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Giovanni T

#142

Total Matches: 1

Community: Madrid

0 Wins 1 Losses

976

S10 End of Season ELO

1000

S10 Peak ELO

0.0%

Win Rate

ELO History

Match Outcome Constellation

✨

⭐

🌟

✨

💫

◯

Wins

Losses

Draws

⭐ Star size = ELO impact

Tournament Performance Profile

Performance Metrics

Consistency Rating

0.0%

ELO Growth -24

Tournament Dominance 0/1

Recent Matches

vs RubenMB

MADVERSE - Dumbo Challenge • Nov 16, 2025

LOSS

-23

Tournament History

MADVERSE - Dumbo Challenge NR

Victory Road • Nov 16, 2025

Record: 0-1-0 • 73 players • Avg ELO: 1015

Player Insights

Max Win Streak

Max Loss Streak

-24

ELO Gained

ELO Range

Strong Tournaments (60%+ WR)

Different Stores

Tournaments Played

Advanced Analytics

⚔️

Giant Killer Analysis

No matches against higher-ranked opponents yet

📈

ELO Momentum

Recent Trend: ➡️ Stable

Average Change: 0

Consistency: 100%

📅

Performance Patterns

Not enough data to determine patterns yet

⚡

Current Form

Current Streak: 1 Losses

Recent Form:

Recent Achievements

0 achievements earned

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No achievements yet - play some matches to start earning them!

🎮 Achievement Categories

No achievements yet.

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Top Rivalries

🎯

RubenMB

1 matches played

0-1

0% win rate

🤝 Occasional Opponent

❄️

Current Streak

Losses

📊

65%

ELO Percentile

vs All Players

📅

This Month

Matches Played

⚡

-24

Avg ELO Change

Per Match

Stores Played

Victory Road

1 tournaments

Most Faced Opponents

Opponent	Matches	Wins	Losses	Draws	Win Rate
RubenMB	1	0	1	0	0%

RubenMB

Matches

Wins

Losses

Draws

Win Rate

Understanding ELO Rating System

What is ELO?

The ELO rating system is a method for calculating the relative skill levels of players in zero-sum games such as chess, or in our case, Lorcana tournaments. It was originally developed by Arpad Elo for chess competitions.

Each player starts with a base rating (1000), and this rating changes after each match based on the outcome and the relative skill difference between players.

How Does It Work?

Basic Formula:

New Rating = Old Rating + K × (Actual Score - Expected Score)

Expected Score Formula:

E = 1 / (1 + 10^((Rb - Ra) / 400))

Where Ra is your rating and Rb is opponent's rating

K-Factor:

Determines how much ratings change per game:

• New players: K = 32
• Experienced: K = 16
• Masters: K = 10

Examples

Beating a Higher-Rated Player

You (1000) vs Opponent (1200)

Expected: You have ~24% chance to win

If you win: +24 rating points!

Losing to a Lower-Rated Player

You (1200) vs Opponent (1000)

Expected: You have ~76% chance to win

If you lose: -24 rating points

Key Points

• Ratings are relative - they only matter in comparison to other players
• Beating stronger opponents gives more points than beating weaker ones
• Your rating stabilizes over time as you play more games
• Draws result in no ELO change for both players
• The system is self-correcting - ratings tend toward your true skill level

Giovanni T

ELO History

Match Outcome Constellation

Tournament Performance Profile

Performance Metrics Explained

Performance Metrics

Recent Matches

Tournament History

Player Insights

Advanced Analytics

Giant Killer Analysis

ELO Momentum

Performance Patterns

Current Form

Recent Achievements

🎮 Achievement Categories

Top Rivalries

RubenMB

Stores Played

Most Faced Opponents

Understanding ELO Rating System

What is ELO?

How Does It Work?

Basic Formula:

Expected Score Formula:

K-Factor:

Examples

Beating a Higher-Rated Player

Losing to a Lower-Rated Player

Key Points

📜 Full Match History

🏆 Giovanni T's Achievements