This website is for a project called “ego Analytics” (evenly gifted organizations). It is based on the idea that every NFL organization originally started with the general resources as the other organization did, specifically the same draft capital and cap space, and how NFL teams have used those resources over time have determined their relative levels of success.

The name is additionally a reference to how many front office decisions are ego-based, such as holding out for a new contract. Sports analytics typically focuses on just what happens on the field, but ego Analytics includes what happens off the field. These off-the-field actions by NFL front offices are guided by rules, and understanding these rules for and the resulting actions of front offices can help predict what should happen on the field.

The cyclical nature of performance that NFL teams typically experience is driven by the structure of the league. If one can find a way to measure and track the cycles of the organizations, one can then predict the future with some increased level of accuracy.

This project is designed to do just that – an economic model of metrics for coaching staffs, front offices, and the organizations overall. Using those metrics, one can generate predicted win totals for each team. Then one could bet Win Total Futures using those predictions.

Due to a high level of aggregation and more level playing field, Win Total Futures are high value bets. They are the virtual opposite of a “prop bet,” which has the lowest aggregation and can sometimes be based on a single play or part of a play. This level of aggregation makes prop bets essentially random and nearly impossible to predict.

The results below are based on 1 unit bets on every bet. The table also includes IMP (Impact on the Model’s Performance), which summarizes the external conditions that affect the results of the model, and the variables that go into IMP (Stability, Power Utilization, and Upset Luck). The range of IMP is roughly 60-100, and the long term average should be around 82.

Menu Options

Specific Season Homepages: overview of the metrics for each NFL organization and the betting results

How It Works: overview of the project and the metrics that make up the economic model

Model Evaluation: describes various methods to evaluate the accuracy and effectiveness of the model

Organizational Scores: in-depth view of those metrics which includes the lower level metrics upon which those higher level metrics are generated

Organizational Graphs: visualizations of the Scores over time for each of the NFL organizations

Heat Check: a metric created to determine how likely a Head Coach or General Manager is to be fired

Gambling Performance: in-depth view of the Win Total Futures (Over/Under # of Wins) performances for each season

Miscellaneous Information: definitions, sources, and additional content using the metrics of this project

Legend for the Table Below

The green section shows the elements external to the model and their effects on the performance of the model

The blue section shows the results of the model via Win Total Futures betting

The purple section shows the return of the S&P500 during the 2nd half of the year (coinciding with the season)

The red section shows the return of the Nasdaq Composite during the 2nd half of the year (coinciding with the season)

The orange section shows the results of the “Top 20” bets for the model in each season

These bets are based on the 20 largest differences between ego prediction and Win Total line (see specific years)

This style of betting has its advantages and pitfalls in that they should be the highest value bets, but in bad years, the profit can be significantly lower due to risk consolidation

The yellow section shows the most equal comparison to the results of this project in the form of hedge fund performance (uses https://hedgefollow.com/top-hedge-funds.php) *Note: ranking only includes the 20 “Most Popular” funds*

The website above uses the Top 20 investments to rank hedge funds (to compare with Top 20 of this project)

These returns are not a 1:1 comparison, as they are over years and not individual 6 month periods