Friday, 6 January 2017

How Good Are Betting Tipsters? The Ultimate Guide To Evaluating Betting Tipsters

Betting tipsters often promise high strike rates that will win you a fortune. Although the promise of big profits is alluring, how can you tell the difference between a profitable tipster and a salesman. Read on to find out the two factors you need to evaluate before you decide which expert picks to trust.
In a previous article, we explained how luck influences betting and whether profit is always a sign of skilful betting. Using a series of coin tosses as an example, we used a binomial calculator to calculate the probability of making a profit after ten consecutive wagers.
The binomial distribution is well suited to 50-50 propositions like point spread or Asian handicap markets, where the odds for each side are close to even money, or a little shorter after the bookmaker applies his betting margin. Often, however, bettors bet on all sorts of different prices with all sorts of different stakes, for example 1X2 markets in soccer or match betting in tennis.
In such circumstances, we can rely on what is known as the t-distribution and the student’s t-test for statistical significance, which uses it. In this article, I explain how to use the t-distribution to gauge the performance of a betting tipster.
The length of a tipster’s record
The t-distribution is very similar to the bell-shaped normal distribution and for numbers of wagers above about 30, is to all intents and purposes the same thing. The t-test investigates the likelihood that a profit from a series of wagers could have happened by chance.
A return of 120% from 100 wagers at odds of 10.00 or longer is most likely to be a consequence of luck. Same returns from betting odds-on prices is an indication of skill.
The smaller the likelihood, the more probable it is that something else, like the bettor’s skill, is explaining their profitability. The t-test simply compares the bettor’s observed return to a theoretical expectation (assuming chance only) defined by the market they’re betting in. 
Usually this would be a loss equivalent to the bookmaker’s margin, or break even if the bettor is taking the trouble to find best prices using an odds comparison tool. The resulting t-score is then analysed to determine whether the difference is statistically significant.
It should be intuitively obvious that the larger the profitability the bigger the t-score and more statistically significant the betting history, in other words, the more likely it is that skill has played a part. The t-score is directly proportional to a bettor’s excess average return over expectation.
Similarly, the longer the history (for an equivalent return) the more likely it is that something other than chance is at work. Consider, for example, two bettors with a 120% return on investment. The first achieved it from 10 wagers, the second from a 1,000. Who is more likely to be the skilled bettor?

If in doubt, think of tossing coins again. Landing six plus heads out of 10 tosses is obviously far more likely than landing 600 plus from 1,000 assuming only chance. Were one to land 600 or more heads, one might reasonably suspect the coin to be biased.
Similarly, we are more likely to conclude that a bettor with a long record of profitability is demonstrating skill. The t-score is in fact proportional to the square root of the number of wagers.
Long vs. short odds
Less intuitive is the influence of the betting odds. In fact, a 120% return on investment from betting odds around 1.25 will be a much better indicator of skill than an equivalent profitability from betting odds around 5.00. Betting on lower probability outcomes (longer odds) is inherently riskier (assuming equivalent stakes) because it is more at the mercy of random variability.
To put it another way, returns are more volatile. 19 or 21 winners at odds of 5.00 will give returns of 95% or 105% respectively. In contrast, 79 or 81 winners at odds of 1.25 will show 98.75% or 101.25% profit over turnover. Betting longer odds implies taking more risk to get more reward. 
We can see the influence of the betting odds by means of the standard deviation in profits and losses of the betting history. For level staking, the standard deviation can be approximated by the following expression.

Where o represents the average odds for the betting history and r is the bettor’s actual return. The standard deviation in profits and losses betting at odds of 5.00 is more than eight times larger than when betting at odds of 1.25. Assuming that expected returns (based on luck only) are 100% (or break even), the t-score is given by the following equation:

