Determining playoff clinching scenarios in the NHL using constraint programming

The AWS Generative AI Innovation Center developed an automated system to determine NHL playoff clinching scenarios using constraint programming and custom tree search. The system consists of a 0-day solver that checks if a team has already clinched the playoffs and an n-day lookahead solver that generates scenarios for teams that could clinch based on upcoming games. The approach accounts for the NHL's complex tie-breaking rules and was validated against official NHL results. The practical implication for engineers building AI systems is the application of constraint programming to solve complex combinatorial challenges in real-world domains.

⚡ Key Takeaways

• The system uses the CP-SAT solver from Google OR-Tools to solve the constraint programming model.
• The NHL's tie-breaking rules involve a cascade of seven tie-breakers, including point percentage, regulation wins, and goal differential.
• The 0-day solver formulates the problem as a feasibility problem to determine if a team has already clinched the playoffs.
• The approach accounts for the full complexity of the NHL's tie-breaking rules and game outcomes, including six possible outcomes for each game.
• The system was validated against official NHL results, demonstrating its accuracy and reliability.

The application of constraint programming to determine NHL playoff clinching scenarios demonstrates the potential of AI to solve complex combinatorial challenges in real-world domains, such as sports and finance. This approach can be extended to other domains, enabling engineers to build more efficient and accurate systems for decision-making and prediction.

✅ Practical Steps

1. Apply the concepts from this article to your own system design, using constraint programming to solve complex combinatorial challenges.
2. Utilize the CP-SAT solver from Google OR-Tools to solve constraint programming models in your own applications.

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