Name
Abstract | ||
|---|---|---|
This paper presents the model for the competitiveness of soccer matches played in the top four European soccer leagues. Every soccer match in every league holds some importance and contributes towards the overall performance of the league compared to other leagues. These individual results constitute a single season. A lot of aspects of a team and a season are attributed to their final positions in the league. These positions, however, do not detail the competitiveness of a single match. This research aims to highlight the competitiveness in each match without any relation to how the season may have ended. A match gives out a lot of details towards how it was approached by a team. A win may not constitute competitiveness, but the approach does. The idea is to look at individual statistics of a match and use them to construct a model using SEMMA approach of data mining, that classifies the matches based on how competitive they were. This research constructs various models for classification as each model provides its own variant based on the different methodologies used in the individual models. Our analysis is mainly depended on, but not limited to, the number of attempted shots on goal and on the number of those shots that were on target. An important characteristic of the attempts on goals is that they are subjective to the performance of a team and its ability to try and secure a win in a match. This performance formulates competitiveness which is the basis of our research. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22338-0_11 | HCI IN BUSINESS, GOVERNMENT AND ORGANIZATIONS: INFORMATION SYSTEMS AND ANALYTICS |
Keywords | Field | DocType |
Competitiveness, Data mining, Shots on target, Performance evaluation, Misclassification rate | Data mining,SEMMA,Computer science,League | Conference |
Volume | ISSN | Citations |
11589 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neetu Singh | 1 | 10 | 9.56 |
| Apoorva Kanthwal | 2 | 0 | 0.34 |
| Prashant Bidhuri | 3 | 0 | 0.34 |