Where n is the number of wagers. Consequently, the t-score for equivalent returns and length of betting history will be more than eight times smaller betting at odds of 5.00 compared to 1.25.
It should be clear that superior yields achieved through betting longer odds, as is typical in markets like horse racing, are not necessarily a sign of better forecasting talent. The same amount of luck will deliver much bigger percentage returns.
Hence, comparisons of betting histories that consider only percentage returns, as is common when ranking tipsters, are fundamentally misleading. By taking into consideration the betting odds, the t-score provides a measure of the quality of the risk-adjusted return in excess of expectation.
Calculating chance 
The final step is to convert the t-score into a probability (the p-value) that a history of profitability could arise by chance only. Those with Microsoft Excel can make use of the TDIST function. This takes the form TDIST (t, degrees of freedom, tails), where t is the t-score whilst degrees of freedom is the number of independent pieces of data and is equivalent to the number of wagers minus one.
The tails argument can be either one (for the one-tailed t-test) or two (for the 2-tailed t-test). Since we’re really only interested in whether a profit is statistically significant, the former is preferred in this case. Alternatively, one can make use of an online calculator where these values can be inputted.
The table below shows some examples of t-scores and their corresponding p-values for betting histories of 100 wagers and a return on investment of 120%.
Odds
t-score
p-value
1.5
3.33
0.06%
1.75
2.46
0.78%
2
2.04
2.19%
2.5
1.60
5.63%
3
1.36
8.83%
4
1.09
13.89%
5
0.94
17.56%
10
0.62
26.98%
25
0.37
35.45%
50
0.26
39.72%
Evidently, the average odds at which someone bets has a big influence on whether their profitability can be considered lucky or skilful. A return of 120% from 100 wagers at odd of 10.00 or longer should clearly be considered largely a consequence of luck.
By contrast, if a bettor was to show the same return betting odds-on prices, it is far more likely that the profitability has arisen because of their skill. Consequently, when comparing betting histories, for example from tipsters, it is not enough just to analyse their percentage returns, but also the length of their records and the odds at which they were achieved.


Friday, 13 May 2016

How To Find Value Bets In The Champions League

You might have heard of Elo ratings but what is it exactly and how can they be applied to betting to increase your winnings? A must read for anybody planning to bet on the second leg matches of the UEFA Champions League semi-final phase.
Originally invented as an improved chess rating system by Hungarian-born American master level chess player and physics professor Arpad Elo, the Elo ratings is a way of comparing the skill levers of players in competitor-versus-competitor games.
The method has since been adapted for several sports and is now widely used in assessing the performance of soccer teams. Here’s how Elo ratings work with examples from the upcoming Champion League fixtures.
The theory
When applying the Elo system in soccer, every team has a rating for each point in time and the higher it is the stronger the team. The ratings are constantly re-calculated in order to include the outcomes of matches between rated teams.
The winner takes points from the loser, with the total number of points at stake depending on the rating difference between the opposing teams.
The essence of Elo ratings is that the winner takes points from the losing team. The number of points in the total pool for each match depends on the difference between the ratings of the teams.
When a high-rated team wins against a low-rated team only a few ratings points will be deducted from the losing team, whereas if the lower-rated team scores an upset, a proportionally larger number of rating points will be transferred. In the event of a draw, the pot is shared equally.
The basic maths
Here’s how the basic calculation for Real Madrid vs. Manchester City, playing on Wednesday 4th May for the second leg of Champion league’s semi-final. The teams are both within the top 10 with 2074 and 1861 points respectively, according to the ClubElo rankings.
By calculating 5% of their respective ranking, Real Madrid will be risking 104 points and Man City 93, the total pot contains 197 points. Therefore, here are the three possible outcomes:
1.              Real Madrid wins (1.518*). Their ratings increases by 93 to 2167 and Man City’s rating decreases to 1768.
2.              Man City wins (6.86*). Their rating increases by 104 to 1965 and Real Madrid’s rating decreases to 1970.
3.              Draw (4.64*). The pot (197) is divided equally between the two teams 98.5:98.5 resulting in a slight increase to 1866.5 for Man City and a slight decrease to 2068.5 for Real Madrid.
Including other factors
The above calculations demonstrate the basic mechanics behind the Elo values in soccer. However, anyone with a basic understanding of soccer would guess that there is a major problem. Crucial factors, such as home advantage, are not taken into consideration. 
In the above example, it is obvious that a draw of 1-1 between the two sides in Madrid is a clearly better result for the Englishmen than it is for the Spaniards, considering that the first game ended in a goalless draw, and it is therefore irrational for Real Madrid to walk away at full-time slightly penalised for failing to progress to the Champions League final. 
For that reason the calculation for Elo values in soccer is usually adjusted to include major factors such home advantage, goal difference, two-leg matches etc. Below are the Elo ratings of the top 10 teams according to ClubElo:
Top 10 Elo ratings 
Team
Elo value
Real Madrid
2074
FC Barcelona
2060
Atlético Madrid
2017
Bayern München
2011
Juventus
1940
Paris SG
1894
Dortmund
1887
Villareal
1872
Manchester City
1861
Bilbao
1844
Updated on 04.05.2016
Using Elo ratings in betting
The main limitation bettors should keep in mind when referring to Elo ratings is the fact that the calculations of Elo values are based exclusively on past performance. With no account for individual players, it can be argued that the Elo system may lose some of its predictive power when certain circumstances - such as injuries, new manager/players, tactics etc - arise. Elo will only respond to such changes over time.
Despite that, the Elo system is widely used by sharp bettors as a reliable tool in the search of value bets, i.e.bets with positive expected value, because it provides a solid statistical foundation for calculating the probabilities of the different outcomes with great accuracy. This is particularly the case when a competition progresses towards its final stages, like the Champion League round of 16, as the weaker teams have been eliminated and it is getting harder and harder to predict the winners.
Ready to find value bets in the Champions League? Pinnacle Sports is the Nr.1 for Champions League semi-final odds, meaning you win more on every game.  



