FolderLoft
Analytics for esports teams

Read every scrim. Walk into the bracket prepared.

FolderLoft applies machine learning to your team's scrims and official matches and turns them into what coaches and analysts work with: round-by-round breakdowns, draft and map-pool statistics, per-player numbers, and short insights that point to the moments worth opening in VOD review.

Upper bracket semifinalMap 2 · Harbor

13 : 9 Win

Opening duels
13–9
Map control
56%
Clutches
3–1
Sample data for illustration.

For the roster and the staff behind it

Each invited team gets one shared space for everyone who prepares for match day.

  • Coaching staff

    Plan the week from what happened in scrims: which rounds to review, which map to drill, which habit to fix before the next official match.

  • Analysts

    Spend the bootcamp on the opponent, not on spreadsheets. Map pools, veto habits and round patterns arrive assembled for every match.

  • Players

    Your own numbers, read against your role, with the rounds behind them and a timestamp to open in the VOD.

From raw match data to the next VOD review

A match is decided across hundreds of small moments. FolderLoft reads all of them and gives your staff and players the numbers and the reasons in one place.

Round-by-round breakdowns

Each scrim and official match becomes a summary and a timeline. Every round shows its side, its outcome, how it ended, its tempo and who won the opening duel.

  • Score progression and both halves at a glance
  • How each round ended and how long it took
  • A VOD timestamp on every round

Per-player numbers

Score, duels won, assists, accuracy, objectives taken, opening duels and clutches for every player, for one match or a whole split.

  • Numbers read against each player's role
  • Trends across matches, not single spikes
  • The rounds behind every number, one click away

Drafts and map pools

Pick rate, ban rate and win rate for every map in the pool, for your team and for the next opponent in the bracket.

  • The map veto history of both teams
  • Win rate by map and by side
  • The compositions you field and how they fare

Scrims, bootcamps and official matches

Every match is tagged as a scrim or an official match, and a bootcamp's scrim blocks are kept together, so practice and stage results are read side by side and never mixed.

  • Scrim blocks grouped by bootcamp
  • Practice form next to official form
  • Opponents followed across events

Tournament preparation

Follow your path through the tournament bracket. Before each series FolderLoft gathers what the next opponent played, picked and banned in its recent matches.

  • The bracket and the next series in one view
  • The opponent's map pool and veto habits
  • Their rounds worth a look before match day

Generated insights

Models trained on match data look for the patterns that decided a game and describe them in short, plain sentences, each linked to the rounds that support it.

  • What turned the match, in a few lines
  • Recurring habits, good and bad
  • Points to open in the next VOD review

How it works

Your team keeps scrimming the way it does. FolderLoft does the reading.

  1. Bring your matches

    Add the match recordings your team already keeps from scrims and official matches. Each one is checked and queued for analysis.

  2. Models read every round

    Machine-learning models reconstruct each round: positions, map control, duels, tempo and the moments that changed it.

  3. Statistics are assembled

    Breakdowns, draft and map-pool numbers, per-player statistics and comparisons join your team's history, by event, bootcamp and map.

  4. Ready for VOD review

    Short findings in plain language, each tied to the rounds and timestamps behind it, ready for the next review with your coaching staff.

Request access

FolderLoft is open by invitation only. There is no public sign-up: invitations go directly to teams, and each invited team receives an access token for its coaching staff, analysts and players.

If your team has received a token, sign in with it.

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