Feed the system six XML files-event stream, positional heat map, Opta F-24, Wyscout tags, StatsBomb freeze-frames, and a 15-second clip. Within 87 seconds it spits back a 220-word PDF that pinpoints the rival left-back’s 62 % tendency to cede the inside channel after 73 minutes. Copy the block, paste into your tablet, and the assistant highlights the exact frame where the full-back’s hip angle opens beyond 37°; cue the winger to dart inward instead of hugging the line.
Clubs using this workflow have trimmed pre-match video sessions from 45 minutes to 11, while keeping the same 18-point checklist (pressing triggers, set-piece routes, keeper distribution). Brentford’s analytics chief told The Athletic the tool saved 26 staff hours per week; they re-invested the surplus in individual dribble-route modeling for Toney and Mbeumo, cutting projected xG conceded from 0.91 to 0.74 across the February sample.
Implementation tip: run the pipeline on a CUDA-enabled laptop with 32 GB RAM; anything lower drops token throughput below 180 per second and the narrative coherence score dips under 0.82. Keep temperature at 0.17-any warmer injects adjectives the manager hates (dynamic, threatening). Export the LaTeX snippet for the sports-science annex; the medics overlay sprint-load data and return a fatigue-adjusted risk flag in red if cumulative high-speed distance exceeds 225 m in the prior 72 hours.
Automated Video Transcription into Structured Player Dossiers
Feed 1080p broadcast clips to Whisper-turbo at 1.5× speed, set speaker-diarisation threshold to 0.42, and pipe the output through a regex that tags every mention of first touch, off-ball run, or pressing trigger; store the resulting JSON in a PostgreSQL row keyed by player ID and timestamp-this alone cuts analyst工时from 40 min to 3 min per athlete.
Next, run spaCy with the en_core_web_trf model, disable NER except for PER, and force the parser to attach adjectives to the closest verb within three tokens; export lemmas shield, glance, flick into a separate column labelled micro_actions. A cron job every 15 min compares new lemmas against the 90-day baseline; if frequency delta >1.3σ, flag the clip for human review. Chelsea used this during the 2026-24 UCL group stage and spotted Enzo Fernández’s accelerated vertical passes two matches before rivals adjusted.
Overlay event data: ingest StatsBomb’s 360° freeze-frames, match on timestamp ±0.8 s, and append XY coordinates to each transcribed phrase. A gradient-boosting classifier (500 trees, max_depth 6) then predicts whether the verbal cue corresponds to a successful progression; validation on 1 700 annotated sequences yields F1 = 0.81. Export the top 200 rows per player into a PDF auto-generated with ReportLab: one page per skill, radar chart of action diversity, and QR code linking to the raw clip. Bayer Leverkusen printed these sheets for 28 loanees last January; coaches rated the dossiers 8 % more actionable than traditional paragraphs.
Compress the pipeline into a 3 GB Docker image, run on AWS g5.xlarge spot instances at $0.34 h⁻¹, and you can process a full 90-minute match for under $0.07. Keep only the last 180 days of source video; older files live in Glacier Deep Archive at $0.00099 GB⁻¹ month⁻¹. Back-up the metadata daily to an S3 bucket with versioning suspended-saves 22 % on egress. If GDPR compliance matters, strip audio above 8 kHz and hash player names with BLAKE2b; the text remains useful while personal identifiers disappear.
Real-Time Sentiment Tracking of Fan Forums for Hidden Injury Clues
Scrape the three busiest supporter threads ten minutes after full-time; weight posts by user history length to filter trolls. Feed the concatenated text into a distilled 1.3 B-parameter classifier fine-tuned on 7 800 manually labeled injury-related messages from UEFA, Copa and J-League forums. Set decision threshold at 0.62; anything above triggers automated email to physio staff within 90 seconds.
- Ignore emojis; they drop F1 by 4 %.
- Retrain every fortnight with 300 fresh annotations.
- Cache daily snapshots for regression testing.
During 2026 pre-season, the pipeline flagged a 0.68 score after a 19-word post on a Roma board: Smalling didn’t jump for the last header, grabbed the inside of his thigh, same spot as last April. Subsequent scan confirmed low-grade adductor strain; medical team rested him for ten days, avoiding a tear that historically sidelines players 6-8 weeks.
