Gaming & Esports Desk — Strategy & Data Inventory

Panel: Opus, Sonnet, Gemini Pro 3.1, Grok 4.2 | Ruling: Boss (Mar 28, 2026)

What This Document Is

Complete strategy and build spec for the Gaming & Esports trading desk. Covers match markets, tournament markets, and meta/awards/Twitch markets on Kalshi. An engineer should be able to read this and know exactly what to build, what data to collect, and how to find edges.

The Business Model

Same as all sports desks. We trade on Kalshi prediction markets. We find mispriced lines by comparing Kalshi prices to sharp reference fair values. For esports, our anchor is a Sharp Composite Line (SCL) built from multiple sharp books, with Pinnacle as the heaviest weight.

Market Size

$56.5M+ in settled Kalshi esports volume. This is real money:

STRATEGY

The Anchor: Sharp Composite Line (SCL)

Pinnacle is the sharpest generalist book for esports but WEAKER than for traditional sports — wider vig (2.5-4% vs 1.5-2%), lower limits, slower line movement on tier-2/3 events. Their esports traders are generalists covering dozens of titles.

We do NOT rely on Pinnacle alone. We build a weighted no-vig composite across multiple sharp books:

SCL = w_pinnacle * pinnacle_novig + w_bet365 * bet365_novig + w_ggbet * ggbet_novig + ...

Weights based on historical closing line accuracy and volume/limit size per book, per title. Pinnacle gets ~50% weight, others split the rest.

Anchor hierarchy by market type:

Data access:

• Pinnacle: Direct scraping via stealth Chromium (already built — same scraper as sports). PRIMARY source.
• The Odds API: BACKUP ONLY — used when Pinnacle scraping fails. Covers Pinnacle + bet365 + Betway for esports.
• GG.bet: Scrape for cross-reference on LoL/CS2 markets.

Why Esports Markets Are Inefficient

Structural reasons edges exist — larger and more persistent than traditional sports:

1. Roster instability — Esports rosters change mid-season constantly. A CS2 team benching their AWPer is equivalent to losing a starting QB, but books are slow to reprice (12-24 hours). Stand-ins for online matches often not repriced at all.

2. Patch meta shifts — Game patches every 2-3 weeks fundamentally change agent/champion/weapon viability. Books price off historical data that is immediately stale post-patch. The 2-6 hour window after patch notes publish is a recurring edge.

3. Map pool asymmetry — CS2 and Valorant matches are best-of-N across maps. Teams have wildly different win rates per map (85% on Mirage, 40% on Ancient). Map veto is predictable, and the actual maps played dramatically shift true probabilities vs. the aggregate line.

4. Online vs. LAN gap — Some teams are "onliners" who overperform online and underperform at LAN (and vice versa). Documented but rarely modeled quantitatively by books.

5. Regional strength miscalibration — Cross-regional matches are systematically mispriced. Books anchor on regional form without adjusting for meta differences and style matchups.

6. Low-information markets — CoD, R6, Overwatch, Rocket League have minimal sharp action. Fattest edges but also lowest Kalshi volume.

7. Information speed — Scrim results, roster leaks, player health/motivation issues travel through Discord, Twitter, and Reddit hours before books react.

8. Casual bettor dominance on Kalshi — Favorites systematically overpriced, underdogs underpriced. Verify this with our $56.5M settled data.

Strategy By Title

CS2 (highest priority after FIFA)

• Best data availability of any esport. HLTV.org is the gold standard.
• Map veto modeling is the single biggest edge. Predict which maps get played, then use map-specific win rates.
• Pistol round win rate is a leading indicator (outsized economic impact on subsequent rounds).
• Track AWP economy — AWP is the highest-impact weapon. Track AWP kill rates per player.
• Focus: map veto prediction + roster-adjusted Elo + patch sensitivity.

LoL ($8.2M+ volume)

• Champion draft is the equivalent of map veto. Draft modeling predicts team comp strength.
• Patch cycle every 2 weeks — champion win rates shift dramatically. Solo queue win rates are a leading indicator of pro meta (react faster).
• Early game metrics (first blood %, first tower %, gold diff at 15) are highly predictive and underweighted by books.
• Regional meta differences: LCK plays fundamentally differently than LEC.
• Oracle's Elixir is the canonical free stats database.