Friday, 8 January 2016

Is The Labouchere Betting System Profitable?

To be profitable in sports betting, staking strategy is as important as finding value odds. With many progressive betting systems, such as the Labouchere betting system, promising almost infinite wealth, are they a good idea to follow? Here’s the academic answer to the question.
The Labouchère staking system is a progressive staking method acording to which a bettor continues to bet until a particular winning amount is reached, and is traditionally used for Roulette.
The Labouchère staking method in sports betting
Labouchère can be easily extended to sports betting and the steps involved in applying this staking method are the following:
1.     Decide how much money you want to win. Let's aim, for example, to win $100.
2.     Determine how you will split this money. Say you split it up over five values: $10, $20, $40, $20, $10.
3.     Place a bet that would win the sum of the first and last numbers. If the European/Decimal odd is 3 (+200American/MoneyLine) for example, you would stake $10, so as to win $20 back (the sum of the first and last number).

4.     If you win, you tick off the first and last number. Otherwise, you add the amount of the stake you made, so that you need to win this back. In this case, you would have $10, $20, $40, $20, $10 and $10.
5.     Repeat steps 3 and 4 until you win the amount you are aiming for.
The rationale behind this strategy is that you tick two items off the list when you win, but you only add one; so you are ticking it faster than you are adding. Below I discuss why this doesn’t make sense.
To recommend or not to recommend?
While it is fun to deal with, I would not recommend any progressive staking methods. In essence, they suggest you bet and bet until you win the desired amount, without heeding the amount you afford to bet. Let ‘s assume you are betting on even odds and have a run of four losses. The next bet would then be $50, even if you had already lost $100.
Unlike the Kelly Betting strategy, no consideration is taken to the size of the portfolio.
It is important to remember that the key decision in sports betting should be whether the odds represent value, rather than how much can be won.
A similar progressive system is the Fibonacci strategy, where you increase your betting stake according to the Fibonacci sequence 1, 1, 2, 3, 5, 8, 13, 21 etc.
The academic answer
From an academic perspective, there have been three good papers discussing the use of this strategy to bet on draws. In 2007, Archontakis and Osborne stated that betting on draws at an odd of 3 using a Fibonacci Strategy would result in wins, if applied to World Cup Finals data.
Yet they didn’t use real data, so another team of researchers focused on real odds for draws and simulated the results (Is the soccer betting market efficient? A cross-country investigation using the Fibonacci strategy). They found that in 95% of the cases, the worst case scenario is to have to stake up to 43, if the Fibonacci strategy is used.
More recently, however, Lahvička debunked the theory of succes behind these methods by finding that all tested versions of this strategy eventually result in money loss. I would expect any progressive system to provide similar results.
So, tread on with caution. Progressive systems are fun, but only while the fun lasts. 

