Sports Betting as an Investment

Section 3: Handicapping Theory 2/3 (Technical Analysis & Team Trends)

Fundamental Analysis:

Fundamental analysis is the old fashioned way of handicapping. Fundamentalists look at matchups or try to envision how a game will play out. They study the strengths and the weaknesses of teams and try to determine if one team has a significant advantage over another based on their ability to exploit a team’s weakness with their strength. For example, a fundamental analyst might claim that, ‘Denver is a great running team and they need their rushing attack to be effective to help their play-action pass attack thrive. The Broncos’ offense should work well against a Raiders’ defense that has trouble stopping the run.’ Or that, ‘Carolina’s All-Pro defensive end will take advantage of their opponents rookie left tackle and be in the quarterback’s face all day.’

Such a style of handicapping depends on a very keen knowledge of each team and their personnel. The problem with this sort of fundamental analysis is that most mismatches in a game are already reflected in mathematical models, as well as in the point spread.

The key to fundamental analysis is finding statistical indicators that have led to point spread success, beyond the most obvious observations which everyone already knows, and odds makers have already taken into account. Handicapping based solely on fundamental analysis (which is what you’ll see from the majority¬†of touts and analysts) is lazy, inaccurate and unlikely to gain an edge over odds makers and/or other bettors. However, more nuanced fundamental analysis can be a useful addition to more comprehensive and predictive analytical models.

Technical Analysis:

Technical analysis is the study of patterns and is based on the psychological ups and downs of teams as well as the psychological patterns of those that bet sports. Obviously teams have their ups and downs, and I use trends and situations to identify when teams are likely to play well and when they are not based on patterns that have led to point spread success and failure in the past. In general, technical analysis is becoming less and less predictive as some trends are being incorporated into the line and betting biases that used to supply plenty of value for those using contrary indicators are not influencing the lines as they once did.

Team Trends:

The most widely used and understood type of technical analysis is the study of team specific patterns which I simply call team trends. When I started handicapping back in the mid-80’s team trends were an important part of selecting which teams I bet on. I would go back into my logs of results and study how teams performed at home and on the road, as a favorite or as an underdog, after a win or after a loss, and under other circumstances. I found that the most insightful team trends were the ones that were based on recent results and explained how teams performed after good and bad performances. For instance, the 49ers were 48-17 ATS (Against The Spread) from 1981 through 1997 when they lost straight up and failed to cover the point spread in their previous game. What this told me is that the 49ers had a strong tendency to be more focused after a poor outing and had the talent to raise their level of play when they were more focused. This is a trend that worked for many years despite coaching and player changes over the years. Of course, the 49ers basically had the same type of team over all of those years, and the 49ers’ tradition started under Bill Walsh in the early 80’s has been handed down through the generations of players in that time frame. It also helped that San Francisco had only two quarterbacks during those 17 years, and that both Joe Montana and Steve Young had the personality types that made them perform better after poor outings.

Most of the personality of a team comes from the head coach, and I have noticed that patterns follow coaches from team to team. For instance, Jon Gruden’s teams had a tendency to play well as a favorite after a loss (10-5 ATS from 1998 through 2001 in Oakland and 17-10-2 ATS from 2002 through 2008 with Tampa Bay) and poorly when favored by three or more the week following a victory (5-15 ATS with Oakland and 9-17 ATS with Tampa Bay). I can imagine Gruden being very good at motivating his team and also knowing how to adjust his game plans, after a bad performance while perhaps being less intense after a good performance.

On the other hand, there are some types of team trends that simply do not predict what will happen in the future. Over the past 20 years, I have studied the results of all statistically significant team trends that I have used in my game notes and tallied the results, broken down by the type of trend. The type of team trends that were the best indicators are what I call personality trends, which are the trends that explain how teams react to recent performances, such as how a team performs after a win or a loss, or after two straight spread wins, or after allowing 30 points or more (like the ones I used in the examples above). Certain types of team trends don’t work at all, such as series history trends or trends that deal with a specific game number. A series history trend is a trend that states that Team A has covered 10 straight times against Team B. I have found that regardless of how many times in a row a team has covered against another team, the chance that they cover in the next meeting remains 50%. A trend that says that Team A has gone 13-1 in their second road game of the season or is 8-0 in week number 5 doesn’t make any sense and does not have any value in predicting the future. I know what types of trends tend to work and to what degree they work.

While the use of team trends worked very well during the 80’s and through the mid-90’s, the advent of free agency and the constant changing of head coaches in the league changed the personalities of teams every few years, making previous patterns of these teams meaningless in most cases. I tend to shy away from most team oriented trends unless the head coach or core of star players has been intact over the term of the trend. I certainly wouldn’t pay much attention to a Carolina Panthers¬†trend that included games prior to the arrival of quarterback Cam Newton and head coach Ron Rivera, who both changed the personality of that team. On the other hand, longer term trends of the Pittsburgh Steelers do have some validity due to the long tenure of head coach Mike Tomlin- even though the personnel have changed over the years.

