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Clares

#18

Total Matches: 35

Community: Levante

24 Wins 11 Losses

1165

S10 End of Season ELO

1165

S10 Peak ELO

68.6%

Win Rate

ELO History

Match Outcome Constellation

✨

⭐

🌟

✨

💫

◯

Wins

Losses

Draws

⭐ Star size = ELO impact

Tournament Performance Profile

Performance Metrics

Consistency Rating

68.6%

ELO Growth +165

Tournament Dominance 7/9

Recent Matches

vs ❄️ Kumiho ❄️

LIGA SIMBA 29/01/2025 - 18:30H • Jan 29, 2026

WIN

+24

vs Fiordor

LIGA SIMBA 29/01/2025 - 18:30H • Jan 29, 2026

WIN

vs Yisus

LIGA SIMBA 29/01/2025 - 18:30H • Jan 29, 2026

WIN

+15

vs Tenedor

LIGA SIMBA 29/01/2025 - 18:30H • Jan 29, 2026

WIN

+16

vs Jotaeme

LIGA SIMBA 22/01/2026 • Jan 22, 2026

WIN

vs Laura Rodríguez

LIGA SIMBA 22/01/2026 • Jan 22, 2026

WIN

+14

vs paulisknowsbest

LIGA SIMBA 22/01/2026 • Jan 22, 2026

WIN

vs Yisus

LIGA SIMBA 22/01/2026 • Jan 22, 2026

LOSS

-19

vs Rubbyrubss

LIGA SIMBA 15/01/2026 • Jan 15, 2026

WIN

+21

vs Mizuki

LIGA SIMBA 15/01/2026 • Jan 15, 2026

WIN

+13

Tournament History

LIGA SIMBA 29/01/2025 - 18:30H #1

La Escotilla • Jan 29, 2026

Record: 4-0-0 • 14 players • Avg ELO: 1004

LIGA SIMBA 22/01/2026 #2

La Escotilla • Jan 22, 2026

Record: 3-1-0 • 15 players • Avg ELO: 1023

LIGA SIMBA 15/01/2026 #2

La Escotilla • Jan 15, 2026

Record: 3-1-0 • 11 players • Avg ELO: 999

Liga Simba #2

La Escotilla • Jan 08, 2026

Record: 3-1-0 • 12 players • Avg ELO: 1002

Liga Simba - Viernes Afternoon Weekly Play (Constructed) #8

La Escotilla • Jan 02, 2026

Record: 1-2-0 • 11 players • Avg ELO: 1008

SIMBA 23/12/2025 #2

La Escotilla • Dec 23, 2025

Record: 3-1-0 • 10 players • Avg ELO: 1018

LIGA SIMBA #2

La Escotilla • Dec 18, 2025

Record: 3-1-0 • 8 players • Avg ELO: 1090

Liga Simba #9

La Escotilla • Dec 11, 2025

Record: 1-3-0 • 12 players • Avg ELO: 1048

LIGA SIMBA 4/12 #1

La Escotilla • Dec 04, 2025

Record: 3-1-0 • 9 players • Avg ELO: 1032

Player Insights

Max Win Streak

Max Loss Streak

+165

ELO Gained

169

ELO Range

Strong Tournaments (60%+ WR)

Different Stores

Tournaments Played

Advanced Analytics

⚔️

Giant Killer Analysis

Upset Victories: 1/2

Upset Rate: 50.0%

Biggest Upset: +132 ELO

📈

ELO Momentum

Recent Trend: ➡️ Stable

Average Change: 0

Consistency: 100%

📅

Performance Patterns

Not enough data to determine patterns yet

⚡

Current Form

Current Streak: 6 Wins

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.

🔍 View All Achievements

Top Rivalries

🏆

Yisus

8 matches played

6-2

75% win rate

👑 Dominates

🎯

Rubbyrubss

5 matches played

2-3

40% win rate

🎯 Rival

🎯

❄️ Kumiho ❄️

3 matches played

1-2

33% win rate

🤝 Occasional Opponent

🏆

Fiordor

3 matches played

3-0

100% win rate

🤝 Occasional Opponent

🏆

Tenedor

3 matches played

2-1

67% win rate

🤝 Occasional Opponent

🔥

Current Streak

Wins

📊

78%

ELO Percentile

vs All Players

📅

This Month

Matches Played

⚡

Avg ELO Change

Per Match

Stores Played

La Escotilla

9 tournaments

Most Faced Opponents

Opponent	Matches	Wins	Losses	Win Rate
Yisus	8	6	2	75%
Rubbyrubss	5	2	3	40%
❄️ Kumiho ❄️	3	1	2	33%
Fiordor	3	3	0	100%
Tenedor	3	2	1	67%
Jotaeme	2	2	0	100%
Mizuki	2	1	1	50%
Cubitoito	2	2	0	100%
Elvira	2	2	0	100%
Laura Rodríguez	1	1	0	100%

Yisus

Matches

Wins

Losses

Draws

Win Rate

75%

Rubbyrubss

Matches

Wins

Losses

Draws

Win Rate

40%

❄️ Kumiho ❄️

Matches

Wins

Losses

Draws

Win Rate

33%

Fiordor

Matches

Wins

Losses

Draws

Win Rate

100%

Tenedor

Matches

Wins

Losses

Draws

Win Rate

67%

Jotaeme

Matches

Wins

Losses

Draws

Win Rate

100%

Mizuki

Matches

Wins

Losses

Draws

Win Rate

50%

Cubitoito

Matches

Wins

Losses

Draws

Win Rate

100%

Elvira

Matches

Wins

Losses

Draws

Win Rate

100%

Laura Rodríguez

Matches

Wins

Losses

Draws

Win Rate

100%

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

Clares

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

Yisus

Rubbyrubss

❄️ Kumiho ❄️

Fiordor

Tenedor

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

🏆 Clares's Achievements