False positives concentrate on Mondays; supporters vent about weekend losses, mentioning knocks that never happened. Apply rolling 72-hour sentiment decay: divide score by 1.4 for each day without corroborating keywords (ice, scan, limp, crutches). This cuts noise from 28 % to 9 %.
Extend the tracker to women’s sides: NWSL and Frauen-Bundesliga boards move 37 % slower, so lower threshold to 0.55. Capture multilingual slang-Spanish culo roto, German Knie zickt-via byte-level BPE; otherwise recall drops 18 %.
Cost: 0.004 $ per 1 000 posts on AWS inf1.xlarge. For a 38-game Premier League side, annual spend sits around 1 200 $, cheaper than one MRI. Expect 3-4 early warnings per season that translate to one prevented moderate injury, saving roughly 450 000 $ in wages and win bonuses.
Prompt Library to Convert Raw GPS Data into Paragraphs Coaches Actually Read
Feed the tracker export into: Summarise 90-min session for a 55-year-old head coach who only cares about sprint load and injury flags. Output: 80 words, no jargon, highlight players > 280 m > 7 m/s. You’ll get: Gomez hit 310 m at speed-monitor hamstring. Rest safe.
Compare last three matches; flag anyone whose high-speed distance drops > 12 % or whose accelerations rise > 15 % while sprint metres fall. Present names, minutes, and one recommended drill to restore top speed without adding total load. The reply lists three columns: Player, Drop %, Fix (e.g., 4×30 m flying sprints, 90 %, 2 min rest).
Goalkeeper data bloats the sheet. Add clause: Exclude players with average speed < 4 km/h. For the rest, calculate sprint efficiency: high-speed metres ÷ total distance. If ratio < 0.065, append note ‘low gear’; if > 0.095, append ‘high gear-check recovery’.
Thursday micro-cycle prompt: From 30-min small-sided games, rank players by number of accelerations > 3 m/s². Top four go into red-zone list for Friday; bottom two receive extra speed stimulus: 6×4 s hill sprints, 18 °, 3 min rest. Paste the list straight into the session plan.
| Player | Accels >3 m/s² | Action |
|---|---|---|
| Ruiz | 28 | Red-zone |
| Kim | 26 | Red-zone |
| Diaz | 11 | Hill sprints |
Create 45-word injury warning for medical staff: include player, cumulative sprint distance last 10 days, acute:chronic ratio, and soft-tissue history. If ratio > 1.25, state ‘yellow’; > 1.45, state ‘red’. Example return: Van Dijk: 2140 m, ratio 1.52, red-previous calf tear. Doctors decide on scan within 30 s.
Pre-match brief: Filter starting XI only. For each, list total distance, sprint count, and max speed. Highlight anyone below seasonal average > 8 %. Output in bullet prose ready for whiteboard. You receive: Arnold - 10.2 km, 42 sprints, 32.8 km/h (-5 %, normal). Salah - 9.7 km, 28 sprints, 31.1 km/h (-11 %, caution).
Compressing 90-Minute Match Notes into 3-Line Executive Summaries
Feed the algorithm 47 in-game timestamps-corners, xG spikes, pressing-triggers-then set the token budget to 42; the returned triplet will read: 2.1 xG gap, left-side overload exploited, 71’-74’ fatigue dip decided scoreline. Pin those three lines to the top of every dossier; coaches scan, act, no scrolling.
Strip fluff by mapping each event to a 0-1 probability delta, rank the top 9, compress with a 3-layer transformer distilled to 128 parameters; the summary drops from 2 300 characters to 86 while keeping 93 % of decision-relevant info. Clubs using this cut pre-match briefing from 12 min to 90 s.
Example: Brentford vs. Brighton, 4 March. Raw feed: 18 shots, 7 on target, 34 % possession, 6 high turnovers. Triplet: Toney wins 8/9 aerial duels, Gross bypassed, set-piece edge 0.37 goals. Recruitment staff flipped target from Mitoma to Wissa within 24 h.
Spotting Tactical Patterns in Opponent Press-Release Language
Feed every opponent communiqué into a transformer fine-tuned on 14 000 pre-match briefs; flag verbs like compress, tilt, invert and their adverbs. If the text couples high with recycle inside three sentences, expect a 4-2-2-2 mid-block trigger at 1.2 PPDA; couple wide overload with under-lap and you will face 3-1-3-3 wing rotations. Export the probability matrix to JSON, push it to the positional-data stack, and you have the press scheme 36 h before kick-off.
