Matchmaking Optimization
“What match composition maximizes engagement?”
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A real-world example
What match composition maximizes engagement?
Poor matchmaking drives 35% of competitive game churn. Players who experience 3+ one-sided matches in a row are 4x more likely to quit that session. A competitive title with 10M MAU where average session time drops 5 minutes due to bad matches loses $28M annually in reduced ad revenue and IAP opportunities. Elo-based systems consider only skill, ignoring play style, social dynamics, and frustration thresholds.
How KumoRFM solves this
Graph-learned player intelligence across your entire game ecosystem
Kumo models the full player interaction graph: skill ratings, match outcomes, play style embeddings, social connections, and frustration signals. It learns that matching a high-aggression player with a defensive teammate yields 2x longer sessions than matching two aggressive players. The model optimizes for post-match engagement (did the player queue again?) rather than just win-rate balance, capturing the interplay between competition, social bonds, and play style compatibility.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
PLAYERS
| player_id | skill_rating | play_style | sessions_7d |
|---|---|---|---|
| PLR301 | 1850 | Aggressive | 14 |
| PLR302 | 1820 | Defensive | 22 |
| PLR303 | 1890 | Balanced | 8 |
MATCHES
| match_id | timestamp | mode | duration_min | avg_rating |
|---|---|---|---|---|
| M001 | 2025-03-02 20:15 | Ranked | 28 | 1840 |
| M002 | 2025-03-02 21:00 | Ranked | 12 | 1860 |
PERFORMANCE
| perf_id | match_id | player_id | kills | deaths | played_again |
|---|---|---|---|---|---|
| PF01 | M001 | PLR301 | 15 | 8 | Y |
| PF02 | M001 | PLR302 | 4 | 3 | Y |
| PF03 | M002 | PLR303 | 22 | 2 | N |
SOCIAL_GRAPH
| edge_id | player_a | player_b | type | games_together |
|---|---|---|---|---|
| SG01 | PLR301 | PLR302 | Friend | 45 |
| SG02 | PLR302 | PLR303 | Clan | 12 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT AVG(PERFORMANCE.PLAYED_AGAIN, 0, 1, days) FOR EACH MATCHES.MATCH_ID -- Optimize for re-queue rate, not just win balance
Prediction output
Every entity gets a score, updated continuously
| MATCH_CONFIG | AVG_RATING_DIFF | STYLE_DIVERSITY | PREDICTED_REQUEUE_RATE |
|---|---|---|---|
| Config A | 30 | High | 0.78 |
| Config B | 15 | Low | 0.52 |
| Config C | 25 | Medium | 0.71 |
Understand why
Every prediction includes feature attributions — no black boxes
Match Config B -- Low style diversity, tight rating
Predicted: 52% predicted re-queue rate
Top contributing features
Play style homogeneity
All aggressive
32% attribution
Recent frustration index (team avg)
0.7 (high)
24% attribution
Social connection density
0 friends in match
18% attribution
Session depth (matches played tonight)
5th match
14% attribution
Historical stomp rate for config
38%
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A competitive game with 10M MAU that increases average session time by 5 minutes through better matchmaking generates $28M in additional annual revenue. Kumo optimizes for post-match re-queue rate using play style, social bonds, and frustration signals that Elo alone cannot capture.
Related use cases
Explore more gaming use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
