Austin Jazz Week 12 Update
The Austin Jazz is on a three game Win Streak!!! Yahoo!!!
All right, getting a little ahead of myself, but I’m rather proud of how I’ve done the last few weeks. Last week, there was once again very little drama. I had a small lead, 91-80 after the early Sunday games, but by the end of the Sunday night game I was ahead 137-86. Both my opponent and I had one player each playing Monday night, but I was already celebrating. The Austin Jazz won 160-116, and that wasn’t even the largest margin of victory for my league for the week!!
I’m currently tied for 4th place, but with the tiebreaker being points scored, I’m actually in 5th place. This week I go up against the 2nd place team in the league so I’m going to need all the luck I can get. My win streak needs to continue in order to be in the playoffs in Week 15. In Week 14 I’m up against the 8th place team in the league so, if I win today, I think I may have a good chance at those playoffs.
Win Probabilities
Yesterday I was thinking about trying to predict the results for NFL games for the rest of the season and throughout the playoffs. I decided to try a very simple model that predicts the probability that a team will win based on whether they are home or away as well as the points scored. Obviously this is a very, very simple way at looking at something like this, but through Week 12 of this season Home Teams have scored an average of 25.4 points while Visiting Teams have scored an average of 22.9 points. That would indicate the old home-team advantage!
The table below reflects the results of my model and compares them with the implied probabilities based on money line odds from Pinnacle that was gathered this morning (12/2/2018) at 9 AM CST. My win probabilities don’t necessarily add up to 100% since, the way I constructed my model, I take into account the possibility of a tie (which has happened earlier in the season). The remaining percentage for each game would be probability of a game ending in a tie. Also, and since I know very little about sports gambling, I noticed that Vegas odds equal to more than 100%. I did not know that until today! According to Wikipedia, “it is ideal for bookmakers to price/mark up a book such that the net outcome will always be in their favor: the sum of the probabilities quoted for all possible outcomes will be in excess of 100%. The excess over 100% represents profit to the bookmaker in the event of a balanced/even book. In the more usual case of an imbalanced book, the bookmaker may have to pay out more winnings than what is staked or may earn more than mathematically expected. An imbalanced book may arise since there is no way for a bookmaker to know the true probabilities for the outcome of competitions left to human effort or to predict the bets that will be attracted from others by fixed odds compiled on the basis personal view and knowledge.” Okay….
Comparison of Win Probabilities with Vegas Money Line Implied Probabilities
| Game | Team | Ed's Model | Vegas | Difference |
|---|---|---|---|---|
| BAL vs ATL | BAL | 76.66% | 45.66% | 31.00% |
| ATL | 18.98% | 56.72% | -37.74% | |
| CHI vs NYG | CHI | 86.84% | 64.56% | 22.28% |
| NYG | 10.33% | 37.89% | -27.56% | |
| ARI vs GB | ARI | 1.54% | 15.58% | -14.04% |
| GB | 97.72% | 87.87% | 9.85% | |
| CAR vs TB | CAR | 48.73% | 62.85% | -14.12% |
| TB | 46.17% | 39.68% | 6.49% | |
| BUF vs MIA | BUF | 20.61% | 37.33% | -16.72% |
| MIA | 74.31% | 65.19% | 9.12% | |
| IND vs JAC | IND | 62.00% | 66.58% | -4.58% |
| JAC | 31.76% | 35.84% | -4.08% | |
| CLE vs HOU | CLE | 19.18% | 32.79% | -13.61% |
| HOU | 76.50% | 69.74% | 6.76% | |
| DEN vs CIN | DEN | 62.16% | 67.34% | -5.18% |
| CIN | 32.44% | 35.09% | -2.65% | |
| LAR vs DET | LAR | 95.57% | 81.83% | 13.74% |
| DET | 3.32% | 20.62% | -17.30% | |
| KC vs OAK | KC | 98.84% | 88.81% | 10.03% |
| OAK | 0.09% | 14.14% | -14.05% | |
| NYJ vs TEN | NYJ | 14.53% | 20.88% | -6.35% |
| TEN | 81.47% | 81.57% | -0.10% | |
| SF vs SEA | SF | 3.15% | 20.92% | -17.77% |
| SEA | 95.63% | 82.03% | 13.60% | |
| MIN vs NE | MIN | 20.43% | 32.79% | -12.36% |
| NE | 75.23% | 69.74% | 5.49% | |
| LAC vs PIT | LAC | 26.47% | 43.29% | -16.82% |
| PIT | 68.71% | 59.21% | 9.50% | |
| WAS vs PHI | WAS | 43.65% | 31.95% | 11.70% |
| PHI | 49.58% | 70.52% | -20.94% |
Just looking at who wins (based on probabilities being over 50%), my predictions aren’t quite that much different than Vegas. The one obvious difference, though, is right at the top with Baltimore and Atlanta. I have Baltimore with a 76.66% chance of winning while Vegas has Atlanta favored. As far as how close my probabilities are with Vegas, I have Tennessee with an 81.47% while Vegas has the Titans with an 81.57% chance of winning. That’s pretty much the closest comparison. The only other prediction with a difference of the winning percentage being less than 5% is the outcome of the Indianapolis/Jacksonville match-up.
Anyway, just wanted to start simple and over time add various other features to try and tune the model. I have ideas of what I want to add, but, as with most projects, it’s a matter of getting the data, cleaning it up, and putting it into a format that will work, and, of course, make sense.
Fantasy Football Projections
Here are your updated projections for the week. Good luck this week!