Tuesday, 27 October 2015

How To Successfully Bet On NHL And Which Stats To Use

Analytically, hockey has many similarities to soccer. Both are invasive, fluid team sports, where games are won by the number of goals scored and conceded and there are a large number of readily identifiable and recorded precursors to scoring, namely shots.
Therefore, the overlap in how teams and players are statistically evaluated, both descriptively and predictively, in the two sports has much common ground.
Methods of analysis for NHL betting
The Poisson distribution used extensively to model past outcomes and predict future matchups in soccer also adequately describes the likely number of goals scored and allowed in regulation time in hockey. The sudden death nature of overtime scoring in hockey is better described as a binomial distribution.
Inevitably, Bill James’ Pythagorean Expectation has also been adapted for hockey in an attempt to identify those teams who may be experiencing unsustainable levels of luck. But, you need to account for the awarding of a point for losses in overtime or during a shootout.
However, the most accessible tool to evaluate the NHL involves counting shots.
Even the wins or losses from an 82 game sample size for a single NHL team in the regular season will contain elements of random variation that may mislead when attempting to assess a side’s true abilities in a relatively low scoring sport such as hockey.
Goals are more numerous than wins, and shots, both on and off target, even more so. The more extensive shots at goal data are seen as better predictors to future performance in the NHL than goals or simple wins, losses or points.
Collecting "Corsi" and "Fenwick" data
“Corsi” named after the originator of the stat describes shots that are directed towards the net and they include goals, saves, misses as well as blocked shots. This may be quoted as a single number, either for or against for a single side, as a differential of total shots or as a ratio of the total shots taken in a game or over a series of matches.
A similarly named stat, “Fenwick” removes blocked shots from the calculation and more recently attempts have been made to improve the predictive ability of shot data by weighting each individual component differently.
These core hockey stats can be further subdivided by collecting shot numbers when the score line is close and teams are playing in a more normal tactical setup.
Score effects, also referred to as “game state” in football, can skew shooting data, as trailing sides may adopt more risky approaches in search of a goal, while the team which leads may devote more of its resources to defence to maximize their chances of winning the game.
Shot domination, particularly in hockey is often taken as a proxy for possession of the puck and a side which dominates possession in hockey would appear to be more likely to be successful in the long term.
Therefore, by demonstrating an ability to consistently out shoot the opposition is often taken as evidence that a side excels in the process that will bring success, even if short term bouts of good or bad fortune may prevent this ability from revealing itself in a team’s current win and loss record.
The pitfalls of PDO
Unsustainable levels of shot conversion rates or save percentages are powerful short-term factors, which may temporarily elevate a side’s record above or reduce it below their core abilities.
These are combined to create the final useful hockey stat, “PDO”, or SPSV% to the NHL. Typically, a team’s PDO, which is the sum of their save percentage and their scoring percentage, will cluster around 100, although the stat is sometimes scaled to 1 or 1,000.
PDO values that are either significantly in excess or below 100 are often signs that a side has experienced unsustainably high levels of good or bad luck.
However, the tendency over time is for such extreme luck-driven outcomes to become less extreme in the future, and PDO values that may enhance or confound the rewards from a side’s Corsi or Fenwick data should be expected to gravitate towards 100.
It has become increasingly recognised that a team’s win record, even over a season is the product not only of the talent of the players, but also natural variation the exists in any sample of data and small sample sizes are frequently imprecise measures of true talent.
Just as a coin may produce “heads” in excess of the expected 50% through natural variation, a side may convert and save shots, and ultimately, win hockey games by a combination of luck and skill, with the former unlikely to persist at the extremes.
A team’s 5v5 Fenwick ratio from one season shows a much higher correlation to their wins in the following season compared to their PDO under the same playing conditions from the previous campaign.
Therefore, when projecting a team’s performance, either using last year’s data or a recent run of results, shot data should perhaps carry more sustainable predictive weight as measured by either Fenwick or Corsi ratios, rather than a particularly high or low PDO value, which will have a tendency to regress towards the league mean of 100.



Tuesday, 8 September 2015

How To Use NFL Yards Per Play Rankings To Gain A Betting Edge

Football betting punters should learn to evolve betting strategies to get ahead of bookmakers in the NFL. One way of doing this is to place close attention to Schedule changes and to use yards per play rankings to exploit discrepancies in Handicap markets.
The NFL has always seen a gulf in quality between the best and worst franchises. Since the scheduling rule changes in 2002 dictated that only two games per year would be based on a team’s record from the preceding year, the number of mismatched fixtures has increased, resulting in the bad teams losing regularly, and the good teams winning more often.
So what does this mean for punters betting on the NFL? This article sets out to explain how to use yards per play rankings, from previous seasons, to your advantage.
Using Yards Per Play Rankings To Your Advantage
With the emergence of big mismatches, there are now more profitable opportunities to change betting strategies and do what is often unthinkable – back the quality team as large favourites.
One way to evaluate a team is to look at its offensive yards gained per play and the defensive yards allowed per play. As you would expect, the teams that gain more yards than they allow, tend to win more than they lose.
While this rule isn’t exclusively accurate, it is as good a predictor of future performance, as past head-to-head results.
The table below shows yardage data from the 2014 NFL season for each franchise competing in the 2015 NFL season. By calculating the yardage difference between offensive and defensive yards per play (Y/P) for each franchise, will give you a solid base for predicting results.
Yards Per Play NFL 2014
Team
Offensive yards per play
Defensive yards per play 
Net yards per play
Seattle Seahawks
5.9
4.7
1.2
Denver Broncos
5.9
4.8
1.1
Green Bay Packers
6.1
5.3
0.8
Indianapolis Colts
5.8
5.3
0.5
Baltimore Ravens
5.7
5.3
0.4
Detroit Lions
5.3
4.7
0.4
New England Patriots
5.5
5.3
0.2
Dallas Cowboys
6.0
5.8
0.2
Philadelphia Eagles
5.6
5.4
0.2
Miami Dolphins
5.4
5.3
0.1
Buffalo Bills
5.0
4.9
0.1
Kansas City Chiefs
5.3
5.2
0.1
Pittsburgh Steelers
6.1
6.0
0.1
San Francisco 49ers
5.2
5.2
0.0
Houston Texans
5.2
5.2
0.0
New Orleans Saints
6.0
6.0
0.0
Carolina Panthers
5.2
5.3
-0.1
San Diego Chargers
5.4
5.5
-0.1
Cincinnati Bengals
5.4
5.5
-0.1
Washington Redskins
5.7
5.8
-0.1
Cleveland Browns
5.1
5.2
-0.1
Tennessee Titans
5.3
5.4
-0.1
Minnesota Vikings
5.2
5.4
-0.2
St. Louis Rams
5.3
5.6
-0.3
Atlanta Falcons
5.8
6.1
-0.3
New York Jets
5.0
5.4
-0.4
Tampa Bay Buccaneers
5.0
5.5
-0.5
New York Giants
5.4
6.0
-0.6
Jacksonville Jaguars
4.7
5.5
-0.8
Arizona Cardinals
5.0
5.8
-0.8
Chicago Bears
5.2
6.0
-0.8
Oakland Raiders
4.5
5.5
-1.0
By looking at the table it is clear to see that the four worst teams in terms of yardage per play differential in 2014 were the Oakland Raider (-1.0), Jacksonville Jaguars (-0.8), Arizona Cardinals (-0.8) and the Chicago Bears (-0.8), neither of which qualified for the playoffs.
In comparison, the four best teams by yardage per play differential were the Seattle Seahawks (1.2), Denver Broncos (1.1), Green Bay Packers (0.8) and the Indianapolis Colts (0.5). All teams qualified for the playoffs bar the Philadelphia Eagles who won eight but also lost eight.
Betting odds can often fail to reflect the difference between the elite teams and the teams that struggle.
While the results are not surprising, a yardage differential can be used to analyse matches between two teams. There is a simple rule that is used by sharp bettors using the NFL yardage differential to calculate a betting advantage – each 0.15 yards per play is worth 1 point on the handicap market.
Using Yards Per Play To Calculate Handicap Discrepancies
By using the 2014 stats above, we can examine the 2015 NFL week one match between the Seattle Seahawksand the St. Louis Rams.
By looking at the table from last year, the Seahawks had a yards per play differential of +1.2, while the Rams net differential was -0.3. The difference between the two franchises was 1.5, so by dividing this number by 0.15 suggests the Steelers should be around a 10 point favourite on a neutral field.
However, the Steelers are actually offered at -4, while at home the Broncos are offered at +4. The handicap odds offered with Pinnacle Sports suggest the teams are much closer than the yardage per play calculation would suggest.
With this said, as with all tools for evaluating teams, this calculation is best used in conjunction with other handicapping methods. Many statistical methods become far more viable after 4-6 games, at which point the 2014 data could be analysed.
It is also vital to gather as much knowledge on betting variables to accompany your yards per play calculations such as preseason results, squad rosters, and which players are sidelined through injury.
These betting variables coupled with the yards per play calculation will give you the best chance to work out any NFL handicap discrepancies.