Esport - CS2 demo analysis¶

This jupyter notebook can be found here https://drive.google.com/file/d/11yr8qwfT3YO204pEjOpYRfRXDcGdGQQV/view?usp=sharing

  • Demo file: https://drive.google.com/file/d/1AVZ3IdrN-TCkclVUcSEyjwrYMH58aqWe/view?usp=sharing
  • Map: https://drive.google.com/file/d/1jQZULatBDxeBHUeQfj9XUgFDbwjwvKhm/view?usp=sharing

Project requirements:
numpy
pandas
matplotlib
demoparser2
jupyterlab
seaborn

Useful links:

  • HLTV results - https://www.hltv.org/results
  • CS2 events - https://cs2.poggu.me/dumped-data/game-events/
  • CS2 map sizes - https://github.com/pnxenopoulos/awpy/blob/main/awpy/data/map_data.py

CS2 Parser:

  • Parser - https://github.com/LaihoE/demoparser
  • Documentation - https://github.com/LaihoE/demoparser/tree/main/documentation/python
  • Example - https://github.com/LaihoE/demoparser/blob/main/examples/1vX/main.py
  • Examples - https://github.com/LaihoE/demoparser/tree/main/examples

Imports¶

In [3]:
from demoparser2 import DemoParser
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from matplotlib.collections import LineCollection
from matplotlib import cm
import seaborn as sns

Functions¶

In [4]:
def draw_map(df):
    plt.figure(figsize=(10, 6))
    plt.scatter(df['X'], df['Y'], c='blue', marker='o', label='Player Positions')
    plt.xlim(-2800, 1500)
    plt.ylim(-3000, 1000)
    plt.xlabel('X Position')
    plt.ylabel('Y Position')
    plt.title('2D Map of Player Positions')
    plt.grid(True)
    plt.legend()
    plt.show()
In [5]:
def draw_movement(df_all, df_path=None):
    plt.figure(figsize=(10, 6))
    plt.figure(figsize=(10, 6))
    
    img = mpimg.imread('Cs2_mirage_radar.webp')
    # Plot the image behind the points
    plt.imshow(img, extent=[-3230, 1883, -3490, 1713], aspect='auto')  # Extent sets the image limits
    
    plt.scatter(df_all['X'], df_all['Y'], c='blue', marker='o', label='Player Positions')

    if df_path is not None:
        plt.scatter(df_path['X'], df_path['Y'], color='red', linewidth=2, label='Highlighted path', marker='o')

    plt.xlim(-2800, 1500)
    plt.ylim(-3000, 1000)
    
    # Adding labels and title
    plt.xlabel('X Position')
    plt.ylabel('Y Position')
    plt.title('2D Map of Player Positions')
    
    # Display the plot
    plt.grid(True)
    plt.legend()
    plt.show()

Data overview¶

In [7]:
parser = DemoParser("demos/eternal-fire-vs-natus-vincere-m2-mirage.dem")

Events¶

In [8]:
df_deaths = parser.parse_event("player_death", player=["X", "Y"], other=["total_rounds_played"])
df_weapon_fires = parser.parse_event("weapon_fire", player=["X", "Y"], other=["total_rounds_played"])
df_player_spawns = parser.parse_event("player_spawn", other=["total_rounds_played"])
df_player_teams = parser.parse_event("player_team", other=["total_rounds_played"])
df_player_hurts = parser.parse_event("player_hurt", other=["total_rounds_played"])
df_round_starts = parser.parse_event("round_start")
df_round_ends = parser.parse_event("round_end")
df_last_round_half = parser.parse_event("round_announce_last_round_half")
df_item_purchases = parser.parse_event("item_purchase", other=["total_rounds_played"])
df_item_pickups = parser.parse_event("item_pickup", other=["total_rounds_played"])
df_bomb_plants = parser.parse_event("bomb_planted", other=["total_rounds_played"])
df_bomb_defuses = parser.parse_event("bomb_defused", other=["total_rounds_played"])
df_bomb_explosions = parser.parse_event("bomb_exploded", other=["total_rounds_played"])
df_bomb_drops = parser.parse_event("bomb_dropped", other=["total_rounds_played"])
df_bomb_pickups = parser.parse_event("bomb_pickup", other=["total_rounds_played"])
df_hegrenade_detonations = parser.parse_event("hegrenade_detonate", other=["total_rounds_played"])
df_flashbang_detonations = parser.parse_event("flashbang_detonate", other=["total_rounds_played"])
df_smokegrenade_detonations = parser.parse_event("smokegrenade_detonate", other=["total_rounds_played"])
df_smokegrenade_expirations = parser.parse_event("smokegrenade_expired", other=["total_rounds_played"])
df_inferno_startburns = parser.parse_event("inferno_startburn", other=["total_rounds_played"])
df_inferno_expirations = parser.parse_event("inferno_expire", other=["total_rounds_played"])
df_player_pings = parser.parse_event("player_ping", other=["total_rounds_played"])
df_player_sounds = parser.parse_event("player_sound", other=["total_rounds_played"])
df_ticks = parser.parse_ticks(["X", "Y"])
In [9]:
max_round = df_player_spawns["total_rounds_played"].max() + 1
max_round
Out[9]:
np.int32(18)

