Comparative Analysis for Predicting Football Match Outcomes based on Poisson Models

Maozad, Syasya Nadhilah; Razali, Siti Noor Asyikin Mohd; Nanthaamornphong, Aziz; Wahab, Mohd Helmy Abdul; Razali, Nazim

doi:10.1109/ecti-con54298.2022.9795385

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The de Finetti distance formula forms like the not-rooted Euclidean distance. The distance between the prediction and the actual result is given by equation (5), where 𝑏 1 and 𝑏 2 are represented by (1,0) and (0,1), respectively. The distance is acceptable if the space is under the threshold.…”

Section: De Finetti Measurementioning

confidence: 99%

“…Several methods are applied to solve the prediction problem based on a probabilistic model or machine learning approach. Some of them are Poisson models [5], an attention-based LSTM network [1], a hybrid of LSTM and RNN [4], a combination of ANN and DNN [6], and a Priori prediction algorithm [7]. Most prediction models train many features of team performance data, such as the goals, ball possession, corners, crosses, or pass accuracy, which is necessary winning factors.…”

Section: Introductionmentioning

confidence: 99%

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World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model

Pinasthika¹,

Fudholi²

2023

Indonesian J. Comput. Cybern. Syst.

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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. 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.

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Section: De Finetti Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model

Pinasthika¹,

Fudholi²

2023

Indonesian J. Comput. Cybern. Syst.

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“…He used an independent Poisson model to describe football scores. Contemporary work in this field is also rather extensive [3,16,10,12]. Hucaljuk and Rakipović [12] use classification models to predict the outcome of Champions league matches.…”

Section: Background and Motivationmentioning

confidence: 99%

Betting the system: Using lineups to predict football scores

Peters¹,

Pacheco²

2022

Preprint

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This paper aims to reduce randomness in football by analysing the role of lineups in final scores using machine learning prediction models we have developed. Football clubs invest millions of dollars on lineups and knowing how individual statistics translate to better outcomes can optimise investments. Moreover, sports betting is growing exponentially and being able to predict the future is profitable and desirable. We use machine learning models and historical player data from English Premier League (2020-2022) to predict scores and to understand how individual performance can improve the outcome of a match. We compared different prediction techniques to maximise the possibility of finding useful models. We created heuristic and machine learning models predicting football scores to compare different techniques. We used different sets of features and shown goalkeepers stats are more important than attackers stats to predict goals scored. We applied a broad evaluation process to assess the efficacy of the models in real world applications. We managed to predict correctly all relegated teams after forecast 100 consecutive matches. We show that Support Vector Regression outperformed other techniques predicting final scores and that lineups do improve predictions. Finally, our model was profitable (42% return) when emulating a betting system using real world odds data.

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Predicting Game Results for Football League Using Deep Learning

Satyapanich,

Somkheawwan

2023

2023 27th International Computer Science and Engineering Conference (ICSEC)

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Comparative Analysis for Predicting Football Match Outcomes based on Poisson Models

Cited by 3 publications

References 15 publications

World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model

World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model

Betting the system: Using lineups to predict football scores

Predicting Game Results for Football League Using Deep Learning

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