Learning the Value of Teamwork to Form Efficient Teams
Authors: Ryan Beal, Narayan Changder, Timothy Norman, Sarvapali Ramchurn7063-7070
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our model to predict team performance and validate our approach using real-world team performance data from the 2018 FIFA World Cup. Our model is shown to better predict the real-world performance of teams by up to 46% compared to models that ignore inter-agent interactions. |
| Researcher Affiliation | Academia | 1School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom {ryan.beal, t.j.norman, sdr1}@soton.ac.uk 2The National Institute of Technology, Durgapur, West Bengal 713209, India narayan.changder@gmail.com |
| Pseudocode | No | The paper presents mathematical formulations (MIPs) in equations 4 and 6, but it does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | Yes | To evaluate our models we use a dataset collected from the 2018 FIFA World Cup.5 The dataset breaks down the 64 games from the tournament into an event-by-event analysis where each event gives different metrics including: event type (e.g., pass, shot, tackle etc.), the pitch coordinates of the event and the event outcome. This type of dataset is industry-leading in football and used by top professional teams. Thus, we believe that this is a good, real-world, dataset with the richness and challenge appropriate to rigorously assess the value of our model. 5All data provided by Stats Bomb www.statsbomb.com. |
| Dataset Splits | Yes | To learn the model weights, we use a 10-fold cross-validation approach, splitting the dataset randomly into 70% training and 30% test. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using a Logistic Regression algorithm and a Random Forest approach, and mixed-integer programming (MIP) techniques, but does not provide specific software names with version numbers for reproducibility (e.g., Python, PyTorch, CPLEX versions). |
| Experiment Setup | Yes | To learn the model weights, we use a 10-fold cross-validation approach, splitting the dataset randomly into 70% training and 30% test. |