I can’t recall where I first heard it, but my favorite fantasy draft quote is “you can’t win your league with your first pick, but you can lose it.” Drafting a stud in the 1st round does not guarantee a championship. In most cases, the league champ hit on a sleeper in the later rounds and picked up important pieces on the waiver wire. On the other hand, when your 1st round pick is a complete bust, it can be a crippling blow. This is why I have always prioritized safe, conservative picks in the 1st round over players who might carry more risk. In the article below, I examine the fantasy football value and risk between quarterbacks, running backs, and wide receivers using preseason rankings from ESPN.com and season-long scoring totals from pro-football-reference.com.
Fantasy Points Above Expectation (FPAE) is a metric we created to better capture how much fantasy production each team is giving up. FPAE measures how many fantasy points a team gives up to a certain position, relative to what they were expected to give up. For example, Miami’s FPAE against QBs in 2016 was 6.1, meaning that they gave up an average of 6.1 points more than expected to QBs. In this context, “expected” is referring to their opponents’ average fantasy points. As a further example, let’s say Sam Bradford is averaging 15 fantasy points going into a Week 6 game against Detroit, and he scores 22 points in that game. Detroit would get a +7 FPAE vs. QBs for Week 6, since Bradford scored 7 points more than his average. Detroit’s +7 FPAE for Week 6 would be combined with their QB FPAE values from Weeks 1-5 to get their average QB FPAE. This process is repeated for each team against each position to calculate our FPAE values. Negative FPAE values indicate that a team is holding players they face below their average production.
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After an otherwise forgettable first few weeks, this year’s NFL Playoffs were capped off by the greatest comeback in Super Bowl history, led by the league’s undisputed G.O.A.T, better known as Tom Brady. Facing a 25 point deficit part way through the third quarter, the legacy of the Brady-Belichick era was put on the line, and the light at the end of the tunnel was looking brighter and brighter for Roger Goodell. Little did he know, that light was a reflection beaming off of Brady’s fifth Super Bowl ring, and he’d soon be on the field coughing up the Lombardi trophy to the team he’s conspired against for the past several seasons. Although he dodged the bullet last year when the Broncos knocked the Patriots out in the AFC Championship Game, the four-game suspension he imposed on Brady to begin the year proved to only fuel the fire in the Patriots’ season of vengeance on the commissioner. Although our Models didn’t contain a variable of this nature, 5 of them correctly predicted the Patriots to beat the Falcons and our spread model had them winning by 9. For the playoffs as a whole, we had two models finish 10-1 (only missing the DAL/GB game) and our average Model finished 9-2. See the tables below for our predictions for all of this year’s games.
Continue reading 2016 NFL Playoffs Recap
This will be the 7th Super Bowl that the Brady-Belichick Patriots have played. While everyone remembers that they are 4-2 in Super Bowls, with both losses coming at the hands of Eli Manning and the Giants, we took a quick look at other takeaways from those 6 games. Continue reading A Look at the Brady-Belichick Super Bowls
We use team statistics to create models that predict outcomes of NFL games. Our models generate either a win probability for the home team or a point spread, with each model generating a unique prediction. See this article for a more detailed breakdown of each model. So far this postseason, our average probabilities have picked 8/10 games correctly, and Models 2 and 3 have been the most accurate individual models, both going 9 for 10.
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In the two conference championship games on Sunday, our average probabilities went 1-1, correctly picking the Falcons (57%), but missing the Steelers (55%), to bring the average probability record for the playoffs to 8-2. Models 2 and 3 remained the most accurate, as they each had the Falcons winning (78% and 75%, respectively) and the Patriots winning (61% and 59%, respectively) to bring their records to 9-1 for the playoffs as a whole. Model 5 also went 2-0 this weekend, and improved to 8-2 for the playoffs. Our spread missed the Falcons (had ATL winning by only 3 points), but correctly predicted that the Patriots would cover (had NE winning by 7). Check back later this week for our Super Bowl predictions!
Continue reading 2016 NFL Championship Round Recap
We use team statistics to create models that predict outcomes of NFL games. Our models generate either a win probability for the home team or a point spread, with each model generating a unique prediction. Our models consider many different metrics, such as turnovers, first downs, points scored, pace of play, offensive and defensive efficiency, as well as a few metrics that we have created ourselves. Beyond these statistics, some models consider factors such as “did a team make the playoffs last year?” and “how far is the away team traveling?” See this article for a more detailed breakdown of each model. So far this postseason, our average probabilities have picked 7/8 winners correctly, and Models 2 and 3 have been the most accurate – also going 7 for 8.
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In the second round, our average probabilities went 3-1, and all models individually were either 3-1 or 2-2. Model 1 was the only one to correctly pick the Packers over the Cowboys. For the playoffs as a whole, Models 2 and 3 are performing best, as they have correctly picked 7 of the 8 games, while Model 6 is performing worst, only picking 5 of 8 games correctly. The average probabilities are also 7-1, only missing the Packers/Cowboys game. Click here to see all of our predictions from the past weekend. Check back later this week for our Conference Championship game predictions!
Continue reading 2016 NFL Divisional Round Recap
We use team statistics to create models that predict outcomes of NFL games. Our models generate either a win probability for the home team or a point spread, with each model generating a unique prediction. Our models consider many different metrics, such as turnovers, first downs, points scored, pace of play, offensive and defensive efficiency, as well as a few metrics that we have created ourselves. Beyond these statistics, some models consider factors such as “did a team make the playoffs last year?” and “how far is the away team traveling?” See this article for a more detailed breakdown of each model. The article below focuses on the four divisional round matchups for the coming weekend, but a breakdown of predictions for the entire 2016 NFL Playoffs can be found here, and our predictions from round 1 are here, where our average probabilities correctly predicted all four winners.
Continue reading 2016 NFL Divisional Round Predictions
This article provides some background on the components of each NFL Playoff Model. By making use of multiple models that are comprised of different modeling techniques and variables, we can better assess each game. For example, if all the models are predicting the Pittsburgh Steelers to beat the Miami Dolphins, we can feel very confident in picking the Steelers to win. If the models are split, we can explore each model individually based on their predictors to identify areas where the model may be taking into account less than perfect information. For instance, if one model emphasizes defensive performance and Seattle is currently missing Earl Thomas, maybe that model does not represent Seattle as accurately as the others. Ultimately, by looking at multiple models we are able to reduce the noise inherent in all predictive models.