AI eSports predictions across CS, Dota, LoL & Valorant

In CS2, map pool and individual map win rates are among the strongest predictors of match outcomes. A team with an 80% win rate on Inferno playing against a team that bans it aggressively tells a different story than their overall head-to-head record. We weight recent map-specific performance and veto patterns when computing match-level probabilities.

Game patches in titles like League of Legends or Dota 2 can drastically shift which team compositions and strategies are viable. Teams that adapt quickly to patch changes perform above their recent average; teams slow to adapt underperform. We track patch-change timing and apply a recency weight that deprioritises pre-patch results when the meta has shifted significantly.

Roster changes are one of the highest-impact variables in eSports. A stand-in player — particularly under tournament bracket pressure — carries meaningful performance uncertainty not reflected in team-level historical data. We flag and discount picks where a team is playing with a confirmed stand-in unless sufficient recent data for that stand-in is available.

We focus on Counter-Strike (CS2), Dota 2, League of Legends and Valorant at tier-1 tournaments and league play where Pinnacle offers deep markets. ESL Pro League, BLAST Premier, The International and Worlds are examples of events with sufficient liquidity for reliable fair-value anchoring. Tier-2 and below are excluded due to thinner market depth.

Yes. Every settled eSports pick appears in the public ledger on /track-record, showing the edge at publication, the match result and whether the pick won or lost. eSports is a relatively new coverage area for us, so sample sizes may be lower — the sample-size chip appears when we have fewer than 50 settled picks, giving you a transparent view of the data maturity.