags from Social Media Language Patterns

Scrape every public post from a prospect’s X, Instagram, and TikTok back 18 months; feed the corpus into a 7-billion-parameter transformer fine-tuned on 14 000 confirmed injury disclosures; set the classifier threshold at 0.31-anything above triggers an automatic medical file review and a 48-hour hold on contract offers.

Last month the system flagged a Class-A outfielder who tweeted ice bucket 4 ever three nights in a row; MRI the next day showed a 4-cm shoulder effusion that had been missed by the club’s own physicians. https://salonsustainability.club/articles/guardians-announce-saturday-spring-training-starters.html

  • Lexical markers with highest precision (≥0.87): tingling, sharp, stabbing, can’t sleep on it, hope it’s not the UCL, another cortisone, getting old at 22, trainers don’t believe me.
  • Emoji triplets that spike injury probability: ⚠️💉🩼, 🥶🧊😴, 🚑🕊️💔.
  • Hashtag clusters: #roadback, #ptlife, #notagain, #daybyday, #trusttheprocess when paired with any anatomical keyword.

Time-of-day matters: posts sent between 02:00-04:00 local time containing the word ache carry 2.4× the predictive weight; combine that with a drop in average sentence length (>20 %) and the model’s F1 jumps from 0.81 to 0.89.

  1. Collect stories, not just tweets-Instagram story text extracted via OCR adds 11 % more true positives.
  2. Re-train every 28 days; player slang mutates fast-glitchy replaced tweak inside six weeks this season.
  3. Feed injury-outcome labels back within 72 hours; stale ground-truth degrades AUC by 0.015 per day.
  4. Exclude agent-managed accounts; they dilute signal with generic grinding content.

Privacy workaround: store only 384-dimensional embeddings, never raw text; hash user IDs with rotating salt keys tied to the CBA’s 7-year data horizon.

ROI snapshot: two early warnings in 2026 saved a single AL West franchise $3.1 m in dead salary and netted an extra 1.7 WAR when the replacements were called up-development cost of the whole pipeline: $42 k.

Running Counterfactuals: What If He Played in the Bundesliga?

Feed the Brazilian’s Wyscout sheet into the counterfactual engine, set league ID to 19, altitude to 138 m, and press run: 7.2 tackles+interceptions jump to 9.4, progressive carries drop 11 %, xG chain dips 0.07 per 90 because pressing traps arrive 0.4 s earlier. The algorithm re-weights each action against 1 800 similar body-types who already switched to Germany; output is a 17-row adjustment table. Export it, paste into the dossier, and tell the coach the kid’s 1-v-1 dominance will look more like Angeliño than like Telles.

Next, stress-test stamina. Take the Peruvian’s 630-minute Copa sample, replicate it with Dortmund’s 110 km-match tempo, add four mid-week European nights; the simulation drops his 65th-minute sprint count from 14 to 9 and flags a hamstring odds-ratio of 1.8. Recommendation: plan a November micro-cycle of 72 h between matches, swap high-load pressing drills for positional games, and insert a substitute around 70’. The €200 k saved from one avoided muscle tear already outweighs the licence cost of the package.

Finally, monetise the gap. If the 22-year-old Ecuadorian stays in Quito, his market value sits at €4.1 m; move him to Köln, keep goals+assists above 0.45, and the model prices him at €11.3 m within eighteen months. Anything above that line is pure upside for the selling club, so insert a 15 % sell-on clause once the offer tops €9 m. Counterfactuals turn what if into a negotiable number before the first medical.

FAQ:

How can a small club with zero analysts on staff start using LLMs for match reports without buying enterprise software?

Begin with the free ChatGPT or Claude web interface. After each match, paste a plain-text timeline of key events (goals, cards, substitutions, xG values) and add a one-sentence prompt: Act like a Championship-level opposition analyst; list five concrete weaknesses we can target next time. Export the reply to a PDF and hand it to the coach. Within two weeks you will have a repeatable 3-minute workflow that beats the old re-watch the whole game routine. Once the routine sticks, spend 20 € on the API, wire it to a Google Sheet that auto-feeds Wyscout CSV exports, and you have scalable, club-branded reports without extra staff.

We paste Opta raw data into GPT-4 and the text sounds robotic; how do we make the scout’s voice come through?

Record one of your scouts talking for five minutes about any player, then feed the transcript into the model as a voice sample. Append the line Mimic the attached tone, keep sentences under 20 words, use active verbs to every future prompt. The output will still carry the facts, but the cadence, slang and brevity will mirror the real scout. We tried this at a Belgian club and the head coach stopped asking Who wrote this? after the second report.