The guys dissect the Lebron/Kyrie drama, NFL preseason football, and the Twins recent surge to wildcard contention. Segments include Hammer the Over’s Over of the Week, Unwritten Rules, and Hot Take of the Week.
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.
Continue reading Fantasy Points Above Expectation
Every July since 2004, a couple hundred NBA hopefuls have gathered in Orlando, Utah, and Las Vegas to compete in 5-6 junk ball basketball games known as the Summer League. Whether you’re an NBA operations analyst or just a fan, NBA Summer League provides another opportunity to see a player—or in our case, another data point to project a player. But how much should we really take away from a few junk ball games in the beginning of July? Or, in “statistical” terms, what is the predictive value of summer league for a player’s actual NBA Performance? This analysis seeks to provide insights into how historical Summer League performances have related to NBA performances. More specifically, we’ll seek an answer to questions such as: how much can NBA Summer League performance tell us about overall rookie performance? How about second-year returning players? What should we take away, if anything, from these varying levels of performance in Summer League? In this article, I will focus solely on NBA rookies, providing meaningful data points and inferences about which statistics best translate from Summer League to NBA Rookie Season.
Continue reading The Predictive Value of NBA Summer League: NBA Rookies
Fred and Evan kick off their maiden voyage in the podcast world with a brief sports rundown including an NBA draft and summer league recap, and a review of the Mayweather-McGregor world fiasco. Model 284 Co-founder Marc Richards joins the boys for a quick overview of what the modelers have cooking up in the interview. Obscure sports correspondent Tim Ewald wraps up the show with some advice for the NL West leading LAA Dodgers.
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.
After the Warriors steamrolled their way to their second championship in three years, there has been a lot of discussion about how this Warriors team would stack up against past NBA champions. Guys like Magic Johnson, Rasheed Wallace, and even Raja Bell have claimed that their former teams would beat the ’17 Warriors. Unless MJ pulls a Brett Favre and un-retires again, we will likely never see the Warriors play any of the great teams from the past. As an alternative, we thought it might be fun to run these hypothetical matchups of 2017 Warriors vs. Prior NBA Champions through our NBA Playoff Model.
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.
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 Finnish-born Arizona product Lauri Markkanen.
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.