Football season is officially back. With that, here is a breakdown of our model’s predictions for all Week 1 matchups of the 2017 NFL season. We promise we did not make manual adjustments in favor of the Vikings, who the model loves this week.
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All posts by Sam Walczak
The Impact of Situational Factors on NFL Games
In Week 1 of 2015, a 49ers team that went onto finish 5-11 and fire their head coach (RIP Tomsula) mopped the floor with a Vikings team that would finish 11-5 and make the playoffs. The Jeff Fischer-led Rams somehow beat the mighty Seahawks multiple times. Things don’t always go according to plan in the NFL. Situational factors such as weather, bye weeks, divisional opponents, travel distance, and time of the game are often cited as an explanation for why those things don’t go according to plan. In the article below, I explore whether any of these factors have been statistically associated with wins, points, and offensive efficiency. These factors by themselves should not be the sole driver of decision making for picking fantasy football lineups, making bets, or predicting wins, but they do serve as a piece to the puzzle and should not be ignored. They can certainly highlight situations that should be avoided (such as starting QBs playing in 20+ MPH winds), as well as situations that should be targeted (such as the under on Thursday Night Football). Many of the factors covered below are included in our NFL models and serve as contributing factors to our win, spread, total predictions, and ELO ratings, which we will be publishing every week of the upcoming NFL season.
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Fantasy Points Above Expectation
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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2017 NBA Playoffs Wrap-Up
Now that the Cavs and Warriors finally met up in the highly anticipated NBA Finals rematch only to let us down with a 4-1 blowout, we take a look back at how our models fared in the NBA Playoffs as a whole. To recap, we have a Playoff Series Win Probability Model that predicts a percent chance that a team wins a given series, as well as our Playoff Betting Models that predict point spreads and totals for each game.
Prospect Profile: Justin Patton
Leading up to the 2017 NBA Draft, we will be diving into what our Draft Models tell us about this year’s top prospects. Our NBA Draft Models include the PNSP Model, NBA Role Probability Model, and Similarity Scores which each provide unique ways of evaluating college prospects. Our Prospect Profiles look at which stats positively/negatively affect NBA projections, unique data points from a player’s stats, and relevant comparisons to current NBA players. You can find links to all of our Prospect Profiles in the header menu above (NBA –> NBA Draft –> Prospect Profiles). In this article, we look at Minnesota Timberwolves selection Justin Patton.
Model 284 NBA Draft Board
Using a combination of our NBA Draft Models (PNSP, NBA Role Probabilities, & Similarity Scores), we have created a consensus ranking for the 2017 NBA Draft. For example, Jawun Evans ranks 40th by PNSP but has the 7th highest All-Start probability in the class, so we have him ranked higher than 40th on our draft board. These rankings are meant to take into account the predictions from all of our models, while also considering any outside factors that the models do not directly account for (e.g., off-court issues, NBA fit, ranking within a position, model biases, etc.). It should also be noted that international prospects are not included in our models or our draft board.
Prospect Profile: Zach Collins
Leading up to the 2017 NBA Draft, we will be diving into what our Draft Models tell us about this year’s top prospects. Our NBA Draft Models include the PNSP Model, NBA Role Probability Model, and Similarity Scores which each provide unique ways of evaluating college prospects. Our Prospect Profiles look at which stats positively/negatively affect NBA projections, unique data points from a player’s stats, and relevant comparisons to current NBA players. You can find links to all of our Prospect Profiles in the header menu above (NBA –> NBA Draft –> Prospect Profiles). In this article, we look at Final Four sensation Zach Collins.
Prospect Profile: Dennis Smith Jr
Leading up to the 2017 NBA Draft, we will be diving into what our Draft Models tell us about this year’s top prospects. Our NBA Draft Models include the PNSP Model, NBA Role Probability Model, and Similarity Scores which each provide unique ways of evaluating college prospects. Our Prospect Profiles look at which stats positively/negatively affect NBA projections, unique data points from a player’s stats, and relevant comparisons to current NBA players. You can find links to all of our Prospect Profiles in the header menu above (NBA –> NBA Draft –> Prospect Profiles). In this article, we break down the NBA prospects of Dennis Smith Jr.
Prospect Profile: Malik Monk
Leading up to the 2017 NBA Draft, we will be diving into what our Draft Models tell us about this year’s top prospects. Our NBA Draft Models include the PNSP Model, NBA Role Probability Model, and Similarity Scores which each provide unique ways of evaluating college prospects. Our Prospect Profiles look at which stats positively/negatively affect NBA projections, unique data points from a player’s stats, and relevant comparisons to current NBA players. You can find links to all of our Prospect Profiles in the header menu above (NBA –> NBA Draft –> Prospect Profiles). In the article below, we provide everything our models tell us about Kentucky guard Malik Monk.
Prospect Profile: De’Aaron Fox
Leading up to the 2017 NBA Draft, we will be diving into what our Draft Models tell us about this year’s top prospects. Our NBA Draft Models include the PNSP Model, NBA Role Probability Model, and Similarity Scores which each provide unique ways of evaluating college prospects. Our Prospect Profiles look at which stats positively/negatively affect NBA projections, unique data points from a player’s stats, and relevant comparisons to current NBA players. You can find links to all of our Prospect Profiles in the header menu above (NBA –> NBA Draft –> Prospect Profiles). In this article, we look at a prospect who propelled his draft stock in this year’s NCAA Tournament in De’Aaron Fox.