Making a Successful Strategy
Why is Strategy Important?
Strategy is crucial in Botball because it determines how you approach the game and what you aim to achieve. A good strategy can help you maximize your score, make the most of your resources, and ensure that your robot performs well during the competition. Without a strategy, you risk wasting time and effort on tasks that don’t contribute to your overall goal.
What makes a good strategy?
A good strategy isn’t just about how many points you score at the end of a competition. It’s also about how well you use the resources you have, how much time you have to prepare, and the team you’re working with, both in terms of experience and size.
For example, if you have a team of six experienced people and three months to prepare, you can aim for a high-scoring run. But if your team has only two people who are less experienced, it’s better to aim for a run that’s easier to execute and has high accuracy. In this case, consistently scoring 50 points is already a success.
Accuracy Over Points
In my experience, it’s better to aim for a strategy that’s reliable in scoring the points you plan for, rather than going for a high-scoring strategy that isn’t reliable. A dependable robot is much easier and less stressful to work with during a competition than one that can score a lot of points but might fail when it counts.
Let me give you an example from GCER 2024. My team had a strategy that could score 2,700 points in theory, but at the competition, we only managed to score 1,100 points. The issue was that our high-scoring strategy wasn’t reliable. Even a small mistake cost us a lot of points, and unfortunately, our robot made more than one mistake. The reason we ended up in fourth place wasn’t because we could achieve a 2,700-point run, but because we were able to consistently score around 1,100 points.
This experience taught me that being able to consistently achieve a lower, but reliable score is often more valuable than risking everything for a higher, but unpredictable score. As a side note, the reliability of our robot made GCER much more enjoyable for us, as we didn’t have to worry about our robot failing during the competition.
Creating the Initial Strategy
Step 1: Setting Team Goals and Time Commitments
The day before the game review was released, my team and I had a Discord call to discuss our goals for the year and how much time each of us could commit to the project. To clarify, we were all students, and Botball wasn’t a school subject—we had to work on it over the weekends.
Step 2: Reviewing the Game and Scoring Rules
With a general idea of what we wanted to achieve, we started looking at the game review as soon as it was released. We studied the scoresheet and the game table, trying to identify which areas could score the most points. At this stage, we weren’t thinking about what our team could realistically do, but rather about what was possible. We asked ourselves questions like:
“What can be done?”
“Which items score well together?”
At this point, we weren’t worrying about risks or how the robot would accomplish specific tasks. This approach is important because it helps you understand what the game allows and what other teams might also be aiming for. We also asked questions to the KIPR staff to clarify the scoring rules. This whole process took us about 3-4 hours of discussions and questions.
Step 3: Brainstorming Possible Strategies
Once we had a solid understanding of what was possible in the game, we started brainstorming what our robot should be able to do. We used a whiteboard session (in our case, the free tool excalidraw.com) to sketch out possible mechanisms, our robot design, and the tasks we wanted to accomplish. We also discussed the order in which the tasks should be done and how the robot should be built to handle them. We even took a closer look at the new Create3 robot to see how we could use it.
As you can see in the picture, we mapped out the driving paths for the robot on the game table and sketched possible mechanisms, like a pom sorting system and a creative astronaut pickup mechanism. We also drew some robot designs, although we didn’t end up using all of them. The image also includes some features of the Create3 robot and screenshots of our Excel scoring sheet.
Examples of Other Strategies
To give you a broader perspective, consider how other teams might approach the same challenge:
Team A might focus on fewer, high-point tasks, ensuring they can execute them flawlessly.
Team B might aim for a balanced strategy, completing a wide range of tasks to maximize their overall score.
Both approaches can be successful, depending on the team's strengths and available resources.
Refining the Strategy
After the brainstorming session, we had an initial idea of what our robot should be able to do. The next steps were a bit more diverged between the team members. Some of us started to build our concept robot, while others experimented with the Create3 robot, and others tried to come up with more detailed movement strategies for the robot.
To focus on the refinement part of the strategy, I will talk about the movement strategy. When you want your robot to be moving accurately on the game table, there's a simple rule you should follow: **The robot should always know where it is **. This concept is described in more detail in the Increasing Robot Accuracy guide. Essentially, this refinement step is about implementing the different methods to ensure the robot knows its x and y position introduced in the guide.
In this picture, you can see how we refined our strategy. We used the whiteboard to sketch out the different methods we wanted to implement to ensure the robot knew its x and y position. The orange-pink lines represent the robot’s driving path. The blue solid lines show where we wanted the robot to do line-ups, the blue dotted represents where we use the distance sensor to drive our robot till it sees a certain distance, and the green dotted lines represent where we wanted to drive with back-EMF.
By the end of the refinement phase, we had a plan where we marked where we want the robot to do line-ups, line-following, etc.
Adapting and Learning from Mistakes
It’s important to understand that even the best-laid plans can go awry. During the competition, we had to change our plan multiple times because we realized that some parts weren’t working as expected. Don’t worry about having a perfect plan from the start. It’s more important to have a plan that you can improve on later. Being adaptable and willing to learn from mistakes is key to success.
Conclusion and Feedback
This guide is meant to help develop a successful strategy based on my (& my teams) experiences. Remember, strategy is about balancing your team’s capabilities with the goals you want to achieve. Consistency, adaptability, and a solid understanding of the game are more valuable than aiming for an impossibly high score that you might not achieve.
Authors
Tobias Madlberger