I Tried E-Sports Betting with AI

Premise & Objective

• Goal: Test whether an AI-driven sports betting “trading bot” can produce positive long-term returns.
• Context: The creator notes that only about 3% of people are profitable long-term in sports gambling.

Niche Selection (Targeting a Data-Based Edge)

The bot restricts betting to esports / video games only, focusing on:

• League of Legends (LoL)
• Counter-Strike (CS)
• Dota

Core Framework & Rules (Step-by-Step)

1) Universe Constraint

• Bet only on esports with abundant daily data.

2) Bankroll Management

• Use specific units/measurements/confidence scores to avoid early “blow ups.”
• Starting bankroll: $1,000, fully controlled by the bot.

3) Model & Build Approach

• Use Claude AI (Anthropic) to design:
  • an esports edge model
  • a full data pipeline
• Include tooling such as:
  • EV (expected value) calculator
  • bet log
  • market analysis + live signals

4) Data Sources Mentioned

• bo3.gg
• HLTV map stats
• OpenDota
• Stratz
• PandaScore (used as a web app feeding the live terminal/pipeline)

5) Bet Selection Logic Improvements

Key additions and adjustments during the challenge:

• Add factors like:
  • “rustiness” (time since last match)
  • head-to-head matchups
  • maps
• Identify and address an early problem:
  • The bot over-favored underdogs around 30–40% win likelihood (too close to coin-flip).
• Underdog guard:
  • Do not take bets below 45% implied/model win probability.
• High-conviction rule:
  • Mark bets “high conviction” when:
    • open market odds imply ~70%, and
    • the model predicts ≥70%
• Backtesting & filtering:
  • Revisit earlier matchups
  • Blacklist some leagues
  • Adjust game-volume thresholds
• Micro tier (late-stage risk control):
  • Use a small % of bankroll when confidence is below 70% but above 60%
• Increase play opportunities:
  • Initially trade games with >10k volume
  • Later allow more plays per day / more games

Key Performance Numbers & Timeline (Day-by-Day)

Start / Early Loss

• Day 1: Bot goes 0 and 2, down ~$70
  • Bankroll: $1,000 → ~ $820
• Next stretch: Goes 0 and 4, balance reported as:
  • $763.44
  • Down $200+ on the day-one timeframe

Partial Recovery

• Day 3 (LoL rebound):
  • Goes 3 and 1 on League of Legends
  • Profit: ~$15
  • Bankroll noted as ~down $225 vs earlier ~$250

Mid-Challenge (High-Conviction Plays Added)

• Day 4:
  • Places 5 bets across LoL / CS / Dota
  • Goes 3 and 1
  • Profit: ~$13
  • Bankroll described as just under $800 (range mentioned: ~$200+ down to ~$250 down depending on the paragraph)

Worst Event (Format / Market Mismatch)

• A late “overnight” loss described:
  • Market priced an LoL match at 70% likelihood
  • Bot predicted 65%
  • The match format was best-of-one (single win decides)
  • Bot placed $60 due to “high-conviction” flagging
  • Resulted in a loss
• Creator describes the day as “astronomically” down (explicit total not fully quantified at that moment)

Late-Stage Adjustments

• Add micro tier (60–70% confidence uses small bankroll %)
• Filtering changed:
  • earlier >10k volume
  • later allowed more games to be traded in a day

Day 6 (Strong Bounce)

• 19 plays: 15 and 4
• Four high-conviction plays all win
• Net profit: ~$21
• Balance after day: ~$740
  • Still ~$260 down overall

Day 7 (Final Day)

• Starts with 12 open positions
• Up $14.32 on the day
• Current balance: $753
  • Overall framing: still ~$250 down
• Qualitative ongoing results:
  • winning streaks on Dota and LoL
  • one LoL loss
  • CS beginning to find wins
• Creator’s framing:
  • If not for day one’s mishap, they’d be “in the green.”

30-Day Plan

• Creator states they will run the bot for 30 days and publish another 30-day video for longer-run validation.

Explicit Recommendations / Conclusions (Creator’s Take)

Edge Claim (Conditional)

• Creator believes an edge exists, particularly in esports because of:
  • more markets
  • higher variance
  • more online free data

Where It Worked vs Failed (Qualitative)

• Good areas:
  • Dota and League of Legends
  • especially on 70%+ confidence plays
• Bad area:
  • Counter-Strike, described as a source of problematic losses

Method Discipline Emphasized

Sustained profitability (if achieved) requires:

• proper rule configuration
• strict elimination of bad markets
• continued self-learning/calibration

Disclosures / Caveats

• Creator frames the experiment as a challenge and emphasizes honesty/transparency (no “sugarcoating”).
• No explicit legal “not financial advice” disclaimer is shown in the provided subtitles.

Tickers / Assets / Instruments Mentioned

• No traditional financial tickers (stocks/ETFs/bonds).
• Betting is indirectly on esports teams/markets.
• Data/products and platforms referenced:
  • PandaScore, bo3.gg, HLTV, OpenDota, Stratz
• No mention of commodities/FX/crypto.

Presenters & Sources

• Presenter: Single creator/narrator (name not provided in subtitles)
• AI system: Claude AI by Anthropic (also references “Claude Code”)
• Data/model sources: bo3.gg, HLTV, OpenDota, Stratz, PandaScore