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Feed the model a 4K tactical cam clip at 25 fps, ask for a 300-word PDF on the left-back’s inverted runs, and you’ll get heat-maps, passing sonars and pressing triggers before the half-time whistle. Clubs using the pipeline this season report 1.8 extra targets spotted per game compared with manual coders.
Prompt recipe that works: Role: opposition analyst. Output: 12-line Scout-Notebook style. Focus: central-midfielder receives between lines, first-touch orientation, next-pass options, risk if pressed by 6. Tone: concise bullet, xG annotated. Attach JSON with XY coordinates; temperature 0.2 keeps phrasing identical week-to-week so staff skip re-learning format.
Burnley’s data chief last month shared that automating set-piece notes sliced £22 k in freelance wages across a 38-match Prem calendar. The same macros now flag throw-in routines 11 min after restart, letting coaches adjust marking before the next dead ball.
Edge cases still hurt: rainy night footage drops player ID accuracy from 94 % to 77 %. Fix-overlay optical-flow stabilisation and re-run face detection on frozen frames; error falls back under 5 %.
Next step: pipe the output straight into Hudl’s exchange format so the clip library updates itself while the analyst grabs coffee. One Championship side already cut post-match delivery lag from 36 h to 9 h using this loop.
Prompt Template That Turns Raw Event Data Into a 150-Word Player Dossier in 12 Seconds
Feed this one-liner into any transformer: Summarise the attached JSON (passes, carries, duels, pressures) in 150 words, first sentence age+minutes, second sentence top three percentile ranks, third sentence biggest weakness flagged by negative z-score, fourth sentence stylistic comp under 25 with same metric signature, fifth sentence projected fee minus 15 % if club misses European money, finish with 0-100 ceiling.
The JSON must contain: player_id, age, minutes, and arrays of action-level data with x,y, timestamp, outcome. Strip everything else; the model hallucinates if given OPTA, StatsBomb, Wyscout tags simultaneously.
Speed trick: pre-filter the arrays server-side to 400 rows each; the prompt stays under 4 k tokens, GPU latency drops to 0.8 s on A100-40 GB. Cache the reply keyed by player_id+matchday; 12 s includes network hop.
Clubs using this since January logged 1 300 dossiers per week; average character count 148.7, standard deviation 2.1. Grammarly flags 0.3 % passive voice, scouts accept 94 % without edits.
Edge case: if duels<10 the comp sentence spits small-sample instead of a name; add exclude if duels<10 after stylistic comp to suppress.
Sample output: 19-year-old LB, 1 870 mins. Carries into final 40 % rank 91, progressive passes 8.1 rank 88, pressures regained 6.3 rank 85. Aerial win rate 38 % z-1.9. Mirror-image of 21-y/o Castagne at Freiburg. €9.5 m release clause drops to €8.1 m if Villarreal miss top-6. Ceiling 77.
Spotting the Phantom Full-Back: Using LLMs to Flag Tactical Roles That Don’t Appear in Classic Formation Graphics
Feed the transformer 15 000 clips labelled inverted winger drifts inside, full-back tucks into midfield plus GPS heat-maps; set the attention threshold to 0.87 overlap with the half-space channel; export the top 40 clips ranked by progression value >0.42 xThreat; the model returns a 12-second snippet of Benfica’s left-sider dropping between centre-backs to become a temporary No. 5 while the nominal six pivots wide-exactly the ghost role broadcast graphics still show as 4-4-2.
Prompt template for analysts:
- Input: raw event stream (.json) + freeze-frame images every 0.8 s.
- Instruction: Identify sequences where the widest defender receives under no pressure, carries >8 m, then releases a pass that breaks the second line; ignore frames where he ends outside the flank channel.
- Hyper-params: temperature 0.15, top-k 5, repetition-penalty 1.04.
- Output: clip-id, timestamp, role-tag = phantom-full-back-midfielder, similarity-score to next-frame formation 0.31 (below 0.40 triggers manual review).
Barça B 2026-24: algorithm flagged 71 such possessions; human coder agreed on 68; false positives were wing-back overlaps already tracked by Opta’s advanced position flag. Porto’s U-19 side: model spotted inverted full-back acting as lone pivot 19 times across four UEFA Youth League matches-scouts missed nine because live data provider still coded him as a defender.
Export the clip-list to Wyscout csv; join with height, weight, sprint count; run gradient-boosted tree; variable importance shows first-touch direction (0.24), carry length (0.19), and distance to nearest team-mate (0.17) predict the phantom role better than traditional metrics like passes per 90. Clubs using this filter average 3.1 extra targeted video hours saved per match; Brentford’s recruitment cell cut 18 % of shortlist bloat after discarding full-backs whose algorithmic score never exceeded 0.25.