Team interpretation in numbers:

  • 3 = CT
  • 2 = T
In [10]:
df_player_teams
Out[10]:
disconnect isbot oldteam silent team tick total_rounds_played user_name user_steamid
0 False False 2 True 3 87273 12 jL 76561198176878303
1 False False 3 True 2 87273 12 Calyx 76561198052546265
2 False False 2 True 3 87273 12 w0nderful 76561199063068840
3 False False 3 True 2 87273 12 Wicadia 76561198812513923
4 False False 3 True 2 87273 12 MAJ3R 76561197967432889
5 False False 2 True 3 87273 12 Aleksib 76561198013243326
6 False False 3 True 2 87273 12 XANTARES 76561198044118796
7 False False 2 True 3 87273 12 b1t 76561198246607476
8 False False 2 True 3 87273 12 iM 76561198050250233
9 False False 3 True 2 87273 12 woxic 76561198083485506
In [11]:
# Another approach
df_team_info2 = parser.parse_player_info();
df_team_info2
Out[11]:
steamid name team_number
0 76561198176878303 jL 3
1 76561198052546265 Calyx 2
2 76561199063068840 w0nderful 3
3 76561198812513923 Wicadia 2
4 76561197967432889 MAJ3R 2
5 76561198013243326 Aleksib 3
6 76561198044118796 XANTARES 2
7 76561198246607476 b1t 3
8 76561198050250233 iM 3
9 76561198083485506 woxic 2
In [12]:
df_deaths
Out[12]:
assistedflash assister_X assister_Y assister_name assister_steamid attacker_X attacker_Y attacker_name attacker_steamid attackerblind ... total_rounds_played user_X user_Y user_name user_steamid weapon weapon_fauxitemid weapon_itemid weapon_originalowner_xuid wipe
0 False NaN NaN None None 296.651398 -1586.485962 Aleksib 76561198013243326 False ... 0 150.391434 -1526.738159 Wicadia 76561198812513923 glock 17293822569165815812 20660576547 0
1 False NaN NaN None None -452.761658 -1758.071045 Aleksib 76561198013243326 False ... 0 -531.607178 -2378.742432 XANTARES 76561198044118796 glock 17293822569165815812 20660576547 0
2 False -531.607178 -2378.742432 XANTARES 76561198044118796 -1570.978027 -760.632751 MAJ3R 76561197967432889 False ... 0 -1272.865112 -1050.418091 iM 76561198050250233 usp_silencer 17293822569145761853 13626608964 0
3 False NaN NaN None None -812.001831 -2082.587646 w0nderful 76561199063068840 False ... 0 -465.970337 -2350.779785 Calyx 76561198052546265 glock 17293822569105195012 34398553971 0
4 False NaN NaN None None NaN NaN None None False ... 0 -1693.296631 -1375.607422 Aleksib 76561198013243326 planted_c4 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
120 False NaN NaN None None -1698.991211 682.782166 jL 76561198176878303 False ... 17 -1064.924072 151.871338 XANTARES 76561198044118796 m4a1_silencer 17293822569131868220 34241486015 0
121 False -2065.616211 -559.346924 iM 76561198050250233 -1699.885620 681.705627 jL 76561198176878303 False ... 17 -1210.908081 285.837585 MAJ3R 76561197967432889 m4a1_silencer 17293822569131868220 34241486015 0
122 False -1064.924072 151.871338 XANTARES 76561198044118796 -1022.358459 120.646042 woxic 76561198083485506 False ... 17 -1699.831299 683.073303 jL 76561198176878303 ak47 17293822569170534407 25065970543 0
123 False -1699.792603 683.121765 jL 76561198176878303 -2495.580322 328.931458 Aleksib 76561198013243326 False ... 17 -1021.273499 123.897125 woxic 76561198083485506 deagle 17293822569105129473 29398974889 0
124 False NaN NaN None None -1847.084473 -98.833923 w0nderful 76561199063068840 False ... 17 -1736.028687 849.204041 Calyx 76561198052546265 awp 17293822569146089481 36570875316 0

