Football Mining with R

@inproceedings{Carpita2014FootballMW,
  title={Football Mining with R},
  author={M. Carpita and Marco Sandri and Anna Simonetto and P. Zuccolotto},
  year={2014}
}
This chapter presents a data mining process for investigating the relationship between the outcome of a football match (win, lose, or draw) and a set of variables describing the actions of each team, using the R environment and selected R packages for statistical computing. The analyses were implemented with parallel computing when possible. Our goals were to identify, from hundreds of covariates, those that most strongly affect the probability of winning a match and to construct a small number… Expand
Spatio-Temporal Movements in Team Sports: A Visualization Approach Using Motion Charts
Filtering procedures for sensor data in basketball
Space-Time Analysis of Movements in Basketball using Sensor Data
...
1
2
...

References

SHOWING 1-10 OF 38 REFERENCES
Random Forests
  • L. Breiman
  • Mathematics, Computer Science
  • Machine Learning
  • 2004
Classification and regression trees
  • W. Loh
  • Computer Science
  • Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
  • 2011
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
Data Mining with R: Learning with Case Studies
...
1
2
3
4
...