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Amy Mhir

#14

Total Matches: 11

Community: Islas Baleares

5 Wins 6 Losses

1006

S10 End of Season ELO

1009

S10 Peak ELO

45.5%

Win Rate

ELO History

Match Outcome Constellation

✨

⭐

🌟

✨

💫

◯

Wins

Losses

Draws

⭐ Star size = ELO impact

Tournament Performance Profile

Performance Metrics

Consistency Rating

45.5%

ELO Growth +6

Tournament Dominance 1/3

Recent Matches

vs LNT_Shadow

Magic Mallorca miércoles Evening Weekly Play (Constructed) • Dec 17, 2025

WIN

+29

vs Magnus_90

Magic Mallorca miércoles Evening Weekly Play (Constructed) • Dec 17, 2025

WIN

+15

vs LNT_Sensei

Magic Mallorca miércoles Evening Weekly Play (Constructed) • Dec 17, 2025

LOSS

-19

vs LNT_Dasein

Cooperativo ~ Night Weekly Play (Constructed) • Dec 05, 2025

LOSS

-9

vs LNT_Marionetas

Cooperativo ~ Night Weekly Play (Constructed) • Dec 05, 2025

WIN

+22

vs LNT_Poncio

Cooperativo ~ Night Weekly Play (Constructed) • Dec 05, 2025

LOSS

-12

vs LNT_Nozmu

Cooperativo ~ Night Weekly Play (Constructed) • Dec 05, 2025

WIN

+17

vs LNT_Velveck

Magic Mallorca Evening Weekly Play (Constructed) • Nov 26, 2025

LOSS

-14

vs LNT_Gladyum

Magic Mallorca Evening Weekly Play (Constructed) • Nov 26, 2025

LOSS

-16

vs Nare

Magic Mallorca Evening Weekly Play (Constructed) • Nov 26, 2025

LOSS

-26

Tournament History

Magic Mallorca miércoles Evening Weekly Play (Constructed) #2

Magic Mallorca • Dec 17, 2025

Record: 2-1-0 • 5 players • Avg ELO: 1063

Cooperativo ~ Night Weekly Play (Constructed) #6

The Gathering Mallorca • Dec 05, 2025

Record: 2-2-0 • 15 players • Avg ELO: 1020

Magic Mallorca Evening Weekly Play (Constructed) #11

Magic Mallorca • Nov 26, 2025

Record: 1-3-0 • 16 players • Avg ELO: 1031

Player Insights

Max Win Streak

Max Loss Streak

ELO Gained

ELO Range

Strong Tournaments (60%+ WR)

Different Stores

Tournaments Played

Advanced Analytics

⚔️

Giant Killer Analysis

Upset Victories: 1/3

Upset Rate: 33.3%

Biggest Upset: +190 ELO

📈

ELO Momentum

Recent Trend: ➡️ Stable

Average Change: 0

Consistency: 100%

📅

Performance Patterns

Not enough data to determine patterns yet

⚡

Current Form

Current Streak: 1 Wins

Recent Form:

Recent Achievements

0 achievements earned

�

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

🎮 Achievement Categories

1-0

100% win rate

🤝 Occasional Opponent

🔥

Current Streak

Wins

📊

67%

ELO Percentile

vs All Players

📅

This Month

Matches Played

⚡

Avg ELO Change

Per Match

Stores Played

Magic Mallorca

2 tournaments

The Gathering Mallorca

1 tournaments

Most Faced Opponents

Opponent	Matches	Wins	Losses	Win Rate
LNT_Shadow	1	1	0	100%
Magnus_90	1	1	0	100%
LNT_Sensei	1	0	1	0%
LNT_Dasein	1	0	1	0%
LNT_Marionetas	1	1	0	100%
LNT_Poncio	1	0	1	0%
LNT_Nozmu	1	1	0	100%
LNT_Velveck	1	0	1	0%
LNT_Gladyum	1	0	1	0%
Nare	1	0	1	0%

LNT_Shadow

Matches

Wins

Losses

Draws

Win Rate

100%

Magnus_90

Matches

Wins

Losses

Draws

Win Rate

100%

LNT_Sensei

Matches

Wins

Losses

Draws

Win Rate

LNT_Dasein

Matches

Wins

Losses

Draws

Win Rate

LNT_Marionetas

Matches

Wins

Losses

Draws

Win Rate

100%

LNT_Poncio

Matches

Wins

Losses

Draws

Win Rate

LNT_Nozmu

Matches

Wins

Losses

Draws

Win Rate

100%

LNT_Velveck

Matches

Wins

Losses

Draws

Win Rate

LNT_Gladyum

Matches

Wins

Losses

Draws

Win Rate

Nare

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

Amy Mhir

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

LNT_Shadow

Magnus_90

LNT_Sensei

LNT_Dasein

LNT_Marionetas

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

🏆 Amy Mhir's Achievements