Technical Data

Battlemech: Awesome
Variant: AWS-8Q
Chassis Mass: 80 Tons
Tech Base: Inner Sphere
Battle Value: 1605

Engine: Fusion
Gyro: Standard
Cockpit: Standard
Structure: Standard
Armor: Standard

Movement: 3/5/0
Dissipation: 28
Armor Mass: 15.0 Tons
Armor Factor: 240

   HTAL


Weapons & Equipment

Weapon Class Location
PPC LT
PPC RT
PPC RA
SLas H

Equipment Location

   Threat Assessment



Key Metrics

Run ACD Heat
Baseline 152.5 19
Optimized 165.6 39
Redline 91.2 179

Test Results

The AWS-8Q has average baseline damage output with solid damage optimization potential (+13 versus baseline). This Awesome variant will see penalties from heat quickly but can be managed with good fire control.

   Redline Benchmark


   Optimized Benchmark


   Benchmark Comparison



Key Metrics

Damage/Hit 9.3
Critical Hits 3.0 per Game
Time to Kill 9.0 Turns

Test Results

The AWS-8Q deals an extremely high amount of damage per hit, generates an average number of critical chances and has the capability to take down targets quickly.

   Lethality Test


   Cumulative Lethality



Key Metrics

Armor Coverage 97.2%
Survival Rate 88.6%
Destruction Rate 11.5%

Test Results

This configuration of the Awesome has extremely heavy armor coverage that is vulnerable on the center torso and is extremely over spec on the rear torso. The AWS-8Q is very difficult to bring down and carries no ammo.

   Survivability Benchmark


   Ammo Analysis


   Cumulative Survivability



Key Metrics

Ground MP 3/5
Jump MP 0
Target Mod (avg) +2.0

Test Results

The Awesome AWS-8Q has very poor mobility with no jump capability. This battlemech sees very few motive hits while maintaining a competitive average target modifier.

   Motive Hits


   Target Modifer Analysis



Key Metrics

Battle Value: 1605
Effective Loss 4.6%
Skill Sensitivity 0.712

Test Results

The AWS-8Q is a solid choice that outperforms most other battlemechs while responding very well to increases in gunnery skill.

   Effective Damage Analysis


   Efficiency Rating


   Gunnery Sensitivity Analysis





Preparing Analysis...