Valorant ($5.5M)

• Agent composition matters enormously. Track agent pick rates by team, map, and side.
• Highly patch-sensitive — agent nerfs and map pool rotations are game-changing.
• Scene is young — historical data is shallow, books rely on stale priors.
• VLR.gg is the primary data source.

CoD ($8.2M — surprisingly high volume)

• Least efficient market. Very few modelers.
• Hardpoint, Search & Destroy, and Control have different skill correlations.
• Respawn modes (Hardpoint, Control) have higher variance → map count overs systematically underpriced.
• Small player pool at elite level — individual performance is high-variance but trackable.

FIFA ($17.4M — volume leader)

• Likely competitive FIFA / EA FC. Player skill gaps are enormous at top level.
• Head-to-head records are king. Top FIFA players have consistent matchup edges.
• Simple to model: Elo + H2H + recent form explains 80%+ of variance.
• Patch meta matters — annual release cycle changes which tactics are overpowered.

Dota 2

• Hero draft even more impactful than LoL champion draft.
• Pub (ranked) meta is a leading indicator of pro meta.
• OpenDota API is genuinely excellent — free, no auth, structured JSON.

R6 / Overwatch / Rocket League

• Thin sportsbook coverage. Build internal models from scratch.
• Lower-confidence markets — bet sizing should reflect model uncertainty.
• Octane.gg has a full public API for Rocket League.
• Siege.gg has R6 pro stats.

Non-Match Markets (Awards / Steam / Twitch)

No book anchor exists — we ARE the model.

Game Awards:

• Aggregate critic scores (Metacritic, OpenCritic) as priors
• Historical voting patterns (action-adventure wins GOTY ~45%, FromSoft overperforms, late-year releases have recency advantage)
• Social media volume and sentiment for popular vote categories
• Track influential journalists (IGN, GameSpot, Keighley's circle)

Steam Rankings:

• Player counts are public via SteamDB/Steam Charts — nearly deterministic for short-horizon markets
• Model: regression on log(players) with features: day_of_week, days_since_major_update, competing_releases

Twitch Sub Counts / Streamer Bans:

• TwitchTracker and SullyGnome have channel analytics
• Sub counts estimated from chat velocity, emote usage, sub-only chat activity
• Ban markets: pure sentiment + behavioral pattern recognition. Certain streamers have predictable ban cycles.

DATA SOURCES

Odds & Lines (Anchor Sources)

CS2 Data Sources

LoL Data Sources

Valorant Data Sources

Dota 2 Data Sources

CoD / FIFA / Other Title Sources

Cross-Title Sources

Meta / Awards / Streaming Sources

Sentiment & Intelligence Sources

CUSTOM CALCULATIONS & METRICS

1. Map Veto Prediction Model (CS2 / Valorant) — HIGHEST VALUE METRIC

Predicts which maps will be played in a best-of-N series based on historical veto patterns.

Inputs:

• Team A and Team B map win rates (per map, last 90 days, decay-weighted)
• Team A and Team B map ban/pick history (last 20 matches)
• Map pick order rules for the tournament format
• Current active map pool

Algorithm (Monte Carlo, 10,000 runs):

For each simulation:
  1. Simulate Team A ban: weighted random from their ban frequency distribution
  2. Simulate Team B ban: weighted random from their ban frequency distribution
  3. Team A picks best remaining map (by opponent-adjusted win rate)
  4. Team B picks best remaining map
  5. Decider is the remaining map

Output:
  P(map_i played) for each map
  P(A wins match) = avg across sims of product of map-specific win probs

Edge: Generic match odds treat all maps equally. A team that is 90% to win Mirage and 30% to win Nuke has a VERY different Bo3 probability depending on which maps are played. Expected edge: 3-8% on map-dependent markets.

2. Roster Change Impact Score (RCIS) — ALL TITLES

Quantifies expected impact of a roster change before books reprice.