125 rows × 33 columns

In [13]:
df_deaths.keys()
Out[13]:
Index(['assistedflash', 'assister_X', 'assister_Y', 'assister_name',
       'assister_steamid', 'attacker_X', 'attacker_Y', 'attacker_name',
       'attacker_steamid', 'attackerblind', 'attackerinair', 'distance',
       'dmg_armor', 'dmg_health', 'dominated', 'headshot', 'hitgroup',
       'noreplay', 'noscope', 'penetrated', 'revenge', 'thrusmoke', 'tick',
       'total_rounds_played', 'user_X', 'user_Y', 'user_name', 'user_steamid',
       'weapon', 'weapon_fauxitemid', 'weapon_itemid',
       'weapon_originalowner_xuid', 'wipe'],
      dtype='object')

Movements¶

In [14]:
df_movements = parser.parse_ticks(["X", "Y", "team_rounds_total", "team_name", "is_alive", "total_rounds_played"])
df_movements
Out[14]:
total_rounds_played team_rounds_total team_name X Y is_alive tick steamid name
0 0 0 TERRORIST 1136.047729 -160.015564 True 1 76561198176878303 jL
1 0 0 CT -1656.000000 -1800.000000 True 1 76561198052546265 Calyx
2 0 0 TERRORIST 1216.000000 -307.000000 True 1 76561199063068840 w0nderful
3 0 0 CT -1656.000000 -1976.000000 True 1 76561198812513923 Wicadia
4 0 0 CT -1776.000000 -1976.000000 True 1 76561197967432889 MAJ3R
... ... ... ... ... ... ... ... ... ...
1405715 18 13 CT -2216.906006 621.395386 False 140592 76561198013243326 Aleksib
1405716 18 5 TERRORIST -1064.924072 151.871338 False 140592 76561198044118796 XANTARES
1405717 18 13 CT -701.487671 -1357.026489 False 140592 76561198246607476 b1t
1405718 18 13 CT -1807.516724 -54.575256 True 140592 76561198050250233 iM
1405719 18 5 TERRORIST -1021.273499 123.897125 False 140592 76561198083485506 woxic

1405720 rows × 9 columns

Rename "TERRORIST" into "T" to match the naming.

In [15]:
df_movements.loc[:, "team_name"] = df_movements["team_name"].replace("TERRORIST", "T")
df_movements
Out[15]:
total_rounds_played team_rounds_total team_name X Y is_alive tick steamid name
0 0 0 T 1136.047729 -160.015564 True 1 76561198176878303 jL
1 0 0 CT -1656.000000 -1800.000000 True 1 76561198052546265 Calyx
2 0 0 T 1216.000000 -307.000000 True 1 76561199063068840 w0nderful
3 0 0 CT -1656.000000 -1976.000000 True 1 76561198812513923 Wicadia
4 0 0 CT -1776.000000 -1976.000000 True 1 76561197967432889 MAJ3R
... ... ... ... ... ... ... ... ... ...
1405715 18 13 CT -2216.906006 621.395386 False 140592 76561198013243326 Aleksib
1405716 18 5 T -1064.924072 151.871338 False 140592 76561198044118796 XANTARES
1405717 18 13 CT -701.487671 -1357.026489 False 140592 76561198246607476 b1t
1405718 18 13 CT -1807.516724 -54.575256 True 140592 76561198050250233 iM
1405719 18 5 T -1021.273499 123.897125 False 140592 76561198083485506 woxic

1405720 rows × 9 columns

In [265]:
draw_movement(df_movements)

<Figure size 1000x600 with 0 Axes>
No description has been provided for this image

Player "Calyx" movements¶

All selected movements for a specific player with name Calyx.

