Third trading post and this one is a direct consequence of my incursion in the mighty world of Twitter. I already knew about betting advice based on statistical data, what I wasn’t expecting was the magnitude and the reach of it. Bear in mind I’m probably biased when it comes to this issue due to my research background, which involved statistics, data and computational modeling. So, let’s begin…
Would a person who never cooked be able to make a top dish, if given a large amount of ingredients, of variable quality, to choose from?
When it comes to cooking, there are at least two important parts of the equation: technique and ingredients quality. If you’re like me and don’t know a lot of cooking techniques, the only way you’ll succeed is by choosing good ingredients and somehow don’t spoil them in the process. So, what does this have to do with the use of statistics in the betting world? Probably there are better analogies, but my point is: we live in a world where the amount of data is huge and readily available for everyone. Adding to that, nowadays you don’t need to have a Math degree to somehow be able to extract some odds out of that data. That being said, I can assure you that if you don’t have an edge statistically-wise and you don’t know how to select quality data from the whole bunch, the odds you are cooking will suck!
Enough of cooking, let’s get down to business!
Fair enough, let’s start with the “problem” of the amount of data available. Manchester United are playing against Swansea on Sunday and with some tweaking here and there, you can select two sets of data: one that supports Swansea to win and other in favor of Man Utd. Furthermore, you can also find evidence supporting the over and the under for the same match. Politicians and economists with some agenda do this all the time, it’s all about cooking it the right way.
When it comes to the punting world, I understand the commercial side of it: you’re selling something and a bit of data can validate your tips. If you’re right, you’re the man, if you’re wrong blame the variance!
This leads us to the other part of the problem, the selection of meaningful data. In science, if you want to obtain credible results, from an experience, the number of observations is an important issue. Furthermore, each observation must be obtained in the exact same circumstance as the others. So, transposing this to football databases, how can you reach any conclusion about Van Gaal’s Man Utd, based on historical data from Ferguson or Moyes tenures? Even the first Van Gaal matches are not meaningful in the present, as he experimented a lot in the beginning; ex: what does a 3-man defense with the likes of Tyler Blackett playing as to do with the current setup?
Football: the sport where results lie!
One of the reasons behind football popularity is the frequent unfair nature of the final result; in some sense, it’s a bit like trading sometimes: a team can take more EV+ decisions than the opponent and still lose or draw the match. Final results lie in football!
As an example let’s take two 0-0 results from last week: Man Utd vs Newcastle and Arsenal vs Liverpool. In the future, these results will be used to support some Under bet or to model the odds of the Under/Over market. While in Man Utd game, the 0-0 can be considered a fairly true result, anyone who saw Arsenal vs Liverpool can’t possibly say that the 0-0 reflect what happened. Both GK had amazing performances, plus there was a goal unfairly ruled out for Arsenal and 3 shots off the woodwork. My point being: don’t follow or use stats blindly, they don’t tell the whole story!
Final remarks – Take advantage of stats and not the other way round!
As I stated in the introduction, my professional background probably introduces some bias in my views, as I know it’s relatively easy to drown in the sea of statistics available nowadays.
Nonetheless, I’d like to state I truly believe it’s possible to take advantage of stats in order to score some profits out of the markets. What I don’t believe is that anybody can do it, just because they are available. As always, you have to have an edge: it can be your data selection, your statistical knowledge, your ability to construct customized data from the databases available or others I don’t even know about.
As a final remark, you may ask me: “so, if you work with stats and data, why do you approach the markets with an intuitive-based approach?”. Fair question, but I guess I choose to follow the old proverb: “In the house of a blacksmith, the ornaments are made of wood”.