Meet a Creator: Jaime Brugueras

Rokfin
Rokfin
Nov 7 · 6 min read

Sports betting is reaching the mainstream as more states in the U.S. legalize sports gambling.

Rokfin has its own resident NFL sports betting expert in Jaime Brugueras, who serves as VP of Product by day and super sharp by night.

Brugueras has been sharing his weekly NFL picks via NFLPickles.com for years, and his in-depth machine learning analysis is now available exclusively through his Rokfin channel.

Below is a Q&A with Brugueras to help you understand his approach to NFL picks and how he began “sports investing,” as he refers to it.

Jaime Brugueras, the man behind NFL Pickles

How long have you been handicapping NFL games?

About 12 years. NFLPickles.com started as a personal notebook to tally my own results and document my thoughts on every game. That hobby turned into a passion. The last 5 years, I have sold my NFL picks for $99 for the postseason. This is a deal compared to other NFL handicappers. But now all my NFL regular season and postseason picks are available exclusively on Rokfin for $9.99/mo.

I’ve enjoyed being very transparent with my picks and win-loss record. Other handicappers are always on a “streak.” For me, some weeks are bad and some years are bad. But in the long run, my model and the results consistently beat the lines set by oddsmakers.

Why did you choose the NFL vs. other sports leagues?

Other sports require player level data and day-to-day attention. For instance, in baseball you need to track who is pitching each game. That’s not as much fun to me, plus I hold a day job. Football is once a week, so it takes less time.

What qualifies you as an expert?

In 2005, I received a PhD in Mathematics from the University of Illinois at Chicago. My thesis was on Statistics (now called data science) and Game Theory. I have spent the last 15 years building analytical products, and I’ve seen the impact that data-driven decisions have on business performance. I’ve used business intelligence and data science to help businesses build successful products, understand customer needs, and measure marketing impact.

Once I attended a conference where a well-respected professor explained the results of his latest paper, which was forecasting figure skating scores by taking into account some judge bias. That professor and I talked for hours about applying his idea to the NFL. Using his methodology as a starting point, I developed and refined algorithms to predict the NFL spreads and understand oddsmakers’ accuracy. I have built more than 40 different models and have stuck with the Top 5 performers. I look at their results every week.

What are the levers in your statistical model?

Offensive and defensive stats (e.g. passing yards per game, points scored, rushing yards allowed, etc.), home field advantage, divisional games, and TV primetime games are some of the levers that factor into the forecasting models.

How far back does your model go? Which NFL season?

I use 20 years of football data, scores and Las Vegas point spreads. Of course, predicting the point spread with data from 20 years ago is useless, but the historical data is useful in understanding the accuracy of the model over time. In data science, we call that backtesting.

Backtesting is my special sauce. It tells me in which situations my model is very accurate. For example, when a team with a strong offense faces a weak defense, and the spread is less than 7 points, and I predict the spread should be more than 10 points, my model’s accuracy is 64%.

I have dozens of similar situations that I measure to see the percentage of times where my prediction is most accurate. Then I tie that info to current games and situations. On average, my model finds three or four games each week where the situations cover the spread 58% of the time or more, which is the minimum threshold I use to select my picks.

What do you think is the most overlooked variable when NFL oddsmakers set the lines for games?

Home field advantage is overrated. Oddsmakers often assign home field advantage 3 points. One of the situations I like betting is when there is a road team that is favored to win by one or two points. This typically means that oddsmakers know the visiting team is superior, but they’re afraid to push the spread higher. Backtesting this scenario, my model is successful 65% against the spread.

What is your career record against the line for NFL games?

  • 11–1 against the spread (ATS) in the last 12 Super Bowls.
  • 64% ATS during the postseason
  • 55% ATS during the regular season

With more data, your model should improve. So does your record vs. the line typically get better as the NFL season progresses?

Yes, definitely. For forecasting the week’s spread, I only use the last 12 to 18 weeks of play and recent games receive higher weights. The further into the season, the more of those weeks fall into this year. By the time the playoffs arrive, only the current year’s data is used.

Legal sports gambling is becoming more commonplace. What do you envision the sports betting landscape in the U.S. to be like in 5 years, 10 years?

The Supreme Court overturned PASPA, opening the path for states outside of Nevada to decide whether they want to legalize sports betting. I think in 5–10 years we will have more states legalize sports betting, similar to marijuana.

You predict the line based on the data, while oddsmakers set the line based on bet volume. Explain why this is such an important distinction.

The point of using machine learning to predict NFL games is to find inefficiencies in the spread. One of those inefficiencies is the fact that the spread is based on bettors’ perceptions about where the money is going. For example, an oddsmaker might think that a 3-point spread will divide the population in half, therefore guaranteeing profits for the sportsbook. But they know that in reality, one team is better and should be favored by 5 or 6 points.

Other types of inefficiencies that my model finds:

  • Recency bias: One team blew out another team in the previous week, so now they’re heavy favorites.
  • Home field bias: We’ve talked about this above.
  • Big market bias: Teams in big markets get more bets.

What are your Top 5 tips for novice NFL bettors who are just getting started?

Follow these rules:

  1. Don’t bet with emotions (your favorite team, etc.)
  2. Do the research: injuries, historical trends, etc.
  3. Set up a bankroll at the beginning of the season and bet a percentage of that (less than 30%) every week.
  4. Realize that anything can happen. There is no sure thing. Expect to lose a “sure” win.
  5. Buy the NFL Pickles picks from Rokfin.com/nflpickles for only $9.99/mo.

If someone subscribes to Rokfin to see your picks, what can they expect? Why should they care about your picks vs. other experts’ picks?

My subscribers are people that love betting and love football, but they don’t have the time necessary to do the research and analysis. Or they love betting, but they can’t seem to find a way to a positive return on investment (ROI.)

Following my picks, subscribers can expect to win money over the course of the season and to come out on top at the end. I haven’t seen anyone do this type of analysis on the point spread except for me. The situational analysis is unique to NFL Pickles and it’s been delivering winners for more than 12 years. And they can expect transparency. If I’m on a streak, I’m on a streak. My results are there for everyone to see.

Subscribers can expect an article with my NFL locks of the week. These are the games where the model is expected to win ATS 58% of the time or more. In the article, I explain why those picks made the cut, looking at ATS trends using https://killersports.com/nfl/query, finding out where the money is being bet and other related news.

Additionally, I do a short podcast going through the 3–5 NFL games that seem interesting for the week. In the podcast, I’m accompanied by high-stakes poker player Scott Augustine, who challenges my picks and brings his own betting and football expertise to the table.

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