On September 7, the NFL Season got underway when the Detroit Lions beat the Kansas City Chiefs 21-20 in an upset. The Lions opened as a -6.5 underdog against the spread. Not only did the Lions win the game, but Detroit paid backers handsomely. In this blog, we use the Lions versus Chiefs game to analyze how big data could have pointed to high-probability betting decisions. We also list some of the pitfalls sports bettors face if they overanalyze big data.

The Role of Big Data in Sports Betting:

What is big data in sports betting?

Big data is simply a term used to describe data sets that are too large or complex to be dealt with via old school applications.

When it comes to sports betting big data, we’re talking the data that’s underneath the apparent data. For example, we can say Kansas City is 9-1 against the spread in their last home games.

But the big data underneath paints a fuller picture. KC might be 9-1 against the spread in their last 10 home games and the 1 loss happened versus a quarterback with a QB rating threshold.

Or a big data point could be Kansas City is 9-1 ATS in their last 10 home games, but all 9 wins came versus AFC opponents, or to dig deeper into a big data point, AFC opponents that run spread offenses.

None of the above big data points are true, but it gives us an idea of how big data in sports betting works.

Examples of big data information from KC versus Detroit that could have swayed live betting decisions

Travis Kelce, Patrick Mahomes’ top target since he took over as starting quarterback for the Kansas City Chiefs, didn’t play in the game. Once oddsmakers saw Kelce wasn’t going to step onto the field, the line dropped from KC -6.5 to Kansas City -4.

The line fell even further in some sportsbooks to KC -3.5. So an important data point for sports bettors was how many times has Kansas City covered when Travis Kelce doesn’t play?

When it comes to live betting, there are plenty of data points we could have considered. One swung the game in Detroit’s favor, could have had to do with dropped passes. KC player Kadarius Toney had three dropped passes. One of the dropped passes resulted in a pick-6 that tied the game at 14-14.

How many dropped passes does Kadarius Toney have as a starter? Digging deeper we could ask, how many dropped passes does Kadarius Toney have as a starter in home games?

Another big data point that could have helped us make live betting decisions is how successful is David Montgomery getting the ball over the goal line when carrying inside the 10-yard line? Montgomery scored the TD that gave the Lions the victory.

Big data sports betting data point creation

There is no place to find data points like the examples in this blog. AI programs, which use neural network machine learning that mirrors how our brains work, could create data points based on past information and results and make predictions.

Right now, though, there is no site where we can grab specific data points like the ones outlined above. But we can create big textual data points.

Travis Kelce caught 110 passes last season. It was the most passes Kelce has ever caught in his career. Last season was also the first in a while where Patrick Mahomes didn’t get a chance to throw to Tyreek Hill.

So the fact Kelce was out of the game should already have us leaning towards backing the Lions. Another factor? Patrick Mahomes changed left tackles this season from Orlando Brown Jr. to Donovan Smith.

So we could have created a textual data point that says something like, “Patrick Mahomes and the Kansas City Chiefs struggle on offense when Mahomes plays in the first game with a new left tackle and Travis Kelce doesn’t play.”

Big Data Pitfalls: The human element in sports betting

More big data exists for sports than ever before, but it’s difficult for sports handicappers to find winning bets. Sports are played by human beings or in case of horse racing, animals and human beings.

Animals and human beings are flawed and highly unpredictable, which is why underdogs win on the field and long shots take down favorites at the racetrack. We must take that into consideration.

We must also beware of overthinking. Detroit averaged 26 points last season. Their opponents averaged 25. The game ended Lions 21 and Kansas City 20, a 1-point difference.

So we can’t get too much involved in analyzing big data. Instead, we take the data points necessary to make an informed betting decision. Finally, and this is ultra-important, gut handicapping is still more powerful than big data. The reason is that since we’re dealing with human beings and animals, we understand emotional factors that even AI big data programs can’t assess. So go with your gut, always, before leaning on big data analysis.

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