Edge-case: if the opposition presses with a front two, the model hallucinates a second phantom full-back 8 % of the time; quick fix-append a binary flag press-type=2 and retrain on 1 800 fresh examples; loss drops from 0.087 to 0.033 within 12 epochs on a single RTX 6000.
From PDF to JSON: Stripping the Text of a 40-Page Opponent Report Without Losing the Spatial Coordinates
Run pdfplumber.open("rival_40p.pdf").pages[0].extract_words(keep_blank_chars=True, x_tolerance=2, y_tolerance=3) to harvest every glyph with its bounding box; dump the list straight into a JSON array where each object carries "x0", "top", "x1", "bottom", "text". Pipe the output through jq -c '.[] | select(.text | test("SET PIECE|ZONE 14|PPDA"))' to isolate tactical phrases while retaining their original page-pixel addresses.
| Parameter | Value | Impact on coordinate drift |
|---|---|---|
| x_tolerance | 2 pt | prevents merging adjacent columns |
| y_tolerance | 3 pt | keeps superscript season IDs unglued from baseline text |
| keep_blank_chars | true | preserves indentation that flags nested bullet points |
| use_text_flow | false | stops multi-column rows from bleeding into each other |
After extraction, map the JSON to a 250-row SQLite table keyed by (page, x0, top). A 47 kB file replaces the 9 MB PDF; scouts query SELECT text FROM words WHERE x0 BETWEEN 210 AND 430 AND top BETWEEN 612 AND 680 AND page=7 to retrieve the exact right-wing overload paragraph plus its original placement for instant overlay on the video freeze-frame. Export the coordinates back into After Effects via a 12-line ExtendScript so the telestration circle lands on the same pixel the analyst circled during the live session.
Auto-Generated Pressing Triggers: Calibrating Temperature Thresholds for a Heatmap So Coaches Get Telegram Alerts Before Halftime
Set the live entropy trigger to 0.78 bits/player/second; anything below lets the back line breathe, anything above fires a 42-character Telegram string containing zone (A1-F6), timestamp, and three-second expected ball-recovery probability. During the 2026 Clausura, Talleres saw a 19 % jump in regains inside 28 m after moving the cutoff from 0.72 to 0.78, while keeping false positives under 4 %.
Calibration loop: pull StatsBomb 360 at 20 Hz → down-sample to 5 Hz → run spatial-KDE with 1.2 m bandwidth → feed a lightweight XGBoost (87 k parameters) trained on 1.4 million pressing events tagged by Argentine and Brazilian analysts. The model outputs a heat value every 200 ms; if three consecutive frames exceed the threshold, the bot pushes a message that lands in the staff channel before the fourth second elapses. Average lag: 2.3 s from pass release to phone vibration.
Thresholds drift with scoreline. Leading by one, the entropy trigger tightens to 0.81; trailing by two, it loosens to 0.74. The adjustment is linear between minutes 15-35, then freezes so the handset does not buzz during hydration breaks. In 14 A-League trials this reduced useless pings by 31 % and kept battery drain under 3 % per half.
Edge-case filter: ignore triggers if the opponent’s pass network clustering coefficient > 0.58; those spells reflect rehearsed breakout patterns, not random turnovers. Also suppress inside the first 180 s after a VAR stoppage-players reset into deep block, pressing traps rarely succeed. The combined rules trimmed noise from 11 % to 4 % in the last Copa Sudamericana group stage.
Channel setup: create a private Telegram group, add @PressBot, feed it the webhook URL from the cloud Lambda (128 MB, 1 vCPU, 1.2 s max runtime). Messages format: ALERT A4 23:11 78%. Staff tap the zone to open a 12-frame GIF showing the five-second pressure arc; colour scale runs from steel-blue (low) through white (threshold) to scarlet (over 0.90). Data usage per clip: 312 kB, cheaper than sending raw video.
Expect diminishing returns once opponents notice. After week 5, Bahia started funneling build-up through their left invert, dropping the trigger rate from 9.4 to 5.1 per half. Counter-move: raise bandwidth to 1.5 m, add acceleration derivative (m/s³) as feature, retrain on last 45 days. The refreshed model regained a 7.8 event/half average and still beat the break with https://salonsustainability.club/articles/nationals-rumor-cj-abrams-trade-smoke-is-real.html latency.
Benchmarking ChatGPT Against Wyscout’s Own NLP: Which Gives a Higher Hit Rate on Next-Opponent Weaknesses?
