What Actually Wins League of Legends Games? ML Analysis of 250K Matches

Source: Dev.to

The Science Behind League Victories

Our machine‑learning models achieved 90.8 % accuracy in predicting game outcomes by the 27‑minute mark. This precision lets us pinpoint which factors matter most at each stage of a match.

Early Game (0–12 minutes): It’s All About Gold

• Gold differential explains 42 % of win variance in the first 12 minutes.
• First blood adds only 3.2 % to win probability on average.
• CS differential accounts for just 8 % of early win probability.

Takeaway: Prioritize consistent gold generation through CS and objective bounties rather than risky early kills.

Mid Game (12–24 minutes): The Experience Advantage

• Level advantage becomes the second‑strongest predictor after gold.
• XP differential explains 28 % of win variance.
• Team‑fight deaths have a larger impact than early‑game deaths.

Takeaway: Split‑pushing and proper wave management are crucial for maintaining or gaining experience leads.

Death Is More Costly Than You Think

• Each death reduces win probability by 4.2 % on average.
• A 1‑for‑1 kill trade results in a net loss of win probability.
• The negative impact of deaths grows significantly after 20 minutes.

Takeaway: Play conservatively and avoid deaths, especially in the late game.

Late Game (24 + minutes): Levels Trump Gold

• Level differential explains 38 % of win variance after 24 minutes.
• Gold importance drops to 32 %.
• CS differential remains a weak predictor at 7 %.

Takeaway: Level advantages often decide late‑game team fights, even when gold totals are similar.

Common Misconceptions vs. Data Reality

• “CS doesn’t matter late game.”
  While CS is a weaker direct predictor, it still fuels gold and experience advantages. Consistent CS remains important throughout.
• “Kill trading is worth it.”
  Trading kills is nearly always a net negative, except when you are significantly behind.
• “Early game decides everything.”
  Early advantages matter, but prediction accuracy improves markedly in the late game, and comebacks are viable until major objectives fall.

Methodology and Data Collection

• Analyzed 250,000+ Diamond+ ranked games from major regions.
• Used XGBoost models to rank factor importance at two‑minute intervals.
• Achieved 90.8 % prediction accuracy by 27 minutes, providing high confidence in the findings (individual games may still deviate).

Practical Applications for Players

• Prioritize consistent gold generation over high‑risk plays.
• Maintain experience parity through proper wave management.
• Avoid death‑trading, especially in the late game.
• Track level differentials as carefully as gold differentials.
• Remember the outsized impact of each death on win probability.

Looking Deeper Into the Data

Interactive tools are available to explore these patterns further. Visit macromind.gg/insights to analyze win conditions based on game states, team compositions, champion pools, and roles.