Before making any bet, punters first arrive at a selection. That process can either be rudimentary or seriously complex. Examples of the former would be simply backing the top merit rated horse, or the soccer team that is positioned higher on the log than their opposition. On the other hand, computer syndicate team may crunch thousands of variables, weighing up each in precise, quantifiable detail, before deciding on their preferences in a horse race.
Which method is better? Nobel Prize winning economist, Daniel Kahneman wrote in a wonderful book, “Thinking, Fast and Slow,” that simple algorithms are best. Apgar scores which use just five variables rated 0,1 or 2 (measuring heart rate, breathing, reflex, muscle tone and colour) are an excellent way of assessing a new- borns’ health. A baby scoring 8 or more is doing great, less than 4 needs urgent help.
A Princeton academic and oenophile, Orley Ashenfelter demonstrated that even something tricky like predicting the future price of French wine from a particular region can be done accurately by taking only three key factors into account; temperature over Summer, amount of rain at harvest and total rain fall during past Winter.
Maybe there is a lesson here for horseplayers and sports bettors who tend to be too intuitive and haphazard in their analysis or else overcomplicate things unnecessarily. This is however not a justification for mere superficial study. Trying to come up with a wager by glancing at the most obvious data, then supporting, say, the champion jockey’s mount, is also not going to cut it in a tight betting market.
Instead, consider applying this balanced approach in whatever your favourite sport to bet on may be – focus on the most fundamental factors that have an influence on match outcomes and use that to come up with smart selections. The bigger the sample size the better.
A sensible application for making tennis predictions could involve looking at each player’s performance over the past season. Go to the Stats section of the atptour.com website. Then, simply add up the %’s of points won on first and second serves and the %’s of points won when receiving first and second deliveries for each player, then divide those four %’s by two to come up with an overall skill rating for ATP players.
Using this method, here are some scores from the recent past, before the Tour was suspended due to Covid – 19: Nadal was the head honcho with 112, Federer and Djokovic scored 110, Medvedev = 108, Bautista Agut = 106, Thiem and upward- moving, JL Struff rate 105’s, whilst a journeyman pro like Adrian Mannarino managed 101.
The figure is an instructive metric of playing ability with forecasting value, as it can reveal emerging players who are better than their bare official ranking may indicate. It takes only a minute to calculate and by updating it every month or so you can also track who is improving or in decline.
Young Greek sensation, Stefanos Tsitsipas’’s rating has crept up from 102 to 104 and is now 105, whilst ace-machine John Isner’s scores, (perhaps due to injury battles and the distractions of parenthood), are regressing from 102 – 99.
You might want to take it a step further and factor in Head to Heads on the specific surface and with just those two metrics you can, with some confidence, strike a rationally- worked out tennis bet!
It applies for any selection process – the method does’nt have to be complicated, so long as it’s fundamentally sound. I wonder what bright ideas Interbet punters can come up with for making sharp selections in different sports like golf, cricket and rugby – or the suddenly popular Belarusian Premier soccer league?