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LBT_Creddish

#166

Team Libertalia

Total Matches: 11

Community: Madrid

Team: Team Libertalia

4 Wins 7 Losses

949

S10 End of Season ELO

1026

S10 Peak ELO

36.4%

Win Rate

ELO History

Match Outcome Constellation

✨

⭐

🌟

✨

💫

◯

Wins

Losses

Draws

⭐ Star size = ELO impact

Tournament Performance Profile

Performance Metrics

Consistency Rating

36.4%

ELO Growth -51

Tournament Dominance 0/3

Recent Matches

vs LBT_D1AMANT1D1S

lunes Evening Weekly Play (Constructed) • Dec 01, 2025

LOSS

-16

vs LBT_Zer0

lunes Evening Weekly Play (Constructed) • Dec 01, 2025

LOSS

-21

vs LBT_Miky20hz

lunes Evening Weekly Play (Constructed) • Dec 01, 2025

LOSS

-24

vs Viti

Libertalia domingo 29/11 • Nov 30, 2025

WIN

+20

vs LBT_Elros

Libertalia domingo 29/11 • Nov 30, 2025

LOSS

-6

vs LBT_SrCipo

Libertalia domingo 29/11 • Nov 30, 2025

WIN

+15

vs LBT_Issel

Libertalia domingo 29/11 • Nov 30, 2025

LOSS

-13

vs Cristian Sanz

Torneo Aniversario Libertalia 23/11 • Nov 22, 2025

LOSS

-18

vs Antonio De Pablos

Torneo Aniversario Libertalia 23/11 • Nov 22, 2025

WIN

+20

vs LBT_Miky20hz

Torneo Aniversario Libertalia 23/11 • Nov 22, 2025

LOSS

-22

Tournament History

lunes Evening Weekly Play (Constructed) NR

Libertalia • Dec 01, 2025

Record: 0-3-0 • 9 players • Avg ELO: 1061

Libertalia domingo 29/11 #5

Libertalia • Nov 30, 2025

Record: 2-2-0 • 12 players • Avg ELO: 1055

Torneo Aniversario Libertalia 23/11 #10

Libertalia • Nov 22, 2025

Record: 2-2-0 • 28 players • Avg ELO: 1028

Player Insights

Max Win Streak

Max Loss Streak

-51

ELO Gained

ELO Range

Strong Tournaments (60%+ WR)

Different Stores

Tournaments Played

Advanced Analytics

⚔️

Giant Killer Analysis

Upset Victories: 0/2

Upset Rate: 0.0%

📈

ELO Momentum

Recent Trend: ➡️ Stable

Average Change: 0

Consistency: 100%

📅

Performance Patterns

Not enough data to determine patterns yet

⚡

Current Form

Current Streak: 4 Losses

Recent Form:

Recent Achievements

8 achievements earned

✨

Fabled Debut

Play 1 match in Season 9.

🧞

Friend Like Me

Play 10 matches in Season 8.

📚

Well-Versed

Play 10 matches.

📜

Street Rat

Play 5 matches in Season 8.

📖

The First Chapter

Play your first match.

🎮 Achievement Categories

Seasonal_Participation

Participation

1 matches played

0-1

0% win rate

🤝 Occasional Opponent

❄️

Current Streak

Losses

📊

63%

ELO Percentile

vs All Players

📅

This Month

Matches Played

⚡

-5

Avg ELO Change

Per Match

Stores Played

Libertalia

3 tournaments

Most Faced Opponents

Opponent	Matches	Wins	Losses	Win Rate
LBT_Miky20hz	2	0	2	0%
LBT_D1AMANT1D1S	1	0	1	0%
LBT_Zer0	1	0	1	0%
Viti	1	1	0	100%
LBT_Elros	1	0	1	0%
LBT_SrCipo	1	1	0	100%
LBT_Issel	1	0	1	0%
Cristian Sanz	1	0	1	0%
Antonio De Pablos	1	1	0	100%
BOLLITO🍓🍀	1	1	0	100%

LBT_Miky20hz

Matches

Wins

Losses

Draws

Win Rate

LBT_D1AMANT1D1S

Matches

Wins

Losses

Draws

Win Rate

LBT_Zer0

Matches

Wins

Losses

Draws

Win Rate

Viti

Matches

Wins

Losses

Draws

Win Rate

100%

LBT_Elros

Matches

Wins

Losses

Draws

Win Rate

LBT_SrCipo

Matches

Wins

Losses

Draws

Win Rate

100%

LBT_Issel

Matches

Wins

Losses

Draws

Win Rate

Cristian Sanz

Matches

Wins

Losses

Draws

Win Rate

Antonio De Pablos

Matches

Wins

Losses

Draws

Win Rate

100%

BOLLITO🍓🍀

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

LBT_Creddish

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

LBT_Miky20hz

LBT_D1AMANT1D1S

LBT_Zer0

Viti

LBT_Elros

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

🏆 LBT_Creddish's Achievements

Season 9 - Fabled

Season 8 - Reign of Jafar

Global Achievements