What is Technical Sports Analysis?

Technical sports analysis is a method of evaluating teams by analyzing the statistics generated by team activity, such as past winning percentages and point margins. Technical analysts do not attempt to measure a team’s intrinsic value, but instead use charts and other tools to identify patterns that can suggest future activity.

Just as there are many analysis styles on the fundamental side (DVOA, Sabermetrics, Sagarin), there are also many different types of technical methods. Some rely on chart patterns, others use technical indicators and oscillators, and most use some combination of the two. In any case, technical analysts’ exclusive use of historical wins and scoring data is what separates them from their fundamental counterparts. Unlike fundamental analysts, technical analysts don’t care whether a team is undervalued - the only thing that matters is a team’s past scoring data and what information this data can provide about where the team might move in the future.

The field of technical analysis is based on three assumptions:

1. The standings discount everything.
2. Scoring moves in trends.
3. History tends to repeat itself.

1. The Standings Discount Everything

The main criticism of technical analysis is that it only considers scoring movement, ignoring the fundamental factors of the team. However, technical analysis assumes that, at any given time, a team’s trend reflects everything that has or could affect the team - including fundamental factors. Technical analysts believe that the team’s fundamentals, along with broader competition factors and league psychology, are all evaluated into a team’s won-lost record, removing the need to actually consider these factors separately. This only leaves the analysis of scoring movement, which technical theory views as a product of the competition for a particular standing in the league.

2. Scoring Moves in Trends

In technical analysis, scoring movements are believed to follow trends. This means that after a trend has been established, the future scoring movement is more likely to be in the same direction as the trend than to be against it. Most technical analysis strategies are based on this assumption.

3. History Tends To Repeat Itself

Another important idea in technical analysis is that history tends to repeat itself, mainly in terms of scoring movement. The repetitive nature of scoring movements is attributed to league psychology; in other words, league participants tend to provide a consistent reaction to similar league stimuli over time. Technical analysis uses chart patterns to analyze league movements and understand trends. Although many of these charts have been used for more than one hundred years, they are still believed to be relevant because they illustrate patterns in scoring movements that often repeat themselves.

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So, now that you know what technical analysis is, I can tell you about the Walters Trend Method.

Several years ago I was given a book of NFL stats and figures called Pro Football Revealed 1996: The 100-yard War. I was interested in stats as a kid so it was a great gift. At the end of the book there were several essays about various statistical findings, fun facts, etc. One essay was about a “foolproof” method for predicting Super Bowl winners. The author broke down the correlation between inter-conference play dominance and Super Bowl wins, postulating that the dominant conference almost always wins the Super Bowl. This meant that in any given year, you could look at inter-conference records between conferences and if the AFC led in ICPD during the year, they’d probably win the Super Bowl. It wasn’t until two years ago that I really caught hold of that concept and ran with it.

The first thing I did was break down the same stats into a table. The finished product was a list by year of each conference’s stats in categories like inter-conference play dominance (ICPD), how many teams in each conference won 10 or more games (10+), and how many eams in each conference were .500 or better (.500+). Just like the book said, the correlation was pretty clear. From that list I created a graph with binary data, i.e. if the NFC won the Super Bowl in 1994 they received a value of 1 for that year and the AFC received a 0. I also created graphs with ICPD data, 10+ and .500+ data. When I assigned order 4 polynomial trend lines to each set of values, the trends matched up almost perfectly. I knew there had to be something more there.

Needless to say, the past two years have brought a lot of new information that has helped to bring out WTM. I found that you can keep track of an individual team trend by simply plotting out their winning percentages by year. Thanks to some research into stock market analysis (yes, stock market analysis) I was able to create a rating system based on High, Low and Closing margins of lead or loss. Research into polarity in international relations shed light on a formula that would quantify the amount of power a certain team held in relation to other teams in the league, and even estimated the overall level of parity in the league at any given time.

WTM is more than just stats and estimated wins. It’s a system that tells not why, but when you can expect a certain team to perform well or vice-versa. Last year I successfully predicted a Super Bowl victory by the Indianapolis Colts, in November when everyone was on the Bears bandwagon. By the end of the year I was calling for regression in 2007 by the Saints and Chargers…look where we stand in week six of the very next season. I even predicted a rise in wins for teams like Tampa Bay and Washington. Every day I figure out something new, so make sure to check back often for the latest and greatest technical sports analysis.

-Doug Walters, The Football Professor



KEY TERMINOLOGY:

RSI - Relative Strength Index - a measurement of a team’s momentum derived from their average gain and average loss over a certain period of time. Teams generally fluctuate between RSI values of 45 and 55.

MA - Moving Average - shows the relationship between a team’s long term movement and short term movement. Crossovers signify fluctuations in the team’s trend and can be used to forecast winning and losing streaks.

Trend Line - generated by using a polynomial equation (anywhere from 2nd order to 6th) or combination of equations. The line generated by graphing the results of the equation/s show movement over time and can be used to forecast winning and losing streaks.

Candlestick Chart - a chart that shows the High, Low and Closing values for each game or season for a particular team.

CF - Closing Factor - the relationship between a team’s average High, Low and Close. The closer a team’s average Close is to their High, the better the team is and vice-versa.

SF - Stability Factor - the relationship between a team’s average High, Low and Close to the league’s maximum High, Low and Close. Teams that win by blowouts are more stable than teams that win in comebacks or narrow victories and can therefore expect more wins.

Regression to the Mean - in theory, teams fluctuate between emotional and physical peaks and valleys. The fluctuations are for the most part predictable as it is extremely difficult to maintain an emotional or physical high or low for very long. It is also hard to break the cycle and establish consecutive peaks or valleys.

Mallios - Dr. William Mallios was the first person, that we’ve found, who made the connection between stock market analysis and sports analysis. He created candlestick charts using the High/Low/Close margin method and used them to refer to betting lines and gambling strategies.

Singer - J. David Singer created a group called the Correlates of War which analyzes international relations. He created a formula that determines how much parity there is between a group of nations, which we modified and now use to measure parity in sports leagues. We call this Distribution and Concentration of Power.