In [16]:
df_calyx_movement = df_movements[(df_movements["name"] == "Calyx")]
df_calyx_movement
Out[16]:
total_rounds_played team_rounds_total team_name X Y is_alive tick steamid name
1 0 0 CT -1656.000000 -1800.000000 True 1 76561198052546265 Calyx
11 0 0 CT -1656.000000 -1800.000000 True 2 76561198052546265 Calyx
21 0 0 CT -1656.000000 -1800.000000 True 3 76561198052546265 Calyx
31 0 0 CT -1656.000000 -1800.000000 True 4 76561198052546265 Calyx
41 0 0 CT -1656.000000 -1800.000000 True 5 76561198052546265 Calyx
... ... ... ... ... ... ... ... ... ...
1405671 18 5 T -1736.228027 849.113464 False 140588 76561198052546265 Calyx
1405681 18 5 T -1736.228027 849.113464 False 140589 76561198052546265 Calyx
1405691 18 5 T -1736.228027 849.113464 False 140590 76561198052546265 Calyx
1405701 18 5 T -1736.228027 849.113464 False 140591 76561198052546265 Calyx
1405711 18 5 T -1736.228027 849.113464 False 140592 76561198052546265 Calyx

140572 rows × 9 columns

Selected movements for the first round only.

In [17]:
df_calyx_movement_round1 = df_calyx_movement[(df_calyx_movement["total_rounds_played"]) == 0]
df_calyx_movement_round1
Out[17]:
total_rounds_played team_rounds_total team_name X Y is_alive tick steamid name
1 0 0 CT -1656.000000 -1800.000000 True 1 76561198052546265 Calyx
11 0 0 CT -1656.000000 -1800.000000 True 2 76561198052546265 Calyx
21 0 0 CT -1656.000000 -1800.000000 True 3 76561198052546265 Calyx
31 0 0 CT -1656.000000 -1800.000000 True 4 76561198052546265 Calyx
41 0 0 CT -1656.000000 -1800.000000 True 5 76561198052546265 Calyx
... ... ... ... ... ... ... ... ... ...
58601 0 0 CT -465.557495 -2350.714355 False 5861 76561198052546265 Calyx
58611 0 0 CT -465.557495 -2350.714355 False 5862 76561198052546265 Calyx
58621 0 0 CT -465.557495 -2350.714355 False 5863 76561198052546265 Calyx
58631 0 0 CT -465.557495 -2350.714355 False 5864 76561198052546265 Calyx
58641 0 0 CT -465.557495 -2350.714355 False 5865 76561198052546265 Calyx

5865 rows × 9 columns

First round movement¶

In [18]:
draw_movement(df_calyx_movement_round1)

<Figure size 1000x600 with 0 Axes>
No description has been provided for this image

Filter all movement for comparison to make calculation faster.

In [19]:
df_movements_filtered = df_movements[::5]
df_movements_filtered
Out[19]:
total_rounds_played team_rounds_total team_name X Y is_alive tick steamid name
0 0 0 T 1136.047729 -160.015564 True 1 76561198176878303 jL
5 0 0 T 1296.000000 32.000000 True 1 76561198013243326 Aleksib
10 0 0 T 1136.047729 -160.015564 True 2 76561198176878303 jL
15 0 0 T 1296.000000 32.000000 True 2 76561198013243326 Aleksib
20 0 0 T 1136.047729 -160.015564 True 3 76561198176878303 jL
... ... ... ... ... ... ... ... ... ...
1405695 18 13 CT -2216.906006 621.395386 False 140590 76561198013243326 Aleksib
1405700 18 13 CT -1699.792603 683.121765 False 140591 76561198176878303 jL
1405705 18 13 CT -2216.906006 621.395386 False 140591 76561198013243326 Aleksib
1405710 18 13 CT -1699.792603 683.121765 False 140592 76561198176878303 jL
1405715 18 13 CT -2216.906006 621.395386 False 140592 76561198013243326 Aleksib

281144 rows × 9 columns

In [292]:
print(f"df_movements:\t\t{df_movements.size}\ndf_movements_filtered:\t {df_movements_filtered.size}")

df_movements:		12651480
df_movements_filtered:	 2530296
In [273]:
draw_movement(df_movements_filtered, df_calyx_movement_round1)

<Figure size 1000x600 with 0 Axes>
No description has been provided for this image

All movements as a CT¶

All movements for player named Calyx in the CT team.

In [264]:
draw_movement(df_calyx_movement[(df_calyx_movement["team_name"] == "CT")])

<Figure size 1000x600 with 0 Axes>
No description has been provided for this image

Comparison of Calyx movements in the CT team with all movements.

In [274]:
draw_movement(df_movements_filtered, df_calyx_movement[(df_calyx_movement["team_name"] == "CT")])

<Figure size 1000x600 with 0 Axes>
No description has been provided for this image

All movements as a T¶

All movements for player named Calyx in the T team.

In [21]:
draw_map(df_calyx_movement[(df_calyx_movement["team_name"] == "T")])
No description has been provided for this image

Comparison of Calyx movements in the T team with all movements.

