Star Player Win Share

Overview

This pillar analyzes a team's ability to secure a win even when their star player has a statistically poor performance. It's a key metric for identifying deep, resilient teams that are often undervalued in markets.

What It Does

It isolates historical matches where a team's designated star player significantly underperforms, based on key metrics like Kill/Death ratio or damage output. The pillar then calculates the team's win rate in these specific scenarios. This quantifies the team's dependency on a single player for success.

Why It Matters

The market often overweights the recent performance of star players. This analysis provides a contrarian signal, highlighting teams that can win through superior strategy and teamwork, making them strong candidates for upset victories.

How It Works

First, we identify each team's star player using historical performance data. Second, we define an 'underperformance' threshold, such as a Kill/Death ratio below 1.0 or performance 25% below their 90-day average. Finally, we filter all matches meeting this criteria and calculate the team's win percentage to derive the score.

Methodology

The core metric is the Star Underperformance Win Percentage (SUWP). It is calculated as (Wins where star player K/D < 1.0) / (Total matches where star player K/D < 1.0). The 'star player' is defined as the player with the highest average rating over the last 3 months. Analysis window is typically the last 6-12 months of official matches.

Edge & Advantage

This provides a crucial edge in betting on match upsets, as it identifies teams that do not fold under the pressure of their main carry having an off day.

Key Indicators

• Star Underperformance Win %

  high

  The team's win rate in matches where the designated star player's K/D ratio is below 1.0.

• Non-Star Damage Share

  medium

  The combined percentage of team damage dealt by players other than the star in wins.

• Clutch Success Rate

  low

  The frequency at which non-star players win late-round, high-pressure situations.

Data Sources

• HLTV.org

  Provides detailed match statistics and player performance data for Counter-Strike.

• VLR.gg

  Source for Valorant match results, stats, and news.

• Dotabuff

  Comprehensive statistics and analytics for Dota 2 players and matches.

Example Questions This Pillar Answers

• → Who will win the upcoming match between Team A and Team B?
• → Will Team X cover the -1.5 map spread against Team Y?
• → Is Team Z a good value bet to win the tournament outright?

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