<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Introduction</h3>
<p>Starting from the information feedback of cybernetics theory, mathematical statistics methods have powerful application and interpretation functions in sports events to solve practical problems of football player evaluation.</p>
</sec>
<sec><h3>Data Source</h3>
<p>The data comes from Sofascore.com. Using the players’ position on the field as a classification, their technical statistics were checked one by one, and the original database of the 2021 Chinese Super League was established. The team consisted of 289 players, with 61 forwards, 133 midfielders, and 95 defenders.</p>
</sec>
<sec><h3>Method</h3>
<p>Factor analysis was conducted on the original data using IBM SPSS Statistics (Version 26.0). The evaluation model was obtained after determining the comprehensive score of players using loading and variance contribution rates according to the relevant methods and steps.</p>
</sec>
<sec><h3>Conclusions</h3>
<p>The total cumulative variance contribution rate of these factors is 74.155%. The five evaluation factors of players are Direct Attack, Basic Pass, Cooperative Defense, Aggressive Pass, and Risky Defense. In the pentagon evaluation model, F1 accounted for 26.88%, F2 for 20.42%, F3 for 20.40%, F4 for 18.54%, and F5 for 13.74%. Finally, the player’s score and club’s ranking are calculated and tested, and the results indicate that the effect is good.</p>
</sec>
</abstract>

Introduction
Starting from the information feedback of cybernetics theory, mathematical statistics methods have powerful application and interpretation functions in sports events to solve practical problems of football player evaluation.

Data Source
The data comes from Sofascore.com. Using the players’ position on the field as a classification, their technical statistics were checked one by one, and the original database of the 2021 Chinese Super League was established. The team consisted of 289 players, with 61 forwards, 133 midfielders, and 95 defenders.

Method
Factor analysis was conducted on the original data using IBM SPSS Statistics (Version 26.0). The evaluation model was obtained after determining the comprehensive score of players using loading and variance contribution rates according to the relevant methods and steps.

Conclusions
The total cumulative variance contribution rate of these factors is 74.155%. The five evaluation factors of players are Direct Attack, Basic Pass, Cooperative Defense, Aggressive Pass, and Risky Defense. In the pentagon evaluation model, F1 accounted for 26.88%, F2 for 20.42%, F3 for 20.40%, F4 for 18.54%, and F5 for 13.74%. Finally, the player’s score and club’s ranking are calculated and tested, and the results indicate that the effect is good.

Exploratory Factor Analysis: A Pentagonal Evaluation Model Based on Football Player Stats

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Introduction
Starting from the information feedback of cybernetics theory, mathematical statistics methods have powerful application and interpretation...

Exploratory Factor Analysis: A Pentagonal Evaluation Model Based on Football Player Stats

Online veröffentlicht: 11. Dez. 2023

Seitenbereich: -

Eingereicht: 22. März 2023

Akzeptiert: 21. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01450

Schlüsselwörter
Factor analysis, Evaluation model, Technical statistics

© 2023 Tao Quan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploratory Factor Analysis: A Pentagonal Evaluation Model Based on Football Player Stats

Online veröffentlicht: 11. Dez. 2023

Seitenbereich: -

Eingereicht: 22. März 2023

Akzeptiert: 21. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01450

SchlüsselwörterFactor analysis, Evaluation model, Technical statistics

© 2023 Tao Quan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Factor analysis, Evaluation model, Technical statistics