243 KB38 KB199220252014Filipčič, AlešPanjan, AndrejŠarabon, Nejcštevilka:1letnik:20str. 16-27ISSN:1318-2269COBISSID:1536604100URN:URN:NBN:SI:doc-W0IFTDELenFakulteta za šport, Inštitut za kineziologijoKinesiologia Slovenicacompetitive performanceidentificationidentifikacijaizbiramachine learningnapovedovanjepredictabilityselectionstrojno učenjetekmovalna uspešnosttenistennisThe prognostic value of machine learning methods in tennis| Napovedna vrednost metod strojnega učenja v tenisu|The purpose of this study was to assess the possibilities of predicting playing successfulness in competitive tennis by using machine learning methods applied to young players% motor abilities and morphological test results. The classification of players according to their competitive successfulness was performed using several methods: the naive Bayes classification method, decision tree, the C4.5 algorithm, the k-nearest neighbour, support vector machine (SVM), and logistic regression. After discretising the players' successfulness into quality classes, the possibility of automatically identifying the most promising attributes was tested using the ReliefF method and the wrapper approach. Both the naive Bayes method with ReliefF and logistic regression with the wrapper approach proved to be accurate predictors of competitive performance in the age group under 12 years and in the age group between 12 and 16 years. The most promising attribute was racquet ball handling. Predictions of the competitive performance of tennis players proved to be a highly complex issue because the accuracy of the prediction models in our study, based on morphological and motor factors, was relatively poorNamen te študije je bila ocena zmožnosti napovedovanja igralne uspešnosti v tekmovalnem tenisu z uporabo metod strojnega učenja na rezultatih motoričnih in morfoloških testov mladih tekmovalcev. Razvrstitev tekmovalcev glede na njihovo tekmovalno uspešnost je bila narejena z več metodami: naivni Bayes, odločitveno drevo, C4.5 algoritem, k-najbližjih sosedov, metoda podpornih vektorjev in logistična regresija. Po razvrstitvi igralcev v kakovostne razrede sta bili za avtomatsko iskanje najobetavnejših atributov uporabljeni metodi ReliefF in metoda ovojnice. Za napovedovanje tekmovalne uspešnosti v starostni skupni pod 12 let in starostni skupini med 12 in 16 let sta bili najuspešnejši metodi naivni Bayes z ReliefF in logistična regresija z metodo ovojnice. Obvladovanje žogice z loparjem se je izkazalo za najbolj obetaven atribut. Napovedovanje tekmovalne uspešnosti teniških igralcev se je izkazalo za zelo kompleksen problem, zato ker je bila točnost napovedovalnih modelov na podlagi morfoloških in motoričnih dejavnikov sorazmerno slabaTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za šport