AI Language Models Streamlining the Creation of Scouting Reports and Analysis Briefs

Feed 12.7 hours of match video into SportLogic’s neural scout and it returns a 1,800-word talent card in 18 minutes-work that used to tie up three analysts for two days. Bayern München used the same pipeline last season and trimmed the average dossier turnaround from 11 hours 14 minutes to 5 hours 6 minutes, freeing scouts for 19 extra live appearances.

Clubs pay $0.04 per player row for the cloud service; Brentford’s head of recruitment, Luke McCabe, reports a $138k annual saving after canceling two outsourced video-tagging contracts. The algorithm re-watches every corner, grades first-touch quality on a 0-100 scale, and flags any winger whose sprint frequency drops below 85% of their season mean after the 70-minute mark-criteria Ajax now embed in every youth report.

To replicate the gains, compress your footage to 1080p/30 fps, upload via the XML gateway, and set the tactical focus slider to inverted wing-backs; the system will auto-append heat-maps and 8-second video snippets for each highlighted action. Benfica’s B-team applied those exact settings and delivered 34 mid-season assessments in four nights, allowing the first team to sign Andrey Nascimento 11 days before market value spiked 38%.

Auto-Tagging Game Footage in 3 Clicks

Load mp4 into Hudl Sportscode 13.2, hit Auto-Tag, select Soccer 11v11 preset, press Run-labels appear in 38 s for a 90-minute match, 14 event types pre-sorted by half.

Accuracy: 91 % on corner kicks, 87 % on pressing traps, 94 % on counter-attacks; false positives drop to 4 % if you feed 1080 p @ 30 fps or higher. Export XML straight to Nacsport or export csv to PowerBI; both routes keep timestamp and XY coordinates.

• Label set is editable: add Third-man run or Inverted overlap in under 15 s; the neural net retrains overnight on your local GPU (RTX 4060, 6 Gb VRAM) with 200 user-checked examples.
• Keyboard shortcut: Ctrl-Shift-A toggles overlay so you can eye-check every 5th clip; average review lasts 4 min 12 s for a U-18 fixture.
• Cloud offload: push 4 K clips to AWS g5.xlarge, pay $0.98 per full game; turnaround 6 min east-coast, 8 min west-coast.

1. Shoot wide 16:9, never vertical; algorithm loses 12 % precision on portrait video.
2. Deactivate scoreboard overlay in broadcast feed; the ticker confuses the timestamp parser and shifts tags by 0.7 s on average.
3. Store labels in a PostgreSQL schema with match_id, half, minute, second, label_id; indexing on (match_id, minute) keeps queries under 120 ms for 5-season archive.

Clubs in Danish Superliga used the tri-click workflow across 212 matches; tagging manpower shrank from 3 staff × 4 h to 1 staff × 45 min, freeing 21 man-days per season for set-piece rehearsal.

Next upgrade due Q3 adds audio cue fusion-whistle detection plus crowd roar lifts counter-attack recall to 97 %-and introduces a silent tag mode that writes metadata without preview, trimming the whole operation to 22 s per game.

Prompt Template Library for 90-Second Player Sketches

Feed the LLM: Role: position} | Minutes: xG+xA/90: Sprint freq: per match | Key action clip: {URL} | Output: 3-line snapshot, 1 comparable, 1 risk flag, 1 price bracket in €. Stick to 65 tokens; anything longer dilutes punch.

Goalkeepers swap sprint for sweeps/90 and add PSxG-GA/100 instead of xG. Full-backs need passes into final 3rd accuracy plus progressive carries p90. Centre-backs drop attacking metrics, insert aerial win % and fouls committed p90. Tweaks fit inside the same 65-token ceiling.

Example return: 2006-born No. 6, 1.83 m, 1,260 mins Ligue 2, 0.87 xG+xA/90, 28 sprints, 62 % duels. Similar to 17-yr-old Idrissa Gueye. Hip flexor flare-ups. €3-5 M ceiling. Paste the clip link behind Key action clip so the bot can timestamp the 12-second sequence.

Keep a canned list of comparables by age band: 16-18, 19-21, 22-24, 25-27. Update every quarter using Opta’s Similarity Score. If the algorithm spits out a retired name, replace manually; stale comps erode credibility inside the room.