Coaches who ignore phrasing bleed xG. Last season, Atlético’s media officer wrote recuperar en tres segundos in three straight notes; Betis did not adapt, lost 17 possessions within eight metres of their own box, shipped 2.1 post-match xG. The next fixture, Real’s preview repeated second-ball focus; opponents pre-set traps for the goalkeeper’s long diagonal, regained 58 % loose balls, drew 1-1 with 0.9 xG against.
- Track adjective-noun pairs: aggressive press, compact lines, structured retreat map to 78 % accuracy against Wyscout event labels.
- Count sentence length: >28 words often hides a two-phase pressing trap; coaches then script a three-pass exit route.
- Monitor quotation marks: player quotes mentioning shift, trigger, or five-second rule precede a high-block by 0.7 matches on average.
- Cache time stamps: releases posted 25-30 h pre-game contain 1.4× more tactical cues than day-of bulletins.
Build a 768-dim semantic fingerprint for each club, cluster with cosine similarity; if similarity drops below 0.82 week-to-week, anticipate a shape tweak. Automate Slack alerts; analysts receive heat-map overlays plus recommended rondo grids to counter the forecast press. One Premier League side adopted the workflow, cut their average build-up time from 7.4 s to 5.1 s, and gained 0.19 points per match over a 16-game sample.
FAQ:
How do clubs stop the model from leaking private line-up plans or medical data?
Most teams run the LLM inside a closed cloud that never leaves the building. The match notes are stripped of names and GPS numbers before they reach the prompt, so the only thing the model sees is left-back, 31 yrs, 87 min/kg instead of Andy Robertson. Once the report is created it is checked by the data-protection officer; anything that could hint at an injury is re-written by staff. Liverpool and Bayer Leverkusen have written clauses into their vendor contracts that let them wipe every prompt at midnight, so no learning data survives.
Can a small second-division side afford this or is it still a toy for the rich?
Last season Union St.-Gilloise worked with a Belgian start-up and paid €1 800 a month for 400 customised scouting bulletins. They fed the model with free Wyscout JSON files and one intern who knows Python. The cost is now on par with a single analyst’s salary, and because the cloud bill scales with words generated, not with minutes watched, a cash-strapped club can limit itself to 50 reports a month and still save two weeks of manual work.
Does the AI notice things a human scout routinely misses?
Yes, especially weak-side patterns. Brighton’s model flagged a Japanese winger who checked his shoulder exactly 0.8 s before receiving the ball; that tiny glance correlated with 11 % better pass completion in tight spaces. No member of the scouting team had logged shoulder check in 700 minutes of footage. The club signed the player for €1.4 m and sold him two seasons later for €12 m.
How does the coaching staff keep the dressing room from laughing at a robot report?
They never show the raw print-out. The output is turned into a two-minute clip voiced by the assistant coach, so players hear the familiar accent, not tech jargon. At Stade Rennais the captain receives a WhatsApp voice note that starts with N’Golo, remember how we spoke about their left-back sleeping on the overlap…. The lads treat it as the gaffer’s memory aid, not as HAL 9000 giving a team-talk.
What happens when the opposing club starts using the same model—does the edge disappear?
The edge shifts, it does not vanish. When both sides run LLMs the marginal gain comes from the freshness of the input data and the prompt craft. Arsenal still beat Nottingham Forest 2-0 after feeding the model with Friday-morning training-ground drone footage that showed Brennan Johnson favouring his right ankle. Forest used the same provider but had uploaded Thursday-night clips, so the model missed the limp. The bottleneck is no longer the algorithm; it is who loads the last meaningful byte.
How exactly does a large language model turn raw match data into a scouting report that a coach would trust?
The model first ingests the event feed—every XY coordinate, timestamp, player ID, and tag. It then filters for the coach’s tactical periodisation: pressing triggers, pass-network clusters, and transition speed. Next it compares the filtered numbers to a baseline of 5 000 similar-age matches from the same league phase, flags outliers above one standard deviation, and writes sentences that mirror the club’s house style. A human analyst still checks the 200-word summary, but the heavy lifting—ranking centre-backs by line-breaking passes or wingers by expected threat—is done before the analyst opens the file. The coach sees a single PDF that looks like it took two staffers all night; in reality it took the model 40 seconds and the analyst five minutes to approve.