Prediction of Season-End Point for Football using Pythagorean Expectation

Year 2019, Issue: 27, 63 - 73, 01.03.2019

Sezer Baysal Engin Yıldıztepe

Abstract

The use of data collected
on players, teams, and games for performance evaluation, player selection, score-outcome
estimation, and strategy development using data mining tools and techniques are
defined as sports data mining. Performance measures,
unlike the common statistical methods, developed for each sport branch have an
important role in sports data mining processes. Performance measures calculated
for team sports can be used to predict the expectation of winning. The
Pythagorean expectation developed for this objective was originally used in
baseball games. The Pythagorean Expectation has also been adapted for other
team sports with two results, such as basketball. However, the studies using
Pythagorean Expectation for sports which have three possible outcomes are very
limited. In this study, a suggestion for the calculation of Pythagorean Expectation
for football is presented. In the application section, end-season rankings and
points for the 2017/2018 season of  the
selected fifteen European football leagues are predicted by using the suggested
method. The data of the past five seasons of the selected European football
leagues is used as the training dataset. All calculations are performed in R.

Keywords

Sports data mining, Pythagorean Expectation, Point prediction, Soccer, Football

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