Polymarket Esports Trader

Trades esports tournament, game release, and streaming milestone prediction markets on Polymarket. Exploits three stacked edges — game data richness (HLTV Elo, Oracle's Elixir), series format variance reduction (Bo5 vs Bo1), and Asian session timing lag for Korean/Chinese team matches.

Esports & Gaming Trader

This is a template. The default signal is keyword-based market discovery combined with conviction-based sizing and esports_bias() — three stacked structural edges, no external API required. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Esports markets are mispriced in two directions simultaneously. Data-rich titles (CS2, LoL, Dota 2) have published Elo models, map win rates, and patch-level performance metrics that retail ignores entirely. At the same time, fan-favourite teams (T1/Faker) are systematically overcrowded by fanbases trading loyalty rather than skill assessment. Three structural edges compound cleanly without any API.

Signal Logic

Default Signal: Conviction-Based Sizing with Esports Bias

1. Discover active esports and gaming markets on Polymarket
2. Compute base conviction from distance to threshold (0% at boundary → 100% at p=0/p=1)
3. Apply esports_bias() — three layers: game data quality × series format × Asian session timing
4. Size = max(MIN_TRADE, conviction × bias × MAX_POSITION) — capped at MAX_POSITION
5. Skip markets with spread > MAX_SPREAD or fewer than MIN_DAYS to resolution

Esports Bias (built-in, no API required)

Layer 1 — Game / Market Type

Game / market type	Multiplier	Key data source retail ignores
T1 / Faker markets	0.75x	Fandom overcrowds YES by 10–20% vs Elo model — documented 2023–2025
CS2 / Counter-Strike	1.20x	HLTV.org Elo ratings, map win rates, head-to-head history
League of Legends (non-T1)	1.15x	Oracle's Elixir patch-level stats — meta shifts change team win rates ±15%
Dota 2 / The International	1.15x	OpenDota comprehensive match stats — consistency rewarded in long series
Valorant / VCT	1.10x	VLR.gg agent win rates, map pools — growing and increasingly accurate
Mobile esports (HoK, PUBG Mobile, MLBB)	1.15x	Deep Asian stats with Western info lag
Game release date milestone	1.10x	Publisher delay history documented — ~70% re-delay rate for prior delayers
Twitch / streaming peak viewership	1.10x	TwitchTracker daily historical peaks — viewership growth curves trackable
Steam concurrent player milestone	1.10x	SteamCharts real-time — launch peaks predictable from pre-order velocity

The T1 / Faker Rule — The most precisely documented single-team overcrowding in all of esports. Faker's global fandom spans every region, every language, every platform. The result is systematic YES overpricing on T1 outcomes by 10–20% relative to what HLTV/Oracle's Elixir Elo models imply. T1 are genuinely elite — but the market price of T1 wins is almost always too high because the fan base is the dominant pricing force, not analysts. This is not a bet against T1 — it is a sizing discipline: trade T1 markets very conservatively.

Layer 2 — Series Format: Variance Reduction by Match Length

This is the cleanest mechanic in the entire trader — no data needed, just understanding how best-of series work:

Format	Multiplier	Statistical reality
Bo5 / Grand Final / Championship	1.20x	Stronger team wins ~72–78% — retail says "anything can happen" which is statistically false
Bo3 / Playoff / Semifinal / Elimination	1.10x	Stronger team wins ~65–70% — meaningful variance reduction
Bo1 / Group Stage / Swiss / Round Robin	0.90x	~40% upset rate — genuine uncertainty, reduce conviction

The Grand Final insight: retail treats championship matches as the most uncertain because "the stakes are highest." The opposite is true statistically. Teams playing Bo5 Grand Finals have survived multiple elimination rounds — they are the two best teams in the tournament, playing the format that most reliably selects the winner. This is maximum-edge territory, not minimum.

Layer 3 — Asian Session Timing

LoL LCK/LPL, mobile esports, and Dota 2 SEA feature Korean, Chinese, and Southeast Asian teams competing at 01:00–09:00 UTC. Polymarket is US-dominated — match results in these regions take 30–90 minutes to fully reprice when US retail is asleep.

Condition	Multiplier
Asian-dominant game + 01:00–09:00 UTC	1.15x — lag window open
All other times	1.00x

Combined Examples

Market	Type	Format	Timing	Final bias
CS2 Bo5 Grand Final	1.20x	1.20x	1.0x	1.35x cap
T1 Bo3 match	0.75x	1.10x	1.0x	0.83x
LoL LCK Bo5 at 04:00 UTC	1.15x	1.20x	1.15x	1.35x cap
Dota 2 Bo1 group stage	1.15x	0.90x	1.0x	1.04x
Any Bo1 group match	type_mult	0.90x	1.0x	Edge compressed

Keywords Monitored

text

esports, League of Legends, CS2, Counter-Strike, Dota 2, Valorant, Fortnite,
World Championship, tournament, Steam, Twitch, game release, PlayStation,
Xbox, Nintendo, gaming revenue, Riot Games, Blizzard, grand final, bracket,
LCK, LPL, LEC, BLAST, ESL, VCT, The International, HLTV, peak viewers,
concurrent players, T1, Faker, NaVi, Vitality, patch

Remix Signal Ideas

• HLTV.org Elo ratings: Compare published Elo-implied win probability to Polymarket price for CS2 matchup markets — the gap is consistently 8–15% for non-marquee matches
• Oracle's Elixir: LoL team stats by patch — when a meta patch hits 2 days before a tournament, markets haven't adjusted; the data has
• Liquipedia API: Real-time bracket data, match results, team stats for 30+ esports titles — feed bracket position into p to trade next-round markets
• TwitchTracker: Daily peak viewer history for "will X reach Y viewers" markets — compare trajectory to market price

Safety & Execution Mode

The skill defaults to paper trading (venue="sim"). Real trades only with --live flag.

Scenario	Mode	Financial risk
python trader.py	Paper (sim)	None
Cron / automaton	Paper (sim)	None
python trader.py --live	Live (polymarket)	Real USDC

autostart: false and cron: null — nothing runs automatically until you configure it in Simmer UI.

Required Credentials

Variable	Required	Notes
SIMMER_API_KEY	Yes	Trading authority. Treat as high-value credential.

Tunables (Risk Parameters)

All declared as tunables in clawhub.json and adjustable from the Simmer UI.

Variable	Default	Purpose
SIMMER_MAX_POSITION	20	Max USDC per trade — reflects esports market liquidity
SIMMER_MIN_VOLUME	3000	Min market volume filter (USD)
SIMMER_MAX_SPREAD	0.10	Max bid-ask spread (10%)
SIMMER_MIN_DAYS	2	Min days until resolution — tournaments move fast
SIMMER_MAX_POSITIONS	10	Max concurrent open positions
SIMMER_YES_THRESHOLD	0.38	Buy YES if market price ≤ this value
SIMMER_NO_THRESHOLD	0.62	Sell NO if market price ≥ this value
SIMMER_MIN_TRADE	5	Floor for any trade (min USDC regardless of conviction)

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

• PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk