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Skill-based matchmaking systems, ranking algorithms, and queue management for fair multiplayer matches
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Asynchronous programming models including coroutines, async/await, and reactive patterns
Game server communication protocols including gRPC, REST, and custom binary protocols
Efficient data serialization for game networking including Protobuf, FlatBuffers, and custom binary
基于 SOC 职业分类
正在显示 SKILL.md
| name | matchmaking |
| description | Skill-based matchmaking systems, ranking algorithms, and queue management for fair multiplayer matches |
| sasmp_version | 1.3.0 |
| version | 2.0.0 |
| bonded_agent | 03-matchmaking-engineer |
| bond_type | PRIMARY_BOND |
| parameters | {"required":["algorithm"],"optional":["queue_timeout_s","skill_range_expansion","team_size"],"validation":{"algorithm":{"type":"string","enum":["elo","trueskill","glicko2","custom"]},"queue_timeout_s":{"type":"integer","min":30,"max":600,"default":120},"skill_range_expansion":{"type":"number","min":0.1,"max":2,"default":0.5},"team_size":{"type":"integer","min":1,"max":100,"default":5}}} |
| retry_config | {"max_attempts":3,"backoff":"linear","initial_delay_ms":5000,"max_delay_ms":30000,"retryable_errors":["QUEUE_TIMEOUT","INSUFFICIENT_PLAYERS"]} |
| observability | {"logging":{"level":"info","fields":["player_id","mmr","queue_time_ms","match_quality"]},"metrics":[{"name":"matchmaking_queue_time_seconds","type":"histogram"},{"name":"matches_created_total","type":"counter"},{"name":"queue_size","type":"gauge"},{"name":"match_quality_score","type":"histogram"}]} |
Implement fair skill-based matchmaking for competitive multiplayer games.
| Algorithm | Accuracy | Convergence | Use Case |
|---|---|---|---|
| Elo | Good | Fast | 1v1 games |
| TrueSkill | Excellent | Medium | Team games |
| Glicko-2 | Excellent | Slow | Chess, turn-based |
| Custom | Variable | Variable | Specialized |
def calculate_elo_change(winner_mmr, loser_mmr, k=32):
"""Calculate MMR change for a match result."""
expected = 1 / (1 + 10 ** ((loser_mmr - winner_mmr) / 400))
return k * (1 - expected)
# Example
mmr_change = calculate_elo_change(1500, 1400)
winner_new = 1500 + mmr_change # ~1512
loser_new = 1400 - mmr_change # ~1388
import trueskill
def calculate_trueskill_match(team1, team2, winner):
"""Update ratings after team match."""
env = trueskill.TrueSkill(draw_probability=0.0)
# Convert to Rating objects
t1_ratings = [env.create_rating(p.mu, p.sigma) for p in team1]
t2_ratings = [env.create_rating(p.mu, p.sigma) for p in team2]
winner == :
new_t1, new_t2 = env.rate([t1_ratings, t2_ratings], ranks=[, ])
:
new_t1, new_t2 = env.rate([t1_ratings, t2_ratings], ranks=[, ])
new_t1, new_t2
class MatchmakingQueue:
def __init__(self, config):
self.queue = []
self.config = config
def add_player(self, player):
entry = QueueEntry(
player_id=player.id,
mmr=player.mmr,
enter_time=time.time(),
skill_range=self.config.initial_range
)
self.queue.append(entry)
self.try_match()
def try_match(self):
# Sort by wait time
self.queue.sort(key=lambda e: e.enter_time)
for entry in self.queue:
# Expand range over time
wait_time = time.time() - entry.enter_time
entry.skill_range = min(
self.config.initial_range + wait_time * self.config.expansion_rate,
self.config.max_range
)
# Find compatible players
candidates = [p for p in self.queue
if abs(p.mmr - entry.mmr) <= entry.skill_range
and p != entry]
if len(candidates) >= self.config.team_size * 2 - 1:
self.create_match(entry, candidates)
return
def calculate_match_quality(team1, team2):
"""Score match quality 0-1 based on skill balance."""
avg_t1 = sum(p.mmr for p in team1) / len(team1)
avg_t2 = sum(p.mmr for p in team2) / len(team2)
mmr_diff = abs(avg_t1 - avg_t2)
# Sigmoid decay based on MMR difference
quality = 1 / (1 + mmr_diff / 200)
return quality
| Error | Root Cause | Solution |
|---|---|---|
| Long queue times | Too strict matching | Increase expansion rate |
| Unbalanced matches | Fast expansion | Tune quality threshold |
| Rating inflation | No decay | Add seasonal reset |
| Smurf accounts | No placement | Require placement matches |
# Monitor queue health
def queue_diagnostics(queue):
print(f"Queue size: {len(queue.queue)}")
print(f"Avg wait time: {queue.avg_wait_time():.1f}s")
print(f"Match rate: {queue.matches_per_minute():.2f}/min")
# MMR distribution
mmrs = [e.mmr for e in queue.queue]
print(f"MMR range: {min(mmrs)} - {max(mmrs)}")
print(f"MMR median: {statistics.median(mmrs)}")
# Check match quality
def validate_match(match):
quality = calculate_match_quality(match.team1, match.team2)
assert quality >= 0.6, f"Low quality match: {quality}"
import pytest
class TestMatchmaking:
def test_elo_calculation(self):
change = calculate_elo_change(1500, 1400)
assert 10 < change < 20 # Expected range
def test_queue_matching(self):
queue = MatchmakingQueue(config)
# Add 10 similar-skill players
for i in range(10):
queue.add_player(Player(mmr=1500 + i*10))
# Should create a match
assert queue.matches_created == 1
def test_match_quality(self):
team1 = [Player(mmr=1500), Player(mmr=1520)]
team2 = [Player(mmr=1480), Player(mmr=1540)]
quality = calculate_match_quality(team1, team2)
assert quality >= 0.9 # Good balance
def test_queue_timeout(self):
queue = MatchmakingQueue(config)
queue.add_player(Player(mmr=3000)) # Outlier
# Fast forward time
time.sleep(config.queue_timeout_s)
# Should trigger timeout callback
assert queue.timed_out_count == 1
assets/ - Matchmaking templatesreferences/ - Algorithm papers