2014-2015 Bowl Pick'em Picks: Large Pool

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 Large Pool 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 large 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 Large Pools

Once we get into the 300-500 person range, optimal strategy shifts more to a boom-or-bust mode. A 95th percentile finish in a 400-person pool probably won’t even crack in the top 20, so we need to start going for broke a bit if we want to maximize our odds to cash.

The biggest risk with large pools, typically, is the opposite of small pools -- not taking enough risk. Just throwing a few minor twists into a conservative strategy isn’t going to cut it. So we’re going to make some more significant changes, and live with the risk that our highly aggressive strategy may well bomb if things don’t pan out in several key games.

And in this case, we’re also going out on a limb more to get some additional upset picks into the mix.

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.

For large pools, we’re still going to take a few riskier upset picks, but it’s likely to be fewer this year than in future years.

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

  • To ramp up the risk/reward scale, this set includes picking upsets of the two riskiest TR favorites out of those that are severely overvalued by the public: LSU (65% win odds, 86% pick rate) and Oregon State (58% win odds, 74% pick rate)
  • All TR favorites whose win odds are at more than 10% higher than their public pick rates have gotten a confidence point boost compared to a conservative strategy. This includes Arkansas State (68% win odds, 28% pick rate), Rice (54% win odds, 24% pick rate), Western Kentucky (69% win odds, 53% pick rate), and Louisiana Monroe (70% win odds, 56% pick rate).
  • Many TR favorites that are overvalued by the public in terms of both pick percentage and confidence points have been dropped down the list. This includes Florida (77% win odds, 97% pick rate), Georgia (76% win odds, 95% pick rate), Cincinnati (68% win odds, 86% pick rate), and Stanford (69% win odds, 87% pick rate).

A Closing Point On Expectations

An average picker will win a 500-pool...pretty much never. Our ROI projections indicate these picks should increase give you an 8-10x edge, however.

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Large Pool Picks

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