發(fā)布時(shí)間：2018-02-12 20:26

  本文關(guān)鍵詞： 板球 運(yùn)動(dòng)數(shù)據(jù)挖掘 預(yù)測(cè) 排序 機(jī)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(shì)分析方法已經(jīng)從傳統(tǒng)的簡(jiǎn)單的數(shù)據(jù)統(tǒng)計(jì)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(yùn)動(dòng)中,對(duì)板球態(tài)勢(shì)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(bǔ)球員預(yù)測(cè)和等級(jí)劃分等方面的空白,同時(shí)消除當(dāng)前傳統(tǒng)統(tǒng)計(jì)方法的局限性,基于數(shù)據(jù)挖掘的態(tài)勢(shì)分析作為一種非常有效的方法應(yīng)運(yùn)而生。數(shù)據(jù)挖掘的效率和準(zhǔn)確性等方面的優(yōu)勢(shì),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(jì)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(jià)值的度量標(biāo)準(zhǔn)和機(jī)制,用以提高決策的有效性。此外,這樣的分析對(duì)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(shí),能夠獲得球員和球隊(duì)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(jī)器學(xué)習(xí)和隨機(jī)游走算法),提供板球運(yùn)動(dòng)的有效解決方案。本文的創(chuàng)新點(diǎn)如下:·基于機(jī)器學(xué)習(xí)的方法,提出了預(yù)測(cè)新星的綜合性能評(píng)價(jià)函數(shù),能夠魯棒準(zhǔn)確地預(yù)測(cè)新星。我們首次整合協(xié)作球員、團(tuán)隊(duì)以及敵手的概念,并通過實(shí)驗(yàn)的方法針對(duì)擊球手篩選出Co-batsmen Runs和Co-batsmen Average 等 9 方面的潛在特質(zhì) ,對(duì)投球手篩選出 Team Average和Team Strike Rate等11方面的特質(zhì),然后對(duì)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(jī)器學(xué)習(xí)算法。交叉驗(yàn)證表明算法可以高精度的預(yù)測(cè)新星,并且該算法不但具有魯棒性,而且統(tǒng)計(jì)效果顯著�！せ跇湫捅O(jiān)督機(jī)器學(xué)習(xí)方法,提出了預(yù)測(cè)明星球員的貝葉斯模型,取得了好的預(yù)測(cè)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(gè)體特征。在合并測(cè)試集上篩選出六種合適的監(jiān)督機(jī)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(jī)器學(xué)習(xí)算法給出融合的算法。最終,利用該融合算法對(duì)貝葉斯機(jī)制、樹形機(jī)制、函數(shù)機(jī)制下的兩類模型進(jìn)行評(píng)估,實(shí)驗(yàn)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(duì)優(yōu)先級(jí)等信息,提出了團(tuán)隊(duì)得分能力的預(yù)測(cè)算法,實(shí)驗(yàn)結(jié)果表明該算法具有較高的預(yù)測(cè)精度。在球隊(duì)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(qiáng)的團(tuán)隊(duì)。針對(duì)以前的工作使用特性較少的情況,我們提出新的預(yù)測(cè)評(píng)估算法,卷入更多的屬性,使得該算法具有以下特點(diǎn):一、在球隊(duì)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(cè)擊球、投球得分能力;三、通過獲勝的球隊(duì)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(gè)比賽跨度期間最有得分能力的隊(duì)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(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13;G80-3
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本文編號(hào)：1506487