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Angie

#182

Total Matches: 23

Community: Levante

1 Wins 19 Losses 3 Draws

798

S10 End of Season ELO

1000

S10 Peak ELO

5.0%

Win Rate

ELO History

Match Outcome Constellation

✨

⭐

🌟

✨

💫

◯

Wins

Losses

Draws

⭐ Star size = ELO impact

Tournament Performance Profile

Performance Metrics

Consistency Rating

5.0%

ELO Growth -202

Tournament Dominance 0/7

Recent Matches

vs PT_Logan 🚀

Tuesday Evening Weekly Play (Constructed) • Jan 27, 2026

WIN

+29

vs PT_JJ 🚀

Tuesday Evening Weekly Play (Constructed) • Jan 27, 2026

LOSS

-4

vs PT_Soriano851🚀

Tuesday Evening Weekly Play (Constructed) • Jan 27, 2026

LOSS

-7

vs PT_George 🐳

Tuesday Evening Weekly Play (Constructed) • Jan 27, 2026

LOSS

-3

vs PT_Soriano851🚀

martes Evening Weekly Play (Constructed) • Jan 20, 2026

LOSS

-5

vs PT_GuЯu 🚀

martes Evening Weekly Play (Constructed) • Jan 20, 2026

LOSS

-3

vs PT_AJ42🚀

martes Evening Weekly Play (Constructed) • Jan 20, 2026

LOSS

-7

vs PT_Figgy 🚀

Martes de lorcana • Jan 13, 2026

DRAW

vs PT_Lumbre🔥

Martes de lorcana • Jan 13, 2026

LOSS

-11

vs anvid

Martes de lorcana • Jan 13, 2026

DRAW

Tournament History

Tuesday Evening Weekly Play (Constructed) #9

Pulsar Store • Jan 27, 2026

Record: 1-3-0 • 12 players • Avg ELO: 1079

martes Evening Weekly Play (Constructed) NR

Pulsar Store • Jan 20, 2026

Record: 0-3-0 • 12 players • Avg ELO: 1089

Martes de lorcana #10

Pulsar Store • Jan 13, 2026

Record: 0-2-2 • 11 players • Avg ELO: 1088

Resaca post navidad NR

Pulsar Store • Dec 26, 2025

Record: 0-3-0 • 8 players • Avg ELO: 972

martes Evening Weekly Play (Constructed) NR

Pulsar Store • Dec 02, 2025

Record: 0-3-0 • 6 players • Avg ELO: 1012

martes Evening Weekly Play (Constructed) NR

Pulsar Store • Nov 18, 2025

Record: 0-3-0 • 8 players • Avg ELO: 1070

jueves Evening Weekly Play (Constructed) #6

Pulsar Store • Nov 13, 2025

Record: 0-2-1 • 7 players • Avg ELO: 1058

Player Insights

Max Win Streak

Max Loss Streak

-202

ELO Gained

216

ELO Range

Strong Tournaments (60%+ WR)

Different Stores

Tournaments Played

Advanced Analytics

⚔️

Giant Killer Analysis

Upset Victories: 1/12

Upset Rate: 8.3%

Biggest Upset: +187 ELO

📈

ELO Momentum

Recent Trend: ➡️ Stable

Average Change: 0

Consistency: 100%

📅

Performance Patterns

Not enough data to determine patterns yet

⚡

Current Form

Current Streak: 3 Losses

Recent Form:

Recent Achievements

0 achievements earned

�

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

🎮 Achievement Categories

0-1-1

0% win rate

🤝 Occasional Opponent

❄️

Current Streak

Losses

📊

53%

ELO Percentile

vs All Players

📅

This Month

Matches Played

⚡

-9

Avg ELO Change

Per Match

Stores Played

Pulsar Store

7 tournaments

Most Faced Opponents

Opponent	Matches	Wins	Losses	Draws	Win Rate
PT_Logan 🚀	4	1	3	0	25%
PT_Soriano851🚀	2	0	2	0	0%
PT_George 🐳	2	0	2	0	0%
PT_AJ42🚀	2	0	2	0	0%
PT_Figgy 🚀	2	0	1	1	0%
PT_Lumbre🔥	2	0	2	0	0%
anvid	2	0	1	1	0%
RafcKen7	2	0	2	0	0%
PT_JJ 🚀	1	0	1	0	0%
PT_GuЯu 🚀	1	0	1	0	0%

PT_Logan 🚀

Matches

Wins

Losses

Draws

Win Rate

25%

PT_Soriano851🚀

Matches

Wins

Losses

Draws

Win Rate

PT_George 🐳

Matches

Wins

Losses

Draws

Win Rate

PT_AJ42🚀

Matches

Wins

Losses

Draws

Win Rate

PT_Figgy 🚀

Matches

Wins

Losses

Draws

Win Rate

PT_Lumbre🔥

Matches

Wins

Losses

Draws

Win Rate

anvid

Matches

Wins

Losses

Draws

Win Rate

RafcKen7

Matches

Wins

Losses

Draws

Win Rate

PT_JJ 🚀

Matches

Wins

Losses

Draws

Win Rate

PT_GuЯu 🚀

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

Angie

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

PT_Logan 🚀

PT_Soriano851🚀

PT_George 🐳

PT_AJ42🚀

PT_Figgy 🚀

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

🏆 Angie's Achievements