Chart 1 / drill-and-cone problem
What practice types are generated by age band
The youngest players get the most isolated drill-work.
U6-U9 practices generated on FootballGPT are 59.7% technical, while only 26.5% are small-sided games, the format kids actually learn from. Technical-drill bias is heaviest at the youngest ages and eases as players get older, while tactical work scales up with age.
n = 16,796 categorised animated practices with age band. Bands with fewer than 50 hidden. methodology
Real coach question · what this looks like
“I’m coaching 9U and 10u what are the best ways to teach through passing”
Chart 2 / planning rhythm
When training practices are generated during the week
Monday, not Sunday, is peak planning night.
Practice generation peaks on Monday and Tuesday — coaches plan their week early, not last-minute. Monday is the busiest day overall, with a strong evening peak around 8pm UTC, and the single hottest cell on the heatmap is Tuesday at 9am UTC.
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n = 16,835 animated practices. methodology
Real coach question · what this looks like
“I have a girls team aged 14-15 and I want to work on possession as well as counter pressing and a bit of finishing as well.”
Chart 3 / audience mix
Who actually uses football coaching AI tools
More than one in four queries comes from a Football Manager video-game player.
Coach-mode activity makes up the majority of mode-tagged queries, but Football Manager video-game players are the second-largest audience — sharing the same tool with very different intent.
n = 39,932 mode-tagged queries. Modes with fewer than 50 hidden. methodology
Real coach question · what this looks like
“Can you give trigger examples for defense?”
Chart 4 / age band distribution
Most-generated age bands on FootballGPT
Teenagers, not under-9s, are the most-generated age band.
The 'grassroots = wee kids' assumption is wrong. Senior Youth and Junior bands account for over half of all practice volume; Mini-soccer is the third smallest band by activity.
n = 16,796 animated practices with assignable age band. methodology
Real coach question · what this looks like
“How do we handle wide overloads?”
Cut 5 / pitch concentration
86%
Almost every animated practice puts the action in the middle third.
Computed from the average y-coordinate of all players in each AI-generated practice. Of these, only 1% came from prompts where the coach actually named a pitch zone — the other 99% is where the AI placed players when no zone was requested. Read the middle-third concentration as "where the AI puts the action by default", not as proof coaches ignore defensive or attacking work.
n = 16,786 practices with at least one player; of which 225 (1%) came from prompts that explicitly named a pitch zone. Pitch thirds are computed from each practice's average player y-coordinate (0-100, where 0 is the defending goal line). 'Middle' covers y=33-66. methodology.
Cut 6 / player counts
90%
Of Mini-soccer practices use 5v5 or smaller — the format kids learn best in.
At the smallest age band coaches do design appropriately small. The story changes higher up: Adult coaches favour 8v8+ work; Senior Youth split fairly evenly between 2v2 and full-format. Bands are by total players in the practice: 1v1 (≤2), 2v2-3v3 (3-6), 4v4-5v5 (7-10), 6v6-7v7 (11-14), 8v8+ (15 or more). Note: only 10% of these came from prompts that explicitly named a player count (e.g. "4v4") — the rest is the AI deciding how many players to draw.
| 1v1 | 2v2-3v3 | 4v4-5v5 | 6v6-7v7 | 8v8+ | total | |
|---|---|---|---|---|---|---|
| Mini (U6-U9) | 167 | 843 | 714 | 177 | 15 | 1,916 |
| Junior (U10-U12) | 276 | 1,584 | 1,745 | 922 | 203 | 4,730 |
| Youth (U13-U15) | 229 | 870 | 938 | 966 | 453 | 3,456 |
| Senior Youth (U16-U18) | 275 | 878 | 1,012 | 807 | 692 | 3,664 |
| Adult (U19+) | 183 | 550 | 607 | 605 | 623 | 2,568 |
| Mixed | 29 | 167 | 129 | 72 | 55 | 452 |
n = 16,786 practices with both age band and player count; of which 1,756 (10%) came from prompts that explicitly named a player count (e.g. "4v4"). Player count is derived from drill_data.players[]; bands are 1v1, 2v2-3v3, 4v4-5v5, 6v6-7v7, 8v8+.
Cut 7 / cohort profile
60%
Mini-soccer's category profile is the most technical-dominant of any age band.
Each polygon is one age band; each axis is that band's share of practices in that category, normalised within the six axes plotted. Older bands open out into tactical, game-based and set-piece work, but technical still dominates everywhere — the shape change is gradual, not a flip. Coach-intent caveat: 25% of these rows came from prompts that explicitly named a category; the remainder reflects the AI's category fallback when no signal was given.
Categories: technical, tactical, game-based, set-piece, warm-up, physical. Each axis is the band's share of practices in that category. Bands plotted: Mini, Junior, Youth, Senior Youth (top 4 by volume). 25% of underlying rows had an explicit category in the coach prompt. methodology
Cut 8 / weekly trends
8,782
Practices generated across the last 12 weeks. Peak week: 1,387.
The cadence reveals coaching seasonality and the impact of FootballGPT releases. The mode-mix stack underneath shows whether new audiences (FM, Player, Scout) are growing or shrinking against the coach baseline.
