NASEF x Skillquest: Rocket League Bot Building Beyond the Game Challenge Recap and Winners
Dec 22, 2025
Final standings from the tournament:
1st - Wheatley
2nd - theyhard_maybe3
3rd-4th - Glad0s
3rd-4th - Cortana
5th-6th - The_beginner_part_2
5th-6th - GIR
7th - 11th - R.O.B.
7th - 11th - Baymax
7th - 11th - HAL 9000
7th - 11th - J.A.R.V.I.S.
12th - 15th - Gerald Jaemz The XIV
12th - 15th - Ti-80 Silver Plus
12th - 15th - the bot
Throughout the fall, Skillquest users created their bots using our new online coding platform, enabling them to write code and watch their bots update and run in real time, all within a web browser.
While bots don’t exactly “make plays” the way human players do in traditional Rocket League, the winning bot Wheatley distinguished itself through effective aerial mechanics and exceptionally strong decision-making around when and how to shoot. Any Rocket League player understands how difficult aerials are to master, requiring precise control of speed, positioning, ball trajectory, and boost management. Translating even a basic aerial into a reliable routine for a bot is a significant challenge, which makes Wheatley’s execution especially impressive.
Shooting, while easier to break down mechanically, is often overlooked in bot design. Many bots in the competition treated movement as if driving straight forward were the only option, leading to suboptimal positioning and rushed shots. Wheatley’s creator clearly invested extra effort into refining the bot’s shooting logic, resulting in smarter positioning and more consistent conversions. Together, these strengths set Wheatley apart and ultimately secured its victory.
One example of Wheatley’s advantage can be seen in its shot selection logic. In the snippet below, the bot evaluates possible ball hits and prioritizes them based on intent:
At a high level, this code asks a simple but critical question: Can I shoot toward the opponent’s goal? If the answer is yes, the bot immediately commits to that option. Only if no viable offensive shot is available does Wheatley fall back to a safer “hit anywhere” option, which helps maintain pressure or avoid dangerous situations. This prioritization is important. Rather than blindly hitting the ball forward or reacting late, Wheatley evaluates multiple outcomes and selects the most effective one first. That decision-making layer—choosing what kind of hit to take before executing it—is a major reason the bot consistently found better shots and positioning than many of its competitors.
Join the NASEF Skillquest Beyond the Game challenge and Rocket League tournament in January - April, 2026!