Icon

C29_​Table Tennis Tournament

There has been no title set for this workflow's metadata.

You work for a Berlin startup and it's Friday night. A table tennis tournament with your colleagues is due! The following problem comes up: how to randomly split all your co-workers in random teams of two players, and then randomly define the direct elimination matches as a dendrogram? Hint: You can use the Hierarchical Cluster Assigner in this challenge.

URL: Dataset https://hub.knime.com/alinebessa/spaces/Just%20KNIME%20It!%20Season%202%20-%20Datasets/latest/Challenge%2029%20-%20Dataset~E4tX_Id3c0ziPaws/
URL: Challenge 29 Forum Thread https://forum.knime.com/t/solutions-to-just-knime-it-challenge-29-season-2/73429

3. spilt up playr pairs {P1, P2} into 2 datasets before concantenating them row-wise 1. Get distance (Euclidean) between each player via their scoresIt'll generate a matrix of pairings using distance as a measure ofsimiliarity. We'll use this distance to pair up players of similar strength 2. Each player pair (e.g. {P1, P2}) has a distance. The smaller it is, the more similar in strenght theyare. By sorting the dataset by distance (ascending), we now have a dataset that contains all possiblepermutations of player pairs. Algo rundown1. Take out the first player pair {P1, P2}. This pair is most suited for each other. Save to a table 2. Remove all instances of P1 and P2 from the main dataset3. Repeat until there are no more rows in the main dataset 4. Join with original dataset to get useable names.Create the bracket dendogram Overview C29: Table Tennis Tournament should do the following: 1. Match players of similar strengths 2. Create playoff brackets ala dendogramIssueIf we were to just do a h clustering on the dataset as is, it will result in a dendogramthat's not playoff bracket-like. Simply because there are 3 players or more who havesimilar scores, and 1 outlier who overshadows everyone (don't believe me? run part 4for yourself!). Hence, we'd have to figure which players should be matched to each other first; beforecreating the dendogram. Approach1. Find the strength difference between each playerThis will give you many player pairs (approx 90) on a strength difference scale (0 to 60)2. Sort the pairs by strength difference from lowest to highest. 3. Pick out the pairs that have low strength difference scores Repeat until there are no more pairs to pick out4. Generate the playoff brackets There isn't much randomisation in this approach. One way to introduce randomness isto create "discrete buckets" in the list. Then draw a player pair randomly from eachbucket and repeat 3. Node 1Node 5Node 23Node 24Node 25Node 26Node 30Node 31Node 32Node 33Node 34Node 35Node 36Node 37Node 38Node 40Node 42Node 43Node 44 CSV Reader Distance MatrixPair Extractor Recursive Loop End RecursiveLoop Start Row Splitter Sorter Table RowTo Variable Rule-basedRow Filter Column Filter Column Filter Column Renamer Concatenate HierarchicalCluster Assigner Hierarchical Clustering(DistMatrix) Distance MatrixCalculate RowID Joiner RowID Distance MatrixCalculate 3. spilt up playr pairs {P1, P2} into 2 datasets before concantenating them row-wise 1. Get distance (Euclidean) between each player via their scoresIt'll generate a matrix of pairings using distance as a measure ofsimiliarity. We'll use this distance to pair up players of similar strength 2. Each player pair (e.g. {P1, P2}) has a distance. The smaller it is, the more similar in strenght theyare. By sorting the dataset by distance (ascending), we now have a dataset that contains all possiblepermutations of player pairs. Algo rundown1. Take out the first player pair {P1, P2}. This pair is most suited for each other. Save to a table 2. Remove all instances of P1 and P2 from the main dataset3. Repeat until there are no more rows in the main dataset 4. Join with original dataset to get useable names.Create the bracket dendogram Overview C29: Table Tennis Tournament should do the following: 1. Match players of similar strengths 2. Create playoff brackets ala dendogramIssueIf we were to just do a h clustering on the dataset as is, it will result in a dendogramthat's not playoff bracket-like. Simply because there are 3 players or more who havesimilar scores, and 1 outlier who overshadows everyone (don't believe me? run part 4for yourself!). Hence, we'd have to figure which players should be matched to each other first; beforecreating the dendogram. Approach1. Find the strength difference between each playerThis will give you many player pairs (approx 90) on a strength difference scale (0 to 60)2. Sort the pairs by strength difference from lowest to highest. 3. Pick out the pairs that have low strength difference scores Repeat until there are no more pairs to pick out4. Generate the playoff brackets There isn't much randomisation in this approach. One way to introduce randomness isto create "discrete buckets" in the list. Then draw a player pair randomly from eachbucket and repeat 3. Node 1Node 5Node 23Node 24Node 25Node 26Node 30Node 31Node 32Node 33Node 34Node 35Node 36Node 37Node 38Node 40Node 42Node 43Node 44 CSV Reader Distance MatrixPair Extractor Recursive Loop End RecursiveLoop Start Row Splitter Sorter Table RowTo Variable Rule-basedRow Filter Column Filter Column Filter Column Renamer Concatenate HierarchicalCluster Assigner Hierarchical Clustering(DistMatrix) Distance MatrixCalculate RowID Joiner RowID Distance MatrixCalculate

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Dreamcore
Created at: 2023-10-12
On NodePit since: 2023-10-13
Last update: 2024-04-27
Created with KNIME version: v5.1.1
Tags: KNIME ITJKISeason2-29

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!