Store 30 variants in a single Google Sheet; each row is a prompt. Add a checkbox Injury flag; tick it and the prompt auto-inserts medical red zone into the risk line. Export as CSV, import into club Slack via bot slash-command /sketch. Staff trigger 90-second bursts during live matches.

One intern once mislabelled a striker’s sprint count, inflating it by 18 %. The bot still spat out a flashy line, the DoF bit, and the club bid €1.2 M above market. Post-medical, hip issues surfaced; the player lasted 11 matches. Since then every prompt ends with Source-check within 5 min or kill the thread. https://salonsustainability.club/articles/jake-paul-undergoes-jaw-surgery-after-olympic-gold-watch.html

Exporting AI Drafts Straight to Club CRM Fields

Map the AI output node player_summary to the CRM field talent_notes with a 400-character hard limit; anything beyond triggers an auto-truncate at the last complete sentence to avoid mid-word chops.

Configure the webhook payload to POST a JSON object containing player_id, summary, position_key, foot, height_cm, contract_expiry directly to https://club-api.domain.com/v2/talent; use OAuth2 bearer token refreshed every 55 minutes.

Set the confidence threshold to 0.82; any AI prediction below this value routes to a queue labeled manual_review instead of overwriting existing CRM data, preventing low-accuracy updates for attributes like sprint speed or injury proneness.

Run a nightly AWS Lambda (Python 3.11, 512 MB, 15-sec timeout) that compares the updated_at timestamp in the CRM against the AI batch timestamp; if the CRM entry is newer, skip the push and log the mismatch to CloudWatch with metric SkippedOverwrite.

For academies, mirror the same pipeline into a separate CRM tenant keyed by academy_id; keep the field names identical so U-18 analysts can reuse senior-team dashboards without recoding.

Back-fill historical data by exporting 1 500 profiles per CSV zip, then use the bulk import endpoint /talent/bulk with mode=upsert; expect 90 seconds per thousand rows and a 3 % failure rate mostly from duplicate player_id keys.

After go-live, track two KPIs: average analyst clicks to confirm an AI push (target 2) and CRM sync latency (target < 45 seconds); post these numbers on a Grafana board refreshed every 30 seconds so staff spot lag before agents complain.

Spotting Red-Flag Injury Phrases Before Send-Out

Replace day-to-day with exact ligament grade and MRI date; recruiters bin anything fuzzy within 30 s.

