• Gaming
  • 17 columns
  • 200 rows
  • 8 formats

Gaming Matchmaking Performance Metrics

This dataset offers detailed, player-level records for online competitive matches, including matchmaking balance, player performance, satisfaction ratings, and algorithm usage. It enables game studios to evaluate matchmaking systems, optimize player experience, and identify factors influencing match outcomes and satisfaction. Ideal for analytics, A/B testing, and improving competitive game design.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Analyzing matchmaking algorithm effectiveness and balance

Sample rows

preview · 8 of 200 rows · all 17 columns
match_idstringmatchmaking_algorithmstringmatch_balance_scorefloatregionstringgame_titlestringmatch_start_timedatetimematch_end_timedatetimematch_duration_secondsintegerplayer_idstringplayer_teamstringplayer_skill_rating_pre_matchfloatplayer_skill_rating_post_matchfloatplayer_performance_scorefloatplayer_satisfaction_ratingintegermatch_outcomestringplayer_feedback_textstringplayer_ping_msinteger
M-10001MM-Elite0.98NA-EastBattle Legends2023-09-12T18:07:43Z2023-09-12T19:08:03Z3620P-RA401Red2125.52146.291.45winAmazing match! Very intense and well balanced.24
M-10002MMv2.00.39AsiaMystic Quest2024-03-20T13:24:18Z2024-03-20T13:50:40Z1582P-BE963Beta675.8650.218.72lossUnfair teams, not enjoyable.204
M-10003MMv3.00.65EU-WestSpace Turf2024-05-08T22:55:11Z2024-05-09T00:38:17Z6246P-GN712Green2380.72380.741.23drawLong match, ended in a draw. Balanced overall.51
M-10004MM-Experimental0.03AfricaBattle Legends2022-04-19T14:41:56Z2022-04-19T14:42:08Z12P-BL101Blue110.3110.301drawMatch crashed, couldn't play.823
M-10005MMv3.00.86EU-NorthMystic Quest2023-12-15T09:05:09Z2023-12-15T09:31:43Z1594P-OM228Omega2987.92998.567.14winIntense game, loved the challenge!39
M-10006MMv1.00.17OceaniaSpace Turf2021-07-11T21:33:49Z2021-07-11T21:40:11Z382P-YE341Yellow210.7207.99.21lossPoor connection and lag.645
M-10007MMv2.10.71NA-WestBattle Legends2024-02-28T17:19:22Z2024-02-28T17:59:44Z2422P-RE532Red2050.32050.335.73drawClose match, teams felt even.48
M-10008MMv2.00.29AsiaMystic Quest2024-04-07T10:05:31Z2024-04-07T10:11:49Z378P-AL458Alpha1482.21468.215.42lossTeams were very unbalanced.217

What the 200 rows show

from the 200-row sample

MMv1.0 (matchmaking algorithm) stands out: mean match_balance_score is 0.14, against 0.65 for the rest.

  • 0.66median match_balance_score
  • 3game titles
  • 3match outcomes
  • 7player teams
  • 2,400median match_duration_seconds
  • 1,448median player_skill_rating_pre_match
Mean match_balance_score by matchmaking_algorithm200 rows
00.510.60MMv2…48 ro…0.68MMv2…48 ro…0.94MM-E…30 ro…0.90MMv3…23 ro…0.14MMv1…23 ro…0.04MM-E…14 ro…0.28MMv1…14 ro…
match_balance_score200 rows, in bands of 0.1
0204027105814232434154000.51match_balance_score →

Median 0.66, from 0.01 to 1.0.

region200 rows · top 10 of 11 values
  1. Asia36
  2. NA-East23
  3. EU-North23
  4. EU-West21
  5. Africa19
  6. EU-Central19
  7. Oceania18
  8. NA-West16
  9. SA14
  10. NA-Central7
17 columns by typefrom the column list below
  • string 8
  • integer 3
  • float 4
  • datetime 2

Columns

17 columns in three groups
blueprint · 17 columns
columntypedescriptionexample
Text 8 columns
match_idstringUnique identifier for each game match.uniqueM-10001
game_titlestringName of the game for which the match was played.3 titlesBattle Legends
player_idstringUnique identifier for each player in the match.P-RA401
player_teamstringTeam name or identifier the player was assigned to during the match.7 teamsRed
matchmaking_algorithmstringName or version of the matchmaking algorithm used for this match.7 algorithmsMM-Elite
match_outcomestringOutcome of the match for the player (e.g., win, loss, draw).win · loss · drawwin
player_feedback_textstringOptional free-text feedback provided by the player after the match.optionalPoor connection and lag.
regionstringGeographical region or server location where the match was hosted.11 regions · optionalNA-East
Numbers 7 columns
match_duration_secondsintegerTotal duration of the match in seconds.0 or more3620
player_skill_rating_pre_matchfloatPlayer's skill rating before the match started.0 or more2125.5
player_skill_rating_post_matchfloatPlayer's skill rating after the match ended.0 or more2146.2
player_performance_scorefloatComposite score representing player's performance in the match.0 or more91.4
player_satisfaction_ratingintegerPlayer's post-match satisfaction rating (e.g., 1-5 scale).1 to 5 · optional5
match_balance_scorefloatScore indicating how balanced the match was (e.g., based on skill disparity).0 to 10.98
player_ping_msintegerPlayer's average network latency (ping) in milliseconds during the match.0 or more · optional24
Dates and times 2 columns
match_start_timedatetimeTimestamp when the match started.2023-09-12T18:07:43Z
match_end_timedatetimeTimestamp when the match ended.2023-09-12T19:08:03Z

Use it for

  • median match b…0.66200 rowsmean match balance sc…0.60MMv2…0.68MMv2…0.94MM-E…0.90MMv3…

    A gaming dashboard

    Match_balance_score by matchmaking_algorithm and a breakdown of region. Excel, Power BI or Tableau.

  • Why do the 23 MMv1.0 rows have a mean match_balance_score of 0.14?

    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 matches with game_title, match_start_time and match_end_time to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 17 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows17 columns
Yours1,000 rows17 columnsregion: UK only

blueprint · gaming-matchmaking-performance-metrics

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 17 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
  • Player skill ratings and ranks
  • Match outcomes and durations tracked
  • Balance scores calculated per match
  • Satisfaction feedback included
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
gaming-matchmaking-performance-metrics

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