Torrent Gear

Tournament Participant Segmentation with AI

1) Why segment tournament players

AI segmentation helps:

Honestly sow and match make (MMR/leagues, qualifying baskets).
Personalize tasks and schedules (time slots, event length).
Manage the prize economy (target coverage and award issuance).
Reduce risk and burden (RG guards, anti-abuse).
Increase retention due to relevant goals and sensitive meta-progression.

2) Data and signals

Gaming/Tournament Behavior

Temp: spin/min, medium and dispersion.

Nature of participation: frequency of events, length of qualifiers, share of finishes.

Variety of content: providers/genres, novelty.

Skill & Competition

Position history (top X%, final tables), result stability.

MMR/Elo, K-factor, league promotion response.

Economy

Proxy values: turnover/frequency of deposits (aggregated), sensitivity to rewards (conversion to participation at the announcement).

Social cues

Chat/clip/community activity, reporting and hustling posts.

Context and RG

Time of day, device, consecutive sessions, limits and RG flags (to reduce load).

💡 All signals - aggregated, no PII above regulatory minimum.

3) Ficering (examples)

Stability of the result: coefficient of variation of the position, P75→P25 delta.

Skill gradient: MMR gain/loss after inter-divisional transition.

Time participation: hits by hour/day of the week, autocorrelation.

Content diversity: provider/genre entropy.

Economic sensitivity: uplift participation to promo/boosts.

RG load: average duration and speed of sessions, streak warnings.

4) Segmentation model stack

1. Clustering (unsupervised): K-Means/HDBSCAN for behavioral segments.

2. Embeddings:

User2Vec by provider/event sequences (Skip-gram), Game2Vec for content proximity → better grouping of "interests."
3. Graph segmentation: Community Detection - useful for catching collusions/party games.
4. Supervised: probability of participation/finish/rollback after losses.
5. Mixed typology: final segments = combination of × skill behavior × × risk economics.

5) Example of typology (skeleton)

S1 "Sprinter-qualifier": short intense runs, high peaks, low stability.

S2 "Stayer-tournament": long qualifiers, stable top 25%, average speed.

S3 "Collector Content": high entropy of providers, loves missions of "variety."

S4 "Master Finals": high MMR, narrow pool of providers, high% of final tables.

S5 "Seasonal Hunter": Active in waves during boost/event periods.

S6 "RG risk signal": signs of fatigue/long strike sessions - requires gentle scenarios.

6) Link-up with leagues and seeding

Segments do not replace MMR, but enrich it: the segment affects the length of the qualifiers, the type of tasks, the schedule, but not the mathematical odds/rules.

Placement matches + quick up/down with an explicit mis-match between the segment and the current league.

Fairness: VIP status does not affect MMR and does not give an advantage in the match.

7) Using segments in practice

Tournament formats: sprint/marathon/mixed under S1/S2.

Micro tasks: variety of providers for S3, tempo control for S1.

Schedule: personal slot recommendations for familiar activity.

Awards: Focus on Cosmetics/Sets; rarities - common to all, without pay-to-win.

Communications: text/tonality, strategy tips (ethics-neutral).

RG guards: for S6 - soft pauses, limitation of mission lengths, reduced complexity.

8) Anti-abuse and compliance

Collusion/smurfing: graph signals and behavioral biometrics; random KYCs at master leagues.

Rate limiting: cap on attempts/re-entry; cooling during repeated cycles.

Fairness: the ceiling on the value of awards is the same; segmentation changes path/UX, not win EV.

Transparency: "How segmentation works" screen: general principles, no disclosure of internal weights.

9) Success metrics

Uplift D7/D30 by segment vs control.

Participation Rate/Completion Rate missions and qualifiers.

SP distribution (Gini) - evenness of seasonal progress.

P95 time to reward - variance control.

Complaint/Abuse rate, Smurf/Collusion flags.

RG metrics: proportion of soft pauses, decrease in extra-long sessions.

Prize ROI/Emission to GGR - Sustainability of the Promotional Economy.

10) A/B patterns

1. Segmentation of K-Means vs HDBSCAN (noise immunity, cluster stability).

2. With the addition of embeddings vs without them (quality of format recommendations).

3. Micro-problems: one vs two parallel.

4. Time slots: personal vs fixed.

5. RG-Guards threshold: soft vs strict.

6. Length of qualifiers: short vs long for S1/S2.

11) JSON templates

Player segment card (aggregates + tags):

json
{
"user_id": "u_87421",  "segments": ["S1_sprinter", "S3_collector"],  "mmr": 1420,  "features": {
"pace_spm_med": 52,   "pace_spm_cv": 0. 31,   "finish_top10_rate": 0. 18,   "provider_entropy": 1. 92,   "evening_participation_rate": 0. 64
},  "rg_flags": {"long_sessions": true, "cooldown_suggested": true},  "updated_at": "2025-10-24T10:00:00Z"
}

Decision on the format of the tournament/tasks:

json
{
"decision_id": "d_s3_2025_10_24_1000",  "user_id": "u_87421",  "recommendation": {
"qualifier_format": "sprint_20min",   "time_slot": "evening",   "micro_tasks": [
{"type":"pace_control","max_spm":48,"duration_min":20},    {"type":"provider_diversity","providers":3}
],   "reentry_cap": 1
},  "fairness": {"vip_neutral": true, "reward_cap_equivalent": true},  "rg": {"enforced_break_min": 10}
}

12) Pipeline and Production

Architecture:

Events → Kafka/Redpanda → butch/stream feature (1h/24h/7d windows).
Feature Store (online/offline) with SLA delivery.
Clustering/embedding training once every 1-7 days; Online assignment of segments at entry
Solution orchestration: Segmentation API service → Matchmaking/Tasks/Comms.

Pseudo assignment code:

python ctx = build_context(user_id)
x = feature_store. fetch(user_id)
z = user2vec. embed(x. sequence)
cluster = hdbscan. predict(z)
segment = postprocess(cluster, mmr=ctx. mmr, rg=ctx. rg_flags)
emit_segment(user_id, segment)

13) UX and Communications

Lobby with "for you": format, duration, time slots - in one block.

Non-manipulative tone: "We recommend a short qualifier in the evening - that's how you usually play."

Control options: change format/slot, disable personal recommendations.

Quiet VFX: neat task progress markers, no spam.

14) Integrity checklist and RG

Segmentation does not affect RTP/odds in matches.
The ceiling on the value of awards is the same for everyone.
Transparent operating principles page.
Anti-abuse (collisions, smurfing, rate limits) are included.
RG guards are active: pauses, duration limits, reduced complexity.
Decision logs and reason codes.

15) Implementation plan

1. MVP (3-5 weeks): K-Means + Basic Feature; Format/slot recommendations transparency screen.

2. v0. 9: User2Vec/Game2Vec embeddings; HDBSCAN; anti-abuse graph signals.

3. v1. 0: online segment updates, bundling with bandits for tasks; "integrity" reports and RG analysis.

4. Next: RL configuration of task chains by segment; cross-promo, seasonal patterns.

AI segmentation is a layer of meanings over MMR: it does not change the chances, but selects the format, duration, tasks and communications for the player's style. The combination of clustering, embeddings and propensities gives a stable typology; anti-abuse and RG guards keep the system honest; metrics (Gini, P95, ROI emissions) confirm that the tournament ecosystem has become both fairer and more efficient.