2012-2013 Bowl Pick'em Picks: Conservative / Small Pools
NOTE (Fri Dec 14, Noon ET): Several pick set writeups have been posted, along with comment boxes so you can ask questions specific pick sets. Pro and All Star subscribers also have access to the Q&A Discussion Forum for any more general questions.
Our picks are computer-optimized picks. To create them, we built a computer simulation engine that plays out thousands of hypothetical bowl pick’em contests. For each of these contests, we first simulate how a panel of fictional pool opponents will make their picks, then we project how the 2012-2013 bowl season will play out.
The last step is to iterate through a vast array of potential pick sets you could play in these hypothetical pools, in order to find out which pick sets most often outperform the fictional opponents. This pick set was one of the winners for small pools.
The entire process takes days to run and incorporates a variety of objective data, including public picking trends (published by several large sports sites, but adjusted by us for some artificial biases), Vegas odds, and game predictions from our proprietary mathematical models.
The Data Rules
A lot of people have preconceptions about how to approach bowl pick’em contests. Those preconceptions are often based on what’s happened in past bowl seasons, what the popular sports media are saying about this year’s games, or gut instinct about “intangibles” such as which teams are more likely be motivated for their bowl games.
The objective approach we apply, on the other hand, has no preconceptions. Instead, it evaluates hard data to uncover whatever inefficiencies happen to exist in each unique bowl season. Every year, the dynamics of bowl contests are different. There can be lots of evenly matched teams, or lots of lopsided matchups; lots of big name teams playing no-name schools, or few such games. Oh, and we always love to see games featuring mediocre teams from big name conferences. These types of factors will impact your optimal bowl pick’em strategy.
If you blindly apply the same tactics year after year (e.g. “With 35 games, I’m going to pick at least four upsets”), you’re putting yourself at a huge disadvantage. If your goal is to win your bowl pool, all that matters is making decisions that increase your chances of winning. And with billions upon billions of potential combinations of bowl picks to make, computer simulation is an ideal solution to identify pick sets that should give you a big edge.
So as you review these picks, feel free to ask us questions about why we did this or didn’t do that. After researching contest strategy for many years now, we can usually explain why a particular idea makes sense or not. However, for more nuanced questions, sometimes the answer is simply, “Because we simulated a similar approach for pools of this size, and it didn’t perform nearly as well, no matter how nice it sounds in theory.”
General Strategy For Small Pools
If you’re in a bowl pick’em pool of 10 to 50 people, the biggest threat to your success is almost always being too risky with your picks. A smart, conservative strategy often finishes in the top 5-10% of a national bowl pick’em contest like ESPN or Yahoo!, and that’s typically good enough to win a 10-person pool, or to have a decent shot of finishing in the money for a 25 to 50-person pool.
Why mess up those odds just because you feel like conservative picks are too plain-Jane? As long as the numbers back up the strategy, we love being boring.
Plus, opportunities usually pop up where our models like a team to win even though they are a slight Vegas underdog, or where the public seems to be significantly overrating or underrating a favorite in terms of confidence points. These are the types of value opportunities you can exploit to create some positive-ROI differentiation in small pools without taking on too much risk.
A Quick Thought About Upsets
Still, we tend to get a decent amount of feedback related to upsets, or more commonly, about the perceived lack of upsets in our various strategies.
One reality is that with 35 winners to pick and confidence points to assign to each, the majority of your competitors have way too much information on their hands to process anyhow. They’re going to make a bunch of suboptimal decisions in terms of assigning confidence points, and they’re also likely to get riskier than they should regarding picking upsets themselves. So we prefer to stand by and watch as they shoot themselves in the foot.
Another reality, specific to this year, is that the 2012-13 college bowl season is projected to be one of the most lopsided in recent memory. As a result of many games having a solid favorite, the list of underdogs with a great shot to pull off an upset is thinner than usual. So our strategies this year had to recognize and adjust to this case; it’s much harder to find high-value, low-risk upset picks in this year’s bowls.
Confidence Points Make Things Trickier
Evaluating value pick opportunities in confidence point based pools is a lot more complex than for non-confidence pools. In a traditional pool, it’s pretty clear how to act on value scenarios. If BYU has 55% win odds against San Diego State, but only 45% of your opponents are picking BYU to win, with few exceptions, BYU is going to be a good pick in a non-confidence pool.
Confidence points adds a new dimension, though. What if the 45% of people picking BYU to win have all done so with the maximum of 35 confidence points, and the 55% picking SDSU have also done so with 35 points? In this case, taking BYU with much lower confidence than 35 may be the best call, but it also depends on what else you’ve got going on with other picks.
Notes About This Pick Set
- Since this set is for small pools, it basically reflects making picks by our TR model win odds in descending order, with a few confidence adjustments.
- However, it raises confidence points on several teams that are undervalued by the public, namely Arkansas St (68% win odds, 28% public pick rate), UCF (41st of the 70 bowl teams in average confidence points, but 17th highest projected win odds) and Western Kentucky (69% win odds, 53% public pick rate).
- Similarly, it lowers the confidence points for Arizona (75% win odds, 86% public pick rate), Cincinnati (68% win odds, 86% public pick rate), Boise St (71% win odds, 85% public pick rate) and USC (77% win odds, 87% public pick rate) because they are all overvalued by the public.
A Closing Point On Expectations
An average picker will win a 10-person pool once a decade, and a 50-person pool twice a century. Our ROI projections indicate these picks should at least double those odds, assuming you’re playing against average competition.