RCIS = (player_out_rating - player_in_rating) * role_weight * synergy_penalty

Role weights (CS2):
  IGL (in-game leader): 1.5
  AWPer: 1.3
  Entry: 1.0
  Lurk: 0.9
  Support: 0.8

Role weights (LoL):
  Mid: 1.3, Jungle: 1.2, ADC: 1.1, Top: 0.9, Support: 0.8

Role weights (Valorant):
  IGL: 1.5, Duelist: 1.2, Sentinel: 1.0, Controller: 1.0, Initiator: 0.9

synergy_penalty = max(0.5, 1 - (days_since_roster_change / 90))

Trading signal: New rosters are overbet by the public (name recognition). The synergy penalty means teams underperform their "on paper" rating for ~6-8 weeks. Fade new rosters in their first 15-20 matches.

3. Patch Sensitivity Score (PSS) — LoL / Valorant / Dota 2 / CS2

Measures how much a patch changes a team's expected performance.

PSS(team, patch) = SUM over agents/champions used by team:
  usage_rate * nerf_magnitude * team_dependency

team_dependency = (team_winrate_with_agent - team_winrate_without) / team_overall_winrate

nerf_magnitude: 0.0 (minor tweak), 0.5 (significant), 1.0 (major rework)

LoL-specific: Use solo queue win rate delta in first 7 days post-patch as a leading indicator of pro meta shift. Solo queue reacts faster than pro play.

Trading window: 2-6 hours post-patch before sharp bettors force books to reprice.

4. Online-LAN Adjustment Factor (OLAF)

OLAF(team) = team_LAN_winrate / team_online_winrate

Apply as multiplier when match is LAN:
  adjusted_prob = base_prob * OLAF(A) / (base_prob * OLAF(A) + (1-base_prob) * OLAF(B))

Additional modifiers:
  Travel distance > 8 hours: -0.02
  First international event of split: -0.015
  Timezone difference * hours_since_arrival factor:
    >72h: 0.2 impact, 24-72h: 0.6 impact, <24h: 1.0 impact

Typical OLAF range: 0.80 (onliner) to 1.25 (LAN specialist). HLTV tracks LAN vs online results separately.

5. Custom Elo Power Rating System — ALL TITLES

Foundation layer for all predictions.

K_factor adjustments:
  LAN matches: K * 1.3 (more informative)
  Online matches: K * 1.0
  Tier-1 events: K * 1.2
  Tier-3 events: K * 0.7
  Within 14 days of roster change: K * 0.5
  First 7 days of new patch: K * 0.7

Elo decay:
  No matches in >21 days: regress toward mean by 5% per week of inactivity

Map-level Elo:
  Maintain SEPARATE Elo per map per team (CS2: 7 maps * N teams)
  Match Elo is weighted average of map Elos based on predicted maps played

6. Pistol Round & Economy Model (CS2-specific)

pistol_adj = (team_pistol_winrate - 0.50) * 0.12
  (each 1% pistol WR above 50% = approx 0.12% match WR — empirically derived)

eco_rating = (team_eco_round_winrate - league_avg) / league_std

Teams with elite pistol rounds (>55%) are systematically underpriced. Pistol wins create compounding economic advantage through the entire half.

7. Early Game Composite (LoL / Dota 2)

early_game_score = w1 * first_blood_rate + w2 * first_tower_rate
                   + w3 * first_dragon_rate + w4 * avg_gold_diff_15min / norm

Weights (LoL): w1=0.15, w2=0.25, w3=0.20, w4=0.40
Weights (Dota): w1=0.10, w2=0.20, w3=0.15, w4=0.55

8. Tournament Bracket Simulator

For "tournament winner" Kalshi outright markets.

sim_tournament(teams[], format, n=100000):
  For each simulation:
    Simulate each match using Elo-derived win_prob(A, B, format)
    Account for: upper/lower bracket, fatigue tax (-0.5% per extra match), rest days
  Return: P(X wins tournament) for each team

Compare output to Kalshi outright prices -> find mispriced teams

9. Patch Meta Fluidity (PMF) Index

Measures a team's historical ability to adapt to patches.

