How to model EM 2024 tournament in database?

ghz 9months ago ⋅ 275 views

currently I'm working on some private project. For this I want to make a website for the EM 2024 (football/soccer tournament).

But I don't know what approach I should use, and if I'm even heading into the right direction.

So the Tournament consists of teams, which are organized in groups.

  1. The group games take place
  2. The best teams get promoted to the knock-out matches
  3. In the knock-out matches it goes up from round of sixteen to the final

Now I'm wondering what Database structure I should use. Till now I often have worked with SQL and I like the strong structure. But now I have the problem, that there are a lot of unkown propertiers regarding the knock-out matches.

Now I want to take a look at the NoSQL approach with MongoDB. My idea would be:

  • one collection for the teams (id, name, group_enum, group_score)
  • one collection for the group-games (id, team_a, team_b, score_a, score_b, start_time, localtion)
  • one collection for the knock-out games (id, team_a, team_b, score_a, start_time, localtion score_b, parent_match, child_matches)

For the knock-out games I would add the parent-child relation to build up the binary tree, that gets constructed. A lot of business-logic needs to be done by coding I think. For example: Team wins in knockout-game -> Update team in parent-match

Till know I tried to model the tournament structure with SQL. Because of the unknown teams in the knockout-matches, I need to use a lot of nullable properties, which I don't like.

So now I want to check out a NoSQL approach and need help in my thought-process.

Thank you very much for further suggestions, improvements and the like!

Answers

Your proposed MongoDB approach seems reasonable for modeling the structure of the EM 2024 football tournament. Using MongoDB for this type of application can offer flexibility in handling the varying structure and properties of the knockout matches, as well as the relationships between teams and matches.

Here are some suggestions and considerations for your MongoDB database design:

  1. Teams Collection: This collection can store information about all participating teams in the tournament. As you mentioned, it can include fields like team name, group enumeration, and group score.

  2. Group Games Collection: This collection can store information about the group stage matches, including the teams competing, scores, start time, and location.

  3. Knockout Games Collection: This collection can represent the knockout stage matches. Using a parent-child relationship as you described can help organize the matches into a tree structure. You may also want to include additional fields such as round number (e.g., round of sixteen, quarter-finals, semi-finals), match type (e.g., round-robin, single elimination), and match status (e.g., scheduled, ongoing, completed).

  4. Updating Parent Matches: Implementing business logic to update parent matches based on the results of child matches is essential. You can achieve this using application logic triggered by events such as match results being recorded.

  5. Indexing: Consider creating indexes on fields frequently used for querying, such as team IDs, match start times, and match locations, to optimize query performance.

  6. Data Consistency: Ensure data consistency by handling updates and deletions carefully, especially in cases where matches have dependencies on other matches (e.g., updating parent matches when child matches are updated).

  7. Scalability: MongoDB's flexible schema and horizontal scalability make it suitable for handling large amounts of data and concurrent access, which is essential for a tournament with many matches and teams.

Overall, your proposed MongoDB approach appears to be a good fit for modeling the EM 2024 football tournament. It offers flexibility and scalability, allowing you to adapt to changes in tournament structure and handle the complexities of managing matches and teams effectively. As you develop your application, continuously evaluate and refine your database design to ensure it meets your application's requirements efficiently.

#mongodb