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Soccer Games Results Prediction

This project aims to predict match scores for Brazil's first division soccer league, featuring 20 teams each playing 38 matches annually. By utilizing a multi-layer perceptron neural network, we analyze historical data from publicly available sources (2003-2012) which includes team performance metrics and market values. We employed Python for data extraction and MATLAB for assembling feature vectors, yielding an average classification accuracy of around 40%. This research contributes to enhancing score prediction methodologies in sports analytics.

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Soccer Games Results Prediction

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  1. Soccer Games ResultsPrediction ECE 539 – Introductionto Artificial Neural Networks andFuzzy Systems Henrique Parreiras Couto

  2. Background • The first division of Brazilian soccer league includes 20 teams • Every team plays against all others twice • Total of 380 games per year • The championship format was different before 2003

  3. Project Goal • Predictthe score ofany match ofthefirstdivisionoftheBraziliannationalchampionshipusing a Multi-layerperceptron.

  4. Data Extraction • Public study about the market value of Brazilian teams Source: http://www.pluriconsultoria.com.br/relatorio.php?segmento=sport&id=263

  5. Data Extraction • Publically available game results from 2003 through 2012 • Python program was used to extract and format the data into .txt files according to each team (with Alberto Tavares) • http://www.bolanaarea.com/gal_brasileirao.htm

  6. Feature Vectors • MATLAB program used to assembly the data • Home Team • # of matches playedsince 2003 • Home goals for • Home goalsagainst • Market value • Away Team • # of matches playedsince 2003 • Awaygoalsfor • Awaygoalsagainst • Market value

  7. FeatureVectors - Labels • [1 0 0 0 0] – Largeloss • [0 1 0 0 0] – Smallloss • [0 0 1 0 0] – Tie • [0 0 0 1 0] – Smallvictory • [0 0 0 0 1] – Largevictory

  8. FeatureVectors • Training andTesting files • 380 featurevectorseach

  9. Score prediction • Classifierresultgivesthedifferencebetweenthenumberofgoalsofeachteam • Final score predictionbasedontheclassifierresultandaveragenumberofgoalsscoredbyeachteamsince 2003.

  10. Results • Averageclassification rate ofthe MLP : ~40% • Improvementsneeded

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