Casino Regression Theory

Regression theory in statistics is the idea that we can take a number of factorsand work out how it influences the characteristicsof a certain object or system.And we could have a similar approach in horse racing.We could say, well, let’s just supposethis box is the performance of the horse.And we have lots of different bits of past data.And we could say, well, maybe each one of these bits of dataexplains some amount of the horse’s performance.Of course, this is a bit simplistic, really, isn’t it.Because just like with the characteristics of inheritancewhere, for instance, some of the variationis explained by the parents that are alsogoing to be shared with the grandparents,the characteristics of a horse– these featuresare going to overlap.Some of these will explain multiple aspects.So these kind of things are goingto be a bit more jumbled up.And what’s more, we might not be able to explainall of the horse’s performance. Find more game theories at CasinoSlots.

There might be some chunk which we still can’t explain.And really the aim from a statistical point of view isto try and minimise this unknown quantity.And there’s actually– in the ’80s, Bill Benter visitinga library in Nevada came across a paperby two researchers called Ruth Bolton and Randall Chapman.They work in marketing.They still do actually.And they had essentially outlined this methodfor horse races, this approach of converting datainto some kind of measure of performancethat you could use to make predictions.And as Bill said, it was the ideathat sowed a multibillion pound industry– an incrediblyimportant piece of research.And actually for Ruth Bolton, it was the only papershe wrote about horse racing.It’s during her PhD.And it was really kind of a side project.But this had a huge impact on this industry.And on doing the analysis of those early syndicates in HongKong, certain things would come out as more important.For example, in one of their early bits of research,the number of races a horse had runwould tell you a lot about how it’s going to do.And it’s tempting to come up with a story for that.We say, well, if it’s run more races,then it’s going to be more experienced.And then that’s going to give it a betterperformance in the next race.But they actually avoid doing that,because really they know it’s a jumble,that all of these things are going to overlap and explainone thing.

And it’s not clear that just because something is important,it has a direct explanation.This quite common problem in statisticsis known as this idea that correlation doesn’t alwaysmean causation.Just to give an example, here we have along the bottomis the wine spend per year at the Cambridge colleges.On the vertical is the exam results.So as you–[LAUGHTER]As you can see, there’s a pretty strong correlationbetween colleges that spend more on wine have better results.And this isn’t the only thing that’s happened.Actually it turns out that countriesthat have a higher per capita spend on chocolatetypically win more Nobel Prizes.As lovely as it would be that eating chocolate would make youa Nobel Prize winner and drinking winewould make you better at exams, there’s clearlysomething else going on.