CHAMP: Cross-domain Hybrid Architecture for Matchmaking and Prediction in Online Multi-Player Games

• URL: http://arxiv.org/abs/2609.04870v1
• Date: Fri, 04 Sep 2026 08:30:29 GMT
• Title: CHAMP: Cross-domain Hybrid Architecture for Matchmaking and Prediction in Online Multi-Player Games
• Abstract summary: Multiplayer Online Battle Arena (MOBA) games rely on matchmaking to maintain competitive balance.<n>We present CHAMP, a cross-domain matchmaking framework that resolves deployment bottlenecks.<n>CHAMP replaces the target-mode-only player profile with a hybrid domain feature collection.<n>It achieves 67.73% win-rate prediction accuracy, outperforming all evaluated attention and sequence baselines.
• Score: 11.51314302493261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiplayer Online Battle Arena (MOBA) games rely on matchmaking to maintain competitive balance. Our prior work, CUPID, framed matchmaking as an assignment re-optimization problem and showed that a single-mode win-rate predictor can meaningfully rebalance teams. However, deploying such a system across diverse player populations exposes three practical bottlenecks: most queueing players lack sufficient in-mode match history (cold start), skill distributions shift drastically across rank tiers (distribution inconsistency), and extreme skill segments are severely data-starved. We present CHAMP, a cross-domain matchmaking framework that resolves these deployment bottlenecks. To address data sparsity and cold starts, CHAMP replaces the target-mode-only player profile with a hybrid domain feature collection: a timestamp-ordered cross-mode short-term sequence whose slices are annotated with target-domain features, plus per-mode breakdowns of long-term, real-time and team statistics. We further propose the Domain-Aware Win-rate Network (DAWN): a Domain-aware Knowledge Extractor (DAKE) compiles target-mode attributes into learnable representations that feed Domain-Aware Temporal/Spatial/Permutation OmniNet Encoders (DATOE/DASOE/DAPOE), so that mode-conditioned representations and per-mode debiasing are learned jointly inside a single shared network. Online, one trained DAWN serves every supported mode, with per-mode position-satisfaction thresholds as the only mode-specific knob. Offline, DAWN achieves 67.73% win-rate prediction accuracy, outperforming all evaluated attention and sequence baselines. Online A/B tests across the entire League ladder of a large-scale MOBA game, from novice players up to the top-expert players served by Elite Mode, demonstrate consistent drops in imbalanced matches. For lower-tier players, CHAMP reduces the 5-minute kill crushing rate by up to 20.73%.

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