This comes from the Doctor who uses lots of technical analysis and he majored in Statistics
Technical analysis has come under scrutiny by fundamental handicappers and some sports bettors due to the fact that anybody searching a database randomly for patterns will find situations that have produced very good results. However, the key is to look for situations that make sense. I don't use trends such as "The Steelers are 13-2 in week number 7" (Do they actually know that week 7 is their week and gain confidence from it?) or "bet on home dogs from +2 to +4 if it's a weeknight MAC game" (the more narrow the point spread range is the more likely it is a random occurrence and not a true indicator of a real pattern).
So how can I be sure that technical analysis works? At the beginning of each year, I make a list of the situational angles that I think are meaningful (they are all easily statistically significant). At the end of the year, I tally the results of these angles. In the last 10 years of doing this, I have found that the situational angles that I use (remember, if you're angles don't make sense they are not going to hold up as well) have won at a profitable rate of 55%, and that the situations with a higher statistical significance (i.e. a higher t-value) have proven to be even more predictive.
Many handicappers tend to back-fit past data by adding more and more factors (parameters) to a situation until they have a very high percentage angle (but also a much smaller sample size). However, my research has shown that a situation's predictability is sacrificed with each parameter added to derive that situation. For instance, a situation with a record of 50-20 (71%) that is derived using 10 factors isn't as predictive as the 59% home underdog situation that I presented above, which has just 4 parameters (this game home, this game dog, won last game, dog last game) and a much larger sample size. It's easy to find a very high-percentage situation if you use an unlimited number of parameters to get to that situation, but all that will result in is a situation that explains what has happened rather than something that helps predict what will happen.
My research, and the theories of statistics, shows that more predictive angles have fewer factors and a larger sample size, rather than a smaller sample situation with a high winning percentage that was derived by using too many parameters. Further research I did in the Summer of 2004 (which I update each summer) enables me to accurately assess a situation's future performance based on the win percentage, sample size, number of parameters and more recent performance (i.e. record of the angle over the past 3 seasons). That research led to a more realistic use of situational analysis than I've employed in the past. For instance, I can now tell you that a situation with a record of 140-60-5 ATS that uses 6 parameters has a 56.8% chance of winning the next time it applies if the line is otherwise fair according to my metrics. Having a realistic expectation of a situation's value has helped my overall analysis immensely, and I will continue to devote time each summer to update the research on the predictability of my situational analysis.
Remember, just because a situation is 70% over 200 games in the past does not mean that it will win 70% of the time in the future. A 140-60 situational trend is simply a sample of 200 games selected from a population consisting of all NFL games. Since the NFL is constantly changing (although the league as a whole doesn't change nearly as quickly as most individual teams do), the results of the same situation in the future will not fully reflect the past. Also, by definition, a statistically significant trend has a 5% probability of being caused by no more than chance variation, and the record of those trends can be expected to be 50% as a whole, bringing down the overall percentage of all significant trends. There is also going to be a certain level of back-fitting involved in finding a situation, which also lowers the future percentage of the situation. Of course, the better the record, the greater number of games in the sample, and the fewer parameters there are in an angle the more likely that the situation is real and not just random.
Technical analysis has come under scrutiny by fundamental handicappers and some sports bettors due to the fact that anybody searching a database randomly for patterns will find situations that have produced very good results. However, the key is to look for situations that make sense. I don't use trends such as "The Steelers are 13-2 in week number 7" (Do they actually know that week 7 is their week and gain confidence from it?) or "bet on home dogs from +2 to +4 if it's a weeknight MAC game" (the more narrow the point spread range is the more likely it is a random occurrence and not a true indicator of a real pattern).
So how can I be sure that technical analysis works? At the beginning of each year, I make a list of the situational angles that I think are meaningful (they are all easily statistically significant). At the end of the year, I tally the results of these angles. In the last 10 years of doing this, I have found that the situational angles that I use (remember, if you're angles don't make sense they are not going to hold up as well) have won at a profitable rate of 55%, and that the situations with a higher statistical significance (i.e. a higher t-value) have proven to be even more predictive.
Many handicappers tend to back-fit past data by adding more and more factors (parameters) to a situation until they have a very high percentage angle (but also a much smaller sample size). However, my research has shown that a situation's predictability is sacrificed with each parameter added to derive that situation. For instance, a situation with a record of 50-20 (71%) that is derived using 10 factors isn't as predictive as the 59% home underdog situation that I presented above, which has just 4 parameters (this game home, this game dog, won last game, dog last game) and a much larger sample size. It's easy to find a very high-percentage situation if you use an unlimited number of parameters to get to that situation, but all that will result in is a situation that explains what has happened rather than something that helps predict what will happen.
My research, and the theories of statistics, shows that more predictive angles have fewer factors and a larger sample size, rather than a smaller sample situation with a high winning percentage that was derived by using too many parameters. Further research I did in the Summer of 2004 (which I update each summer) enables me to accurately assess a situation's future performance based on the win percentage, sample size, number of parameters and more recent performance (i.e. record of the angle over the past 3 seasons). That research led to a more realistic use of situational analysis than I've employed in the past. For instance, I can now tell you that a situation with a record of 140-60-5 ATS that uses 6 parameters has a 56.8% chance of winning the next time it applies if the line is otherwise fair according to my metrics. Having a realistic expectation of a situation's value has helped my overall analysis immensely, and I will continue to devote time each summer to update the research on the predictability of my situational analysis.
Remember, just because a situation is 70% over 200 games in the past does not mean that it will win 70% of the time in the future. A 140-60 situational trend is simply a sample of 200 games selected from a population consisting of all NFL games. Since the NFL is constantly changing (although the league as a whole doesn't change nearly as quickly as most individual teams do), the results of the same situation in the future will not fully reflect the past. Also, by definition, a statistically significant trend has a 5% probability of being caused by no more than chance variation, and the record of those trends can be expected to be 50% as a whole, bringing down the overall percentage of all significant trends. There is also going to be a certain level of back-fitting involved in finding a situation, which also lowers the future percentage of the situation. Of course, the better the record, the greater number of games in the sample, and the fewer parameters there are in an angle the more likely that the situation is real and not just random.
Like the Ohio St play!
