Application Of Data Mining In Basketball Match Lineup Recommendation

In recent years,with the continuous development of the Internet,the scope of computer applications has become wider and wider.Similarly,in today’s data-based life,data mining has gradually become a hot spot for people to research and discuss.In data mining,we often use association analysis algorithms.The idea of this type of algorithm is to first collect a large amount of data that we are going to study,and then perform some necessary sorting of these data,and then analyze the existing data according to our selected algorithm Analyze,obtain the interrelated information between the data,and finally visualize the information.Data mining is being widely used in various fields,including basketball.Whether it is the foreign NBA or the domestic CBA,they are all highly concerned leagues.For these high-level leagues,some technical methods are used to match the competition.Analyze a large amount of data,and then analyze a large amount of seemingly unrelated data to obtain some useful data for the game,which will also be very helpful for the coach to arrange the game.This article first introduces some current status and knowledge about data mining and analysis,and introduces the algorithms of data mining in the field of basketball,focusing on the use of the cooperation between players on the court of a certain team to complete the offensive score data.Value information,so as to discover the best team of players on the field,which will help the coach’s arrangement.This article mainly uses the apriori algorithm,fp-growth algorithm and coordination filtering algorithm to mine and analyze the acquired data.In order to conduct comprehensive comparisons and considerations,use a variety of algorithms,on the one hand,for better data mining and analysis,on the other hand,it is also for certain verification of the mining results,and you can also compare the advantages and disadvantages of different algorithms.So as to improve the mining and analysis of basketball data.This article uses crawler technology to crawl the 2019-2020 season NBA Los Angeles Lakers ten players cooperated with each other throughout the season to complete the offensive data,including pick and roll,passing,assists,screens,counterattacks and a series of on-court actions.The data mining algorithm is used to analyze the crawled data.The results show that the players on the field complete the offense to obtain scores through various cooperation.It has strong association rules and can mine the expected results according to the set limit threshold.,So as to achieve the purpose of this paper.Based on the theoretical analysis of this article and the final experimental verification,it also provides ideas and methods for the research of data mining in the basketball field.It not only provides effective help for the coach to arrange the team lineup on the court,but also provides in-depth research on data mining in the basketball field in the future.Lay a good foundation.