Our Wide Receiver draft model incorporates player and team level college statistics and NFL Combine metrics to generate predicted probabilities of possible outcomes for each player’s NFL career. We focus on each player’s likelihood of making a Probowl, becoming a starter, becoming a role player (3rd – 5th wide receiver), or not making it in the NFL. Continue reading 2019 Wide Receiver Draft Model
All posts by Eric King
2019 NBA Bracket and Simulation
2019 NBA Playoffs Bracket and Simulation
Below is our projected bracket and simulation results for the 2019 NBA playoffs. These probabilities were generated from a combination of models that are trained on data from the regular season. Continue reading 2019 NBA Bracket and Simulation
NBA Elo Ratings and Simulation
Below is a table of our NBA Elo ratings for the 2018-2019 season along with win totals and playoff probabilities derived from 500 simulations of the 2018-2019 season. Check back over the course of the season as we will continue to update our elo ratings, along with win projections and playoff probabilities.
Elo ratings are a zero-sum rating system where teams are rewarded points after a win and subtracted points after a loss. The magnitude of this adjustment is larger for more unexpected outcomes. For instance, the Golden State Warriors would not receive much of a boost in their elo if they beat the Phoenix Suns because this is what we would expect to happen, but the Phoenix Suns would receive a larger increase in their elo if they managed to upset the Warriors. At Model284, our elos take into account each team’s prior elo rating, margin of victory, home court advantage, and the number of days off for each team prior to the game. Elo ratings do not immediately adjust for injuries, or roster changes from free agency and trades.
| Team | Elo | Wins | Playoff Probability |
|---|---|---|---|
| Milwaukee Bucks | 1744 | 60.8 | >0.99 |
| Golden State Warriors | 1706 | 56.7 | >0.99 |
| Houston Rockets | 1706 | 52.6 | >0.99 |
| Portland Trail Blazers | 1675 | 52.2 | >0.99 |
| Toronto Raptors | 1658 | 57.3 | >0.99 |
| Utah Jazz | 1649 | 50.2 | >0.99 |
| Philadelphia 76ers | 1646 | 53.2 | >0.99 |
| Denver Nuggets | 1629 | 54.1 | >0.99 |
| Los Angeles Clippers | 1598 | 49.2 | >0.99 |
| San Antonio Spurs | 1596 | 47.3 | >0.99 |
| Orlando Magic | 1574 | 41 | 0.83 |
| Oklahoma City Thunder | 1542 | 46.5 | >0.99 |
| Detroit Pistons | 1541 | 42.7 | 0.99 |
| Boston Celtics | 1528 | 47.5 | >0.99 |
| Indiana Pacers | 1517 | 47.9 | >0.99 |
| Miami Heat | 1515 | 40.3 | 0.42 |
| Brooklyn Nets | 1495 | 40.9 | 0.75 |
| Los Angeles Lakers | 1469 | 36.5 | <0.01 |
| Minnesota Timberwolves | 1459 | 36.6 | <0.01 |
| Sacramento Kings | 1457 | 40.3 | <0.01 |
| Charlotte Hornets | 1453 | 37.2 | 0.01 |
| Washington Wizards | 1414 | 34.4 | <0.01 |
| Atlanta Hawks | 1373 | 29.1 | <0.01 |
| Memphis Grizzlies | 1372 | 32.5 | <0.01 |
| New Orleans Pelicans | 1365 | 33.5 | <0.01 |
| Dallas Mavericks | 1360 | 32.7 | <0.01 |
| Chicago Bulls | 1302 | 22.8 | <0.01 |
| Cleveland Cavaliers | 1293 | 20.4 | <0.01 |
| Phoenix Suns | 1201 | 18.3 | <0.01 |
| New York Knicks | 1175 | 15.4 | <0.01 |
*Last update on April 1st.
2018 Fantasy Football Rankings
FOOTBALL IS FINALLY BACK! And since there is such a shortage of Fantasy Football content out there, we thought we needed to give the people some material to prepare for their drafts. The following rankings are loosely based on model predictions for each position, which use historical player/team data to predict fantasy points for the coming season (i.e., separate models for QB, RB, WR, TE, K, DST). That said, there are plenty of factors that any model will have trouble capturing perfectly (injury status, team depth charts, suspensions, QB/Coach changes, etc.), and thus we have made some subjective adjustments to the rankings where necessary. For example, if a model weighs last year’s cumulative statistics too heavily, Odell Beckham Jr. is not going to come out very high since he only played 4 games. Our rankings can be found below, along with a short description of the model we used for each position. All rankings reflect PPR scoring.
To download our rankings, click here Continue reading 2018 Fantasy Football Rankings
My Model Monday: Modeling NFL Injuries
Injuries are inevitable in a game as physical as NFL football. Every season, numerous star players and important contributors are sidelined, leaving their fans and fantasy owners disappointed. Injuries appear to strike at random; an elite athlete can have his knee blown out in one cut like Dalvin Cook last season. However, is there a way to identify if certain players are more injury prone than others? I dug into the data to find out how well we can predict injuries among skill position players in the NFL.
Continue reading My Model Monday: Modeling NFL Injuries
Prospect Profiles: Kevin Huerter
Leading up to the 2018 NBA Draft on June 21st, we will be using our NBA Draft Models (PNSP Model, Role Probability Model, and Similarity Scores) to investigate this year’s top prospects. These Prospect Profiles look at which stats affect NBA projections, present unique data points from a player’s stats, and give relevant comparisons to current NBA players. You can find all of our Prospect Profiles here or through the header menu above (NBA –> NBA Draft –> Prospect Profiles). In today’s article, we look at Kevin Huerter.
Prospect Profiles: Robert Williams
Leading up to the 2018 NBA Draft on June 21st, we will be using our NBA Draft Models (PNSP Model, Role Probability Model, and Similarity Scores) to investigate this year’s top prospects. These Prospect Profiles look at which stats affect NBA projections, present unique data points from a player’s stats, and give relevant comparisons to current NBA players. You can find all of our Prospect Profiles here or through the header menu above (NBA –> NBA Draft –> Prospect Profiles). In today’s article, we look at Robert Williams
Prospect Profile: Kevin Knox
Leading up to the 2018 NBA Draft on June 21st, we will be using our NBA Draft Models (PNSP Model, Role Probability Model, and Similarity Scores) to investigate this year’s top prospects. These Prospect Profiles look at which stats affect NBA projections, present unique data points from a player’s stats, and give relevant comparisons to current NBA players. You can find all of our Prospect Profiles here or through the header menu above (NBA –> NBA Draft –> Prospect Profiles). In today’s article, we look at Kevin Knox.
Prospect Profiles: Miles Bridges
Leading up to the 2018 NBA Draft on June 21st, we will be using our NBA Draft Models (PNSP Model, Role Probability Model, and Similarity Scores) to investigate this year’s top prospects. These Prospect Profiles look at which stats affect NBA projections, present unique data points from a player’s stats, and give relevant comparisons to current NBA players. You can find all of our Prospect Profiles here or through the header menu above (NBA –> NBA Draft –> Prospect Profiles). In today’s article, we look at Miles Bridges.
Prospect Profile: Collin Sexton
Leading up to the 2018 NBA Draft on June 21st, we will be using our NBA Draft Models (PNSP Model, Role Probability Model, and Similarity Scores) to investigate this year’s top prospects. These Prospect Profiles look at which stats affect NBA projections, present unique data points from a player’s stats, and give relevant comparisons to current NBA players. You can find all of our Prospect Profiles here or through the header menu above (NBA –> NBA Draft –> Prospect Profiles). In today’s article, we look at Collin Sexton.