Trevor Hardy is the CFL's Director, Salary Expenditure Reporting, responsible for the administration of the league's Salary Management System. A Chartered Accountant, He is also one of approximately 225 designated CA specialists in Investigative and Forensic Accounting in Canada. Trevor is an expert in the use of analytics, statistical modeling and forecasting.
The annual CFL bye weeks are upon us. More than anything, the bye week is valuable in that it allows clubs some time to rest and heal, as they gear up for the drive to the Playoffs.
Teams will also use this time to assess what has gone right and wrong during the first seven weeks of the season. It is in that spirit that we at CFL.ca have decided to do the same with our mathematical Power Rankings. How have they performed? What has gone right and what has gone wrong?
Remember that this year, the CFL Power Rankings are being determined using two methods. The first method has Matt Cauz, resident CFL.ca expert, providing his subjective rankings and commentary each week. The second method uses a pre-determined mathematical formula to estimate the scores each week based on nine key statistics.
The Mathematical Formula
This pre-determined mathematical formula was obtained by using a statistical tool known as regression analysis and is based on information gathered from all regular season games played during the 2004 to 2010 seasons, inclusive – 1,044 observations in total.
Regression analysis is an extremely popular and important statistical tool that is used in a wide range of professions and disciplines. Any valid regression analysis has the potential to tell us two important things: whether a certain variable has “predictive value” and how much impact that variable has on a result.
With this in mind, our regression analysis first suggested that – of all the statistics in football – the nine most “predictive”, or important, indicators of team success were:
1. Quarterback Efficiency Rating
2. Time of Possession, as measured by percentage
3. Rushing Yards
4. Turnovers on Downs
6. Sacks Taken
7. Punt Return Yards
8. Kick-Off Yards
9. Kick-Off Return Yards
The impact that these variables are estimated to have on the outcome of a game give us our “Mathematical Formula”, as follows:
Quarterback Efficiency Rating x 0.137, plus
Time of Possession (%) x 8.602, plus
Rushing Yards x 0.027, less
Turnovers on Downs x 1.299, less
Fumbles x 0.499, less
Sacks Taken x 0.629, plus
Punt Return Yards x 0.017, plus
Kick-Off Yards x 0.048, plus
Kick-Off Return Yards x 0.017.
In other words, what this means is that every Quarterback Efficiency Rating point is worth .137 points in any given football game, every Rushing Yard gained is worth .027 points in any given football game, and so on.
Each week, we’ll determine a club’s predicted offensive score by inputting the statistics from the game into the formula (the defensive score is simply the opposing Club’s offensive score). In fact, we encourage you to try it yourself over the rest of the season so you can follow along!
The following table summarizes the results of the mathematical formula by game, and compares the results to the actual score:
Summarizing these results, the mathematical formula has “predicted” the correct winner 27 out of 28 games – the only game where the incorrect winner was predicted was Game #28, where the nine statistics told us that Saskatchewan should have won the game against Calgary 30 to 25. Instead, they lost 22 to 18.
Overall, the mathematical formula has done an outstanding job of predicting scores – whether they are close games or lopsided games. For example, Game 14 between Calgary and BC was an extremely tight game. The formula reflected this, with a score differential of one point. On the other hand, Game 19 was a rather lopsided game in Hamilton’s favour, and the formula reflected this outcome, as well.
We hope that you enjoy reading the CFL Power Rankings each week – we encourage you to continue to track the results and remember to provide us your feedback, as well!
|5||Devon Bailey||St. Francis Xavier||WR|
|9||Matthias Goosen||Simon Fraser||OL|