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Understanding 'Luck' and Making It Work For You
Two Sundays ago, during the end of the Dallas-Philadelphia game, the Cowboys were down 31-24 and driving into Eagle territory with about 30 seconds on the clock. Though Dallas were long shots to score a TD and tie things up, those "wise guys" sitting on the teaser of Dallas +1.5 up to +7.5 were already counting their winnings.


While a bad-beat like that may leave a horrible taste in the mouth, it's surprising how quickly players forget about the impact of luck on a team's performance. Players might later review Dallas's schedule and note the Cowboy's win/loss record, or the points for/against, but miss the subtle nuances of outrageous misfortune - like the 102 yard TD interception.

In a very long season - such as the NBA or MLB - the good and bad breaks tend to balance each other out. After 30-40 games, you get a fair measure of a team's ability. It's more dangerous to ignore luck's affects on an NFL team due to the short season; most successful bettors appreciate how luck adds another dimension to a team's win/loss record.

There are several occasions each NFL season where the better football team does not win because of a lucky or unlucky play that defies reason. We've all seen miracles and disasters cause small fortunes to change hands between bookies and bettors, but still many players ignore this factor when evaluating teams.
In the same way that players perceive a pattern at the roulette table when the ball lands on black for six consecutive spins (there's actually no pattern, just a sequence of random events), many players try to deduce a "pattern" in lucky or unlucky teams. However, the results are better explained by chance.

The "bean counters" do this with correlation tests, trying to validate (or refute) the idea that "bad teams throw more interceptions" by looking at the results of teams in prior seasons. Do bad teams throw more interceptions, or are there simply unlucky events that make a team "look bad"? This question can be answered even if you have no idea what a "correlation test" is.

First off, identify the factor you want to test for luck - in this case, interceptions. Look at league-wide statistics for the first eight weeks of the prior season, to see which teams had the most, and least interceptions. Divide all NFL teams between "good" and "bad" based on interceptions thrown. If they threw more than average, put them in the "bad" group, and otherwise in the "good" stack.

Then utilize the same process while examining the last eight or nine weeks. If the same 16 teams are bad in both halves, it would suggest a very high correlation, meaning that a team that throws picks will probably keep doing so. If exactly half of the "bad teams" from the first half of the season are bad in the second half (regarding interceptions), it would suggest the event is purely random (since a "bad" team in the first half of the season is equally likely to be good or bad in the second).

Practicing this test on NFL teams, will show that interceptions thrown by a team are almost completely random. In fact, you'd also find similar results for all forms of giveaways and takeaways - there's less than a 20% correlation. So how do bettors turn this knowledge into money? Simply recognize when teams are overrated or underrated because of the impact on perception of good or bad luck.

A team with a high turnover differential looks good in a box-score, but it's just as likely to be positive as negative on turnovers going forward. Historical scores do not reflect that, and make these teams (like St. Louis) look much stronger than they actually are. Similarly, teams with negative turnover differentials (like Oakland and Cleveland) tend to outperform the public's expectations (and hence, the spread).

There are other calculations bettors can do that will give a statistical edge in sports betting. Identifying results that are unusual due to luck will put you in position to evaluate teams better than the market as a whole. With any statistic you can isolate and analyze - from the Colts' 3rd-down conversion rate to anomalies in field goal kicking - if you can recognize luck, you can make money off it.

Real statistics geeks can go further with the luck factors identified. Advanced correlation can tell you how much difference a lucky turnover change makes to an NFL game (it's actually about 3.5 points). Or, a hit batter in baseball, which accounts for about 0.5 runs. If you can quantify how much these lucky/unlucky instances change the event outcomes, you can "adjust" your statistics accordingly and gain a better measure of what truly happened. If your assessment is more accurate than the market, you will profit.

What are our players betting?

Miami -17.5 -109 at Duke

Texas -6.5 -105 at Nebraska

San Diego Chargers -5 -103 at Kansas City Chiefs
The sharps were split evenly on the opener of Chargers -4.5 with some sharps buying the Chargers down to -3.5. When we see this type of activity, one of two things must be true - our push percentages are wrong or their numbers are wrong. Although there's fairly balanced action in terms of volume, the public clearly favors the Chargers as we've taken nearly 20 times as many bets on San Diego as Kansas City.

Carolina Panthers +3 +105 at Cincinnati Bengals
We opened the Panthers at +3.5 (-112) and saw a fairly rare event - the sharps AND the public backing the same side. With nearly three times as many bets on Carolina as Cincinnati, this price has drifted down. Similar to the San Diego game, we're seeing some of our sharper players buy through the "4".

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