Sunny Sze

CS²

Franklin Cheung

Hengyi Lin

Mengyu Liang

Ruiling Xu

wcsze@ug.kth.se

hyfch@ug.kth.se

hengyil@kth.se

lmengyu@kth.se

ruiling@ug.kth.se

DH2321 Interactive Data Visualization�2025-02-01

Current Situation

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Their Limitations

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1. Surface-Level Insights: Current data analytics often provide only surface-level insights, missing deeper contextual factors that influence gameplay. Without comprehensive data that captures the nuances of player performance and strategy, coaches may struggle to make informed decisions.
2. Complexity of Data: The raw data are overly complex, requiring technical skills that coaches may not possess. This complexity can hinder their ability to interpret and apply data insights effectively in training and strategy development.

Reference: https://www.hltv.org/news/40128/why-were-still-waiting-for-counter-strikes-data-revolution

Our Project Overview

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1. Visualizes game statistics, including player movements, economies, and other crucial metrics.
2. Comparison of performances and providing insights that can inform strategies and improve team dynamics.
3. Mimic traditional sports by applying visualization methods that are less used in eSports, e.g. maps

Our Target - CS2 eSports Coaches

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Their responsibilities:​

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1. Strategic Planning by developing game strategies tailored to the team's strengths and weaknesses and analyzing opponents’ gameplay to devise counter-strategies.
2. Players’ Training and Skill Development by providing feedbacks on gameplay, encouraging improvement and adaptation.
3. Performance Analysis through reviewing gameplay footage to identify areas for improvement and using data analytics to track player performance and team statistics.

Our Tasks - how to help the coaches

Through Exploring the graphs and data of players’ performances,

The coaches want to Summarize the match result,

By Discovering the Outliers, Trends​ and Correlations

We allow them to,

1. Analyze the reason behind winning or losing
2. Check the match performance of each player​
3. Compare to find out the which player​ performed better
4. Most importantly WHY?

Our data - General Description

Game Replays (Demos) Oriented Pipeline:

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1. Get replay archives from hltv.org
2. Or download your own demo file after the game
3. Use demoinfocs-go lib to expose raw data
4. Transform data in .dem file to json format

General Description

• The data includes statistics and status of player characters
• The data is logged frame by frame in a match, therefore any changes in a single frame can be captured

Our data - How we utilize them?

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Over 30+ dimensions for the raw Data

• We boil down the data for more efficient retrieval

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Large Dataset down to each frame

• We combine all the frames with a 3 seconds interval

Our Coding Methods

Round Bar

Interaction

Active:

Click to choose a round

Passive:

Highlight the round chosen

- T(Terrorists): Plant a C4 bomb at the Bombsite and detonate it, or kill all enemies.

- CT (Counter-Terrorists): Destroy the T Side before time runs out or Defuse the Bomb.

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Winning Method：

• Eliminate All Enemies (T&CT)
• Bomb Explosion (T)
• Bomb Defusal (CT)
• Round Timer Expiry (CT)

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Economy-Equipment Line Chart

Interaction

Active:

Click to choose a round through difference

Passive:

Highlight the round chosen

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- Win Bonus: $3250-$3500 for winning rounds.

- Loss Bonus: Loss bonuses gradually increase to a maximum of $3,400 for consecutive losses.

- Kill Bonus: $100-$900.

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Economy Strategy：

• Full Buy Round
• Force Buy Round
• Eco Round
• Semi Buy / Half Buy Round
• Saving Round-

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Game Map with Position/Density overlay

Interaction

Active:

Zoom/Pan

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Maps：

• Dust II
• Mirage
• Inferno
• Nuke
• Overpass
• Ancient

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T/CT Spawn: Terrorist/Counter-Terrorists starting point, offense.

Bombsite A/B: One of the points where bombs can be planted.

Mid: Usually in the center of the map, a key location that connects multiple zones

Connector: A path that connects different areas

Long and Short: Paths that are longer or shorter

Sniper Spot: A place where an AWP can be mounted

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Passive:

Change with Round or Game

Change with Team or Player

Radar Chart

- K/D Rate

= Kill/Death

- KD Difference

= Kill-Death

- KAST

= （Kill+Assist+Survive+Trade Kill）/Round

- Damage per Round

= Damage/Round

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Player to Player Heat Matrix

Interaction

Active:

Click to choose a Player/ Player to Player

Passive:

Change with Round or Game

Highlight when choosing a player

Our Interactive Demo

https://cssquare.finpluto.tech/