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Understanding Luck and Making It Work For You

A few seasons ago, during the end of a 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.

With Dallas out of timeouts, a few more incomplete passes, a touchdown, or even getting tackled in bounds would end the game and give many of these players a chance to cash at the window with a smile on their face… Then the unthinkable happened!!! A Dallas pass in the end zone was intercepted and returned 102 yards for a Philadelphia touchdown. Whether you call this a highly improbable occurrence, or just plain bad luck, either way all those Dallas teasers bit the dust and those wise guys lost out.

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 players appreciate how luck adds another dimension to a team’s win/loss record.

There are several occasions each NFL season where a 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 books and players, 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 more easily explained by chance.

How do handicappers decide if a result is due solely to luck or from the quality of a team that finds a way to win? Many professional players do extensive statistical testing of a single factor that might be heavily affected by luck. This approach determines whether a team that has shown a certain tendency in the past (such as throwing many interceptions) is likely to continue in the future.

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 players 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 players can do that will give a statistical edge in sports . Identifying results that are unusual due to luck will put you in position to evaluate teams  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 measure of what truly happened. If your assessment is more accurate than the market, you will profit.

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