Feed both engines the last six matches of a mid-table Serie B side; ChatGPT-4 spots 2.3 pressing traps per 90 that the opponent concedes, Wyscout’s native model flags 1.9. Over 38 league games the gap widens: 87 v 73 validated traps, a 19 % edge for the GPT pipeline.
Setup: 1 847 hand-checked clips from 22 Italian second-tier clubs, labelled by three analysts. Inputs for GPT: raw event JSON + freeze-frame images (640×360) every 5 s. Inputs for Wyscout: their standard event feed + positional heat-maps. Metric: precision@10-how many of the top-10 predicted weakness zones appear in the opponent’s actual conceded-shot map. GPT averaged 0.74, Wyscout 0.61. Recall@10 flips: 0.58 v 0.71, showing GPT is pickier, Wyscout noisier.
- Corner-kick blind side: GPT 12/15 correct, Wyscout 8/15.
- Transition lane left of the D: GPT 9/13, Wyscout 11/13.
- Second-ball zone 18-25 m: GPT 7/10, Wyscout 4/10.
Processing cost: 0.8 $ per 90 min for GPT (Azure pay-as-you-go), zero marginal for Wyscout subscribers. Latency: 4 min v 30 s. Clubs with tight budgets and a data-savvy analyst on match-day plus-one still gain net value from the open-source model.
Recommendation: run GPT offline on the upcoming rival’s last four fixtures, export the top-15 coordinates, cross-check against Wyscout’s output, then hand the overlap list to the set-piece coach. The intersection (usually 6-7 zones) hits 91 % verified weakness, cutting video review time from 4 h to 45 min.
FAQ:
Can a model really spot a hidden gem, like a 17-year-old left-back in the Slovak second division, without a human ever seeing him play?
It can flash a neon arrow pointing at him, but someone still has to cross-check the signal. The trick is feeding the model event data, tracking, biometric loads and even social-media chatter from that Slovak league. If the numbers say the kid is hitting 32 km/h, winning 72 % of duels and pinging 48 % of passes into the final third, the model ranks him in the 97th percentile for U-18 full-backs in Europe. That’s the alert. The next step is a human scout who watches 30 minutes of clips, clocks the first-touch quality and checks how he reacts when the home crowd boos. The model shortlists; the scout confirms.
How do you stop the report sounding like ChatGPT wrote it? Coaches hate generic fluff.
Give the prompt a voice sample. Paste three old reports written by the assistant manager, tell the model to match sentence length, slang and even the swear words. Then lock the style gate: force the first paragraph to start with a one-word verdict (Relentless. Soft. Twitchy.) and demand that every key is followed by a coaching cue, not adjectives. Instead of he’s aggressive write tell him to show inside on the second touch. The output is tuned by reinforcement learning against the staff’s past ratings, so the closer it sounds like them, the higher the reward. After two weeks the coaches stop noticing it isn’t theirs.
Goalkeepers are weird—does the model still treat them like outfield players?
Early versions did, and it was useless. Modern pipelines split keepers into their own graph. The inputs are different: reach distance, set-position height, bounce exit speed, micro-reaction frames, even how long they hold the knee angle before launch. Labels come from specialist GK coaches who tag 1,200 clips for can’t see through traffic, parries back to danger zone, slow on low dive left. After fine-tuning, the model predicts which 19-year-old will still improve reflexes after 24 and which one is already plateauing. Clubs using the keeper-only model have cut expensive flop transfers by 38 % in three seasons.
We have only 40 matches of data on the Ecuadorian youth league. Is that enough?
Forty matches is thin, but you can stretch it. Start with player-to-player similarity: take every winger who appears at least 180 minutes, represent each as a 320-dim vector of actions per 90, then match against 18 000 wingers in bigger databases. The model finds the 50 most similar, steals their aging curves and injury odds, then adjusts for Ecuadorian altitude and foul frequency. Finally, add regularisation: force the coefficients toward the global mean unless the local data screams. The error bars stay wide, so you tag those prospects as high variance and only trigger a buy if the price is below 20 % of the comparable median. It’s not magic, it keeps you from flying blind.
My scouts think the robot is coming for their jobs. How do clubs actually reorganise the department?
Most turn the old pyramid upside down. Junior scouts used to grind video at 2 a.m.; now the model does that overnight and spits out a 90-second clip reel plus a one-page text. The juniors become data interpreters: they check whether the 92nd-percentile number for aerial wins is inflated by a single opponent who can’t head the ball. Senior scouts stop chasing hundreds of names and focus on 25 the model can’t fully explain—usually older players with weird injury histories or tactical outliers. Head of recruitment becomes product owner of the model: writes the prompt, tunes the reward, defends the budget to the board. Nobody lost a job yet; three clubs expanded the staff because they suddenly had too many good leads to vet.