All posts by Sam Walczak

Fantasy Points Above Expectation – Week 3

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, if New Orleans’s FPAE against QBs is 5.0, that means they have given up an average of 5.0 points more than expected to QBs. In this context, “expected” is referring to their opponents’ average fantasy points. We give a full explanation of FPAE here. FPAE values against each position group for Week 3 of 2017 are shown below. These values were calculated using each team’s last 7 games (PPR scoring).
Continue reading Fantasy Points Above Expectation – Week 3

Fantasy Points Above Expectation – Week 2

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 is 5.1, meaning that they gave up an average of 5.1 points more than expected to QBs. In this context, “expected” is referring to their opponents’ average fantasy points. We give a full explanation of FPAE here. FPAE values against each position group for Week 2 of 2017 are shown below. These values were calculated using each team’s last 7 games (PPR scoring).
Continue reading Fantasy Points Above Expectation – Week 2

Fantasy Points Above Expectation – Week 1

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 is 5.1, meaning that they gave up an average of 5.1 points more than expected to QBs. In this context, “expected” is referring to their opponents’ average fantasy points. We give a full explanation of FPAE here. FPAE values against each position group for Week 1 of 2017 are shown below. These values were calculated using each team’s last 7 games (PPR scoring).
Continue reading Fantasy Points Above Expectation – Week 1

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.
Continue reading The Impact of Situational Factors on NFL Games

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.
Continue reading Fantasy Points Above Expectation

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.

Continue reading 2017 NBA Playoffs Wrap-Up

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.

Continue reading Prospect Profile: Justin Patton