Sports Geekery

Following Sports with Mobile Apps, Computers, & Gadgets

OSCAR College Football App for iPad Analyzes Rankings and Predicts Game Outcomes

Are you fascinated by College Football computer rankings? Who has strongest schedule and strongest conference? With the OSCAR College Football iPad app, you can generate computer rankings based on a variety of algorithms for the present day or any day in college football history. You can also use the same algorithms to predict the outcome of future games.

The concept behind the app is that not only are rankings based on purely on experts voting biased, computers rankings are also biased. Different computer algorithms place emphasis on different factors. Should margin of victory be heavily weighted? How about home field advantage? Should a victory last week carry more weight than one early in the season? Or should they be equal?  OSCAR gives you a way to apply six different algorithms and compare any two teams for present day or on any given date going back as far as 1869. Yes, back to the 19th Century.


Choose any date in history and see how the different algorithms rank the top 25. Here we see how OSCAR’s default algorithm saw the raknkings after the BCS National Title Game last January.


You can set OSCAR to run calculations for any date in history.

After taking a few minutes to get the hang of the app, I decided to see how OSCAR viewed the 1994 season. Why? The BCS Championship Series had yet to be created and thus #2 Penn State and #1 Nebraska were not able to be matched up for a National Championship game due to conference affiliations with specific Bowl games. Both teams were absolute powerhouses and college football fans were denied what could have been one of the games of the decade. Each team won their Bowl games handily and Nebraska was voted the National Champions.

Interestingly, OSCAR has Penn State ranked ahead of Nebraska in all 6 algorithms at the end of the year. I could see this being a lot of fun for generating arguments (or lively discussions?) between college football fans!



Want to see how the conferences stack up? Due in no small part to the strength of both Stanford and Oregon in 2010, the Pac 10 comes out on top in many of the end of year rankings. Earlier in the season, however, they were a distant 3rd to the SEC and Big 12.



The Matchup feature can be used to predict outcomes of theoretical matchups or the generate realistic predictions for upcoming games. In the example below, you can see that before the National Championship game, Oregon was a favorite using algorithms that put more emphasis on margin of victory.


Other algorithms, such as the Power Rankings which rank recent games more heavily, and PC, which discounts margin of victory, liked Auburn as a favorite.


In hindsight, we all know Auburn won 22-19. Factoring in that game, the Predictive algorithm that showed Oregon as a favorite on a neutral field, now has Auburn as a very slight favorite.


I found OSCAR to be a fascinating app. I began playing around with 2010 rankings and dates with specific interest in the National Championship participants Oregon and Auburn and quickly became absorbed in seeing how the results of different algorithms change from week to week throughout the season. It was quite interesting to see that a couple of the algorithms have Stanford ranked #1 at the end of the year. As a college football fan I’m looking forward to utilizing OSCAR throughout the season and seeing how game outcomes effect various rankings. One thing to note when analyzing historical data with OSCAR, the teams are placed in the conferences they played in at that specific time in history. For example, if you analyze 1994, you’ll find Nebraska in the Big 8 Conference.

You’ll want to take 5-10 minutes to sit down and read the documentation before you really dive into it so you understand how the app functions and how to use it. The app is iPad only and costs $2.99 in the App Store. You can find a couple of quick video demos on their Facebook page.

Download OSCAR College Football App for iPad from the App Store.

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