Using a logistic regression to predict the probability of centre forward’s averaging ten or more goals in a season in Europe’s top five leagues.

Skeels, P. (2018) Using a logistic regression to predict the probability of centre forward’s averaging ten or more goals in a season in Europe’s top five leagues. Masters theses, University of Chichester.

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Abstract

The aim of this study was to use a binary logistic regression to identify the predictive factors that are indicative of goal scoring performance of players in the centre forward position. 200 players playing in the centre forward position were selected from Europe’s top five leagues (English Premier League, French Ligue 1, German Bundesliga, Italian Serie A & Spanish La Liga). OPTA® performance variables that deemed indicative of shooting performance were chosen. A binary logistic regression was used to perform the analysis. Results found that the overall model was statistically significant (χ2(2) = 168.964, p < 0.005) and explained 84.9% (Nagelkerke R2) of the variance. Shots on target from inside the box (χ2 = 20.37, p < 0.005) and shooting accuracy (χ2 = 6.58, p < 0.010) were found to be the two predictor variables that significantly contributed to the model. The odds ratios of 1.23 and 1.21 respectively suggested that both predictor variables would have a positive influence on the likelihood of averaging 10 or more a season. The findings of this study suggest that to increase the likelihood of averaging 10 or more goals a season, players need to increase the number of shots inside the box and increase their shooting accuracy, however future research should look to use a more sensitive analysis to provide more depth.

Publication Type: Theses (Masters)
Additional Information: MSc Sport Performance Analysis
Subjects: G Geography. Anthropology. Recreation > GV Recreation Leisure > GV557 Sports
Q Science > Q Science (General)
Divisions: Academic Areas > Institute of Sport > Area > Exercise Physiology
Student Research > Masters
Depositing User: Ann Jones
Date Deposited: 18 Nov 2020 09:29
Last Modified: 18 Nov 2020 09:29
URI: https://eprints.chi.ac.uk/id/eprint/5464

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