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Statistical Handicapping

In our last article we took a quick look at each of the three main handicapping techniques and will now look at each one a bit more in-depth, as well as show their strengths and weaknesses, along with their use in each of the four major sports.

One of the biggest strengths of statistical handicapping is that it removes all of the guesswork from the selection process. You run your calculations and you get an answer. The numbers that go into the process are entirely up to you, and they allow you to personalize the handicapping process to your liking.

As far as drawbacks, statistical handicapping doesn’t account for the human element of sports, which the other two techniques can do. The other drawback is that you are only as good as your numbers. Rather than explain, I’ll give a good example when we get to college basketball.

Baseball: Baseball would appear to be a statistical bettor’s dream and there is some degree of truth to that. You have countless statistics available, along with a 162-game schedule, so that you have plenty data to draw upon for a current sample size. Bettors have to be careful not to go overboard with the stats; however, as too much information can just dilute the equation.

Basketball: Statistical handicapping works pretty well in the NBA with its 82-game schedule. Bettors have plenty of statistics to choose from and the ability to get a good sample size. The strength of schedule factor is pretty much non-existent in the NBA.

College basketball is a bit more difficult, as there are fewer games and strength of schedule can be quite different between two teams. College athletes tend to be subject to highs and lows to a greater degree than their professional counterparts.

Strength of schedule factors can create problems for handicappers, especially those who use the power ratings of others. I have several different statistical systems that are based on AOPR, or strength of schedule, and I have seen different sets of AOPR ratings come up with opposite sides on the same game using the same stats with the exception of strength of schedule.

Football: There is a bit of differing opinions on how much statistical handicapping is beneficial in football, particularly college football, where not only do you have similar strength of schedule factors as you do in college basketball, but also just a 12-game schedule. By the time you have a solid statistical base, the season is about over. But a good statistical base is important for the bowl games and the conference championship games.

The NFL gives you a few additional games to accumulate stats from, along with a relatively even strength of schedule. Before the free agent rules were made more liberal for players, you could safely use the previous year’s stats for the first four to six games, but it’s a bit different now, as players are moving about more frequently. There’s a place for statistical handicapping in the NFL, but I’d be hesitant of making it the only handicapping technique used.

Hockey: Handicapping hockey on a statistical basis is another sport that’s a bit tricky due to how little scoring takes place. You can estimate shots on goal and a goalie’s save percentage, but you’re going to end up with many projections of 2.48 goals and the like. Those projections do enable to you convert the game to moneyline odds and look for value in that regard, as do looking at offensive and defensive differences compared to the league average.

Advanced statistics, such as Corsi and the like, are frequently debated among diehard hockey fans as to their usefulness, and they are more indicative on what a player or team has done in the past than they are predictive.

So it’s a bit of a mixed bag, in that you have a good-sized schedule to acquire a solid sample size, but it’s really a question of what you can do with those numbers.

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