Esports trading is more complex than traditional sports betting due to its fast-paced nature, evolving game mechanics, and fragmented data sources. Unlike conventional sports, where stable rules and decades of historical data guide betting decisions, esports titles constantly change. In this article, Bogdan Holovnov, Head of Esports, analyses the evolving landscape of esports trading, illustrating key genre-specific strategies and challenges through the example of DATA.BET, a premium esports solution provider.
Esports trading demands 24/7 risk management, deep game knowledge, and progressive data analysis. Frequent game updates, emerging strategies, and roster changes influence betting market volatility. Computerised models alone cannot handle this level of unpredictability, making a balanced approach between AI-driven automation and human expertise is essential.
Another challenge is the diverse range of betting markets. Esports titles track in-game events, all of which affect market dynamics. While a football match consists of 45-minute halves, a CS2 round lasts just two minutes, requiring much faster odds calculations. Betting trends shift based on game updates, major tournaments, and player interest, influencing which games dominate the market.
The two primary esports genres – first-person shooters (FPS) and Multiplayer Online Battle Arena (MOBA) – demand exceptional trading strategies. These genres make up 97% of all esports bets, shaping how traders assess risks and set odds.
FPS games: CS2, Valorant, Rainbow Six
Pre-match trading of this genre relies heavily on structured probabilities. Mathematical models based on historical performance and running systems form the foundation of odds.
However, traders must adjust these probabilities based on key factors such as:
• Roster changes – new players can shift a team’s performance significantly. • Map preferences – teams have strengths on specific maps, with a 5% advantage on their chosen picks. • Tournament formats – the structure of a tournament affects team motivation and strategies.
A crucial factor in FPS trading is the pick-ban phase, where teams select and ban maps. Traders analyse these choices and adjust probabilities to improve accuracy and reduce unexpected outcomes.
FPS live trading is structured due to the round-based format. Traders use dynamic data feeds and AI-driven models but must manually adjust odds based on dynamic match developments. Essential elements include:
• Character selection in games like Valorant and Rainbow Six, where different agents or operators impact team strategies. • Map balance and attack-defence bias, which affect team performance. • Playstyles and momentum shift as underdog teams may peak unexpectedly in major tournaments.
Even with AI-driven tools, human expertise is necessary to interpret gameplay, adjust odds, and ensure accurate pricing.
MOBA games: Dota 2, League of Legends
This genre presents a more significant challenge for automated models due to frequent game patches, unpredictable meta-shifts, and complex strategies. In pre-match, traders must consider:
• Patch updates – regular game updates change hero balance and team strategies. • Regional differences – play styles vary by region, affecting pacing and strategic depth.
Traditional ranking models help with probability estimation but lose accuracy at the start of a season due to game changes and team synergy shifts. Traders refine odds by analysing scrim results, media reports, and recent tournament performances.
Unlike FPS games, MOBA live trading heavily depends on the trader’s expertise rather than models. Due to the complex nature ofnMOBAs, even advanced AI struggles to evaluate the impact of certain in-game decisions, such as item builds and team strategies.
Traders must monitor:
• Hero selection – certain compositions provide inherent advantages before the match even starts. • Gold and objective control – unlike FPS rounds, gold leads do not always determine the winner; game tendency and team fight execution are more important. • Teamplay dynamics – certain teams excel in early-game aggression, while others thrive in late-game scaling, requiring traders to adjust odds accordingly.
Due to the unpredictable nature of matches, human expertise plays a more prominent role in MOBA trading. Experts must interpret game states and adjust odds accordingly.
While traditional sports are subject to seasonal fluctuations, esports betting benefits from a non-stop competitive calendar. Lower-tier tournaments fill gaps between major events, providing operators continuous engagement opportunities.
Esports betting caters to a younger, tech-savvy audience that expects a fast, dynamic experience, highlights Bohdan Holovnov. Personalisation is key – custom bet builders, data-driven insights, and interactive features enhance engagement. Successful trading in esports requires a balance of AI-powered automation and expert risk management to ensure accurate odds and market adaptability. To maximise opportunities in the growing esports sector and keep pace with the industry’s rapid evolution, operators should collaborate with betting providers that offer advanced technology, experienced specialists, and deep esports expertise.