PMF(team) = Avg(WinRate_post_patch - WinRate_pre_patch) across last N major patches

Teams with high positive PMF: undervalued in early days of new meta
Teams with negative PMF: overvalued when meta shifts

NOVEL EDGE VECTORS

1. Scrim Leak Intelligence

Pro teams practice in private scrimmages. Results leak on Twitter, Reddit, Discord. Build keyword-monitoring bot:

• Keywords: "scrim", "bootcamp", "looking good/bad", "form check" + team/player names
• Platforms: Twitter/X, Reddit, Discord (public servers)
• Use Grok API for sentiment classification
• NOT insider trading — this is public information monitoring

2. Patch Notes Speed Advantage

Patches drop at known times (Riot: every other Tuesday). The 2-6 hour window between publication and book adjustment is a recurring edge.

• Automated patch detection via RSS/webhook
• NLP extraction of agent/champion/weapon changes
• Map to pre-computed team dependency matrix -> instant PSS scores
• Compare to current lines -> auto-alert for trades

3. SteamDB Depot Monitoring (Pre-Patch Intelligence)

Before patches go live, game files update in Steam depots. SteamDB tracks this in real-time.

• Monitor depot pages for CS2 (App 730), Dota 2 (App 570)
• Depot update = incoming patch hours before official notes
• Front-run patch impact on markets

4. Pro Player Practice Tracking

Many pros have public Steam profiles and FACEIT accounts.

• Track Steam hours played (Steam API) and FACEIT Elo (FACEIT API) for top 50 players per title
• Aggregate into team "activity index"
• Declining practice hours = underperformance signal
• Sudden champion/agent spam in solo queue = upcoming strategy shift

5. Substitution/Stand-in Detection

Monitor Liquipedia roster pages and HLTV match pages for lineup changes within 24 hours of a match. Stand-in for a star player often not repriced at all for online tier-2 matches.

6. Favorites Bias Calibration

Analyze our $56.5M in settled Kalshi esports volume:

• Compute implied probability at time of bet vs resolution outcome
• Stratify by price bucket (60-70%, 70-80%, 80-90%)
• If teams priced at 70%+ win at only 62%, systematic favorite overpricing exists
• This is our internal calibration edge — run immediately

7. Tournament Format Exploitation

• Swiss format: 1-1 pool is systematically mismatch-prone
• Double elimination: lower bracket teams still have ~35% equity but naive bettors underestimate
• Single elimination bo1: massive variance inflator — even 60% favorites lose ~40%
• Seeding often based on stale rankings -> predictable misseeds

8. Map Pool Rotation Arbitrage (CS2 / Valorant)

When maps are added/removed from competitive pool:

• Teams that practiced the new map have surprise advantage
• Teams that relied on the removed map lose a weapon
• Announced publicly but not quickly priced
• Update team map Elo immediately when rotation announced

KALSHI MARKETS TRACKED (109 series)

Match Markets (23 series)

CS2: KXCS2GAME, KXCS2MAP, KXCS2TOTALMAPS, KXCS2GAMES, KXCS2MAPWINNER Valorant: KXVALORANTGAME, KXVALORANTMAP, KXVALORANTGAMETEAMVSMIBR LoL: KXLOLGAME, KXLOLGAMES, KXLOLMAP, KXLOLTOTAL, KXLOLTOTALMAPS Dota 2: KXDOTA2GAME, KXDOTA2MAP CoD: KXCODGAME Overwatch: KXOWGAME Rocket League: KXRLGAME R6: KXR6GAME, KXR6MAP FIFA: KXFIFAGAME, KXFIFASPREAD, KXFIFATOTAL

Tournament Winners (18 series)

CS2: KXCS2, KXCS2QUALIFY, KXCS2QUALIFIER, KXCS2QUALIFIERS, KXCS2IEMCOLOGNE Valorant: KXVALORANT, KXVALORANTMASTERSFINALS LoL: KXLOL1STTIMEWIN Dota 2: KXDOTA2, KXTORONTOULTRACHAMPIONSHIP CoD: KXCOD PUBG: KXPUBG, KXPUBGGC Overwatch: KXOVERWATCH R6: KXR6 FIFA: KXFIFAADVANCE, KXFIFAUSPULL, KXFIFAUSPULLGAME Fortnite: KXFORTNITEPROAM

Esports World Cup 2025 (21 series)

