用于检测历史体育比赛数据趋势的程序化技术-Programmatictechniquestodetecttrendsinhistoricalsportsmatchdata

Summary

I have been looking at historical Australian Rules outcomes using excel with an eye on Betfair odds to see if is an opportunity to better predict future match outcomes. My progress to-date is covered in more detail under Background below.

我一直在研究使用excel的历史澳大利亚规则结果,着眼于必发赔率,看看是否有机会更好地预测未来的比赛结果。我在今天的进展情况将在下面的背景下详细介绍。

I’d now like to go a step further and look at data mining / pattern matching / algorithim techniques that I could possibly implement. I have had some experience with using dynamic models (Extend) and using Solver in Excel for optimisation but I am unfamiliar with data mining other than the term itself

我现在想更进一步,看看我可能实现的数据挖掘/模式匹配/算法技术。我有使用动态模型(Extend)和在Excel中使用Solver进行优化的一些经验,但我不熟悉除术语本身之外的数据挖掘

Are there viable data mining programming techniques available to me to deploy for this analysis in VBA?

我是否可以使用可行的数据挖掘编程技术在VBA中进行此分析?

(I realise this question may be seen as borderline by some but I think Stack Overflow is better suited to this question than say Math - I am keen to understand potential programming options/algorithims that I can apply in VBA)

(我意识到这个问题可能被一些人视为边界但我认为Stack Overflow更适合这个问题而不是数学 - 我很想理解我可以在VBA中应用的潜在编程选项/算法)

My strong preference is to look at this with VBA \ Vbscript as this is my coding background but I am open to other options if they are significantly better.

我强烈倾向于使用VBA \ Vbscript来看这个,因为这是我的编码背景,但如果它们明显更好,我会对其他选项持开放态度。

Background

I have extracted the data for Australian Rules football over the last few years into Excel. This data gives me:

我在过去几年里将澳大利亚规则足球的数据提取到Excel中。这些数据给了我:

1. Quarter by Quarter results
   (for example WWWL means team 1 leads for the first three quartes before losing the game, DLLL means teams 1 and 2 were level at the end of the first quarter, then team 2 lead for the remainder of the match).

逐季度结果(例如WWWL意味着球队1在失去比赛之前的前三个赛季领先,DLLL意味着第一和第二队在第一节结束时保持水平,然后在第二季结束时队2领先)。

2. The same info is regrouped into Half by Half results

相同的信息被重新组合成一半的结果

3. Home and Away teams (team 1 is home, team 2 away)

Home and Away球队(球队1队主场,球队2队客场)

4. Match Day Stadium

比赛日体育场

5. Month of the year

一年中的一个月

Which I then match up to other data sets such as

然后我将其与其他数据集匹配,例如

1. League ladder by week (completed)

逐周联赛(已完成)

2. Whether the stadium is open air or closed (completed)

体育场是露天还是关闭(已完成)

3. Bookmakers odds pre game (to do)

博彩公司在比赛前的赔率(待办事项)

4. What happened with the weather conditions for open air stadiums (to do)

露天体育场的天气情况怎么样(待办事项)

And then dice and splice with PivotTables (perhaps PowerPivot) to interrogate this data to look for gaming opportunities, for example:

然后用PivotTables(可能是PowerPivot)进行骰子和拼接,以查询这些数据以寻找游戏机会,例如:

• Do certain teams team to lead from start to finish (WWWW) more often than others, and do the odds for a “Four Quarter” win (WWWW) pay disproportionately more than this likelihood would indicate for a vanilla win (so Lay the vanilla win, Back the WWWW)

某些团队团队从头到尾(WWWW)比其他团队更频繁地领导,并且为“四季度”胜利(WWWW)支付的可能性不成比例地超过这种可能性表明赢得了香草的胜利(所以让香草获胜,返回WWWW)

• Looking for marked differentials in Home and Away performances (i.e. does home ground knowledge, or partisan home crowd support lead to more reversals of ¾ times scores)

寻找Home和Away表演中的明显差异(即家庭地面知识,或党派家庭人群支持导致更多的3/4次分数逆转)

• Comparing results of open-air versus closed roof stadiums (removing weather impact)

比较露天与封闭屋顶体育场的结果(消除天气影响)

• Does a long distance travel one week impact the next week(s) outcome

长途旅行一周是否会影响下周的结果

• Do certain teams produce certain scoring patterns more often than standard league results

某些球队是否比标准联赛成绩更频繁地产生某些得分模式

• Is a lower ranked team more likely to lead throughout an entire match than come from behind to beat a higher ranked team

排名较低的球队在整场比赛中更有可能领先,而不是落后于击败排名更高的球队

2 个解决方案