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MAML-QQQQ, MAML-RRRR, MAML-SSSS, MAML-TTTT, MAML-UUUU, MAML-VVVV, MAML-WWWW, MAML-XXXX, MAML-YYYY, MAML-ZZZZ, MAML-AAAAA, MAML-BBBBB, MAML-CCCCC, MAML-DDDDD, MAML-EEEEE, MAML-FFFFF, MAML-GGGGG, MAML-HHHHH, MAML-IIIII, MAML-JJJJJ, MAML-KKKKK, MAML-LLLLL, MAML-MMMMM, MAML-NNNNN, MAML-OOOOO, MAML-PPPPP, MAML-QQQQQ, MAML-RRRRR, MAML-SSSSS, MAML-TTTTT, MAML-UUUUU, MAML-VVVVV, MAML-WWWWW, MAML-XXXXX, MAML-YYYYY, MAML-ZZZZZ, MAML-AAAAAA, MAML-BBBBBB, MAML-CCCCCC, MAML-DDDDDD, MAML-EEEEEE, MAML-FFFFFF, MAML-GGGGGG, MAML-HHHHHH, MAML-IIIIII, MAML-JJJJJJ, MAML-KKKKKK, MAML-LLLLLL, MAML-MMMMMM, MAML-NNNNNN, MAML-OOOOOO, MAML-PPPPPP, MAML-QQQQQQ, MAML-RRRRRR, MAML-SSSSSS, MAML-TTTTTT, MAML-UUUUUU, MAML-VVVVVV, MAML-WWWWWW, MAML-XXXXXX, MAML-YYYYYY, MAML-ZZZZZZ, MAML-AAAAAAA, MAML-BBBBBBB, MAML-CCCCCCC, MAML-DDDDDDD, MAML-EEEEEEE, MAML-FFFFFFF, MAML-GGGGGGG, MAML-HHHHHHH, MAML-IIIIIII, MAML-JJJJJJJ, MAML-KKKKKKK, MAML-LLLLLLL, MAML-MMMMMMM, MAML-NNNNNNN, MAML-OOOOOOO, MAML-PPPPPPP, MAML-QQQQQQQ, MAML-RRRRRRR, MAML-SSSSSSS, MAML-TTTTTTT, MAML-UUUUUUU, MAML-VVVVVVV, MAML-WWWWWWW, MAML-XXXXXXX, MAML-YYYYYYY, MAML-ZZZZZZZ, MAML-AAAAAAAA, MAML-BBBBBBBB, MAML-CCCCCCCC, MAML-DDDDDDDD, MAML-EEEEEEEE, MAML-FFFFFFFF, MAML-GGGGGGGG, MAML-HHHHHHHH, MAML-IIIIIIII, MAML-JJJJJJJJ, MAML-KKKKKKKK, MAML-LLLLLLLL, MAML-MMMMMMMM, MAML-NNNNNNNN, MAML-OOOOOOOO, MAML-PPPPPPPP, MAML-QQQQQQQQ, MAML-RRRRRRRR, MAML-SSSSSSSS, MAML-TTTTTTTT, MAML-UUUUUUUU, MAML-VVVVVVVV, MAML-WWWWWWWW, MAML-XXXXXXXX, MAML-YYYYYYYY, MAML-ZZZZZZZZ, MAML-AAAAAAAAA, MAML-BBBBBBBBB, MAML-CCCCCCCCC, MAML-DDDDDDDDD, MAML-EEEEEEEEE, MAML-FFFFFFFFF, MAML-GGGGGGGGG, MAML-HHHHHHHHH, MAML-IIIIIIIII, MAML-JJJJJJJJJ, MAML-KKKKKKKKK, MAML-LLLLLLLLL, MAML-MMMMMMMMM, MAML-NNNNNNNNN, MAML-OOOOOOOOO, MAML-PPPPPPPPP, MAML-QQQQQQQQQ, MAML-RRRRRRRRR, MAML-SSSSSSSSS, MAML-TTTTTTTTT, MAML-UUUUUUUUU, MAML-VVVVVVVVV, MAML-WWWWWWWWW, MAML-XXXXXXXXX, MAML-YYYYYYYYY, MAML-ZZZZZZZZZ, MAML-AAAAAAAAAA, MAML-BBBBBBBBBB, MAML-CCCCCCCCCC, MAML-DDDDDDDDDD, MAML-EEEEEEEEEE, MAML-FFFFFFFFFF, MAML-GGGGGGGGGG, MAML-HHHHHHHHHH, MAML-IIIIIIIIII, MAML-JJJJJJJJJJ, MAML-KKKKKKKKKK, MAML-LLLLLLLLLL, MAML-MMMMMMMMMM, MAML-NNNNNNNNNN, MAML-OOOOOOOOOO, MAML-PPPPPPPPPP, MAML-QQQQQQQQQQ, MAML-RRRRRRRRRR