In [277]:
draw_movement(df_movements_filtered, df_calyx_movement[(df_calyx_movement["team_name"] == "T")])

<Figure size 1000x600 with 0 Axes>
No description has been provided for this image

Purchases¶

Data overview of purchase event table for all game rounds.

In [22]:
df_item_purchases
Out[22]:
cost custom_name float inventory_slot item_name name paint_seed skin skin_id steamid stickers tick total_rounds_played was_sold
0 650 None NaN 0 item_kevlar Wicadia NaN None NaN 76561198812513923 [] 87 0 False
1 650 None NaN 0 item_kevlar woxic NaN None NaN 76561198083485506 [] 106 0 False
2 300 None NaN 0 P250 jL NaN None NaN 76561198176878303 [] 137 0 True
3 300 None NaN 1 Smoke Grenade jL NaN None NaN 76561198176878303 [] 159 0 False
4 200 None NaN 2 Flashbang jL NaN None NaN 76561198176878303 [] 170 0 False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
751 400 None NaN 2 Molotov woxic NaN None NaN 76561198083485506 [] 134774 17 False
752 200 None NaN 3 Flashbang woxic NaN None NaN 76561198083485506 [] 134774 17 False
753 200 None NaN 3 Flashbang Wicadia NaN None NaN 76561198812513923 [] 134915 17 False
754 300 None NaN 4 Smoke Grenade woxic NaN None NaN 76561198083485506 [] 135029 17 False
755 200 None NaN 3 Flashbang XANTARES NaN None NaN 76561198044118796 [] 135811 17 False

756 rows × 14 columns

All rounds purchases¶

Total number of purchases for each item.

In [23]:
item_name_counts_df = df_item_purchases.groupby('item_name').size().reset_index(name='count')
item_name_counts_df
Out[23]:
item_name count
0 AK-47 33
1 AWP 9
2 Decoy Grenade 1
3 Desert Eagle 11
4 Dual Berettas 2
5 Five-SeveN 1
6 Flashbang 163
7 Galil AR 11
8 High Explosive Grenade 76
9 Incendiary Grenade 38
10 M4A1-S 34
11 M4A4 3
12 MAC-10 3
13 MP9 17
14 Molotov 60
15 P250 6
16 SSG 08 1
17 Smoke Grenade 131
18 Tec-9 4
19 Zeus x27 1
20 item_assaultsuit 84
21 item_defuser 15
22 item_kevlar 52

Plotting the chart of purchases for each weapon using pure matplotlib.

In [24]:
# Plotting
plt.figure(figsize=(10, 6))
plt.bar(item_name_counts_df['item_name'], item_name_counts_df['count'], color='skyblue')

# Adding labels and title
plt.xlabel('Item Name')
plt.ylabel('Count')
plt.title('Count of Each Item Name')
plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability

plt.tight_layout()
plt.show()
No description has been provided for this image

Using seaborn package to plot the chart instead.

In [25]:
# Plotting with hue
plt.figure(figsize=(10, 6))
sns.barplot(x='item_name', y='count', data=item_name_counts_df, hue='item_name', palette='viridis')

# Adding labels and title
plt.xlabel('Item Name')
plt.ylabel('Count')
plt.title('Count of Each Item Name')
plt.xticks(rotation=45, ha='right')

plt.tight_layout()
plt.show()
No description has been provided for this image

Grouped item purchases for each player¶

In [26]:
# Group by 'name' and 'item_name' to get the count of each item per player
item_name_per_player = df_item_purchases.groupby(['name', 'item_name']).size().reset_index(name='count')

# Plotting with seaborn
plt.figure(figsize=(20, 8))
sns.barplot(x='name', y='count', hue='item_name', data=item_name_per_player)

# Adding labels and title
plt.xlabel('Player Name')
plt.ylabel('Item Count')
plt.title('Count of Each Item Name per Player')
plt.xticks(rotation=45, ha='right')

plt.tight_layout()
plt.show()
No description has been provided for this image

Task 1¶

Choose and analyse four situation that you find interesting and can be used as an information used to improve players' knowledge.

Here are some examples:

  • Identifying most frequent weapon used - determine the most frequently fired weapon by each player across the demo
  • Tracking player deaths over time - track the number of deaths for each player across the rounds
  • Finding players with highest damage taken - identify players who took the most damage in the demo
  • Bomb events analysis - track bomb planting and defusing events over rounds
  • Flashbang and HE grenade effectiveness - measure the effectiveness of flashbangs and HE grenades by their usage and detonations
  • Team composition over time - track how player teams changed over the course of the game
In [ ]: