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.