, MAML-SSSSSSSSSS, MAML-TTTTTTTTTT, MAML-UUUUUUUUUU, MAML-VVVVVVVVVV, MAML-WWWWWWWWWW, MAML-XXXXXXXXXX, MAML-YYYYYYYYYY, MAML-ZZZZZZZZZZ, MAML-AAAAAAAAAAA, MAML-BBBBBBBBBBB, MAML-CCCCCCCCCCC, MAML-DDDDDDDDDDD, MAML-EEEEEEEEEEE, MAML-FFFFFFFFFFF, MAML-GGGGGGGGGGG, MAML-HHHHHHHHHHH, MAML-IIIIIIIIIII, MAML-JJJJJJJJJJJ, MAML-KKKKKKKKKKK, MAML-LLLLLLLLLLL, MAML-MMMMMMMMMMM, MAML-NNNNNNNNNNN, MAML-OOOOOOOOOOO, MAML-PPPPPPPPPPP, MAML-QQQQQQQQQQQ, MAML-RRRRRRRRRRR, MAML-SSSSSSSSSSS, MAML-TTTTTTTTTTT, MAML-UUUUUUUUUUU, MAML-VVVVVVVVVVV, MAML-WWWWWWWWWWW, MAML-XXXXXXXXXXX, MAML-YYYYYYYYYYY, MAML-ZZZZZZZZZZZ, MAML-AAAAAAAAAAAA, MAML-BBBBBBBBBBBB, MAML-CCCCCCCCCCCC, MAML-DDDDDDDDDDDD, MAML-EEEEEEEEEEEE, MAML-FFFFFFFFFFFF, MAML-GGGGGGGGGGGG, MAML-HHHHHHHHHHHH, MAML-IIIIIIIIIIII, MAML-JJJJJJJJJJJJ, MAML-KKKKKKKKKKKK, MAML-LLLLLLLLLLLL, MAML-MMMMMMMMMMMM, MAML-NNNNNNNNNNNN, MAML-OOOOOOOOOOOO, MAML-PPPPPPPPPPPP, MAML-QQQQQQQQQQQQ, MAML-RRRRRRRRRRRR, MAML-SSSSSSSSSSSS, MAML-TTTTTTTTTTTT, MAML-UUUUUUUUUUUU, MAML-VVVVVVVVVVVV, MAML-WWWWWWWWWWWW, MAML-XXXXXXXXXXXX, MAML-YYYYYYYYYYYY, MAML-ZZZZZZZZZZZZ, MAML-AAAAAAAAAAAAA, MAML-BBBBBBBBBBBBB, MAML-CCCCCCCCCCCCC, MAML-DDDDDDDDDDDDD, MAML-EEEEEEEEEEEEE, MAML-FFFFFFFFFFFFF, MAML-GGGGGGGGGGGGG, MAML-HHHHHHHHHHHHH, MAML-IIIIIIIIIIIII, MAML-JJJJJJJJJJJJJ, MAML-KKKKKKKKKKKKK, MAML-LLLLLLLLLLLLL, MAML-MMMMMMMMMMMMM, MAML-NNNNNNNNNNNNN, MAML-OOOOOOOOOOOOO, MAML-PPPPPPPPPPPPP, MAML-QQQQQQQQQQQQQ, MAML-RRRRRRRRRRRRR, MAML-SSSSSSSSSSSSS, MAML-TTTTTTTTTTTTT, MAML-UUUUUUUUUUUUU, MAML-VVVVVVVVVVVVV, MAML-WWWWWWWWWWWWW, MAML-XXXXXXXXXXXXX, MAML-YYYYYYYYYYYYY, MAML-ZZZZZZZZZZZZZ, MAML-AAAAAAAAAAAAAA, MAML-BBBBBBBBBBBBBB, MAML-CCCCCCCCCCCCCC, MAML-DDDDDDDDDDDDDD, MAML-EEEEEEEEEEEEEE, MAML-FFFFFFFFFFFFFF, MAML-GGGGGGGGGGGGGG, MAML-HHHHHHHHHHHHHH, MAML-IIIIIIIIIIIIIII, MAML-JJJJJJJJJJJJJJJ, MAML-KKKKKKKKKKKKKKK, MAML-LLLLLLLLLLLLLL, MAML-MMMMMMMMMMMMMM, MAML-NNNNNNNNNNNNNN, MAML-OOOOOOOOOOOOOOO, MAML-PPPPPPPPPPPPPPP, MAML-QQQQQQQQQQQQQQQ, MAML-RRRRRRRRRRRRRRR, MAML-SSSSSSSSSSSSSSS, MAML-TTTTTTTTTTTTTT, MAML-UUUUUUUUUUUUUU, MAML-VVVVVVVVVVVVVV, MAML-WWWWWWWWWWWWWW, MAML-XXXXXXXXXXXXXX, MAML-YYYYYYYYYYYYYY, MAML-ZZZZZZZZZZZZZZ, MAML-AAAAAAAAAAAAAAA, MAML-BBBBBBBBBBBBBBB, MAML-CCCCCCCCCCCCCCC, MAML-DDDDDDDDDDDDDDD, MAML-EEEEEEEEEEEEEEE, MAML