Discovering the crucial factors that contribute to goal success in sports analytics, this thesis aimsto utilize Random Forest classification to predict the outcome of shots and pre-shot events in powerplay situations. Through three experiments, the study evaluated the use of shots, shots with pre-shotevents, and shots with pre-shot events over sections. The first experiment used only shots, while thesecond experiment focused on shots with pre-shot events, where both compared it with shots over anexpected goal value of 0.08 or higher. The third experiment examined shots with pre-shot events acrossdifferent sections. Our findings demonstrated that the models in our experiments achieved accuracyscores ranging from 78% to 96% and F1 scores between 0% and 24%. Notably, the models in experiment3 demonstrated lower recall scores. The feature importance analysis revealed that pre-shotevents played a significant role in the predictive models of the second and third experiments, indicatingtheir substantial impact on the outcomes. A noteworthy conclusion arising from the discussion isthe recommendation for future research to conduct a more comprehensive exploration into the impactof pre-shot events, given their demonstrated significance in predicting goals. Such an investigation isdeemed necessary and justified.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hj-61421 |
| Date | January 2023 |
| Creators | Djup, Philip |
| Publisher | Jönköping University, JTH, Avdelningen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0026 seconds