u/Formentor99

​

Hi everyone,

I’ve been looking into the "World Conquest" promotion currently running on Winamax and wanted to get your thoughts on the expected value (EV).

The Challenge:

Get 60 winning bets .

Minimum stake: €5 per bet.

Minimum odds: 2.00 .

Reward: An equal share of a €500,000 prize pool.

My Calculation:

I’m estimating that I’ll need around 150 bets total to reach the 60 wins (assuming a conservative 40% win rate at 2.00 odds).

Total Turnover: €750.

Estimated Loss (Expected Value): With a bookie margin of at max 10%, I’m bracing for an expected loss of about €75 on the bets themselves during the process.

The Big Unknown:

The profitability depends entirely on how many people reach the goal.

Questions for the community:

Based on previous Winamax promos (like during the World Cup or Euros), how many people usually cross the finish line for these massive pools?

Do you think the "grind" of 150 bets is worth the potential profit?

Hey everyone. I built https://riftcast.gg/, an ML prediction system for LoL Esports with both training stats visible and historic data tracked (if model predictions were correct or not).

The setup:

- 3,091 pro matches in the dataset across 272 teams and 43 tournaments (so far), covering all major regions (LCK, LPL, LEC, LCS) and minor regions

- Series-level predictions (pre-match) and game-level predictions (post-draft)

- Three models running in parallel:

- FastTree (free tier baseline, simplest features)

- LightGBM with patch/meta-aware features (tracks game duration trends, team performance gaps between recent patches and all-time, format interactions like is_bo5 * elo_diff, etc.)

- PCA Sweep — runs a 7000-config hyperparameter search for ~5 hours weekly, PCA-compresses the noisy draft features

- Plus a Consensus prediction combining all three

**Feature engineering:**

The series model uses ~80 features after filtering. Heavy use of:

- Differential features (Blue stat - Red stat) to avoid teaching the model side bias

- Decayed all-time stats + Diff5 rolling windows for recent form

- A custom Elo system with cross-league calibration (this is what handles international events, which only have ~20 games of historical data)

- Hand-crafted composite features (Diff_Composite_EarlyGame, _Combat, _Vision, etc.) to compress correlated signals.

The draft model adds champion-level features: per-lane Overall/Counter/ Mastery/Meta scores weighted by Samples confidence, synergy by lane-pair (Top-Jgl, Mid-Jgl, Bot-Sup), and per-lane "LaneEdge" composites.

I have an "Uncertain tag" which excludes prediction results for predictions with less than 55% certainty which is also shown in the UI for transparency

Accuracy across the last 2 weekly reports published (75 series, 209 games):

https://preview.redd.it/6c3hogqx6a0h1.png?width=1539&format=png&auto=webp&s=ae7009a43f6e84f41b732dbe2d79a75ceb029da3

I also track each model's performance per league and show it on each upcoming match prediction. For example, Consensus (the aggregate from all models) is yet to make a wrong Series prediction for LCS (17/17 correct) and has a fairly good accuracy for Game (Draft) Predictions as well (28/35 correct)

https://preview.redd.it/wv57dtz08a0h1.png?width=930&format=png&auto=webp&s=e0beebbce44f895459e73fd5c9b2d21ae80fabd3

Where I think it's weak:

- International events (~20 cross-region games in dataset) — Elo helps but cross-region calibration is shaky

- LightGBM volatility week-over-week (76%/70% vs 69%/80%) — patch-aware features may be over-correcting

Any feedback will be much appreciated, thanks!