-FFFFFFFFFFFFFFF, MAML-GGGGGGGGGGGGGGG, MAML-HHHHHHHHHHHHHHH, MAML-IIIIIIIIIIIIIIII, MAML-JJJJJJJJJJJJJJJJ, MAML-KKKKKKKKKKKKKKKK, MAML-LLLLLLLLLLLLLLL, MAML-MMMMMMMMMMMMMMMM, MAML-NNNNNNNNNNNNNNN, MAML-OOOOOOOOOOOOOOOO, MAML-PPPPPPPPPPPPPPPP, MAML-QQQQQQQQQQQQQQQQ, MAML-RRRRRRRRRRRRRRRR, MAML-SSSSSSSSSSSSSSSS, MAML-TTTTTTTTTTTTTTT, MAML-UUUUUUUUUUUUUUU, MAML-VVVVVVVVVVVVVVV, MAML-WWWWWWWWWWWWWWW, MAML-XXXXXXXXXXXXXXX, MAML-YYYYYYYYYYYYYYY, MAML-ZZZZZZZZZZZZZZZ, MAML-AAAAAAAAAAAAAAAA, MAML-BBBBBBBBBBBBBBBB, MAML-CCCCCCCCCCCCCCCC, MAML-DDDDDDDDDDDDDDDD, MAML-EEEEEEEEEEEEEEEE, MAML-FFFFFFFFFFFFFFFF, MAML-GGGGGGGGGGGGGGGG, MAML-HHHHHHHHHHHHHHHH, MAML-IIIIIIIIIIIIIIIII, MAML-JJJJJJJJJJJJJJJJJ, MAML-KKKKKKKKKKKKKKKKK, MAML-LLLLLLLLLLLLLLLL, MAML-MMMMMMMMMMMMMMMMM, MAML-NNNNNNNNNNNNNNNN, MAML-OOOOOOOOOOOOOOOOO, MAML-PPPPPPPPPPPPPPPPP, MAML-QQQQQQQQQQQQQQQQQ, MAML-RRRRRRRRRRRRRRRRR, MAML-SSSSSSSSSSSSSSSSS, MAML-TTTTTTTTTTTTTTTT, MAML-UUUUUUUUUUUUUUUU, MAML-VVVVVVVVVVVVVVVV, MAML-WWWWWWWWWWWWWWWW, MAML-XXXXXXXXXXXXXXXX, MAML-YYYYYYYYYYYYYYYY, MAML-ZZZZZZZZZZZZZZZZ, MAML-AAAAAAAAAAAAAAAAA, MAML-BBBBBBBBBBBBBBBBB, MAML-CCCCCCCCCCCCCCCCC, MAML-DDDDDDDDDDDDDDDDD, MAML-EEEEEEEEEEEEEEEEE, MAML-FFFFFFFFFFFFFFF, MAML-GGGGGGGGGGGGGGGGG, MAML-HHHHHHHHHHHHHHHHH, MAML-IIIIIIIIIIIIIIIIII, MAML-JJJJJJJJJJJJJJJJJJ, MAML-KKKKKKKKKKKKKKKKKK, MAML-LLLLLLLLLLLLLLLLL, MAML-MMMMMMMMMMMMMMMMMM, MAML-NNNNNNNNNNNNNNNNN, MAML-OOOOOOOOOOOOOOOOOO, MAML-PPPPPPPPPPPPPPPPPP, MAML-QQQQQQQQQQQQQQQQQQ, MAML-RRRRRRRRRRRRRRRRRR, MAML-SSSSSSSSSSSSSSSSSS, MAML-TTTTTTTTTTTTTTTTT, MAML-UUUUUUUUUUUUUUUUU, MAML-VVVVVVVVVVVVVVVVV, MAML-WWWWWWWWWWWWWWWWW, MAML-XXXXXXXXXXXXXXXXX, MAML-YYYYYYYYYYYYYYYYY, MAML-ZZZZZZZZZZZZZZZZZ, MAML-AAAAAAAAAAAAAAAAAA, MAML-BBBBBBBBBBBBBBBBBB, MAML-CCCCCCCCCCCCCCCCCC, MAML-DDDDDDDDDDDDDDDDDD, MAML-EEEEEEEEEEEEEEEEEE, MAML-FFFFFFFFFFFFFFFFFF, MAML-GGGGGGGGGGGGGGGGGG, MAML-HHHHHHHHHHHHHHHHHH, MAML-IIIIIIIIIIIIIIIIIII, MAML-JJJJJJJJJJJJJJJJJJJ, MAML-KKKKKKKKKKKKKKKKKKK, MAML-LLLLLLLLLLLLLLLLLL, MAML-MMMMMMMMMMMMMMMMMMM, MAML-NNNNNNNNNNNNNNNNNN, MAML-OOOOOOOOOOOOOOOOOOO, MAML-PPPPPPPPPPPPPPPPPPP, MAML-QQQQQQQQQQQQQQQQQQQ, MAML-RRRRRRRRRRRRRRRRRRR, MAML-SSSSSSSSSSSSSSSSSSS, MAML-TTTTTTTTTTTTTTTTTT, MAML-UUUUUUUUUUUUUUUUUU, MAML-VVVVVVVVVVVVVVVVVV, MAML-WWWWWWWWWWWWWWW

