Mackay, 2017 - Google Patents
Predicting goal probabilities for possessions in footballMackay, 2017
View PDF- Document ID
- 18212351314687871235
- Author
- Mackay N
- Publication year
- Publication venue
- Vrije Universiteit Amsterdam
External Links
Snippet
Football analytics has been on the rise, yet no previous efforts have been made to compute the probability a possession becomes a goal, like in basketball. In this paper goal probabilities will be modeled for the English Premier League for the season 2016/2017 …
- 230000035533 AUC 0 abstract description 19
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kapadia et al. | Sport analytics for cricket game results using machine learning: An experimental study | |
| Decroos et al. | Actions speak louder than goals: Valuing player actions in soccer | |
| Vistro et al. | The cricket winner prediction with application of machine learning and data analytics | |
| Pathak et al. | Applications of modern classification techniques to predict the outcome of ODI cricket | |
| Mackay | Predicting goal probabilities for possessions in football | |
| Rahimian et al. | Beyond action valuation: A deep reinforcement learning framework for optimizing player decisions in soccer | |
| Link et al. | A topography of free kicks in soccer | |
| Erickson et al. | Global state evaluation in StarCraft | |
| US12132974B2 (en) | System and method for model driven video summarization | |
| US20240303509A1 (en) | Systems and Methods for Player and Team Modelling and Prediction in Sports and Games | |
| CN107335220A (en) | A kind of recognition methods of passive user, device and server | |
| Abreu et al. | Improving a simulated soccer team's performance through a Memory-Based Collaborative Filtering approach | |
| Beal et al. | Optimising daily fantasy sports teams with artificial intelligence | |
| Houde | Predicting the outcome of NBA games | |
| Beheshtian-Ardakani et al. | CMPN: Modeling and analysis of soccer teams using Complex Multiplex Passing Network | |
| Narayanan et al. | Flexible marked spatio-temporal point processes with applications to event sequences from association football | |
| KR102579203B1 (en) | Apparatus and method for predicting game result | |
| Gramacy et al. | Hockey player performance via regularized logistic regression | |
| Yurko et al. | Ron Yurko and Rebecca Nugent’s contribution to the Discussion of ‘Flexible marked spatio-temporal point processes with applications to event sequences from association football’by Narayanan, Kosmidis, and Dellaportas | |
| Smith | Paul Smith’s contribution to the Discussion of ‘Flexible marked spatio-temporal point processes with applications to event sequences from association football’by Narayanan, Kosmidis, and Dellaportas | |
| Mateu | Jorge Mateu’s contribution to the Discussion of ‘Flexible marked spatio-temporal point processes with applications to event sequences from association football’by Narayanan, Kosmidis, and Dellaportas | |
| KR102104007B1 (en) | Apparatus and method for predicting result of game using predictive model of game result | |
| KR102764154B1 (en) | Method and apparatus for generating sports play recommendation information using sports game records | |
| Shmakov | Application and development of the expected goals for hockey player evaluation | |
| Chen et al. | Improving StarCraft II player league prediction with macro-level features |