Top 20 Quarterback Week 13 Fantasy Predictions
| Player | Team | Week 13 Fantasy Predictions |
|---|---|---|
| Patrick Mahomes | KC | 37 |
| Deshaun Watson | HOU | 29 |
| Jared Goff | LAR | 29 |
| Cam Newton | CAR | 28 |
| Andrew Luck | IND | 26 |
| Aaron Rodgers | GB | 26 |
| Russell Wilson | SEA | 23 |
| Philip Rivers | LAC | 23 |
| Matthew Stafford | DET | 21 |
| Kirk Cousins | MIN | 21 |
| Jameis Winston | TB | 21 |
| Ben Roethlisberger | PIT | 20 |
| Blake Bortles | JAC | 19 |
| Chase Daniel | CHI | 19 |
| Matt Ryan | ATL | 19 |
| Carson Wentz | PHI | 19 |
| Marcus Mariota | TEN | 18 |
| Eli Manning | NYG | 16 |
| Derek Carr | OAK | 16 |
| Case Keenum | DEN | 15 |
Top 20 Running Back Week 13 Fantasy Predictions
| Player | Team | Week 13 Fantasy Predictions |
|---|---|---|
| Todd Gurley II | LAR | 33 |
| Saquon Barkley | NYG | 28 |
| Christian McCaffrey | CAR | 26 |
| James Conner | PIT | 23 |
| Leonard Fournette | JAC | 23 |
| David Johnson | ARI | 18 |
| Aaron Jones | GB | 16 |
| Joe Mixon | CIN | 16 |
| Marlon Mack | IND | 15 |
| Phillip Lindsay | DEN | 15 |
| Tarik Cohen | CHI | 14 |
| Lamar Miller | HOU | 13 |
| T.J. Yeldon | JAC | 12 |
| Chris Carson | SEA | 12 |
| Adrian Peterson | WAS | 12 |
| Tevin Coleman | ATL | 11 |
| Giovani Bernard | CIN | 9 |
| Latavius Murray | MIN | 9 |
| Jordan Howard | CHI | 8 |
| Austin Ekeler | LAC | 8 |
Top 20 Wide Receiver Week 13 Fantasy Projections
| Player | Team | Week 13 Fantasy Predictions |
|---|---|---|
| Tyreek Hill | KC | 31 |
| Adam Thielen | MIN | 28 |
| Davante Adams | GB | 27 |
| Antonio Brown | PIT | 25 |
| Julio Jones | ATL | 25 |
| DeAndre Hopkins | HOU | 25 |
| Mike Evans | TB | 23 |
| Brandin Cooks | LAR | 22 |
| Odell Beckham Jr | NYG | 22 |
| Stefon Diggs | MIN | 21 |
| JuJu Smith-Schuster | PIT | 21 |
| T.Y. Hilton | IND | 21 |
| Robert Woods | NaN | 20 |
| Emmanuel Sanders | DEN | 19 |
| Tyler Boyd | CIN | 19 |
| Tyler Lockett | SEA | 18 |
| Keenan Allen | LAC | 18 |
| Calvin Ridley | ATL | 17 |
| Kenny Golladay | DET | 17 |
| Alshon Jeffery | PHI | 14 |
Top 12 Tight End Week 13 Fantasy Projections
| Player | Team | Week 13 Fantasy Predictions |
|---|---|---|
| Zach Ertz | PHI | 28 |
| Travis Kelce | KC | 26 |
| Eric Ebron | IND | 24 |
| Jared Cook | OAK | 19 |
| Trey Burton | CHI | 15 |
| Greg Olsen | CAR | 14 |
| Robert Tonyan | GB | 12 |
| Jordan Reed | WAS | 12 |
| Vance McDonald | PIT | 10 |
| Ed Dickson | SEA | 9 |
| Jimmy Graham | GB | 9 |
| Jack Doyle | IND | 8 |
Top 12 Kickers Week 13 Fantasy Predictions
| Player | Team | Week 13 Fantasy Predictions |
|---|---|---|
| Greg Zuerlein | LAR | 14 |
| Ka'imi Fairbairn | HOU | 12 |
| Cairo Santos | TB | 10 |
| Michael Badgley | LAC | 10 |
| Harrison Butker | NaN | 10 |
| Aldrick Rosas | NYG | 9 |
| Justin Tucker | BAL | 9 |
| Mason Crosby | GB | 9 |
| Matt Bryant | ATL | 8 |
| Matt Prater | DET | 8 |
| Dan Bailey | MIN | 7 |
| Adam Vinatieri | IND | 7 |
Imported from Manual Input
| Team | Week 13 Fantasy Predictions |
|---|---|
| CHI | 15 |
| WAS | 10 |
| HOU | 10 |
| NE | 10 |
| CLE | 10 |
| IND | 10 |
| MIN | 8 |
| MIA | 8 |
| PIT | 8 |
| ARI | 6 |
| CIN | 6 |
| ATL | 6 |
*The predictions for this model comes from statistics tables scraped from The Huddle and Football Outsiders from 2006-2018.
As usual I used the Fantasy Points-Per-Reception (PPR) scores for my predictions which comes from the following:
- 1 point per 25 yards passing
- 4 points per passing touchdown
- -2 points per interception thrown
- 1 point per reception
- 1 point per 10 yards rushing/receiving
- 6 points per TD
- 2 points per two-point conversion
- -2 points per fumbles lost
All predictive elements for Fantasy Football Models are for entertainment purposes only. Do your own research before making any decisions on your Fantasy Football team especially in money leagues!!