Animated practices per week
Peak week: 27 Jul (1,387 practices)
Mode mix per week (queries)
Cut 9 / animation complexity
3.0 → 3.4steps
Practice complexity barely scales with age.
Average sequence step count per practice (each 'step' is one phase of the animation). Mini practices average ~3 steps; Adult barely reaches 4. Either coaches genuinely want short practices regardless of age, or the AI tends to produce a similar number of steps regardless of prompt.
Deep cuts / FootballGPT
Underneath the four anchor charts
The same FootballGPT data, sliced more ways: what topics coaches are asking about, which features they use most, the formations they pick by team format, the techniques they analyse, the languages they study, and the qualifications they hold.
- Questions answered
- 33,000+
- Practices created
- 1000+
- Countries
- 30+
- Coaches who came back
- 66%
- generated 2+ practices
Cut A / topics
What coaches are asking about
Twelve coaching topics detected by keyword match across every chat query. A single query may match more than one topic.
All topics
Top topics — grassroots
Top topics — academy
Top topics — professional
Cut B / tools
Which tools coaches use
FootballGPT exposes a dozen specialised tools alongside chat. Share of total tool events:
Cut C / formations
Formations coaches actually pick
Coaches state their preferred formation in their profile or pick one in match-prep. Shown by team format.
11v11
9v9
7v7
5v5
Cut D / who they are
Demographics from FootballGPT profiles
Self-stated by users in their FootballGPT profile. Where percentages don't sum to 100, the underlying field is multi-select or partially populated.
Years coaching
Qualifications held
Team formats coached
How they use the tool
Cut E / techniques & languages
Skills coaches analyse, languages they study
Technique-analyser uploads (which skill)
Football Lingo languages studied · 61% average accuracy
Cut F / keywords
What words show up in the practice prompts
Top tokens extracted from the prompts coaches send to the practice generator. Stop-words and the words "drill" / "practice" are filtered out.
Cross-product
Coaching qualifications, post-session reflections and community discussion
The charts above come from FootballGPT. Separate 360TFT product cohorts provide supporting context below. They are not joined coach journeys and are not directly comparable unless stated.
Chart 6 / what coaches reflect on
From CoachReflect: tags, mood, energy, level, session type
After a session, what do coaches think about? CoachReflect users tag each reflection, rate their mood and energy, log session type, and self-classify their coaching level. Free-text reflection content is never published — only the structured fields below. This is a small, early cohort — see the sample size below rather than reading the shares as population-level.
Top reflection tags
Coaching level (self-stated)
Post-session mood (1-5)
Post-session energy (1-5)
Session type
n = 97 reflections from 27 coaches. Most profiles do not specify a coaching level (onboarding does not force one). Source: coachreflection.com.
FAQ
Common questions about grassroots coaching, answered from the data
Each answer below is grounded in the live numbers shown above, refreshed weekly. Where the underlying cohort is small, the answer says so.
▸What do FootballGPT users generate practices for most?
The most common topic detected across FootballGPT activity is General Coaching at 33%. Technical practices also dominate the generation mix — 59.7% for U6-U9, falling to 37% for adult football.
see the chart →▸What practice types work best for U6, U7, U8, U9?
In our data, 59.7% of practices generated for U6-U9 are technical, only 26.5% are small-sided games. Most coaching guidance for this age band recommends the reverse — game-based learning is how children actually internalise football. The data shows the gap between guidance and practice.
see the chart →▸When are training practices generated during the week?
Monday and Tuesday dominate, with Monday the busiest day overall and a strong evening peak around 8pm UTC. The single hottest cell on the heatmap is Tuesday at 9am UTC. Sunday-night planning, despite the stereotype, is not the busiest slot. Generation peaks early in the week, not last-minute.
see the chart →▸What age band is most-generated on FootballGPT?
Senior Youth (U16-U18) and Junior (U10-U12) are the largest bands by practice volume. Mini-soccer (U6-U9) is the third smallest. The "grassroots = wee kids" assumption does not hold up. Largest band: Junior (U10-U12).
see the chart →▸Are grassroots coaches the same audience as Football Manager players?
No. Of all mode-tagged queries, 48.7% come from coach mode and 43.2% from Football Manager video-game mode. They share the same AI tooling but with very different intent. Roughly one in four queries to a "football coaching AI" comes from someone playing the FM video game.
see the chart →▸What do FootballGPT users ask about most?
Topic detection across every query: General Coaching (33%), Formations & Tactics (15%), Session Planning (13%), Pressing & Defending (11%), Passing & Possession (9%). A single query can match more than one topic.
see the chart →▸What do football coaches reflect on after sessions?
From CoachReflect: top tags coaches attach to their post-session reflections include player_development, session_planning, tactical, communication, technique. This is a small, early cohort — see the CoachReflect panel for the current sample size. Free-text reflection content is never published — only structured tags and ratings.
see the chart →▸How is this dataset refreshed and anonymised?
The dataset refreshes weekly and every published chart applies a minimum cohort size before a figure is shown. See the methodology page for the exclusions, thresholds, and what each source does and does not cover.
see the chart →Use the data
All charts are aggregate. No row-level data, no PII, no club or coach identifiers — ever. For interviews, additional cuts, or a press-ready summary, get in touch.