Mar 28, 2017
Mar 28, 2017

Golf betting strategy: Success = talent + luck

Find out why luck is important part of a golf betting strategy

How to quantify luck when betting on golf

Analysing regression to the mean in golf betting

Golf betting strategy: Success = talent + luck

With the 2017 golf season now well underway, bettors will be refining their golf betting strategy. Unlike most sports, luck plays a significant role in determining the result of a golf match or tournament. Regression analysis can help build an understanding of how chance operates thereby providing a valuable insight for golf bettors. Read on to find out how to quantify luck when developing a golf betting strategy.

Golf betting strategy - The rub of the green

Luck plays some part in how all individual sporting events pan out, but more so in golf than in almost any other - some might argue that it is baseball betting that is influenced by luck the most. Famous hustler and gambler Titanic Thompson summed it up neatly: "In poker, pool and dice you've got to worry about your game and the other man's. In golf there is also the weather". Of course, the weather is just one of many factors that make golf a hard sport to predict; getting the rub of the green can have a huge impact on an outcome in golf betting.

It's a general rule in sports that the greater the number of interacting elements (like the weather in soccer betting) the harder it is to predict the outcome. Once bettors know how to bet on golf, they should consider the following characteristics:

  • Golf is an outdoor sport, so is subject to the unpredictable nature of weather.
  • Tournaments last four days with no consistency to tee times for each competitor, so it is unlikely that players will experience consistent conditions.
  • A golf course covers vast distances with widely differing surfaces.
  • Pin-positions (what golfers are aiming for) change each round.
  • The field of potential winners is large by betting standards.
  • The scoring format of 72-hole tournaments is such that a player can lead throughout 71 holes yet potentially lose on the outcome of the final hole.

Given such a large number of random variables that can impact a golfer'€s score, it is no wonder that golf betting appears to offer much higher odds - something that is highlighted by the current US Masters betting odds.

In poker, pool and dice you've got to worry about your game and the other man's. In golf there is also the weather. Famous Gambler, Titanic Thompson.

Despite a small selection of players dominating the overall PGA and European Tour standings, 28 of the last 43 major winners going back to the 2009 Masters have been unique. To further illustrate this point, see the following list of big outsiders that won majors in the last decade and the particular circumstances in which they were won in:

Shock victories in golf majors in the last decade:

PlayerGolf MajorOddsExceptional Circumstances
Brooks Koepka 2017 US Open 41.700 First ever major and joint-lowest score US Open score (16 under par)
Danny Willett 2016 US Masters 101.000 Hasn't finished better than T37 in any major since
Darren Clarke 2011 British Open 126.000 In his next major at the 2011 USPGA he carded +15 for first two rounds
Keegan Bradley 2011 US PGA 151.000 Debut in a major
Louis Oosthuizen 2010 British Open 201.000 Had made 1 cut in 8 previous majors
Lucas Glover 2009 US Open 201.000 Event was wettest and most disrupted in US Open history; Glover missed cut at US Open in three previous attempts
YE Yang 2009 US PGA 151.000 Missed cut in 5 of previous 7 majors
Michael Campbell 2005 US PGA 151.000 Campbell had to sink a 6ft putt just to qualify

Golf betting - Analysing both talent and luck

Any successful golf betting strategy will be able to analyse a player's talent and ability, but also understand how luck can influence results. A bettor's view shouldn't differ that of a player, something that can be summarised by:

Success = talent + luck

If you accept the relevance of this to the world of golf, bettors must also take luck into account alongside handicap. But how do you quantify the element of luck?

One method would be to understand the importance of regression. For example, if you were to wait for completed Round 1 scores of any given major, you might infer from players scoring below par that their talent on the day -€ “judged on that narrow criteria" -€ was above the average participant. Using our formula for success, we should also appreciate that good fortune played some part.

By the same token, those players who shot over par would be judged to have performed below the average, but poor luck could also have contributed. This can produce these broad statements:

Above average Round 1 score:

Above average performance + Good luck

Below average Round 1 score:

Below average performance + Poor luck

The aim of golf betting is to make accurate predictions. This means we need to use this analysis to predict what might happen in Round 2. If you assume that performances remain constant (and this is a broad assumption), luck becomes the variable element, which you have no way of exactly measuring other than to say that with a best guess that it would be very unlikely to be the same as on Round 1.

You can elaborate to say that those players that played above average in Round 1 would play equally well in Round 2, but that the luck they enjoyed wouldn'€t hold so their scores wouldn'€t be as impressive.

The inverse would be true of those under-performers from the first round, whose scores would (on average) marginally improve, as they would not experience the same degree of bad luck.

Why does a large number of the stars that appear on the cover of Sports Illustrated go on to suffer a decline in their fortunes? The statistical answer is regression.

The interesting observation for bettors is that regression suggests that scores of players for Round 2 are more likely to be closer to the average score than the evidence on which it is based (Round 1 for that player). This moderation is the influence of regression to the mean, which we should expect to be more noticeable with extreme scores.

It is important to note that regression doesn'€t guarantee anything, however. Some players who do well on Round 1 will do equally well or better on Round 2, and vice-versa. On average -€“ if we accept the role of luck -€“ scores will regress back towards the mean.

Regression & the Sports Illustrated Jinx

As stated throughout, we are dealing with average expectation, but regression is nonetheless a statistical fact – a particularly misunderstood one. A great illustration of regression in action is shown in the Sports Illustrated Jinx, an urban myth that suggests that a disproportionately large number of the stars that appear on the cover of the famous magazine go on to suffer a decline in their fortunes.

This has nothing to do with jinxes, however, the reality is more mundane and can be explained by simple regression. A large number of cover features should be expected to fall from their giddy heights as the performances that led to their feature are extremes from which they should statistically be expected to regress.

Regression is certainly something that should be considered for betting on major tournaments in golf, but not across a season where luck should average out and the impact of talent dominate. This is even more relevant over longer time periods, hence multiple major winners like Tiger Woods.

But extreme as their talent is, it doesn't insulate them from the impact of luck over the shorter timescale in what is such a uniquely challenging sport for both players and bettors.

Now that you understand the role luck can play in a golf betting strategy, take advantage of the best US Master betting odds online!

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