KXEWCCS2, KXEWCVALORANT, KXEWCDOTA2, KXEWCLEAGUEOFLEGENDS, KXEWCCHESS, KXEWCCHESS2025, KXEWCAPEXLEGENDS, KXEWCPUBGBATTLEG, KXEWCFREEFIRE, KXEWCMLBB, KXEWCMOBILELEGENDSBBWOMENS, KXEWCHONOROFKINGS, KXEWCFATALFURY, KXEWCCALLOFDUTYBLOPS6, KXEWCEASPORTSFC, KXEWCRAINBOW6SEIGE, KXEWCRB6, KXEWCRSS, KXEWCTEAMFIGHTTACTICS, KXEWCSTARCRAFTII, KXEWCRENNSPORT

Game Awards / Rankings (13 series)

KXGAMEAWARDS, KXGAMEAWARDSBET, KXGAMEAWARDSBEA, KXGAMEAWARDSBGD, KXGAMEAWARDSBAD, KXGAMEAWARDSBP, KXGAMEAWARDSBSM, KXGAMEAWARDSBSRG, KXGAMEAWARDSBIG, KXGAMEAWARDSBN, KXGAMEAWARDSBA, KXGAMERANK, KXRANKLISTIGN

Steam Awards / Rankings (12 series)

KXSTEAMGOTY, KXSTEAMBGOSD, KXSTEAMLOL, KXSTEAMBS, KXSTEAMMIG, KXSTEAMBWF, KXSTEAMBGYSA, KXSTEAMVRGOTY, KXSTEAMOVS, KXSTEAMOSRG, KXSTEAMPRICE, KXTOPSELLERS

Twitch / Streamer (15 series)

KXTWITCHSUBSKAICENAT, KXTWITCHSUBSSPEED, KXTWITCHSUBSJYNXZI, KXTWITCHSUBSPLAQUEBOYMAX, KXTWITCHSUBSFAZELACY, KXTWITCHSUBSFAZEJASON, KXTWITCHSUBSFAZEADAPT, KXTWITCHSUBSNINJA, KXTWITCHSUBSK4PACK, KXTWITCHSUBSYOURRAGEGAMING, KXCEOTWITCH, KXBANCLAVICULAR, KXBANDANTES, KXBANXNUBCAT, KXBAN2XRAKAI

Other (7 series)

KXESPORTSTEST, KXAUSTINMAJOR, KXHALOPS, KXXAIGAME, KXNBA2KCOVER, GAMERANK, KXFORTNITEPROAM

COLLECTION SCHEDULE

IMPLEMENTATION PRIORITY

Phase 1 — Foundation (Weeks 1-2)

• Wire Pinnacle scraper for esports URLs (add esports sport paths)
• The Odds API esports integration (backup)
• HLTV scraper: team stats, map win rates, roster change feed
• VLR.gg scraper: team stats, agent comps, map data
• Liquipedia roster change monitor (wiki diff watcher)
• Custom Elo system (foundation for all predictions)
• Kalshi esports market scanner (already done — 109 series)

Phase 2 — Core Models (Weeks 3-4)

• Map veto Monte Carlo model (CS2 + Valorant)
• Roster Change Impact Score — alerting system
• Oracle's Elixir CSV ingestion (LoL)
• OpenDota API connection (Dota 2)
• Patch calendar — flag when next patch drops for each title
• Favorites bias calibration (analyze settled volume data)

Phase 3 — Proprietary Edges (Weeks 5-6)

• Patch Sensitivity Score — automate patch note parsing + NLP
• Online-LAN Adjustment Factor — tag every match, build OLAF per team
• Tournament bracket simulator (Monte Carlo for outright markets)
• Pistol round & economy model (CS2)
• Early game composite (LoL / Dota 2)

Phase 4 — Signals & Intelligence (Weeks 7-8)

• Steam/Twitch data pipeline (SteamDB, TwitchTracker, SullyGnome)
• Social sentiment monitor (Twitter/Reddit keyword bots using Grok API)
• SteamDB depot monitoring (pre-patch intelligence)
• Pro player practice tracking (Steam API + FACEIT API)
• Scrim leak detection pipeline

KEY DIFFERENCES FROM SPORTS DESKS