Scrim Volume & Burnout Indicators

Overview

This pillar analyzes team practice schedules, travel, and game volume to identify signs of player burnout. It provides a crucial, often overlooked, signal for predicting underperformance in high-stakes esports matches.

What It Does

The analysis aggregates data on professional players' public match history, official tournament schedules, and reported travel itineraries. It calculates an overall 'Fatigue Score' for each team by weighing factors like game volume, match density, and time zone changes. This score highlights teams that are likely over-practiced and at risk of exhaustion.

Why It Matters

Mental and physical fatigue directly degrade reaction time, communication, and strategic decision making. This pillar provides a quantifiable edge by spotting vulnerable favorites or resilient underdogs that the general market might miss, leading to better-priced predictions on match outcomes.

How It Works

First, we collect solo queue match history for each player on a team over the last 14 days. Second, we map out the team's official tournament and travel schedule to determine match density and jet lag factors. Finally, these inputs are combined into a weighted model that generates a comparative Burnout Risk score for upcoming matches.

Methodology

A 7-day and 14-day rolling average of solo queue games is calculated for each player, then converted to a Z-score against their 90-day baseline. Each official tournament match in the last 10 days adds a fixed value to the score. Travel days involving more than three time zone changes are weighted 1.5 times higher than non-travel days.

Edge & Advantage

It quantifies the hidden variable of team fatigue, which is not captured in standard win/loss stats but has a direct impact on in-game performance.

Key Indicators

• Solo Queue Volume (7d)

  high

  The total number of public ranked games played by team members over the past week, indicating practice intensity.

• Tournament Density

  high

  The number of official, best-of-3 or longer matches a team has played in the last 14 days.

• Recent Travel Burden

  medium

  A score based on the number of days spent traveling and the number of time zones crossed in the past 10 days.

Data Sources

• Game Publisher APIs

  Provides raw match history data for individual player accounts (e.g., Riot Games API for League of Legends).

• Liquipedia / HLTV

  Community-driven databases for tournament schedules, match results, and team travel information.

• Team Social Media

  Official team and player social accounts often provide unstructured data on travel and practice schedules.

Example Questions This Pillar Answers

• → Will G2 Esports win their next match in the League of Legends LEC playoffs?
• → Will the team that traveled from Asia have a lower win rate in the first round of the Valorant Champions Tour?
• → Will Team Liquid make it to the finals of IEM Cologne after a dense tournament schedule?

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