• Gaming
  • 15 columns
  • 120 rows
  • 8 formats

Esports Team Remote Training KPIs

This dataset provides granular tracking of remote esports team training sessions, capturing attendance, skill assessments, network latency, and communication effectiveness for players across multiple regions. Designed for esports managers and coaches, it enables data-driven optimization of remote training strategies and team development, supporting both operational oversight and performance analytics.

  • last updated 10 Dec 2025
  • by GoMask
The brief that made it

Monitor and optimize remote esports team training and performance

Sample rows

preview · 8 of 120 rows · all 15 columns
team_idstringattendance_statusstringcommunication_effectiveness_scorefloatregionstringteam_namestringplayer_idstringplayer_namestringplayer_rolestringsession_idstringsession_datedatetimeskill_assessment_scorefloatlatency_msintegerfeedback_notesstringcoach_idstringcoach_namestring
TEAM001present9.4South KoreaQuantum TitansP001JinxcarryS100012024-06-02T15:00:0096.218blankC001Minjun Song
TEAM002present8.7GermanyPhoenix ForceP002BlitzsupportS100022024-06-03T18:30:0081.532Excellent support during clutch moments.C002Hans Müller
TEAM003present9.2CanadaPolar VortexP003NovacarryS100032024-06-02T19:45:0090.122blankC003Aiden Clark
TEAM004present8.9USAIron ReignP004AcesupportS100042024-06-04T14:15:0087.728blankC004Jessica Lee
TEAM005late4.1FinlandNordic WolvesP005FrostcarryS100052024-06-02T16:00:0052.3210Arrived late, distracted during teamfight.C005Oskar Virtanen
TEAM005present7.6FinlandNordic WolvesP006RunesupportS100052024-06-02T16:00:0074.890blankblankblank
TEAM006present5.3AustraliaEclipse OriginP007ShirocarryS100062024-06-05T13:00:0061.7370High latency affected precision.C006Brian Miller
TEAM006present6.7AustraliaEclipse OriginP008EchosupportS100062024-06-05T13:00:0067.4320blankblankblank

What the 120 rows show

from the 120-row sample

Late (attendance status) stands out: mean communication_effectiveness_score is 2.9, against 7.7 for the rest.

  • 8.2median communication_effectiveness_score
  • 5player roles
  • 81.5median skill_assessment_score
  • 57.5median latency_ms
Mean communication_effectiveness_score by attendance_status107 rows
0487.7present95 rows2.9late12 rows
communication_effectiveness_score107 rows, in bands of 1
015302787661328301610communication_effectiveness_score →

Median 8.2, from 1.2 to 9.9.

region120 rows · top 10 of 25 values
  1. Germany10
  2. Canada9
  3. South Korea8
  4. Russia8
  5. Sweden8
  6. USA7
  7. Finland7
  8. Australia7
  9. Nigeria7
  10. Mexico6
15 columns by typefrom the column list below
  • string 11
  • integer 1
  • float 2
  • datetime 1

Columns

15 columns in three groups
blueprint · 15 columns
columntypedescriptionexample
Text 11 columns
team_idstringUnique identifier for the esports teamTEAM001
team_namestringOfficial name of the esports teamQuantum Titans
regionstringGeographic region or country where the team is basedSouth Korea
player_idstringUnique identifier for the playerP001
player_namestringFull name or gamer tag of the playerJinx
player_rolestringPlayer's role or position within the team (e.g., support, carry, coach)5 rolescarry
session_idstringUnique identifier for the training sessionS10001
attendance_statusstringIndicates if the player attended the session (present, absent, late)present · absent · latepresent
feedback_notesstringManager or coach feedback notes for the player in this sessionoptionalImproved coordination.
coach_idstringUnique identifier for the coach overseeing the sessionoptionalC001
coach_namestringFull name of the coachoptionalMinjun Song
Numbers 3 columns
skill_assessment_scorefloatNumerical score representing the player's skill assessment for the session (0-100)0 to 100 · optional96.2
latency_msintegerAverage network latency in milliseconds recorded during the session for the player0 or more · optional18
communication_effectiveness_scorefloatScore (0-10) reflecting the player's communication effectiveness during the session0 to 10 · optional9.4
Dates and times 1 column
session_datedatetimeDate and time when the training session occurred2024-06-02T15:00:00

Use it for

  • median communi…8.2107 rowsmean communication ef…7.7present2.9late

    A gaming dashboard

    Communication_effectiveness_score by attendance_status and a breakdown of region. Excel, Power BI or Tableau.

  • Why do the 12 late rows have a mean communication_effectiveness_score of 2.9?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable teams with team_name, region and player_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 15 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset120 rows15 columns
Yours1,000 rows15 columnsregion: UK only

blueprint · esports-team-remote-training-kpis

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Each row represents a unique team member’s training session.
  • Session attendance status must be recorded for every entry.
  • Skill assessment scores range from 0 (novice) to 100 (expert).
  • Latency and connectivity must be logged for remote sessions.
  • Communication effectiveness is rated on a standardized scale.
  • Team members are grouped by regional cluster for analysis.
Rows
Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
esports-team-remote-training-kpis

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