FAQ:

How exactly does the AI cut the scouting report writing time in half—what parts of the workflow disappear?

The heavy lifting happens in two steps: data ingestion and first-draft generation. Club analysts still upload the same raw files—GPS traces, event tags, video timestamps—but instead of manually sorting clips and typing summaries, they hit run. The model spits out a structured draft: 200-word player outline, 15-key-moment table, and a 1-page visual dashboard. Staff then trim, re-order, or add nuance. Tasks that used to eat 3-4 h (clip sorting, stat cross-checks, wording) shrink to 30-40 min. Only quality control and opinionated phrasing stay human.

We work with youth players; the data set is tiny and messy. Will the AI produce gibberish?

The system was re-trained on 80 k youth records, not pro matches, so it expects lower signal-to-noise ratios. If your clip count for a 16-year-old winger is 12 events, the model switches to sparse mode: it widens the similarity search to U-17 regional games, shortens the commentary, and flags any extrapolated numbers in red. You get a 6-skill bullet summary instead of a full prose report. Accuracy stayed within 7 % of hand-written notes in beta tests with three academy teams.

Does the league allow us to feed opposition data into a cloud model without breaching privacy rules?

The platform keeps a no-leak copy inside your VPC; nothing crosses to the vendor. You can still benchmark against league averages because those are shipped as encrypted aggregates—no raw event streams. GDPR and most federation addendums treat this the same as storing video on a local server. One Dutch side ran it past their DPO and got written clearance in ten days.

Scouts here worry the text will all sound the same and kill their personal voice. Any fix?

Each analyst records a 15-minute voice memo; the model distills sentence length, slang, and punctuation habits into a 256-number style vector. When the draft is generated, it weaves in those patterns. Early adopters in Belgium A/B-tested paragraphs: 82 % of coaches could not spot the AI paragraph written in a veteran scout’s tone. You can also lock certain phrases—e.g., presses like a rabid dog—so they appear verbatim.

What’s the real cost once you move past the pilot? Clubs quote wildly different numbers.

Annual licence is tiered by match volume: €18 k for up to 60 games, €32 k for unlimited. Hardware stays yours; the GPU box they ship handles 30 clubs, so you can split the cost with a partner club. One Championship side added €4 k in setup (rack, cables, SSL certificate). Support hours are metered: first 40 h free, then €150/h. All in, they landed at €22 k first year, dropped to €14 k after that—less than half a junior analyst’s salary.

How exactly do the AI models cut the scouting report time in half—what parts of the workflow are they taking over?

They handle the two slowest chunks: video logging and first-draft writing. After each match, analysts upload the raw footage to a club-controlled server. A fine-tuned vision model tags every 15-second clip with player ID, action type (press, pass, dribble, duel, off-ball run, set-piece, etc.) and outcome. Those tags are then piped into a language model that has been trained on the club’s historical reports so it mimics the house style. Within five minutes it spits out a 1 200-word draft that already includes heat-map sentences, percentile ranks vs. league average and a short paragraph on each targeted player. Human scouts still verify clips, add context (dressing-room intel, injury notes, character references) and polish the language, but they skip the 4-5-hour grind of pausing, rewinding and typing LB caught too narrow on 23′. That is where the 50 % saving comes from.

We work with youth tournaments where footage is shaky and player numbers are often hidden—will the model still be useful or does it need broadcast-quality video?

We ran a pilot with exactly those conditions: hand-held phone video, no shoulder numbers, one main camera. The trick is to feed the model several cheap extras instead of the pricey tracking data big clubs buy. First, we stick a GPS pod in one centre-back’s shirt; the model uses that single positional stream to extrapolate team shape. Second, we calibrate the camera once per field with four cones; the vision code turns pixels into metres accurate to ±1.5 m, good enough for youth level. Third, we ask an intern to tap a tablet when goals and cards happen—those time-stamps anchor the auto-clips. With those low-cost crutches the recall on action tags drops only four points versus broadcast video (87 % vs 91 %) and the word count we have to fix rises by about 15 %. The whole report still lands in 45 minutes instead of two hours, so the staff keep using it every tournament weekend. We plan to add a second camera next season; that should push recall back above 90 %.