Best Key Trading Days Of Selected US Stocks iPhone App

Best Key Trading Days Of Selected US Stocks iPhone App

Can the outcome of the international football tournaments be predicted?

Halicioglu (2005) investigated the possibility to predict the most likely winners of the Euro 2000 football tournament on the basic of the seasonal coefficient of variation (CVs) of the end-of-season points, which were computed from the top division final standings of participating countries of Euro 2000.

The CV values calculated from over ten seasons for the respective countries were used as a sole measurement value to rank the countries and to determine the most likely winners of Euro 2000.

According to the three scenarios (long-term, mid-term, and short-term) based on the respective CV values of fifteen countries, France appeared to be the most likely country to win Euro 2000 and was closely followed by Spain. The CVs are calculated as 3 categories of short term, middle term and long term. Comparing with the real FIFA results of 2000, Czech R. got the first place instead of France. The other countries of France, Spain and Germany are higher ranked in CVs. There are also getting good results in real FIFA 2000. The 5th place of Norway got low rank in CVs.

In the football matches, does the persistence in sequences exist? 在足球比赛中，是否坚持序列存在吗？

There are two researchers called Stephen Dobson and John Goddard concerning a interesting question of sports fans in 2003. In academic words, it is about the nature of persistence in sequences of consecutive match results. In non-academic words, the questions are:

(在2003年, 有两位研究者称为Dobson和约翰戈达德处理一个体育迷有趣的问题的。在学术上，它的性质是持久性的序列的连续比赛的结果。用非学术话来说，问题是：)

1. Does a sequences of wins tend to build a team's confidence and morale, increasing the probability the the next match will also be won?; or Does it tend to create pressures or breed complacency, increasing the likelihood that the next match will be drawn or lost?(难道一个胜方序列倾向于建立一个团队的信心和士气， 这增加下一场比赛也将赢了的概率？;还是会倾向于建立压力或滋生自满情绪，越来越多的可能性，下一场比赛将是失败呢？)

2. Does a sequence of losses tend to sap confidence or morale, increasing the probability of a further loss in the next match? Or does it tend to inspire greater effort, increasing the likelihood that the next match will be won or drawn?(难道一个失败序列往往会消磨其意志和士气，增加下一场比赛进一步损失的概率？还是倾向于激发更大的努力，增加下一场比赛是赢得或平手的可能性呢？)

Dobson and Goddard has analyse 30 years of match data for football from the English Premier League and Football League. They have setup an hypothesis based on a computational test of Monte Carlo analysis. The assumption behind the Monte Carlo simulation is parameter constancy and zero persistence. The result is then compared with the real results. (Dobson和戈达德用了从英超联赛和足球联赛30年的对足球比赛分析数据。他们设置一个假设的基础上计算测试 -- 蒙特卡罗分析。假设背后的蒙特卡罗模拟参数稳定性和持久性为零。然后比较其结果与实际结果。)

They got an interesting observation that the actual probability a reversal occurs is higher than the simulated probability under assumptions of no persistence. What does it mean in non-academic wordings? They summed up in the statement "Empirically, the conditional probabilities of a good result are found to decline with the duration of a poor spell, and the conditional probabilities of a poor result decline with the duration of a good spell." (他们得到了一个有趣的观察，实际发生的"反转概率"是高于在假设不持久性的条件下的模拟概率。在非学术字眼，这是什么意思？他们在声明中总结了“根据经验，一个很好的结果的条件概率是随着一个负面的时间下降的，和一个负面结果的条件概率会随着一个良好的时间下降。”)

Reference:

Dobson and Goddard (2003), "Persistence in sequences of football match results: A monte Carlo analysis", European Journal of Operational Reseaarch, 148, 247-256

Using Artificial Neural Network to Predict Tennis Winner 利用人工神经网络预测网球胜利者

Artificial Neural Network (ANN) is one of the very popular artificial intelligent tools for prediction. There are three researchers called Amornchai Somboonphokkaphan, Suphakant Phimoltares, and Chidchanok Lursinsap. They applied ANN modeling to predict the winner of the tennis match. The name of the academic paper is “Tennis Winner Prediction based on Time-Series History with Neural Modeling” which is a conference paper in 2009.

(人工神经网络（ANN）是一种非常流行的人工智能工具预测。有三个研究人员称为 Amornchai Somboonphokkaphan，Suphakant Phimoltares和Chidchanok Lursinsap。他们采用人工神经网络模型来预测网球比赛的胜利者。这个名字的学术论文是“基于时间序列的神经建模历史对网球比赛的预测”，这是在2009年的学术会议文件。 )

They used a ANN network to build a tennis match model. They collected the statistical data of each player in the past few years until the day before prediction. They added an input parameter which is called the “court surface”. According to the “court surface”, it produces an effect to the individual statistic of the player.

(他们用人工神经网络的网络，以建立一个网球比赛模型。他们收集的每个球员在过去数年的统计数据，直到预测的前一天。他们将一个输入参数，它被称为“场地表面”。根据“场地表面”，它产生的效果，以个别的球员统计。)

The selected statistical features includes

1. Winning percentage on the first serve;

2. Winning percentage on the second serve;

3. Winning percentage on the return serve;

4. Winning percentage on the break point;

5. Winning percentage of played match; and

6. Total points win.

7. Hard Court or Clay Court or Grass Court

(选定的统计特征包括

1。第一发球局胜率;

2。在第二发球局胜率;

3。在返回发球局胜率;

4。在决胜局胜率;

5。全场比赛的胜率;及

6。总积分。

7。场地表面)

They used a few year data as training sets. They showed about 75% accuracy in the Australian Open 2003 Tournament in the training set. They also have a time series model to show 70% to 80% accuracy on 2007 -2008 data of Australian Open, French Open, Winbledon and US Open Tournaments.

(他们用数年的数据作为训练集。他们发现在训练集大约 75％的准确率在2003年澳大利亚网球公开赛赛。他们也有一个时间序列模型显示 70％至80％在2007 -2008数据准确性的澳大利亚公开赛，法国公开赛，美国公开赛和锦标赛 Winbledon。)

Reference:

Amornchai Somboonphokkaphan, Suphakant Phimoltares, and Chidchanok Lursinsap (2009), “Tennis Winner Prediction based on Time-Series History with Neural Modeling”, Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol I, IMECS 2009, March 18-20, Hong Kong