World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model
Football is one of the most popular sports in the world. The popularity makes every topic related to football interesting, for instance, the FIFA World Cup winner prediction. This topic is not only for casual discussion but could be a practical decision support for coaching staff to rate the team’s...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Gadjah Mada
2023-04-01
|
| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/82280 |
| _version_ | 1827814626993111040 |
|---|---|
| author | Stanislaus Jiwandana Pinasthika Dzikri Rahadian Fudholi |
| author_facet | Stanislaus Jiwandana Pinasthika Dzikri Rahadian Fudholi |
| author_sort | Stanislaus Jiwandana Pinasthika |
| collection | DOAJ |
| description | Football is one of the most popular sports in the world. The popularity makes every topic related to football interesting, for instance, the FIFA World Cup winner prediction. This topic is not only for casual discussion but could be a practical decision support for coaching staff to rate the team’s readiness. Most prediction methods use large match datasets. Since every national team has a different squad for every world cup and the FIFA World Cup is held every four years, the usage of a large match dataset is irrelevant. Therefore, there is a need for a prediction method based on the relevant data. We applied the Poisson distribution model for predicting the FIFA World Cup 2022 knockout stage match results. We calculate the probability of winning and losing based on their average goal scores and goal conceded and evaluate the difference by the actual result using de Finetti distance. The successful prediction is 8 out of 15 matches, with six inside the round of 16 games. This prediction model is also a brief example to overcome prediction problem using limited dataset. Thus, the new data attributes need to reformulate Poisson’s lambda. Further studies need to add the 3-4 prior world cup matches data to increase the acceptance of prediction. |
| first_indexed | 2024-03-11T23:48:55Z |
| format | Article |
| id | doaj.art-2d6980b4cb654bca80ed0b59a5dec8b5 |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-03-11T23:48:55Z |
| publishDate | 2023-04-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-2d6980b4cb654bca80ed0b59a5dec8b52023-09-19T08:42:31ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582023-04-0117215116010.22146/ijccs.8228033679World Cup 2022 Knockout Stage Prediction Using Poisson Distribution ModelStanislaus Jiwandana Pinasthika0Dzikri Rahadian Fudholi1Department of Computer Science and Electronics, FMIPA UGM, YogyakartaDepartment of Computer Science and Electronics, FMIPA UGM, YogyakartaFootball is one of the most popular sports in the world. The popularity makes every topic related to football interesting, for instance, the FIFA World Cup winner prediction. This topic is not only for casual discussion but could be a practical decision support for coaching staff to rate the team’s readiness. Most prediction methods use large match datasets. Since every national team has a different squad for every world cup and the FIFA World Cup is held every four years, the usage of a large match dataset is irrelevant. Therefore, there is a need for a prediction method based on the relevant data. We applied the Poisson distribution model for predicting the FIFA World Cup 2022 knockout stage match results. We calculate the probability of winning and losing based on their average goal scores and goal conceded and evaluate the difference by the actual result using de Finetti distance. The successful prediction is 8 out of 15 matches, with six inside the round of 16 games. This prediction model is also a brief example to overcome prediction problem using limited dataset. Thus, the new data attributes need to reformulate Poisson’s lambda. Further studies need to add the 3-4 prior world cup matches data to increase the acceptance of prediction.https://jurnal.ugm.ac.id/ijccs/article/view/82280probabilistic predictionde finetti distancefootball match prediction |
| spellingShingle | Stanislaus Jiwandana Pinasthika Dzikri Rahadian Fudholi World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model IJCCS (Indonesian Journal of Computing and Cybernetics Systems) probabilistic prediction de finetti distance football match prediction |
| title | World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model |
| title_full | World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model |
| title_fullStr | World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model |
| title_full_unstemmed | World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model |
| title_short | World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model |
| title_sort | world cup 2022 knockout stage prediction using poisson distribution model |
| topic | probabilistic prediction de finetti distance football match prediction |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/82280 |
| work_keys_str_mv | AT stanislausjiwandanapinasthika worldcup2022knockoutstagepredictionusingpoissondistributionmodel AT dzikrirahadianfudholi worldcup2022knockoutstagepredictionusingpoissondistributionmodel |