發(fā)布時間：2018-02-12 20:26

  本文關(guān)鍵詞： 板球 運動數(shù)據(jù)挖掘 預(yù)測 排序 機器學(xué)習(xí)　出處：《北京郵電大學(xué)》2017年博士論文　論文類型：學(xué)位論文

【摘要】：體育領(lǐng)域的趨勢分析方法已經(jīng)從傳統(tǒng)的簡單的數(shù)據(jù)統(tǒng)計發(fā)展到當(dāng)今的基于數(shù)據(jù)挖掘的深度分析。在一些主流的體育運動中,對板球態(tài)勢的分析仍然非常落后,這引起學(xué)者們的廣泛關(guān)注。為了填補球員預(yù)測和等級劃分等方面的空白,同時消除當(dāng)前傳統(tǒng)統(tǒng)計方法的局限性,基于數(shù)據(jù)挖掘的態(tài)勢分析作為一種非常有效的方法應(yīng)運而生。數(shù)據(jù)挖掘的效率和準(zhǔn)確性等方面的優(yōu)勢,日益凸顯。到目前為止,板球相關(guān)的解決方案中大都用到了正交化統(tǒng)計和優(yōu)化理論,而沒有使用數(shù)據(jù)挖掘。板球組織正在征求可以融合多種方法的有價值的度量標(biāo)準(zhǔn)和機制,用以提高決策的有效性。此外,這樣的分析對于權(quán)威中心是非常有益的,例如教練和管理者在提高專業(yè)技能時,能夠獲得球員和球隊的最佳表現(xiàn)。論文旨在提供有效的解決方案,解決板球領(lǐng)域的之前沒有考慮的問題以及當(dāng)前解決方法仍然受限的問題。更準(zhǔn)確地說,通過整合先進(jìn)的數(shù)據(jù)挖掘工具(包括監(jiān)督機器學(xué)習(xí)和隨機游走算法),提供板球運動的有效解決方案。本文的創(chuàng)新點如下:·基于機器學(xué)習(xí)的方法,提出了預(yù)測新星的綜合性能評價函數(shù),能夠魯棒準(zhǔn)確地預(yù)測新星。我們首次整合協(xié)作球員、團(tuán)隊以及敵手的概念,并通過實驗的方法針對擊球手篩選出Co-batsmen Runs和Co-batsmen Average 等 9 方面的潛在特質(zhì) ,對投球手篩選出 Team Average和Team Strike Rate等11方面的特質(zhì),然后對這些特質(zhì)采用同類聚合歸一的方法進(jìn)行公式化。為了研究分類,采用了生成性和判別性的機器學(xué)習(xí)算法。交叉驗證表明算法可以高精度的預(yù)測新星,并且該算法不但具有魯棒性,而且統(tǒng)計效果顯著�！せ跇湫捅O(jiān)督機器學(xué)習(xí)方法,提出了預(yù)測明星球員的貝葉斯模型,取得了好的預(yù)測結(jié)果。我們首次利用球員的年智發(fā)展?fàn)顩r提取若干擊球和投球特性,并找到主導(dǎo)地位的個體特征。在合并測試集上篩選出六種合適的監(jiān)督機器學(xué)習(xí)分類算法用于明星球員的二元分類,并通過整合使用貝葉斯定理、函數(shù)以及基于樹的監(jiān)督機器學(xué)習(xí)算法給出融合的算法。最終,利用該融合算法對貝葉斯機制、樹形機制、函數(shù)機制下的兩類模型進(jìn)行評估,實驗結(jié)果證明該算法具有出色的魯棒性和高效性�！ぞC合投球、擊球和團(tuán)隊優(yōu)先級等信息,提出了團(tuán)隊得分能力的預(yù)測算法,實驗結(jié)果表明該算法具有較高的預(yù)測精度。在球隊層面,投球手和打球手的能力與輸贏密切相關(guān),并且依據(jù)此能力來選拔出最強的團(tuán)隊。針對以前的工作使用特性較少的情況,我們提出新的預(yù)測評估算法,卷入更多的屬性,使得該算法具有以下特點:一、在球隊得分能力排名方面,我們的算法更高效;二、突破以前算法的限制,高效預(yù)測擊球、投球得分能力;三、通過獲勝的球隊得分能力優(yōu)先次序與世界杯各自冠軍的冠軍進(jìn)行了交叉檢查,發(fā)現(xiàn)各個比賽跨度期間最有得分能力的隊伍未必贏得世界杯。
[Abstract]:The trend analysis method in the field of sports has changed from the traditional simple statistics to depth analysis based on data mining today. In some mainstream sports, analysis of the situation of cricket is still very backward, which attracted the attention of scholars. In order to fill the blank player prediction and classification, at the same time to eliminate the current limitations of traditional statistical methods, data mining analysis of the situation as a kind of very effective method based on data mining came into being. The efficiency and accuracy of advantages, has become increasingly prominent. To date, the most relevant cricket solutions used orthogonal statistics and optimization theory, without the use of data mining the organization is seeking. Cricket can integrate a variety of methods of value measure and mechanism is effective to improve the decision. In addition, the analysis of the In the centre of authority is very useful, for example, coaches and managers in improving professional skills, can obtain the best players and teams. This paper aims to provide effective solutions, the cricket field did not consider before solving problems and the solution method is still limited. More precisely, through the integration of advanced data mining tools (including supervised machine learning and random walk algorithm), to provide effective solutions to cricket. The innovation of this paper is as follows: Based on machine learning method, the performance evaluation of the star prediction function is proposed, which can accurately predict the robust star. We first integrated collaborative team player and opponent concept. And through the experiments for the batter selected Co-batsmen Runs and Co-batsmen Average latent trait in 9 aspects, Team Average of the bowler screening Team Strike and Rate 11 characteristics, and then the formula of these traits by using the method of similar polymerization normalization. In order to study the classification, using the generative and discriminative machine learning algorithm. Cross validation shows that the algorithm can predict the star high precision, and the algorithm is not only robust, but also statistical significant effect based on the tree. The supervised learning method, proposed the Bias prediction model of star players, achieved good prediction results. We first use of years of wisdom to develop players picking batting and pitching characteristics, individual characteristics and find the leading position in the combined test set selected six kinds of machine learning supervision right classification algorithm for classification of two yuan star players, and through the integration of the use of Bias's theorem, function and algorithm is given based on the fusion algorithm of supervised machine learning tree finally. And using the fusion algorithm of Bayesian tree mechanism, mechanism, two kinds of model function under the mechanism of evaluation, experimental results show that the algorithm has good robustness and efficiency. - pitching, hitting and team priority information, put forward the calculation method of pre team scoring ability, the experimental results show that the prediction accuracy of the algorithm high. In the team level, and winning pitcher and hitter is closely related to, and on the basis of the ability to select the strongest team. According to the characteristics of the previous work using less, we propose a new algorithm to forecast and evaluate, involving more attributes, the algorithm has the following characteristics: first, in terms of ranking in the team scoring ability, our algorithm is more efficient; two, to break the previous algorithm, efficient prediction of batting, pitching score ability; three, by winning the first scoring team order A cross examination with the champions of the world cup has been conducted to find that the team who has the most scoring ability during the span of the game does not necessarily win the world cup.

【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP311.13;G80-3
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本文編號：1506487