Team trends can still be an effective handicapping tool, but I do not use team trends that no longer explain the personality of a team. From a statisticians’ point of view, a trend is basically a sample of games taken from a pool/population of results. When the pool from which the sample was taken changes, the sample of games is no longer representative of that pool and should thus not be used as a forecasting tool. Thus, team trends work best with teams that have had the same coach or core group of players for at least as far as the trend goes back.

Situational Trends:

As the use of team trends became more limited because of free agency and coaching changes, I began looking for patterns that explained the results of all teams that were in the same set of circumstances. For instance, how did all teams perform following consecutive games in which they allowed less than 10 points? Or, what is the record of Monday night home underdogs? These league-wide patterns are referred to as situational trends. I have found that situational trends are better indicators of future point spread results than team trends, because team specific changes (such as coaching changes and free agency) have little effect on league wide patterns. The patterns that exist in the NFL and in college football have existed for years and are based on the psychological ups and downs that exist in all teams and in the wagering habits of the betting public.

While all of my situations deal with the patterns that exist in team performance, some of them also are enhanced by the betting patterns of the public, who are influenced by more recent performances of a team. A lot of the situations that I use deal with playing on teams that have been playing below expectation (bounce-back situations) and playing against those that are playing well in recent weeks (letdown situations). Bounce-back and letdown situations work partly because the betting public overreacts to a string of good and bad performances and bets accordingly. A team may become out of favor after a couple of terrible performances while other teams may get more support than warranted from a couple of very good performances. Since the point spread is as much a measure of public perception as it is a projection of a median outcome, the point spread gets over-adjusted in conjunction with the public’s fear of betting on a team on the slide or with their eagerness to bet on a team playing especially well in recent weeks.

This sort of betting behavior based on short-term results leads to line value and that is why these sort of bounce-back or letdown situations produce pretty good results. For instance, NFL teams that won their previous game straight up as an underdog are just 87-145-9 ATS (since 1980) if visiting a non-divisional opponent that is coming home after a road loss and spread loss. That is a good example of a trend that combines a letdown for one team with a bounce-back for the other.

There are a couple of reasons for this. First, teams that just won as an underdog tend to get more support from the betting public while teams coming off a loss in which they also lost money for their backers tend to get less public support. As a result the point spread is adjusted a bit to reflect current public perception. At the same time, the team that has just won in upset fashion generally is not quite as hungry, as the coach has less ammunition to motivate his players with, and playing against a non-divisional opponent gives the team off an upset win even less reason to get fired up. Meanwhile, the team coming home off a bad performance on the road is more likely to be focused in preparation for their home game. So, not only is the team coming off the upset win more likely to play at a lower level but the point spread has also moved in our favor to create value on the team that is likely to rebound from a bad performance. This situation does not work when playing against home teams off an upset win because it is easier to maintain a high level of intensity in front of the home fans.

Not all situations are in the bounce-back or letdown mode and take advantage of misguided public perception and the natural fluctuations of team performance. There are also what I call momentum situations and these deal with playing on teams that are playing well and playing against teams that are playing poorly. For instance, in the NFL home underdogs with a season win percentage of .600 or lower (i.e. mediocre and bad teams) are 122-73-7 ATS if they won straight up as an underdog the previous week and are now hosting a winning team that does not have revenge. Mediocre and bad teams in the NFL generally lack the confidence to beat a good team and there is nothing like an upset win to boost confidence. The confidence of winning as an underdog is enhanced by playing in front of the home fans and thus creates a good momentum situation. A winning team that is favored on the road (thus, a significantly better team) could easily enter that game overconfident or taking their lesser opponent lightly – particularly after playing a good game (71-38-4 ATS if the road favorite is coming off a game last week in which they won and covered the spread). Having a revenge motive certainly could result in the road favorite being more fired up, which is why we have the not facing revenge stipulation as part of that angle. In general, the NFL is a contrary league, meaning that most of the situations involve going against teams that have been playing well and going with teams that have been struggling. College football, meanwhile, is more of a momentum sport and many more of the good technical situations in college football involve playing on a team that has been playing above expectations.

Does Technical Analysis Work?

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 20 years of doing this, I have found that the situational angles that I use (remember, if your angles don’t make sense they are not going to hold up as well) have won at a profitable rate of 53% to 54%, depending on the sport, and that the situations with a higher statistical significance (i.e. a higher z-score) 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 50-20-2 (71%) that is derived using 10 factors isn’t as predictive as the 62.6% home underdog situation that I presented above, which has just 6 parameters (this game home, this game dog, won last game, dog last game, win percentage 60% or lower, and opponent does not have revenge) 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 (i.e. more back-fitting). Further research I’ve done 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). For instance, I can now tell you that a situation with a record of 140-60-5 ATS that uses 6 parameters has a 55.1% 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 (or whatever sport the situation pertains to). 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.

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